EMJ Diabetes 11.1 2023

Volume 11.1 November 2023

Diabetes

Review of

EASD 2023 Editor’s Pick

Chrono-nutrition and its Association with Chronotype and Blood Glucose Control Among People with Type 2 Diabetes

Interviews Maggie Shepherd discusses monogenic diabetes, and Pia Leete highlights research on the pancreas

emjreviews.com

Contents 4

Editorial Board

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Welcome

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Foreword

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Congress Review Review of the 59ᵗʰ European Association for the Study of Diabetes (EASD) Annual Meeting, 2nᵈ–6ᵗʰ October 2023

Congress Features Precision Medicine in Diabetes Natasha Meunier-McVey

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Equity Versus Equality: A Spotlight on Health Inequalities

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Abstract Highlights

35 41

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Anaya Malik

Interviews Maggie Shepherd Pia Leete

Diabetes T Area

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Articles Editor's Pick: Chrono-nutrition and its Association with Chronotype and Blood Glucose Control Among People with Type 2 Diabetes Chandorkar et al.

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Satisfaction with Home Healthcare Provider Service in Adults with Type 1 Diabetes Using a Hybrid Closed Loop System: The SATURN Study Hanaire et al.

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Type 1 Diabetes and Health Information Systems: Lessons from Denmark Beran et al.

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Hidden Complication of Obesity and Diabetes: Is It Time to Put More Focus on Fatty Liver?

Kim and Sebastiani

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Mean Platelet Volume in Type 2 Diabetes: Correlation with Poor Glycaemic Control Agarwal et al.

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Preconception Care for Individuals with Diabetes Mukherjee et al.

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Editorial Board Editor-in-Chief Dr Martin Whyte

University of Surrey, UK

Editorial Board Prof David Simmons

Western Sydney University, Australia

Mrs Anne-Marie Felton

Foundation of European Nurses in Diabetes, UK

Dr Gijs Goossens

Maastricht University Medical Centre+, the Netherlands

Dr Hassan Shora

Port Said University, Egypt

Dr Mohammad Alhadj Ali

Cardiff University School of Medicine, UK

Dr Muthuswamy Balasubramanyam

Madras Diabetes Research Foundation (MDRF), India

Prof Henning Beck-Nielsen

Odense University Hospital, Denmark

Prof Ellen Blaak

Maastricht University Medical Centre+, the Netherlands

Dr Yehuda Handelsman

Metabolic Institute of America, California, USA

Dr Lorenzo Pasquali

Germans Trias i Pujol University Hospital and Research Institute, Spain

Prof Nikolaos Tentolouris

National and Kapodistrian University of Athens, Greece

Dr Dario Rahelic

Dubrava University Hospital, Croatia

Prof Anne Phillips

Birmingham City University, UK

Dr Jonathan Bodansky

Leeds Teaching Hospitals, UK

Prof Dr Baptist Gallwitz

University of Tübingen, Germany

Dr Sampathkumar Rangasamy

Translational Genomics Research Institute (TGen), USA

Dr Simon Williams

University of South Wales, UK

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Aims and Scope EMJ is an online only, peer-reviewed, open access general journal, targeted towards readers in the medical sciences.

We aim to make all our articles accessible to readers from any medical discipline.

EMJ allows healthcare professionals to stay abreast of key advances and opinions across Europe.

EMJ aims to support healthcare professionals in continuously developing their knowledge, effectiveness, and productivity.

The editorial policy is designed to encourage discussion among this peer group.

EMJ is published quarterly and comprises review articles, case reports, practice guides, theoretical discussions, and original research.

EMJ also publishes 18 therapeutic area journals, which provide concise coverage of salient developments at the leading European congresses. These are published annually, approximately 6 weeks after the relevant congress.

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Invited contributors are recognised authorities from their respective fields.

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Congress Notice Staff members attend medical congresses as reporters when required.

This Publication ISSN 2054-6181 EMJ Diabetes is published once a year. For subscription details please visit: www.emjreviews.com All information obtained by EMJ and each of the contributions from various sources is as current and accurate as possible.

However, due to human or mechanical errors, EMJ and the contributors cannot guarantee the accuracy, adequacy, or completeness of any information, and cannot be held responsible for any errors or omissions. EMJ is completely independent of the review event (EASD 2023) and the use of the organisations does not constitute endorsement or media partnership in any form whatsoever.

Front cover and contents photograph: Hamburg, Germany home of the EASD 2023 © Igor Tichonow / stock.adobe.com

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EMJ Podcasts The EMJ Podcast aims to provoke conversations around the latest trends and innovations in healthcare, provide engaging and educational content for healthcare professionals, and hosts conversations with physician entrepreneur, Jonathan Sackier.

Listen today www.emjreviews.com

Welcome letter Editor Evgenia Koutsouki Editorial Manager Anaya Malik Copy Editors Noémie Fouarge Kirsty Hewitt

Evgenia Koutsouki Editor

Editorial Co-ordinator Natasha Meunier-McVey Editorial Assistants Lucy Anderson, Victoria Antoniou, Abigail Craig, Ada Enesco, Evan Kimber, Darcy Richards Design Manager Stacey Rivers Senior Designer Roy Ikoroha

It is an absolute pleasure to be welcoming you to the 2023 issue of EMJ Diabetes. In this issue, I would like to extend a warm welcome to our new Editor-in-Chief, Martin Whyte, University of Surrey, UK. Our Editorial team is delighted to be working with Martin, who is studying the effect of insulin resistance on cardiovascular disease and nonalcoholic fatty liver disease, and has a plethora of publications on diabetes.

This issue contains a fantastic review of the 59th Annual Meeting of the European Association for the Study of Diabetes (EASD). Highlights of the meeting include a featured study on combination antiviral treatments that may help retain pancreatic β-cell function in those with newly diagnosed Type 1 diabetes; screening for diabetes; and reports on a presented study demonstrating that drinking dark tea is associated with reducing the risk of developing, and the progression of, Type 2 diabetes.

Designer Steven Paul Junior Designers Dillon Benn Grove, Shanjok Gurung Head of Sales Robert Hancox Senior Project Manager Lewis Mackie Chief Content Officer Justin Levett Chief Operating Officer Dan Scott Chief Commercial Officer Dan Healy Founder and Chief Executive Officer Spencer Gore

Dear Readers,

We are delighted to be featuring interviews with Maggie Shepherd, who discussed her research in monogenic diabetes, and Pia Leete, who spoke with us about the importance of pancreatic immunopathology in diabetes research.

Be sure not to miss our collection of peer-reviewed articles, which include a highly engaging review on chrono-nutrition, an emerging field studying the interaction between temporal dietary patterns, circadian rhythms, and metabolic health, and its association with chronotype and blood glucose control in people with Type 2 diabetes.

I would like to close by thanking our fantastic contributors, peer reviewers, and Editorial Board for contributing in making this issue a collection of high-quality content. I hope you enjoy reading through this!

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Foreword Dear Colleagues, I am delighted to welcome you to this edition of EMJ Diabetes. It features a host of exciting developments in diabetes research and clinical care that were featured at the 59th meeting of the European Association for the Study of Diabetes (EASD) in Hamburg, Germany. At the conference, experts placed emphasis on the complications of diabetes, ranging from wellstudied cardiovascular issues to less-explored concerns, such as gastrointestinal problems and cardiac conduction anomalies, and how they relate to glycaemia and duration of diabetes.

Diabetic neuropathy is receiving ever greater attention, with data presented on its prevalence, and discussion of LX9211, an oral non-opioid treatment for neuropathic pain, newly approved by the U.S. Food and Drug Administration (FDA).

A theme that has been emerging in recent years, and which was at the forefront of this year’s conference, is the advent of diseasemodifying and disease-disrupting therapies for both Type 1 and Type 2 diabetes. For Type 1 diabetes, the agenda encompasses immune modulation, islet transplantation, and advanced

technology, including automated insulin delivery systems. In the context of Type 2 diabetes, this involves addressing the root causes, notably obesity, with a focus on research related to the incretin system, and the advent of dual and triple incretin agonists, such as the glucosedependent insulinotropic polypeptide/glucagonlike peptide-1 agonist tirzepatide and the dual glucagon/glucagon-like peptide-1 agonist pemvidutide, as well as the development of triple incretin agonists. Developments proceed apace with therapeutic approaches to preserve β-cell function. There was an enthusiastic debate regarding the use of combined therapy for Type 2 diabetes at the point of diagnosis, as well as debating the feasibility of lasting remission. At the conclusion of the meeting was the presentation of the EASD/American Diabetes Association (ADA) report on hyperglycaemic crises in adults with diabetes. Last updated in 2009, this report will be a vital resource for the management of diabetic ketoacidosis and hyperosmolar states.

I hope you enjoy reading this issue of EMJ Diabetes.

Martin Whyte Associate Professor of Metabolic Medicine, University of Surrey, UK

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Congress Review

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EASD 2023

EASD 2023 Review of the 59th European Association for the Study of Diabetes (EASD) Annual Meeting

Location:

Hamburg, Germany, and online

Date:

2nd–6th October 2023

Citation:

EMJ Diabet. 2023;11[1]:10-21. DOI/10.33590/emjdiabet/10308765. https://doi.org/10.33590/emjdiabet/10308765.

On 2nd October, Chantal Mathieu, European Association for the Study of Diabetes (EASD) President, welcomed the more than 11,000 participants to the EASD’s 59th Annual Meeting.

This year, Hamburg, Germany, played host to what Mathieu introduced as "scientific fireworks," while the city celebrated German Unity Day. In her opening address, Mathieu highlighted the exciting times in the field of diabetes, as we are getting to the core of the disease, with new insights into both Type 1 diabetes (T1D) and Type 2 diabetes, as well as better therapies.

Disease-modifying therapies have represented a major advancement in the treatment of Type 2 diabetes, as well as insights into muscles and nutrition, leading to amazing clinical guidance.

Implementing this knowledge, access to treatment, and clinicians being inert remain challenges. For T1D, as well as other forms of diabetes, new technologies, such as sensors, pumps, and artificial intelligence, are now helping patients improve glucose control and quality of life. Recent research has also identified biomarkers that provide better insights into the disease, allowing us to identify patients in early stages of the disease. Promising interventions are also helping delay the onset of clinical T1D, and protect β-cells after onset. In 2023, the first disease-modifying therapy teplizumab was approved by the U.S. Food and Drug Administration (FDA), and Mathieu hopes it will soon be available in Europe. Finally, stem cell

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research has meant that islet transplantation will possibly become a cure for people with T1D soon. Another aspect of this exciting progress, Mathieu explained, is that we now acknowledge the voice of those living with diabetes in trials, research, and clinics. This has led the EASD to sign a pledge to end diabetes stigma and discrimination.

"We now acknowledge the voice of those living with diabetes in trials, research, and clinics."

This year marked the fifth anniversary of the EASD’s e-learning platform. At its launch in 2018, this contained just three courses and five modules, but this has now increased to 31 courses and 83 modules, and the platform now has 9,000 registered users. The platform aims to educate, inform, and engage diabetes healthcare professionals around the world with high-quality content promoting holistic and patient-centred diabetes care. Mathieu further highlighted the EASD Academy, which aims to support, train, mentor, and create opportunities for students, scientists, and clinicians at an early or intermediate stage of their career. Through this initiative, the EASD wants to promote young people, and, as Mathieu stated: “the immersion of the butterfly from the caterpillar.”

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EASD 2023

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EASD 2023

The congress offered a number of different sessions, including 58 symposia, 56 oral sessions, 96 short oral discussion sessions, 15 study groups and non-governmental organisation symposia, and 43 industry sessions. Mathieu took a moment to thank Tina Vilsbøll, EASD Honorary Secretary, as well as all members of the Programme Committee, for creating this exciting programme.

"The platform aims to educate, inform, and engage diabetes healthcare professionals."

The EASD recognised excellent diabetes science with the 2023 prize awardees, including Åke Lernmark, Lund University, Sweden, who received the 55th Claude Bernard Lecture; Stephan Herzig, Helmholtz Munich, Germany, recipient of the 38th Camillo Golgi lecture; and Yuval Dor, Hebre University of Jerusalem,

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Israel, who was awarded the 17th Albert Renold Lecture. The EASD further presented the EASD/ NNF diabetes prize for excellence to Roman Hovorka, University of Cambridge, UK; the 58th Minkowski Lecture to Tino Müller, Helmholtz Munich, Germany; and the EASD-Lilly Centennial Anniversary Prize to Gerald Shulman, Yale School of Medicine, New Haven, Connecticut, USA. Four people were awarded the rising star symposium and fellowship: Lærke Smidt Gasbjerg, University of Copenhagen, Denmark; Theresia Sarabhai, German Diabetes Center, Düsseldorf, Germany; Luca D’Onofrio, Sapienza University of Rome, Italy; and Alessandro Mengozzi, University of Zürich, Switzerland.

The 60th Annual Meeting of the EASD will take place in Madrid, Spain, from 9th–13th September 2024, promising fireworks again, and hopefully some sunshine. Read on for scientific insights from the congress, including universal screening for T1D, the impact of low doses of radiation on the development of diabetes, and more. ●

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Type 1 Diabetes: Could Antiviral Treatments Preserve β-Cell Function?

COMBINATION antiviral treatment may help retain pancreatic β-cell function in those with newly diagnosed Type 1 diabetes (T1D), according to novel research presented at the EASD Annual Meeting 2023.

Ida Maria Mynarek and Knut Dahl-Jørgensen, Oslo University Hospital, Norway, and colleagues conducted a Phase II, placebo-controlled, double-blind, parallel-group trial including 96 children, aged 6–15 years, to elucidate whether combined pleconaril and ribavirin antiviral treatment impacted β-cell function from T1D onset.

The study, affiliated with INNODIA consortium, randomised children to receive either pleconaril and ribavirin (n=47; 19 females) or placebo (n=49; 21 females) for 6 months within the first 3 weeks of T1D diagnosis. Twelve-month residual endogenous insulin production was the study’s primary endpoint. Area under the concentrationtime curve (AUC) for C-peptide level in response to a 2-hour mixed-meal tolerance test was used to measure this.

C-peptide AUC was found to be significantly higher in children treated with combined antiviral therapy at 12 months when compared with children in the placebo group. Those in the

treatment group experienced an 11% decrease in C-peptide AUC at 12 months, compared with 24% in the placebo group. In the treatment group, 86% had maximal C-peptide levels >0.2 pmol/mL, an indicator of residual exogenous insulin production, at 12 months, compared with 67% in the placebo group (p=0.04).

No significant differences in glycated HbA1c, severe hypoglycaemic events or adverse events, glycated albumin, or insulin dosage at 12 months were identified between the two groups, and treatment was found to be safe, with no severe adverse events during the study.

From the findings, the researchers commented that a potential underlying mechanism for developing T1D could be persistent low-grade viral infection, and alluded that development of new vaccines could therefore aid prevention.

They stated: “Among children with newly diagnosed T1D, a 26-weeks course with two antiviral drugs partially preserved stimulated C-peptide secretion 12 months after diagnosis, and a higher proportion of participants with clinically relevant preserved C-peptide secretion than placebo.” They also discussed the rationale for identifying optimal antiviral therapeutics for use in treating T1D to help rescue β-cells at the time of diagnosis. ●

"C-peptide AUC was found to be significantly higher in children treated with combined antiviral therapy at 12 months."

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Universal Screening of Type 1 Diabetes UNIVERSAL screening for Type 1 diabetes (T1D) could increase access to education, monitoring, and disease-modifying therapies, suggests research presented at the EASD Annual Meeting 2023. Researchers have previously established that individuals with multiple islet autoantibodies have almost 100% risk of developing T1D over their lifetime, and screening for these antibodies at ages 2 and 5–7 years would predict most cases of T1D before the age of 15.

Diabetic ketoacidosis is often the first sign of T1D in people who are undiagnosed, and can be dangerous, or even fatal.

"Individuals with multiple islet autoantibodies have almost 100% risk of developing T1D."

Sims concluded: “The costs of screening, optimal ways to scale it up, and how to connect it with access to disease modifying therapies, such as the monoclonal anti-CD3 antibody that was recently [U.S. Food and Drug Administration] FDA-approved in the USA for delay of Stage 3 T1D in individuals meeting criteria for Stage 2 disease (multiple islet autoantibodies and changes in blood sugar), are all still to be worked out. Other important considerations moving forward include reaching traditionally understudied populations, and more tailored approaches for individual patients.” Regarding when we could see the roll out of universal screening for T1D, Sims suggested that increased endorsement will be seen over the next 5 years, and as this occurs, countries will begin incorporating screening into routine care for young children. ●

This study, presented by Emily Sims, Center for Diabetes and Metabolic Diseases, Indiana University School of Medicine, Indianapolis, USA, highlighted that most people who develop T1D (85–90%) have no family history, despite screening programmes commonly focusing on people with a family history of T1D. Recently, increased understanding of the condition suggests T1D gradually develops, following the appearance of multiple islet-autoantibodies.

Thus, by screening children and adults to identify individuals with early presymptomatic stages of disease, we could more accurately predict when they would first need insulin, thus helping to prevent cases of diabetic ketoacidosis.

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Various research programmes aim to establish the best ways of implementing universal screening, ensuring guidelines for monitoring and screening are endorsed by diabetes advocacy societies prior to screening becoming general policy. This will also be helped by broader access to disease-modifying therapies.

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Emergency Department Screening Could Detect Undiagnosed Diabetes Cases RECENT research presented at the EASD Annual Meeting 2023 suggests that introducing screening for Type 2 diabetes (T2D) in accident and emergency (A&E) departments has the potential to uncover thousands of previously undiagnosed cases every year.

The study, presented by Edward Jude, Tameside and Glossop Integrated Care NHS Foundation Trust, Ashton-under-Lyne, UK, included 1,388 patients visiting the A&E department, all selected at random, who did not have a diabetes diagnosis. Each of the patients were screened for T2D using the HbA1c test, which gives an indication of average glucose levels over the previous 2–3 months. For this study, prediabetes was defined as HbA1c 39–47 mmol/mol, and diabetes as 48 mmol/mol or higher.

Patients additionally were required to complete a questionnaire about demographics, ethnicity, and diabetes risk factors to calculate each patient’s Finnish Diabetes Risk Score (FINDRISC), and to assess those at risk of developing T2D. A score of more than 20 indicated a very high risk of developing diabetes (50% chance over 10 years), and scores of 12–20 indicated moderate to high risk (33% chance over 10 years).

Of the patients screened, 848 (61%) had normal blood glucose levels, while 420 (30%) were found to have pre-diabetes, and 120 (9%) were

diagnosed with T2D, with a similar proportion in males and females. Analysis of the data further showed that people of South Asian and other non-White backgrounds had a higher incidence of glucose intolerance than White patients (43% versus 38%), and were, therefore, twice as likely to be diagnosed with pre-diabetes or diabetes.

Additionally, each unit increase in FINDRISC score above zero was found to be associated with a 7% increased risk of prediabetes, and a 15% increased risk of T2D (after adjusting for age and sex).

"Each of the patients were screened for Type 2 diabetes using the HbA1c test."

Jude concluded that although there were limitations to the study, such as only one hospital being taken into account, their results demonstrate the ability of A&E department screening to help discover thousands of undiagnosed diabetes and prediabetes cases across the country. “Our findings suggest that the FINDRISC score could also be used to help ensure those at highest risk of developing T2D are made aware of their risk,” added Jude. ●

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Low Doses of Radiation Could Contribute to Diabetes EXPOSURE to low doses of radiation could contribute to an increased risk of diabetes, according to new research presented at the EASD Annual Meeting 2023. Huan Hu and Toshiteru Ohkubo, both from the Japanese National Institute of Occupational Safety and Health (JNIOSH), analysed the emergency workers who were exposed to radiation when responding to a radiation accident at the Fukushima Daiichi Nuclear Power Plant, Japan.

In March 2011, this nuclear power plant was hit by a huge tsunami, and the NEWS study was established in 2014 to determine the long-term health effects of radiation exposure in emergency workers. A total of 5,326 male emergency workers (average age: 46 years) took part in the study.

"The NEWS study was established in 2014 to determine the long-term health effects of radiation exposure."

The emergency workers’ radiation exposure was measured with a dosemeter for external exposure, and a whole-body counter for internal exposure. Throughout the study, the participants

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underwent health examinations that involved more than 70 component, including inflammation biomarkers, eye examinations, and blood sugar checks.

After adjusting for a range of potential confounders, such as age, BMI, alcohol consumption, smoking, and high blood pressure, the researchers assessed the link between cumulative radiation exposure and incident diabetes. According to the results, the risk of developing diabetes was 6% higher in workers exposed to 5–9 mSv of radiation than those exposed to 0–4 mSv. This risk was 33% and 47% higher in those exposed to 20–49 and 10–19 mSv, respectively.

A total of 392 participants developed diabetes between 2012–2021, indicating that emergency workers are at increased risk of diabetes when exposed to low levels of radiation. However, the study’s findings show observational associations rather than cause and effect due to limitations, including a lack of data on diabetes types, confounding from unaccounted factors, or measurement inaccuracies. Hu stated: “Ongoing follow-up of NEWS participants will provide an even clearer picture of diabetes risk at low radiation doses.” ●

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Can Drinking Dark Tea Reduce Type 2 Diabetes Risk?

DAILY consumption of dark tea could help to reduce the risk of developing and progression of Type 2 diabetes, according to novel research presented at the EASD Annual Meeting 2023.

"These findings were most prominent in those who drank dark tea daily."

Tongzhi Wu, University of Adelaide and Hospital Research Foundation Fellow, Australia, and colleagues performed a cross-sectional, observational study of 1,923 adults across eight Chinese provinces. Of these, 1,135 had normal blood glucose levels, 352 had pre-diabetes, and 436 had a diabetes diagnosis. Participants were aged between 20–80 years and 562 were male.

The authors reviewed tea drinking frequency (never, occasionally, often, and daily) and type of tea consumed (black, dark, green, or other) to examine the association between type and frequency of tea consumption and urinary glucose excretion, insulin resistance, and glycaemic status.

Urinary glucose excretion was measured using morning spot urine glucose-to-creatinine ratio (UGCR), insulin resistance was measured using the triglyceride and glucose index (TyG), and glycaemic status was defined as a history of Type 2 diabetes; current use of antidiabetic medication; or abnormal 75 g oral glucose tolerance test.

Differences in age, sex, and clinical and lifestyle factors were controlled for.

The analysis revealed that, compared with those who never drink tea, daily tea consumption was associated with a 0.11 mmol/mmol increase in UGCR and a 0.23 reduction in TyG score.

Additionally, when compared with never teadrinkers, daily tea consumption was associated with a 28% reduced risk for Type 2 diabetes and 15% reduced pre-diabetes risk. Interestingly, these findings were most prominent in those who drank dark tea daily, with a 0.16 mmol/mmol increase in UGCR and a TyG reduction of 0.31.

The authors stated: “Our findings hint at the protective effects of habitual tea drinking on blood sugar management via increased glucose excretion in urine, improved insulin resistance, and thus better control of blood sugar.” Wu further commented that the findings could be indicative that bioactive compounds in dark tea could directly or indirectly modulate renal glucose excretion.

Whilst these findings are interesting, the authors noted the study was limited by the observational design and the potential for other residual lifestyle and physiological confounders.

Currently, they are in the process of conducting a double-blind randomised trial to further investigate and validate their findings. ●

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Testosterone Replacement Might Improve Blood Sugar Control TESTOSTERONE replacement therapy (TRT) might improve glycaemic control for up to 2 years, according to real-world data from an ongoing international audit of testosterone deficiency in males with Type 2 diabetes (T2D), which was presented at the EASD Annual Meeting 2023. This decrease in HbA1c is likely due to the effects of the hormone on fat reduction and insulin resistance.

The link between low testosterone and prevalence of T2D in males was discovered 20 years ago, with estimates suggesting that approximately 40% of males with T2D have symptomatic testosterone deficiency, a condition that is linked with adverse effects on psychological wellbeing, osteoporosis, and cardiovascular risk factors, as well as a double risk of death in males with T2D. While previous data has shown benefits of TRT, including reducing insulin resistance, cholesterol, HbA1c, obesity, and mortality, uptake of the treatment has been slow due to conflicting findings on cardiovascular risk. Despite evidence on the safety of TRT from a recent randomised trial, “the use of testosterone among endocrinologists remains low and many diabetologists have

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not even heard of the association between testosterone and diabetes,” stated lead author Hugh Jones, Barnsley Hospital, UK. The ABCD audit aimed to determine safety and real-world benefits of TRT on glycaemic control, obesity, and other diabetes complications or cardiometabolic parameters. Patients used either long-acting testosterone undeconoate intramuscular injections or gels. HbA1c was evaluated after 3, 12, and 24 months. The average HbA1c fell by 7.9 mmol/mol after 3 months, 9.6 mmol/mol after 12 months, and 15.4 mmol/mol after 24 months.

Results of the audit provide insights into whether TRT could have a beneficial effect on diabetes and obesity. More data is needed to determine whether there are indicators of which type of patient is likely to respond to TRT. Jones said: “These findings will also form the evidence basis for general practitioners and endocrinologists to proactively ask patients with T2D about their symptoms, and investigate and diagnose testosterone deficiency appropriately and treat them with testosterone where indicated.” ●

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Severe Hypoglycaemia More Common in Adults with Food Insecurity PEOPLE experiencing food insecurity were more likely to be severely hypoglycaemic, according to an analysis of USA data that was presented at the EASD Annual Meeting 2023. Little population-based research has been conducted to investigate the effect of food insecurity on rates of severe hypoglycaemia, so this was the first study of its kind. Alexandria Ratzki-Leewing, Western University, London, Ontario, Canada, led the secondary analysis of data from the US iNPHORM study, a 12-month prospective panel survey of real-world hypoglycaemia risk. Severe hypoglycaemia was 2.2 times more frequent in people experiencing food insecurity.

A total of 1,001 adults (49.6% male) with either Type 1 diabetes (16.1%) or Type 2 diabetes were included, and had to have been treated with insulin and/or secretagogues for at least 1 year. Average age of participants was 51 years, and median diabetes duration was 12 years. Questionnaires at baseline (Spring 2020) and over 12 consecutive months captured data on characteristics and frequency of severe hypoglycaemia. Severe hypoglycaemia was defined as a Level 3 low blood glucose concentration, regardless of blood glucose value, causing altered mental and/or physical status requiring professional or non-professional aid for recovery, based on the American Diabetes Association (ADA) Standards of Care guidelines.

At baseline, participants were asked the question: “Within the past 12 months, did you ever cut the size of your meals or skip meals because there was not enough food?” Those who answered 'yes' were classified as having experienced food insecurity. Around one in five of the cohort reported that they had experienced food insecurity; these rates were similar in Type 1 diabetes (18.6%) and Type 2 diabetes (20.4%). In these individuals, over half experienced at least one Level 3 event in the past year.

The authors performed a multivariable regression to determine if food insecurity caused higher rates of severe hypoglycaemia revealing, after adjustment for potential confounders (e.g., age, annual gross household income, insurance coverage, living arrangements, and diabetes type) that those with food insecurity experienced twice as many severe hypoglycaemic events than those not exposed to food insecurity, during the year studied.

"We showed that food insecurity is alarmingly common across this population."

Ratzki-Leewing described the novelty of this study: “This is the first community-based, prospective study to look at the impact of food insecurity on rates of Level 3 (severe) hypoglycaemia in adults in the US with diabetes on insulin and/or secretagogues.” She went on to stress: “We showed that food insecurity is alarmingly common across this population, and that it more than doubles the rate of severe hypoglycaemia,” and advised “clinicians use our screening question, and exercise vigilance when managing individuals with food insecurity prescribed insulin or secretagogues. Public health strategies to address food insecurity are also vital to prevent severe hypoglycaemia and its profound consequences.” Applying this work to the wider world, and guiding future strategies, Ratzki-Leewing concluded: “Ultimately, our study uncovers a key opportunity to reduce the burden of diabetes-related severe hypoglycaemia, while improving overall health. The results are timely given the rising cost of living, not only in the US, but also globally.” ●

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Breastfeeding Linked to Lower Body Fat Percentage in Children ACCORDING to new research presented at the EASD Annual Meeting 2023, there may be an association between infant formula and higher levels of body fat later in childhood. The study, presented by Catherine Cohen, University of Colorado Anschutz Medical Campus, Aurora, USA, shows that children who were breastfed as infants for at least 6 months had a lower percentage of body fat by age 9 years compared with those who did not receive breast milk.

The research team analysed data from over 700 mother–child pairs who had been taking part in the Healthy Start longitudinal cohort study. Interviews were conducted when the infants were 6 and 18 months old, during which the mothers were asked about feeding practices, including duration and exclusivity of breastfeeding versus formula feeding. The age at which the children were introduced to complementary foods (including solids and any liquid other than breastmilk or formula) was also recorded.

"Interviews were conducted when the infants were 6 and 18 months old."

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The infants were grouped according to the duration of breastfeeding (6 months or more versus less than 6 months); the age at which the baby was introduced to complementary foods (at or before 4 months, or 5 months and over); and the age at which they were introduced to soda (18 months or more versus less than 18 months). The majority of the infants (65%) were breastfed for 6 months or more, and 86% were introduced to soda after 18 months. At an assessment of percentage fat mass at a median age of 5 years, it was 19.7% on average, and at the second assessment, at median age 9 years, it was 18.1% on average.

It was found that infants who were breastfed for less than 6 months had 3.5% more body fat, on average, at age 9 than those who were breastfed for 6 months or more. The team also reported that infants who were introduced to soda before 18 months had about 7.8% more body fat, on average, at age 9 than those who first tried soda at 18 months or older. Cohen commented: “Our findings add to the larger body of evidence supporting the potential health benefits of breastfeeding for both mothers and their children.” ●

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Blood Sugar Control: Natural Light Versus Artificial Light NATURAL light could help to prevent and treat Type 2 diabetes, according to research presented at the EASD Annual Meeting 2023.

Previous research by Ivo Habets, Maastricht University, the Netherlands, and colleagues has shown that people at high risk of Type 2 diabetes are less able to switch from using carbohydrates as an energy source during the day, to using fat during the night. Therefore, the researchers wanted to see if exposure to natural light made this switch easier in people who already have diabetes.

A total of 13 participants (average age: 70 years; BMI: 30.1 kg/m2; HbA1c: 6.1; fasting plasma glucose: 8.1 mmol/L) were exposed to two lighting conditions during typical office hours (8 a.m.–5 p.m.). Participants were exposed to natural daylight from windows and LED lighting for 4.5 days each, with a 4-week gap in between.

Natural light was most intense at 12:30 p.m., with an average reading of 2,453 lux; however, the LED lighting was consistently 300 lux.

The participants’ blood sugar levels were constantly monitored through monitors that were worn on the upper arm. They were provided with the same meals during both lighting conditions, and evenings were spent in light measuring 5 lux, with their sleeping period (11 p.m.–7 a.m.) in darkness.

The researchers found that blood glucose levels stayed in the normal range (4.4–7.8 mmol/L) for longer in natural light than in artificial light (59% of the 4.5 days versus 51%). Furthermore, blood sugar control was better when the participants were exposed to natural light, suggesting that exposure to natural daylight is beneficial to metabolism, and could be used to prevent and treat Type 2 diabetes and other metabolic conditions, such as obesity.

Habets suggested that those who work in an office, with no exposure to natural daylight, should get outdoors, and get as much daylight as possible, as this lack of exposure has an impact on metabolism and risk of Type 2 diabetes. ●

"Participants were exposed to natural daylight from windows and LED lighting for 4.5 days each."

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Precision Medicine in Diabetes Authors:

Natasha Meunier-McVey, EMJ, London, UK

Citation:

EMJ Diabet. 2023;11[1]:22-24. DOI/10.33590/emjdiabet/10307138. https://doi.org/10.33590/emjdiabet/10307138.

THE 59th Annual Meeting of the European Association for the Study of Diabetes (EASD) took place in Hamburg, Germany, as well as virtually. With exciting new developments in diabetes treatment in the forefront of our minds, as described by the EASD President, Chantal Mathieu, presenter Paul Franks from Lund University, Sweden, took to the stage to deliver ‘ADA/EASD Precision Medicine in Diabetes Initiative: Executive Summary of the Second Consensus Report’. Franks provided the key updates to the consensus, published on 5th October 2023, only hours before the presentation.

WHY PRECISION MEDICINE IN DIABETES CARE? Franks began his presentation by framing the issue faced today: living in an era where diabetes is a tremendous global burden. He emphasised that, although we have some excellent diabetes medications and an ever-growing understanding to prevent it, the prevalence of diabetes is rapidly increasing worldwide. “Over the next couple of decades, we will have something like 1.3 billion people on the planet with this disease we call diabetes,” Franks stated, presenting an overwhelming urgency for new and effective treatments, that perhaps are not ‘one size fits all’.

"Living in an era where diabetes is a tremendous global burden."

Franks explained that in trials of evidencebased medicine, we observe results based on a comparison arm, and researchers will often find that placebo cohorts obtain better responses overall compared with those undergoing active treatment. This is due to many different factors; however, he stressed that possible biological variation is likely driving a response to a treatment for the care or prevention of a disease.

The biomarkers of this biological variation can be used to predict patients’ responses to a

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preventative intervention or treatment, allowing doctors to optimise the treatment and patient outcomes, as well as to save resources in the long term. Franks highlighted a real-world example of this in the Look AHEAD trial,1 which recently underwent a post hoc analysis, and emphasised the potential of a more personalised approach to diabetes treatment.

Franks went on to define precision medicine, and what it means in the context of diabetes care: “Precision medicine is essentially reducing error and improving accuracy in health recommendations and medical decisions.”

As error is relatively high in contemporary evidence-based medicine, many patients do not get optimal treatments, which might not be effective for the individual. “I would say that precision medicine is a reduction of that error,” he explained.

AMERICAN DIABETES ASSOCIATION/EUROPEAN ASSOCIATION FOR THE STUDY OF DIABETES PRECISION MEDICINE IN DIABETES INITIATIVE Established in 2018 by the American Diabetes Association (ADA) in partnership with EASD, the Precision Medicine Diabetes Initiative published its first consensus report in 2020. The 2023

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"The 2023 International Consensus report was worked on by 200 academics from 28 countries and 196 institutions."

International Consensus report is a series of systematic evidence reviews, and was worked on by 200 academics from 28 countries and 196 institutions. Described as an ‘herculean’ effort, the report saw the experts conceive primary research questions that should be addressed, in order for precision medicine to be implemented into clinical practice by 2030.

Franks outlined the framework used throughout the evidence reviews, termed the pillars of precision medicine: precision diagnostics, prevention, treatment, and prognostics. He went on to share the strongest evidence for the clinical translation of precision medicine in different types of diabetes. In monogenic diabetes, which is a key focus area given that primary prevention is not an option, next-generation genome sequencing has been found to aid precision diagnostics and treatment decisions.

In gestational diabetes, precision diagnostics have been shown to resolve heterogeneity in the condition for both the mother and offspring, and precision treatment has shown success in the mother. Further research is required for the

development and validation of algorithms to predict treatment success and risk in gestational diabetes, likely in combination with metabolomics.

Franks stated: “There's some promise that in the next few years we will move into a more translational phase in Type 1 diabetes.”

Through precision prevention and diagnostics, polygenic risk assessment, in combination with islet autoantibody testing, has been shown to identify individuals at high risk of developing Type 1 diabetes for early prevention, as well as in predicting disease development. Precision diagnostics have also shown promise in Type 2 diabetes, allowing diagnostic subclassification; however, there is a need for further research to obtain clinically relevant approaches. This, alongside further development of diagnostic algorithms with the submission of new data, may see a translation of precision diagnostics into the clinical setting. Key evidence from the TriMaster trial2 has also shown that simple clinical markers can be used to dictate treatment allocation in second- and third-line therapies.

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RESEARCH GAPS TO ACCELERATE CLINICAL APPLICATION The session moved on to establish a roadmap for the future impact of precision diabetes medicine.

The initial focal point that Franks shared was accelerating the clinical application of precision medicine in diabetes, emphasising its remarkable potential to alleviate existing disparities in diabetes healthcare. However, he also cautioned that precision medicine could widen these gaps if it is exclusively viewed as accessible to well-funded settings, rather than as a tool to implement globally.

Franks stressed the urgency of prioritising diversity, equity, and inclusion in precision medicine, and the importance of not only including vulnerable groups in high-income settings, but also of ensuring their priority status for this treatment. The report advocates for monitoring disparity gaps to ensure their reduction rather than amplification.

The next critical aspect addressed the necessity for new prospective trials designed specifically to test hypotheses related to precision medicine in diabetes. The TriMaster trial2 was presented as an example; however, it was stressed that incorporating such trials into drug development pipelines early is crucial for their clinical relevance and impact. Pregnancy, as a distinct area in diabetes, was revealed as the third focal point, due to its unique characteristics and potential for precision medicine interventions.

However, as Franks shared previously, the existing evidence was flagged as mostly moderate or low in quality, urging a push for more robust studies, particularly focusing on preconception and impacting lifestyle behaviours before pregnancy.

References 1.

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Pi-Sunyer X et al. The Look AHEAD trial: a review and discussion of its outcomes. Curr Nutr Rep. 2014;3(4):387-91.

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The urgency of cost-effective, diseasemodifying interventions in both Type 1 and Type 2 diabetes, particularly targeting microvascular and macrovascular complications, was outlined as the fourth focal point. Franks spotlighted the need to ensure that precision medicine is tailored to meet individual needs, rather than relying on single technologies like genomics. In the pursuit of a global precision medicine approach, emphasis was put on health equity and the mitigation of disparities, stressing the importance of developing solutions specific to target populations. The reliance on data predominantly from high-income countries and populations of European ancestry was highlighted, as it is not deemed reflective of the global burden of diabetes.

Furthermore, Franks stressed the importance of consistent reporting standards, and collaboration with regulators, such as the United States Food and Drug Administration (FDA) and European Medical Association (EMA), to ensure the seamless translation of precision medicine into clinical practice. Collaboration among various stakeholders was underscored as crucial to ensure that the best research is effectively applied in clinical settings.

CONCLUSION Ultimately, this insightful session emphasised that the development of precision medicine in diabetes has been made possible through a huge collaborative effort. The dedication of 200 experts over 3 years, notably during the challenges of the COVID-19 era, formed the cornerstone of the progress made in the field.

There is much excitement to come in this area, and we are one step closer to successfully tailoring diabetes treatment to each patient, optimising patient experience and quality of life. ●

Angwin C et al.; MASTERMIND consortium. TriMaster: randomised double-blind crossover study of a DPP4 inhibitor, SGLT2 inhibitor and thiazolidinedione as second-line or third-line therapy

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in patients with type 2 diabetes who have suboptimal glycaemic control on metformin treatment with or without a sulfonylurea-a MASTERMIND study protocol. BMJ Open. 2020;10(12):e042784.

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Equity Versus Equality: A Spotlight on Health Inequalities Authors:

Anaya Malik, EMJ, London, UK

Citation:

EMJ Diabet. 2023;11[1]:25-27. DOI/10.33590/emjdiabet/10303633. https://doi.org/10.33590/emjdiabet/10303633.

THE Primary Care Diabetes Europe (PCDE) study group shared the results of its educational projects focused on health inequalities in diabetes, in a symposium titled ‘A Spotlight on Health Inequalities by Gender, Ethnicity, and Migration Status’. The session was presented at the European Association for the Study of Diabetes (EASD) 2023 Annual Meeting, chaired by Pinar Topsever, Acıbadem University School of Medicine, Istanbul, Türkiye, and PCDE Board Member.

Current guidelines and classifications for the risk of developing Type 2 diabetes (T2D) place primary emphasis on medical risk factors over aspects of social determinants of health and contextual factors. With the rising prevalence of multimorbidity, closely associated with the ageing population, the approach to most healthcare, medical research, and medical education still focuses on the single disease framework.

EXTERNAL VALIDATION OF DIABETES USING CARDIOVASCULAR RISK MODELS Rimke Vos, University Medical Center (UMC) Utrecht, the Netherlands, presented the first of three studies conducted by the PCDE shared in this session. The study group challenged the single disease framework, and aimed to produce evidence on the importance of social determinants of health in clinical decision-making and risk prediction. There is a need to integrate this knowledge, Vos shared, into our treatment decisions and consider whether decisionmaking algorithms, initially designed for specific populations, are applicable to a diverse and multifaceted patient population.

Their research aimed to externally validate SCORE2-DM and evaluate the model performance within a socioeconomic and ethnic

diverse group in The Hague, the Netherlands.

The risk score was further stratified based on disposable household income, migration background, and origin. The study included 22,878 inhabitants, with 12,124 males and 10,754 females, equally distributed according to socioeconomic status. The median followup period extended to approximately 9 years, during which 2,193 cardiovascular events occurred. Overall, in regions with a low risk of cardiovascular events, SCORE2DM showed strong predictive accuracy for identifying individuals with T2D at a high risk of cardiovascular events.

When stratified by socioeconomic status, there was an underestimation of risk among individuals with lower socioeconomic status, and an overestimation among those with higher socioeconomic status in both sexes. Additionally, females with an immigration background and high socioeconomic status experienced an underestimation of their cardiovascular risk.

"The data revealed the high prevalence of complications in minority populations."

By combining both medical and social determinants of health and analysing the impact of ethnic minorities and low income, the study

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group gained better insight into how applicable the algorithm was for the population. The large dataset used was a key strength of the study, confirmed Vos, although it lacked detail to differentiate between different immigration backgrounds.

DISPROPORTIONATE CARDIO RENAL EFFECTS IN ETHNIC POPULATIONS Samuel Seidu, Chair of the PCDE Research Study Group, University of Leicester, UK, presented on ethnic disparities in the effects of glucagon-like peptide-1 receptor agonists and sodium-glucose co-transporter-2 inhibitors on cardiovascular and renal outcomes. The session highlighted the disproportionate cardio renal effects observed in ethnic populations, with a specific focus on the use of these new agents in Black individuals. Seidu also highlighted the challenges associated with recruiting diverse populations for clinical trials and the impact of limited statistical power. He shared data from the UK that showed a significantly higher prevalence of T2D among ethnic minority groups, with nearly 80% of individuals under the age of 60 affected, compared to less than 60% among the White population. These differences in T2D prevalence

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have consequences on the health and economic status of affected working populations, leading to disproportionate health inequalities.

The data presented by Seidu revealed the high prevalence of complications in minority populations and the suboptimal prescription of treatments. These findings underscore the importance of ensuring that clinical trials are representative of the diverse population proportionate to trial outcomes and the needs of the population.

GESTATIONAL DIABETES PREVALENCE IN VARYING SUBGROUPS Francesc Xavier Cos Claramunt, Chairman of PCDE, and Autonoma University in Barcelona, Spain, reported on a study focusing on gestational diabetes (GD) complications among females accessing sexual and reproductive health care services in primary care in Catalonia, Spain.

GD is one of the most common pregnancyrelated disorders. Its prevalence is increasing worldwide and is associated with complications in delivery, increasing maternal obesity, and

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maternal age. Screening opportunities have increased in some countries, and countries may adhere to different diagnostic criteria for diabetes in pregnancy, including the International Association of Diabetes and Pregnancy Study Groups (IADPSG) criteria. Claramunt explained that existing studies are discordant with GD prevalence in association with socioeconomic status, largely due to the lack of a global consensus on the diagnostic criteria.

"Existing studies are discordant with GD prevalence in association with socioeconomic status."

Claramunt presented a study on the prevalence of GD in Catalonia, which did not demonstrate a significant increase in the prevalence of GD throughout a 10-year period (2010–2019).

However, there were certain categories where increased prevalence was observed, particularly among younger females and those with specific birth characteristics.

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The study suggested that females with a higher socioeconomic status had a lower prevalence of GD compared to those with lower socioeconomic status, with a 24% difference between the greatest extremes. Observation of higher prevalence of disease in particular subgroups garners attention on public health efforts to manage and prevent GD, with an emphasis on weight and obesity management.

SPOTLIGHT ON HEALTH INEQUALITIES The presented studies critically challenge the importance of social factors of health, ethnicity, and socioeconomic status in clinical decision-making and healthcare research. They also highlight the need to address healthcare disparities among different populations, and ensure an approach to healthcare that addresses social factors, equitable trial recruitment, and access to healthcare services. ●

"The studies highlight the need to address healthcare disparities among different populations."

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Abstract Highlights The following highlights spotlight abstracts presented at the 59th European Association for the Study of Diabetes (EASD) Annual Meeting. Selected abstracts cover pertinent and timely topics, including cardiopulmonary performance in patients with Type 2 diabetes and obesity, and gestational diabetes screening in early pregnancy.

EMJ Diabet. 2023;11[1]:28-34. DOI/10.33590/emjdiabet/10301251. https://doi.org/10.33590/emjdiabet/10301251.

Citation:

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Chronic Kidney Disease and Continuous Positive Airway Pressure NOVEL research presented at the EASD Annual Meeting 2023 highlighted the potential use for continuous positive airway pressure (CPAP) in reducing the chronic kidney disease (CKD) burden in patients with Type 2 diabetes (T2D) and obstructive sleep apnoea (OSA).

OSA is a risk factor for CKD in patients with T2D; therefore, researchers sought to investigate whether CPAP (a treatment used in OSA) could help to reduce this risk. Led by E.A. Makhdom, University of Birmingham, UK, and Imam Abdulrahman Bin Faisal University, Damman, Saudi Arabia, researchers performed an openlabel, multicentre, feasibility randomised controlled trial, which took place over a 2-year period. The impact of CPAP on CKD burden was evaluated through measurement of estimated glomerular filtration rate (eGFR) and urinary albumin-creatinine ratio (ACR).

A total of 83 patients with T2D and OSA from across 13 centres were enrolled, and were randomised to either receive CPAP (n=43) or no CPAP (n=40) over the study course. OSA was defined as an Apnoea–Hypopnoea Index (AHI) of ≥10 events/hour. Exclusion criteria included Epworth Sleepiness Score (ESS) of ≥11, central apnoea index >15/hour, or resting O2 saturation <90%.

Of those enrolled, mean participant age was 62.5±10.9 years; average diabetes duration was

12.2±7.9 years; median follow-up duration was 645 days; 71.1% were male; 89.1% were of White European ethnicity; 48.2% were prescribed insulin; and 77.7% had obesity. Due to the impact of the COVID-19 pandemic, study-end renal data was only available for 49 patients. Out of the 43 patients enrolled to receive CPAP, only 23 used it. The median CPAP usage was 3 hours and 40 minutes per night.

The analysis found that randomisation to the CPAP group was associated with a higher eGFR, lower urinary ACR, and lower risk for developing albuminuria and an eGFR <60 mL/min/1.73m2, compared to the no CPAP group. Moreover, the authors reported that those in the CPAP group experienced an improvement in eGFR and ACR, whereas these parameters remained stable in the no CPAP group.

"Those in the CPAP group experienced an improvement in eGFR and ACR." Whilst this study was limited by small size, from the findings, the team concluded that use of CPAP could have a beneficial effect on eGFR and ACR in patients with OSA and T2D, but for the future, this would need to be validated in a full and adequately powered randomised controlled trial. ●

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Type 2 Diabetes and Obesity: Effects on Cardiopulmonary Performance EFFORT intolerance is highly prevalent in patients with Type 2 diabetes (T2D).

Measured through O2 uptake in peak exercise (VO2peak), effort intolerance predicts incident heart failure. However, the quantification and pathophysiological causes of effort intolerance remain to be debated. A research team from University of Pisa, Italy, aimed to investigate the effects of T2D and obesity on cardiopulmonary performance.

Patients at high cardiovascular risk, both with and without T2D, underwent pulmonary spirometry and a thorough cardiovascular characterisation, which included maximal combined echocardiography-cardiopulmonary exercise test to exclude pulmonary and/ or cardiovascular disease, along with overt microvascular complications.

Multivariable models accounted for the major pathophysiological determinants of VO2peak normalised for lean body weight (LBW).

This ensured the effects of T2D were isolated, permitting the identification of the underpinning mechanisms.

A total of 109 patients with T2D and 97 controls without diabetes were included in the analysis. Both test groups had similar baseline characteristics, with the exception of BMI, which was higher in the T2D group.

Patients with T2D achieved lower VO2peak than

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the participants who did not have diabetes (26.1±5.4 versus 29.5±9.6 mL/min/kgLBW).

Subclinical cardiopulmonary impairments were observed in the T2D group, namely chronotropic incompetence, subclinical systolic dysfunction, and more marked cardiac remodelling.

Furthermore, the presence of T2D was an independent negative determinant of VO2peak in all models, maintaining statistical significance and a strong coefficient of determination.

Finally, BMI reduced exercise capacity by 0.2 mL/min/kgLBW, which could be largely explained by reduced peripheral O2 extraction, impaired systolic reserve, chronotropic incompetence, and ventilator inefficiency. Meanwhile, T2D independently reduces VO2peak by 1 mL/min/ kgLBW and this is only marginally explained by any or all of the previously listed determinants of cardiopulmonary fitness.

"Both test groups had similar baseline characteristics, with the exception of BMI." Overall, T2D negatively affects VO2peak independently, even in the absence of cardiac or pulmonary disease. This effect is in combination to all other pathophysiological determinants of O2 uptake. ●

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Using Participant Characteristics to Prevent Gestational Diabetes AN INDIVIDUAL’s unique biological, social, and environmental context can be used to determine their responses to interventions for preventing diseases. However, the effects of intervention for the prevention of gestational diabetes (GD), according to individual characteristics, have yet to be systematically investigated. New data presented at the EASD Annual Meeting 2023 evaluated the participant characteristics associated with GD prevention.

Research involved a systemic review and metaanalysis of 116 studies (n=40,903 females), published up to 24th May 2022, in order to identify lifestyle (diet, physical activity, or both), metformin, myoinositol/inositol, and probiotics interventions. Studies commencing in preconception or antenatal periods with GD as an outcome were included. Participant characteristics were extracted according to the conceptual framework described in the Precision Medicine in Diabetes Consensus Report.

The results showed that physical activity was more effective in GD reduction in participants with a normal BMI compared with an obese BMI (risk ratio [95% confidence interval]: 0.06 [0.03–0.14] versus 0.68 [0.26–1.60]; p<0.001; 17 studies). Diet and physical activity were found to be more effective in reducing risk for patients with GD who did not have polycystic ovary

syndrome (PCOS) than those who did (0.62 [0.47–0.82] versus 1.12 [0.78–1.61]; p=0.030; 59 studies), as well as in patients without a history of GD than those with unspecified history (0.62 [0.47–0.81] versus 0.85 [0.76–0.95]; p=0.030; 59 studies). Additionally, metformin interventions were more effective in participants with PCOS than they were in patients with unspecified status (0.38 [0.19–0.74] versus 0.59 [0.25–1.43]; p<0.001; 13 studies), and in older patients (β coefficient: -0.52 [-1.02–-0.02]; p=0.040; eight studies). The researchers also found that parity, history of having a large for gestational age infant, or family history of diabetes did not impact the effectiveness of any interventions.

"Physical activity was more effective in GD reduction in participants with a normal BMI." The team concluded that females with PCOS or higher age may benefit more from metformin than those without these traits, and that females with a normal BMI could benefit more from physical activity than those with a higher BMI when it comes to preventing GD. This study highlights the importance of further research into precision prevention. ●

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Dietary Interventions and Insulin Requirements in Type 1 Diabetes IMPROVED insulin resistance, glycaemic control, and reduced medication requirements have been associated with a plant-based diet in people with Type 2 diabetes. The current study, presented at the EASD Annual Meeting 2023, compared the effects of this diet with a portion-controlled diet in patients with Type 1 diabetes (T1D).

A total of 58 participants with T1D were randomly assigned to follow either a low-fat vegan diet (n=29), or a portion-controlled diet (n=29) for 12 weeks. Meal content was recorded using the Cronometer mobile application (Revelstoke, Canada), and insulin sensitivity assessed using the carbohydrate to insulin ratio, which was calculated as the number of grams of total carbohydrate versus units of insulin administered.

Total insulin dose was calculated as a sum of basal and bolus insulin units injected per day.

Consumption of carbohydrates increased in the vegan group by an average of 118 g/ day (p=0.002), compared with no significant change in the portion-controlled group (-18 g/day; p=0.110; treatment effect: +137 [95% confidence interval (CI): +65–+208]; p<0.001).

Insulin sensitivity increased in the vegan group by 6.2 on average (p=0.002), compared with no significant change in the portion-controlled group (-0.8; p=0.320; treatment effect: +7.0 [95% CI: +3.1–+10.9]; p=0.001). Total daily dose of insulin decreased by 28%, specifically by 12.1 units/day in the vegan group (p=0.007), compared with no significant change in the portion-controlled group (-1.4 units/day; p=0.660; treatment effect: -10.7 [95% CI: -21.3–-0.2]; p=0.046). HbA1c showed decrease by 0.8% in the vegan group (p<0.001),

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and by 0.6% in the portion-controlled group (p=0.002; treatment effect: -0.2 [-0.7–+0.2]; p=0.340). Total cholesterol decreased by 32.3 mg/dL in the vegan group (p<0.001) and by 10.9 mg/dL in the portion-controlled group (p=0.030; treatment effect: -21.4 mg/dL [-35.6–-7.2]; p=0.004). Low-density lipoprotein cholesterol decreased by 18.6 mg/dL in the vegan group (p<0.001), and did not change significantly in the portion-controlled group (treatment effect: -9.1 mg/dL [-22.6–+4.5]; p=0.180). Triglycerides did not change significantly in either group, but blood urea nitrogen decreased by 6.0 in the vegan group (p<0.001), and did not change significantly in the portion-controlled group (treatment effect: -5.2 mg/dL [-7.9–-2.5]; p<0.001). Blood urea nitrogen to creatinine ratio decreased by 5.1 mg/ dL in the vegan group (p<0.001), and did not change significantly in the portion-controlled group (treatment effect: -6.7 [-10.7–-2.6]; p=0.002).

The results allowed the investigators to conclude that a low-fat vegan diet has beneficial effects upon insulin sensitivity, insulin requirements, glycaemic control, and both markers of cardiovascular and renal health in patients with T1D compared with a portion-controlled diet.

This randomised control trial promotes the use of the low-fat vegan diet in management of T1D, but larger trials are warranted to confirm findings. ●

"This randomised control trial promotes the use of the low-fat vegan diet in management of T1D."

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Impact of Exercise on Body Composition in Type 2 Diabetes RELATIVELY large volumes of concomitant exercise can improve the negative effects of caloric restriction on fat-free mass (FFM), according to an abstract presented at the EASD Annual Meeting 2023. The study aimed to assess the effects of various exercise volumes, combined with diet-induced weight loss, on fat-mass percentage and FFM in patients with Type 2 diabetes (T2D).

Researchers conducted a secondary analysis of a 16-week four-armed randomised trial, which included 82 patients with T2D who were signed randomly to either standard care (CON), caloric restriction (DCON), DCON with exercise three times per week (MED), or DCON with exercise six times per week (HED). Change in fat-mass percentage points (FM%p) was the primary outcome, and secondary outcomes included changes in FFM and visceral adipose tissue (VAT) volume. They used a constrained baseline mixed effects linear model to analyse data, and mean differences were presented as percentages if data were log-transformed.

Mean age at baseline was 58.2±9.8 years, with T2D duration of 4.0 years (interquartile range: 1.9–5.5), and body weight of 101.4±14.6 kg. In the DCON group, FM%p was changed by -3.5%p following intervention, compared with -6.3%p

in the MED group, and -8%p in the HED group; however, there was a further reduction in the MED group (-2.8%p) and HED group (-4.5%p).

The team noted a reduction in absolute FFM following intervention in the DCON (-2.7%) and MED (-1.9%) group, compared with CON, but no reduction in absolute FFM was noted in the HED group (-0.2%). Finally, all groups showed a reduction in VAT volume compared to CON (DCON: -666.0 cm3; MED: -1,264.0 cm3; HED: -1,786.4 cm3), and the MED and HED groups showed greater VAT reductions compared to DCON. The team concluded that all interventions were superior in reducing FM%p and VAT, compared with CON. Caloric restriction alone and combined with exercise three times per week led to reduced FFM, but this was unchanged in those exercising six times per week. These results suggest that relatively large volumes of concomitant exercise can improve the negative effects of caloric restriction on FFM. ●

"Caloric restriction alone and combined with exercise three times per week led to reduced FFM."

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EASD 2023

Gestational Diabetes Screening in Early Pregnancy THE RESULTS of a multicentre cohort study, which used oral glucose tolerance tests (OGTT) and biomarkers to screen for gestational diabetes (GD) in the early stages of pregnancy, have been presented at the EASD Annual Meeting 2023.

GD is usually diagnosed after 24 gestational weeks, according to the majority of clinical recommendations. Providing females with an earlier diagnosis has the potential to greatly improve outcomes during pregnancy; however, at present, a clear consensus regarding accurate risk stratification during early stages of pregnancy does not exist.

The team, made up of researchers from a number of centres across Switzerland, Germany, Austria, and Italy, aimed to examine the predictive performance of using a 75 g OGTT, along with several biomarkers, during the first trimester.

The researchers also assessed the association of OGTT and biomarkers with β-cell function and insulin action.

The study included 703 females who were pregnant, and receiving care at six centres in central Europe. At a median gestational age of 13.4 gestational weeks (interquartile range: 12.7–14.1), researchers considered patient history, biochemical markers, and a blinded 75 g OGTT.

A second 75 g OGTT was carried out between 24–28 gestational weeks, in order to identify those with GD. In a subgroup consisting of 204 females, researchers also performed a detailed investigation of glucose homeostasis during both visits.

It was discovered that glucose concentrations during the early OGTT (OGTT 0 minutes: 70.3%; 95% confidence interval [CI]: 63.8–76.8; OGTT 60 minutes: 75.0%; 95% CI: 69.6–80.5; OGTT 120 minutes: 72.9%; 95% CI: 66.5–79.2) could fairly predict the later development of GD, which occurred in 87 females in the study group (12.4%). Some biomarkers, which were examined using the area under the receiver operating characteristic curve, also identified significant, although modest, predictive accuracy (adiponectin: 61.2%; triglycerides: 60.1%; glycosylated fibronectin: 58.6%; and fructosamine: 56.9%).

The team concluded that a strong association exists between fair predictive accuracy for the development of GD and parameters of glucose homeostasis. In future, this test can be used to pinpoint those at low- and high-risk of developing GD during pregnancy. ●

"Providing females with an earlier diagnosis has the potential to greatly improve outcomes during pregnancy."

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Interviews Maggie Shepherd discusses her research in the pertinent field of monogenic diabetes, and its common misdiagnosis. Pia Leete sits down to highlight the importance of pancreatic immunopathology in diabetes research, and the work being done to improve global equity.

Maggie Shepherd Associate Director for Nursing Research; Consultant Nurse, Royal Devon University Healthcare NHS Foundation Trust; and Honorary Clinical Professor of Monogenic Diabetes, University of Exeter, UK Citation:

Q1

EMJ Diabet. 2023; DOI/10.33590/emjdiabet/10303060. https://doi.org/10.33590/emjdiabet/10303060.

What inspired you to pursue a career in nursing, and to start research?

I was about 14 or 15 when I first decided that I wanted to be a nurse. I started volunteering at my local hospital, and also volunteered at the Red Cross. Both of these experiences convinced me that yes, this was the role for me. So, I started nursing at 18 at King’s College Hospital in London, UK, and have been nursing in the National Health Service (NHS) ever since.

In terms of starting research, that was a bit more of a chance encounter, really. I had been working as a diabetes specialist nurse in London for 8 years, and then was planning a move down to Devon, UK. So, I sent my curriculum vitae round to the diabetes teams around Exeter. It just happened that Professor Andrew Hattersley, who leads the team in monogenic diabetes, and who had started 6 months prior, was looking for a diabetes research nurse, initially collecting samples from individuals with an autosomal dominant family history of diabetes; so, that is how my research career first started.

I subsequently went on to do a PhD to gain an

understanding of patients’ and professionals’ attitudes to genetic testing in diabetes; and postdoctoral awards investigating the impact of a genetic diagnosis and treatment change from insulin to tablets, and also the impact of a diagnosis of neonatal diabetes. My later work has focused on correct identification of monogenic diabetes, and translating these research findings into clinical care.

Q2

You lead the National Genetic Diabetes Nurse Project. How does this project help raise awareness on monogenic diabetes, and support patients and their families?

I started the National Genetic Diabetes Nurse Project in 2002, because I was very aware that we had been finding genes that caused monogenic diabetes, which is diabetes caused by change in a single gene. We were providing this information to clinicians and families, but at that point in time, there was very little understanding of monogenic diabetes, although we were publishing papers and presenting at conferences. As I had a background as a

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diabetes specialist nurse, I thought that there could be a better way of effectively translating these research findings into clinical care. So, I approached the Department of Health to try and gain funding to train diabetes specialist nurses across the UK in genetic forms of diabetes. That was a model that we carried on until 2021, with regional genetic diabetes nurses, who worked half a day per week on monogenic diabetes, and continued to work as a diabetes specialist nurse for the rest of their time. We used a model of training the trainers: the genetic diabetes nurses attended training three times a year in monogenic diabetes. Then we would provide PowerPoint presentations for them to give talks to other healthcare professionals about monogenic diabetes across their regions to raise awareness, with the idea of trying to identify more, and to make sure patients meeting the criteria had genetic testing. For those with a positive result, they were then changed to the optimal treatment, and we made sure that their family members had appropriate follow-up.

However, there were still areas of the UK without a genetic diabetes nurse.

In 2021, I contacted the NHS England diabetes programme, and aimed to work with them to increase awareness of monogenic diabetes even more. They were able to provide us with some financial support, so we were able to change our 2-day course in monogenic diabetes into a virtual format, and offer this

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to healthcare professionals in England at no charge for the past 4 years. These have been attended by clinicians and nurses, nationally and internationally. We recorded the talks in advance, and developed the programme so that people could listen to the talks, and then we have a live question and answer session. Over the past 2 years, we have run that course four times, and have had over 1,000 delegates attend. As part of that, we aimed to identify a named consultant and diabetes specialist nurse in every Trust with a diabetes service in England. In 2 years, we now have 95% of Trusts with an identified monogenic diabetes lead. The vast majority of those have both a diabetes consultant and a diabetes specialist nurse as named leads. We only have 5% of Trusts in England with a service who do not currently have a named monogenic diabetes lead. The idea behind it is that it does not matter where you live, wherever you are, with diabetes; there will be somebody in the local diabetes service with training in monogenic diabetes. In fact, around 80–86% of teams now have additional staff, as well as the named leads who have attended the training as well. We run some virtual masterclasses that staff can attend to increase their skills, and all of our training videos are available on www.diabetesgenes.org for healthcare professionals to access in their own time for free, if they cannot spend 2 days attending the 2-day course, or would like to revisit the content.

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Q3

Your current research involves identifying patients with monogenic diabetes who have been misdiagnosed as having Type 1 or Type 2 diabetes, and coordinating the transfer of these patients to the most appropriate treatment. How common is misdiagnosis, and how can this be improved?

One of our colleagues, Lewis Pang, identified the prevalence of monogenic diabetes as being 248 per 1 million of the population, with an anticipated 12,000 (77%) cases in the UK that still have not received a confirmed diagnosis.1 Historically, the time between the initial diabetes diagnosis and getting the correct genetic diagnosis was around 10–12 years. That is clearly something we want to improve, to make sure that people get the right diagnosis as soon as they possibly can.

It really is about raising awareness, raising recognition. Another colleague, Bev Shields, developed a maturity onset diabetes of the young (MODY) calculator, or a diabetes application,2 where clinicians can enter eight different clinical characteristics very easily, with a patient sitting in front of them in clinic, which can help to identify how likely it is that the patient has monogenic diabetes. There are lots of different aids to differential diagnosis. Of course, we try to publicise and raise awareness of this through conference presentations, publications, podcasts, etc. Obviously, patients can access all the information on our website, and can put their details into the calculator as well.

Q4

What does the current diagnostic/ treatment landscape for monogenic diabetes look like, and what are the unmet needs in this area?

I think the key thing is that anybody who meets criteria for genetic testing should be accessing that genetic testing, under the NHS. As testing is now centrally-funded, costs should not be a barrier for anyone in the UK when accessing genetic testing, which is the only way we can confirm monogenic diabetes. More than 30 different genes have been identified as causing different types of monogenic diabetes. Each have different clinical characteristics and different treatments. By performing genetic testing, and finding the specific gene that is

affected in that case, we can then advise the best treatment for that patient. For example, those who have glucokinase MODY do not need any treatment or any follow-up, so they can be discharged from diabetes screening. For patients who have HNF1A or HNF4A MODY, they are best treated with a low dose of a sulfonylurea. Many, but not all, of these patients can be taken off insulin, and get better glycaemic control on a low dose of sulfonylurea. However, about 50% of the cases of neonatal diabetes are caused by changes in the potassium channel, and these patients are best treated with a high dose of sulfonylurea. So, the key thing, really, is that the optimal treatment depends on the gene that is affected. By doing genetic testing, we can identify which gene is affected, and then we can offer the optimal treatment for that patient, as well as the best advice and management for their care going forward.

Q5

What is the role of genetic testing in diabetes, and what is its impact?

Doing a genetic test is the only way to get a correct diagnosis. Typically, a sample is tested for all known genes that cause monogenic diabetes in one go. So, in a number of cases we will be able to confirm that the patient has monogenic diabetes. However, in some of the cases, we get a negative result, which means that those patients do not have a change in a gene that we know causes monogenic diabetes, so they still may have an atypical Type 1 or Type 2 diabetes.

"Identifying the correct genetic diagnosis can have an impact on treatment."

Identifying the correct genetic diagnosis can have an impact on treatment. However, there are some types of monogenic diabetes that have a wider impact. For example, in HNF1B MODY, many of these patients will have a renal developmental abnormality, such as renal cysts, or a single or horseshoe kidney that may impact on their kidney function or uterine abnormalities; for example, a bicornate uterus or uterus didelphys. Patients with HNF1B typically have a very small pancreas, so the exocrine pancreatic

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function can also be affected, and these patients may need treatment with Creon. So, the impact can be wider than just diabetes.

Another example is mitochondrial inherited diabetes and deafness (MIDD). Typically, patients with MIDD will have a bilateral sensorineural deafness, and early-onset diabetes, and they can have other features as well. These patients tend to need insulin within a couple of years. So, the impact of a genetic diagnosis can include getting an answer for patients for what has caused their diabetes, but may also indicate what has caused some other symptoms that they may have. Sometimes, even though some of those may not seem that positive, it can be helpful to get an explanation of what Is causing these other features in patients, and perhaps in other family members.

Q6

One of your research interests involves bridging the gap between research and clinical care. What steps are needed to overcome this gap?

I was appointed as one of the National Institute of Health and Care Research (NIHR) 70@70 senior nurse research leaders a few years ago.

That was a 3-year part time appointment, and it focused on developing opportunities for nurses and midwives to undertake clinical and research careers, and support progression to a clinical academic career. Historically, there have not been clearly defined pathways for nurses, midwives, or other allied healthcare professionals to undertake a career that combines both clinical work and research at a senior level. However, Ruth May, who is the Chief Nursing Officer for England, recently launched a research strategy, which highlights the importance of nurses at all levels becoming involved and engaged in research. A similar initiative was launched by the Chief Midwifery Officer, and also the lead for allied healthcare professionals.

Within our own organisation, we have developed a nurse, midwife, allied healthcare professional, and healthcare scientist research strategy, which is focused on engaging clinical staff at all levels in research. This could be individuals just being aware of what research is going on in the organisation, which they can signpost patients to, or it may be an awareness of research roles

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within the organisation, or undertaking their own research.

We have also set up a scheme that we have been running for about 4 years now: the Chief Nurse Research Fellow programme, where individuals who might not have been exposed to research before have 1 day out of clinical practice fortnightly for 1 year to learn more about research, and consider potential opportunities to engage more in research in the future. The Chief Nurse Research Fellows also undertake a project based on a clinical problem in their area, to get them interested in the early stages of what research within their role and their organisation could look like. We have also set up a clinical academic network of individuals within the organisation, who are doing research at different levels; some are pre-PhD, some are post-PhD, but the idea is to provide a supportive, collaborative network, with possibilities for mentorship, and practice interviews for those who are applying for research funding. We all talk about evidence-based care, and that evidence has come from research, so I think it is vital to see research as a crucial component of clinical care, and show nurses, midwives, and allied healthcare professionals how they can turn clinical questions into potential research.

Different professions will see things from a different perspective, so we can all have a role to play in research.

Q7

You were awarded the Aster Guardians Global Nursing Award 2023.

Can you tell us more about what led to you receiving this award?

Aster Guardians Global Nursing Award is an initiative that has been developed by Aster DM Healthcare to recognise the great work that nurses are doing across the world, and to inspire others to join the profession. The first award was in 2022, and it was won by an amazing Kenyan nurse, Anna Qabale Duba, who had been doing some fantastic work with women and girls in terms of healthcare and education, and improvements within her own village in Kenya.

I saw the details of the award, and thought it would be a great opportunity to highlight some of the work that we do in Exeter, because we offer genetic testing for neonatal diabetes for anybody

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diagnosed with diabetes under 6 months of age, anywhere in the world. For those countries that cannot afford it, we are able to offer that testing for free. I thought that it would be fantastic to draw attention to the work that we are doing, and to raise awareness of this service, because if countries across the world know that if they have a baby below 6 months of age who is ill, and if they test the glucose and it is raised, they can refer to us for genetic testing, at no cost to them if they cannot afford it.

(UN), International Council of Nurses (ICN), and other groups. We had a virtual interview with the Grand Jury, and that was an opportunity to talk about the different work that I had been doing, including monogenic diabetes, my own research, the clinical care that I am involved with, the educational initiatives that I have developed, and the leadership that I have been involved with, including the Florence Nightingale Foundation Leadership Scholarship.

So, I put in an application. There were five different categories, and I provided evidence for my work in the following categories: leadership, research, clinical care, and education. My application involved a description of the work that I did in each of those four areas, and then the application was verified by the external auditors Ernst & Young (EY; London, UK).

"Move forward 40 years or so, and we now have patients who are able to monitor their blood glucose through sensors."

This year, over 52,000 registrations were received from nurses from 202 different countries, who were then were shortlisted to 10 finalists. We had an interview with the Grand Jury, which included senior individuals from the World Health Organization (WHO), United Nations

For the next year, this is leading to more opportunities to talk to different forums and countries about monogenic diabetes and the work that we do, as well as championing the cause of nursing, and promoting the profession.

So, this was a fantastic opportunity for me.

Meeting the other nine finalists was hugely inspiring: to talk to them, and listen to the work

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that they have been doing. We have been in touch regularly since, so it is a very supportive group as well.

The Aster Guardians Global Nursing award is about to open for nominations for next year, so I am trying to encourage lots of nurses from the UK to apply. It is a great opportunity to raise the profile of nurses, and to encourage a new generation of nurses to come through the system. I think it shows the variety of work that nurses can do in all sorts of different areas.

Q8

Over the years that you have been practicing as a nurse, how have you seen the field change in terms of advancements to the technology used?

There have been absolutely huge advances, specifically in diabetes. I remember the first ward that I worked on when I qualified in 1984 was in King's College Hospital, and the equipment we had then was so different; for example, the blood glucose monitors were very large, and took 2 minutes to give you a reading. We were putting drops of urine in a test tube to see how much glucose was in the urine. Move forward 40 years or so, and we now have patients who are able to monitor their blood glucose through sensors, with an immediate blood glucose result. Not only do we have insulin pens, but we also have insulin pumps and closed loop systems. So, I think that the advances in the technology have just been phenomenal. Managing diabetes is still really difficult, I would not underestimate that at all, but I think that technology has made things so much easier. When we are in the clinic, we can download a patient's blood glucose results onto our computer from the system they are using, which makes things much simpler to assess. I am sure that there will be lots more advances in the future, as well.

There has been a huge increase in the different types of medications that can be used for diabetes, some with some hugely beneficial results. And, of course, genetic testing.

Obviously, genetic testing has been around for a long time, but we used to test for a single gene at a time. So, we would look at a patient's clinical characteristics and think: “that looks like HNF1A MODY,” or “That sounds like glucokinase MODY, maybe we will just test for that genetic change.”

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Now, however, with the systems that we use, we can test for all known genes in a single test.

Q9

As an educator, where can we expect to see your focus lie in the coming years?

One focus for me is looking at the next generation of nurses coming through. I want to focus on how we can inspire people to join the NHS and other healthcare systems, and to take on a career in nursing, for example. However, it is also about the people who are already in nursing or healthcare, and how we can inspire them to undertake roles that are perhaps a little bit outside the box, or incorporating research or genetics, for example, into their role. There is a move to bring genetics into mainstream care, and that is something that I have been involved with, with our work in monogenic diabetes, by training diabetes healthcare professionals in genetics.

Obviously, COVID-19 prompted us to use virtual platforms more than we ever did before, which has given lots more people the chance to access training that may have been inaccessible before; they can just log in from their own home online.

That kind of virtual technology is really helpful as well.

One thing that I have been involved with more recently is podcasts, which are a really good way of getting information across. In fact, within our team, we have been talking about developing podcasts on monogenic diabetes because it makes the information so much more accessible.

There are lots of different initiatives, and I am sure that there will be more ideas that can come through in the future. For me, it is about keeping your mind open to different ways of communicating information, to make it effective for people. ●

References 1.

Pang L et al. Improvements in awareness and testing have led to a threefold increase over 10 years in the identification of monogenic diabetes in the U.K. Diabetes Care. 2022;45(3):642-9.

2.

Exeter Diabetes. Exeter diabetes app. Available at: https://www.diabetesgenes.org/exeter-diabetes-app/.

Last accessed: 29 August 2023.

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Pia Leete Islet Biology Exeter Group, UK; Exeter Center of Excellence in Diabetes, UK; Department of Clinical and Biomedical Sciences, Faculty of Health and Life Science, University of Exeter, UK Citation:

EMJ Diabet. 2023; DOI/10.33590/emjdiabet/10309296. https://doi.org/10.33590/emjdiabet/10309296.

Q1

What led you to a research career focusing on diabetes, specifically exploring the multifaceted nature of the immunopathology of Type 1 diabetes (T1D) in the pancreas?

A career in research had never been something I had considered, and I came to it quite late: having had quite an itinerant youth, settling on being an artist in my mid-20s and then beginning my family, I did not begin formal study until I was 39. I began with a few short Open University (OU) courses and found that I loved learning academically! In a twist of fate, at this point, both my daughters’ lives suddenly changed due to health issues, and as a working single parent with little time, I was faced with a stark choice: give up studying, or get a grant and a loan and commit fully. I talked to my daughters and picked the latter! So, I suppose back-handed serendipity led me toward a career in science, and to believe that anything can be possible, a belief I now share with my girls.

Within 18 months, I had attained a first-class honours BSc in human biosciences, developed a passion for immunology, and a love of being in the laboratory, chasing down my curiosity.

So, when Noel Morgan (now of the University of Exeter, UK) advertised a funded PhD focusing on viruses in T1D degree, and having heard him lecture with what I know now is his distinctive warmth, humour, and clarity, I jumped at the chance to apply, little believing that the opportunity would develop into my dream job, and an exciting career! At the time, I knew very little about T1D, but the more I learned, the more I discovered in the pancreas, and the more I worked with the amazing researchers in the field,

the more embedded and committed I have become.

"I did not begin formal study until I was 39. I began with a few short Open University (OU) courses."

So, I guess abstract curiosity led me here, but the diabetes community and my amazing colleagues are what keep me in a career focused on exploring the immunopathology in T1D.

Q2

Your research team recently proposed that two distinct forms of T1D exist. Can you briefly explain the work that led to this discovery?

Diabetes is diagnosed when a person can no longer produce sufficient insulin to maintain their body’s glucose homeostasis, and over the years, we have discovered that this can have many causes, including different genetic components, the impact of lifestyle choice, and initiating triggers of disease.

In T1D, this dysregulation is driven by an autoimmune-associated destruction of the insulin-producing pancreatic β-cell; however, studying these cells and the processes that destroy them in situ in living humans is not yet possible. This has hampered efforts to characterise the triggers and processes associated with the induction of T1D. To address this barrier to our understanding, and because the organ is challenging to access, pancreatic researchers mainly focus on samples collected

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and preserved after a person’s death (collected during an autopsy initiated to discover if diabetes is responsible or, if granted, at the time of organ donation). Thankfully, the samples collected close to diagnosis in children are now very rare, but they are critical for understanding how the condition manifests in humans, which we now know is significantly different to the model systems we also use to study T1D. So, to answer your question, the work that led up to our discovery was facilitated by the privilege that we are the home of an archival biobank of the rarest of these pancreatic samples (the Exeter Archival Biobank [EADB] collection).

These are so particularly rare because the collection was compiled many years ago by Alan Foulis, a Scottish pathologist, who had the foresight to collate pancreas samples collected from children who had sadly died at, or not long after, a diagnosis of T1D. Using these samples, the Morgan/Richardson laboratory devised a method of studying islets at different stages of β-cell loss in a given individual’s pancreas by

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leveraging the fact that islets are not attacked simultaneously. The team used this phenomenon to piece together ‘processes’ in the fixed tissue in much the same way as you might think of making a stop-motion movie, ostensibly concatenating fixed ‘time-points’ of data collection along the trajectory of destruction in the islets before, during, and after the inflammatory process. The team pooled their data from several individuals, and, for the first time, could describe the influx and efflux of the immunological culprits involved during the targeted destruction of β-cells.

My first task as a PhD student was to examine if the immunological sequence of β-cell destruction observed in the EADB collection was mirrored in similar pancreas samples that were becoming available in geographically distinct cohorts (the USA and Norway).

However, I soon started spotting unexpected patterns in immune profiles and β-cell behaviour between individuals in each cohort, including the EADB collection. These observations then

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became the focus of my PhD, in which I proposed the novel idea that two distinct immunological phenotypes of immunopathology occur in the pancreas of individuals with T1D. One is a B-lymphocyte enriched CD20 ‘hyper-immune’profile, nearly always associated with an early age of diagnosis and very few β-cells at the time of diagnosis, where B cells appear to play a significant role in β-cell destruction, versus a second CD20 ‘pauci-immune’ profile, which contains few B-lymphocytes at the islet margin, less β-cell destruction at diagnosis, and is much more often found in those diagnosed beyond their mid-teens.

At this point, heterogeneity in T1D was not discussed as much, but as probably the most extensive study of insulitis ever undertaken, this work highlighted the importance of B-lymphocytes as potentially key drivers of immune aggression in T1D, and has contributed to the growing awareness that immunological heterogeneity and the impact of age at diagnosis is critical when considering disease trajectory.

More recently, realising that some people (those with the CD20 pauci-immune-immunotype) still had significant β-cell mass at diagnosis, despite an absolute requirement for externally administered insulin, I became curious about the role that the islets themselves might play in their own survival between these two different ‘forms’ of immune attack. Inspired by differences in the staining patterns of insulin in our samples and by work done by Emily Simms (Indiana University School of Medicine [IUSM], Indianapolis, USA) on proinsulin signals in the blood, Clemens Ziller (a second year medical student and co-author on the paper) and I started looking at proinsulin processing in the pancreas, and in collaboration with our clinical colleagues at Exeter, we then found that the immunotypes correlated with phenotypically distinct errors in proinsulin processing between individuals.

Therefore, my recent proposal that endotypes exist in T1D is built on many years of studying the pancreas in these rare, beautiful, and tragic samples, which I believe still have much to tell us.

Q3

As a recipient of a Diabetes UK RD Lawrence Fellowship, where can we expect to see your focus lie in the near future?

I am so grateful to Diabetes UK, and their donors and supporters, for this opportunity to develop a team that will continue to strive to unravel at least some of the heterogeneity we see in T1D, and develop the skills to make this complexsounding science accessible to people living with the condition. I hope that by further targeted exploration of the pancreatic landscape and employing new tools and technologies, we can unveil some of the drivers that ultimately lead to a diagnosis, maybe even suggesting paths to try to prevent them.

We live in exciting times for research, where new technology and data capacity can be employed to integrate all sorts of research modalities, meshing studies that are undertaken in more malleable systems, such as rodent, zebrafish, and cell models, with the work being done with human samples providing tools to deliver an ever more detailed understanding of the biological processes that contribute to T1D, and potential to halt them. I hope that I will also have a voice in advocating to improve our understanding of T1D in the pancreas in all communities; for example, we do not know what diabetes looks like in the pancreas in Africa, which may be important when we consider that the early initiating environmental triggers are likely very different.

Q4

What were the main conclusions of the highly cited paper that you co-authored, called ‘Detection of a Low-Grade Enteroviral Infection in the Islets of Langerhans of Living Patients Newly Diagnosed with Type 1 Diabetes?’

Because the pathology in the pancreas starts so long before we can detect the dysregulation of glucose homeostasis, we are still looking for triggers in T1D. In this particular paper, our main observations in the pancreas were that a viral infection is more evident in the β-cells of people diagnosed with T1D than those without diabetes but, rather than an acute and aggressive lytic β-cell infection by virus, the authors proposed that the infection may be a slow and persistent low-grade infection, potentially explaining the protracted, peripherally undetectable pre-

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diagnostic β-cell loss from the islets. One clue that this protracted viral insult is ongoing can be seen in histopathological investigations (such as that undertaken by Sarah Richardson, University of Exeter, and her colleagues), where the hyperexpression of human leukocyte antigen-1 (a protein that is critical in our immunological tolerance to self, and our battles with intracellular pathogens) is upregulated in all pancreatic samples from individuals who had a diagnosis of T1D and who retained some β-cells.

However, this normal antiviral response may also be responsible for recruiting and activating the army of aberrant auto-immune cells that wreak havoc in the islets, ultimately leading to β-cell loss and diabetes onset.

With the recent news from the co-authors of this paper in Norway that antiviral therapies can delay the progression of T1D, I think there is still much to unravel in the virus story, particularly if the virus may be a trigger in some people but not in others.

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Q5

You are committed to improving societal equity and education, while supporting a growth mindset for students and colleagues. How do you bring these qualities to your role at the University of Exeter?

Idealistically, societal equity, access to healthcare, and education for all are key aims for any society, and maybe naïvely, I also see having a positive, hopeful growth mindset as critical for creative problem-solving. But many of our communities are battling anxiety and mental health concerns. With budgets shrinking, and the world seemingly becoming an increasingly more complex environment to navigate, I try to offer a space for anyone who needs to be seen, heard, and cheered on to come and talk to me.

I truly believe and advocate for the idea that a diverse but cohesive population, combined with a growth mindset based on kindness, hope, and curiosity is our best way forward, particularly if we hope to solve some of our seemingly most intractable problems.

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Interview

I think my eclectic life journey has given me an unusual perspective, and the advice, or space to explore, that I offer often includes taking some time each day to just look at the sky, or a leaf, or your loved one’s face, and invite the question: “Anything is possible. You just have to remember to ask: is worth it?” Mostly, I try to remain cogent that everyone’s story is different, and, when I am asked for my thoughts and opinions, I try to hold the space for an understanding that we are all trying our best to navigate our lives, and each has something meaningful to contribute to the biggest pictures.

"Maybe naïvely, I also see having a positive, hopeful growth mindset as critical for creative problem-solving."

Q6

What do you believe to be the current gaps in diabetes research, and which topics merit greater attention?

With the advent of the teplizumab licence, there is so much to celebrate. The first U.S. Food and Drug Administration (FDA)-approved therapy proven to slow (and maybe, in some people, even prevent) the progression of T1D, with the newly released news showing that it is safe to use in young people, and can slow the progression of the condition. People affected by T1D have waited so long for this win.

However, there are still gaps in our understanding of what makes T1D so heterogeneous, the trigger(s) that lead to β-cell targeting and loss, and why some people seem to have lots of residual β-cells, but none of their own circulating insulin. Is the environment impacting differently on individuals? Is the age at which a person encounters, or succumbs to, the initiating trigger the differentiator? Or is there something more fundamentally different in the biology of subgroups of individuals? When we understand this, we can start looking for diagnostic markers in a much more focused way.

Q7

You also specialise in microscopy imaging and data analysis.

How important are these skills in your research career?

The significance of the technical advances in research methodologies, data capture, data science, and data sharing cannot be understated, and the recent step changes in the microscopy and imaging field have been huge in the last few years. We are now garnering rich and complex histology-derived datasets with thousands of data points from each individual staining experiment. These advances also offer the potential for cross-disciplinary research, which is increasingly commonplace. I am fortunate to be now working with and learning from image-data specialists and mathematicians, to pull apart the nuances in my ongoing observations.

But it all starts with the staining, the pictures, and the pattern spotting; and so, microscopy remains at the heart of my work.

Q8

Over the years you have been researching within the field of diabetes, what are the most significant changes you have observed in the field?

As I said, teplizumab, of course, is huge, as are the huge advances in the technology available for people to use to manage their condition, such as continuous glucose monitoring and closedloop systems.

It has also been deeply powerful to increasingly hear the voices of lived experience in research settings, conferences, and grant panels. This feels incredibly important.

And, although nowhere near as significant, I am also told by the people who live with T1D, that it truly matters when they discover their personal heterogenous ‘version’ of T1D may be responsible for the clinical differences that they experience, rather than something that they are doing better or worse than their peers. I am sure this was happening long before I started my PhD, but this is what keeps me going. It is a really exciting time to be a researcher in T1D. I continue to feel incredibly privileged and grateful for my days.

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Chrono-nutrition and its Association with Chronotype and Blood Glucose Control Among People with Type 2 Diabetes Editor's Pick

Chrono-nutrition, or how the timing of meals and dietary patterns affects health, is gaining attention in the context of Type 2 diabetes. Regarding this issue, Chandorkar et al. show that late diners have significantly worse blood glucose levels, irrespective of the glycaemic index (GI) of the meal. Understanding the relationship between meal timing, circadian rhythms, and diabetes management holds promise for more personalised and effective dietary interventions for individuals with Type 2 diabetes.

Martin Whyte Associate Professor of Metabolic Medicine, University of Surrey, UK Authors:

Suneeta S. Chandorkar,1 Nupur S. Mehta,1 *Neelam M. Rathod1 Department of Foods and Nutrition, The Maharaja Sayajirao University of Baroda, Vadodara, Gujarat, India *Correspondence to neelam_rathod92@yahoo.com

Disclosure:

The authors have disclosed no conflicts of interest.

The study was approved by the Institutional Review Board of the Department of Foods and Nutrition, Faculty of Family and Community Sciences, The Maharaja Sayajirao University of Baroda, Vadodara, Gujarat, India. The ethical approval number of the study is IECHR/FCSC/2020/46. Chandorkar contributed to the conceptualisation of research and to the finalisation of the paper. Rathod contributed to the conceptualisation of the research and to the writing of the paper. Mehta contributed to the data management throughout this study. All authors contributed to the article, and approved the submitted version.

Acknowledgements:

The authors gratefully acknowledge the support provided

by Akash Singh, Manjalpur Hospital Baroda, Gujarat, India; Sushil Patel, Akshar Diabetes Centre, Gujarat, India; and Dishank Patel, GlucoWell Clinic, Gujarat, India. The authors thank the participants who participated in this study.

Received:

07.08.23

Accepted:

11.09.23

Keywords:

Chrono-nutrition, chronotype, diabetes, insulin resistance.

Citation:

EMJ Diabet. 2023; DOI/10.33590/emjdiabet/10300576. https://doi.org/10.33590/emjdiabet/10300576.

Abstract Background: Recent studies have revealed conflicting results for low glycaemic index (GI) meals in the prevention and treatment of metabolic disorders. Diurnal variations in glucose homeostasis, which are frequently overlooked in epidemiological studies, may help to explain some of these discrepancies.

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Article

Food is an external cue to entrain the circadian rhythm, and meal timing is a crucial factor for glucose homeostasis. The study examines the relationship between chrono-nutrition, chronotype, and blood glucose control among people with Type 2 diabetes.

Materials and Methods: Chrono-nutrition questionnaire assessed eating window, breakfast skipping, evening latency, evening eating, night eating, and largest meal of patients with Type 2 diabetes. Glycaemic control was assessed using a HbA1c test, fasting blood sugar, and 2-hour post-prandial blood sugar test. Insulin resistance was assessed by fasting triglyceride glucose index (TyG).

Results: There was a significant association between late dining with dysglycaemia, irrespective of GI of the meal (p<0.05). Participants who had the largest meal during the active phase had better glycaemic control (p<0.05).

Shorter eating windows and evening latency of at least 2 hours not only aided in glycaemic control, but also gave good sleep (p<0.05). Participants with the evening chronotype ate almost twice the amount of carbohydrates and fat at dinner than at breakfast. Evening chronotypes were associated with lesser servings of vegetables and fruits, and greater servings of sweets and caffeinated beverages, in comparison with morning chronotypes.

Conclusions: Late diners had significantly worse blood glucose levels, irrespective of the GI of the meal. This may have public health implications, as calorie-dense meals are often consumed during late evenings, which can desynchronise the circadian rhythms. Eating meals as per the circadian rhythm could be an alternative nonpharmacological strategy to prevent diabetes and its complications.

Key Points 1. Chrono-nutrition is an emerging field, studying the interaction between temporal dietary patterns, circadian rhythm of our body, and metabolic health.

2. Behavioural indicators of the chrono-nutrition profile, such as a shorter eating window, no breakfast skipping, long evening latency, early dinner, and eating the largest meal early positively affect metabolic health.

3. Early low-GI dinners had a better glycaemic response than late low-GI dinners.

INTRODUCTION Type 2 diabetes (T2D) is a persistent metabolic anarchy, described by hyperglycaemia resulting from defects in insulin secretion, insulin action, or in a combination of both.1 T2D, once assumed to be a metabolic condition only seen in affluent Western countries, is now spreading to lowand middle-income countries. It has attained the status of a global pandemic, due to the phenomenon of Sanskritisation, a form of social shift where low-income groups attempt to move up the social scale by emulating the opulent

practices of the affluent population.2,3 The two main drivers of the global rise in T2D are rapid urbanisation and obesogenic food environment.4 The top three countries with the greatest prevalence of diabetes in 2021 are China (140 million cases), India (74 million cases), and the USA (32 million cases).5 The absolute rise in people with diabetes in China and India is being driven by population growth and ageing in the past few decades.6 Mortality from communicable, maternal, neonatal, and nutritional diseases has halved; however, the number of death caused due to non-communicable diseases has

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significantly increased.7 In India, the burden of diabetes has increased significantly since 1990, and at a faster pace from the year 2000, as per the International Diabetes Federation (IDF). The mean healthcare expenditure on diabetes per person is 114 US dollars in India, and total deaths attributable directly to diabetes account for 0.6 million.8 Asian Indians represent an ethnic group with a significant propensity for developing T2D, and have high predilection to progress from pre-diabetes to diabetes at a younger age, and at lower levels of adiposity, as compared with White Europeans.9 The Asian Indian phenotype, identified by high levels of abdominal adiposity and increased insulin resistance, even at low levels of BMI, has been postulated as a risk factor for developing T2D.9 Dietary intervention is a cornerstone of diabetes management.10 Traditional dietary recommendations regarding quantity, order, and composition of meals to be consumed has been well established. However, the timing of food intake is also important for health.

Chrono-nutrition is the circadian timing of meal intake. The circadian rhythms of the body are not precisely 24 hours and rely on Zeitgebers, such as exposure to light and food intake, to synchronise the circadian rhythm of the body.11 Sunlight is considered the most important Zeitgeber, as it influences the suprachiasmatic nucleus, or central clock, located in the anterior region of the hypothalamus, by providing the strongest signal for entrainment; it transmits the information to the peripheral clocks that are located in all the cells in the rest of the body.12 Feeding and fasting cycles, and dietary intake both have an impact on peripheral clocks.

Nutrient timings can change the phase of the independently working liver clock.12 Ongoing research work suggests that the association between chrono-nutrition and health is likely analogous to those between nutrition and health.

The link between circadian rhythmicity and metabolism has been studied in chrono-nutrition studies using animal models at various cellular, molecular, and behavioural levels.

Overall, laboratory findings suggest that chrono-nutrition plays a potential role in general wellbeing. There are six studied behavioural patterns likely to impact one’s chrono-nutrition status: night eating, time-restricted feeding, breakfast consumption, timing of the largest

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meal, timing of evening eating, and evening latency.12 In the area of chrono-nutrition, research on eating at night has helped to lay the groundwork for future dietary studies, by illuminating the potential health effects of eating at different times.

The invention of the electric light led to working or being awake at night hours, which has led to circadian disruption by modifying melatonin levels. Several studies have reported that melatonin levels are lower in night shift workers.13,14 Melatonin, or sleeping hormone, plays a central role in the regulation of circadian rhythms. Melatonin and cortisol secretions are dependent on photoperiods, which are modified by seasonal fluctuations. Melatonin secretion lasts for a longer time in winter, which lengthens the resting phase, while in summer, the activity phase lengthens due to long hours of sunlight.15 The term time-restricted feeding (TRF), which is one of the behavioural indicators of chrononutrition, is eating inside a predetermined window of time throughout a 24-hour day.

Experimental studies of TRF are mostly carried out in animals. TRF aids in counteracting the detrimental effects of a high-fat diet in mice, on various metabolic health indicators. Despite identical caloric intake across experimental groups, mice with ad libitum access to food gained almost 30% more weight than mice with food access restricted to 8 hours.16 Similar to this, independent of the meal composition, an eating window of 9 hours during waking hours increased insulin sensitivity.17 In a pilot study, participants who were overweight and had a baseline eating window of 14 hours were instructed to limit their eating to 10–11 hours for 4 months. Participants reported better sleep quality, along with weight loss.18 Breakfast skippers had significantly higher HbA1c levels and higher BMI than breakfast eaters. After adjusting for age, sex, race, BMI, diabetes complications, insulin usage, depressive symptoms, perceived sleep debt, and caloric intake at dinner, chrono-nutrition behavioural indicator skipping of breakfast was significantly associated with higher HbA1C values (β=0.108, where β is regression coefficient; p=0.01).19 Breakfast skipping has been linked to an increased risk of obesity by 4.5 times.20 Breakfast skippers lack appetite signalling

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due to disrupted circadian rhythm. There was an independent association of skipping breakfast with poor glycaemic outcomes in individuals with T2D.21 Having lunch late (around 3 p.m.) has shown negative effects on gut microbiota diversity.22 Late diners (evening latency within 2 hours) have a deleterious impact on glucose tolerance, especially in G-carriers of the risk allele at MTNR1B rs10830963.22 It is possible that evening chronotypes, also known as night owls, are more prone than morning chronotypes, also known as morning larks, to consume a higher percentage of calories later in the day.22 Evening circadian typology is not genetic, but derives from unhealthy behaviours.22 It was observed that participants had more calories with less than 4 hours of sleep, and later bedtimes than control participants.23 Evening latency is the time duration between the last food intake and sleep onset, which may affect metabolic health. The preference for sleeping and waking hours or chronotype and health have both been associated with the timing of having the largest meal. People who ate the largest meal at breakfast or lunch had a lower BMI than people who had the largest meal in the evening.24 As hormones with circadian rhythms are synchronised by light, times suitable for feeding and fasting for the American and European populations cannot be applied to the South Asian population due to differences in environmental factors, such as latitude, seasons, and hours of day and night; and modifiable factors, like diet and lifestyles.

Therefore, it is important to understand the role of chrono-nutrition and chronotype on blood glucose outcomes among people with T2D, which may be one of the non-pharmacological strategies to better glycaemic outcomes, prevent complications, and improve quality of life. To the best of the authors’ knowledge, this is the first study to explore the association of chrononutrition status and chronotype with blood glucose outcomes in India among people with T2D. This proposed study aims to examine the associations among chronotype, chrono-nutrition, and glucose outcomes. The authors hypothesised that the morning chronotype, shorter eating window, not skipping breakfast, early dinner time, having the largest meal during daytime hours, and

longer evening latency are associated with better glucose tolerance among people with diabetes.

MATERIALS AND METHODS Sample Size

The required sample size of 227 patients with T2D was obtained using formula n=(3.84) pq/ L2. The sample size for estimation of proportion was at 5% level of significance. P=15.9 (average percentage of males and females having diabetes as 15.1% and 16.6% respectively, as per National Family Health Survey [NFHS-5] district factsheet), and q=1–p. Absolute precision (L) was taken as 5%. Design effect was taken as 1.

Sampling Technique

The sample size was selected through random sampling.

Study Design

This study was a cross-sectional study, based in an urban city between June 2020–June 2021.

Participants from three diabetes clinics were identified who met the study criteria.

Inclusion Criteria

Males and females, aged between 35–60 years, diagnosed with T2D in the past 0–5 years, without secondary complications, and willing to participate.

Exclusion Criteria

Participants with poor compliance for follow-up at the clinic.

Questionnaire

The following information was collected through a structured questionnaire using the interview technique.

Personal information

Personal information, including name, age, sex, education qualification, occupation, and family type was collected from the participants.

Education qualification was classified according to the Kuppuswamy socioeconomic scale.

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Anthropometric and body composition data The weight was measured using standardised and well-calibrated Body Composition Monitor BodySCAN™ HBF-224 (Omron Healthcare, Kyoto, Japan). Height was measured using the standardised and well-calibrated portable stadiometer floor model with a 20–210 cm measurement range, and 1 mm graduation.

Height was read to the nearest mm.

BMI is calculated as weight in kg divided by the square of the height in meters (kg/m2), and has been categorised into four groups according to the Asian-Pacific classification. BMI is calculated by dividing weight (in kg)/height (m²). Waist– hip ratio is calculated by dividing the waist measurement by hip measurement, with the formula waist circumference/hip circumference.

According to the American Diabetes Association (ADA), the normal waist–hip ratio for Indian males is 0.88, and for females 0.81. Body fat and muscle percent were assessed using Body Composition Monitor HBF-375 (Omron Healthcare).

Diet information

This was collected using a 24-hour dietary recall for 3 days (2 weekdays plus one rest day).

The 3-day 24-hour diet recall was converted into raw ingredients, and then quantified using the DietCal software (ProfoundTech Solutions, New Delhi, India).

Chronotype

This was assessed using the Horne and Östberg Morningness-Eveningness questionnaire (MEQ), which includes 19 questions to determine morningness-eveningness in human circadian rhythms. Scoring ranges from 16–86. Scores from 16–30 indicate ‘definitely evening’, 31–41 indicate ‘moderate evening’, 42–58 indicate ‘intermediate types’, 59–69 indicate ‘moderate morning’, and 70–86 indicate ‘definite morning’.

Biochemical parameters

Biochemical parameters, like HbA1c, fasting blood sugar, and post-prandial blood sugar collected from the patient’s case records of no older than 1 month, included the following chrononutrition profile. This was assessed by taking several behaviours in consideration:12 eating window, breakfast skipping, evening latency, evening eating, night eating, and largest meal are the behavioural indicators of chrono-nutrition.

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Sleep

The Pittsburgh Sleep Quality index (PSQI) was used to assess the sleep quality among participants. The PSQI contains 19 self-rated questions. The scoring is divided into seven components, which have a range of 0–3 points: a score of 0 indicates no difficulty, while a score of 3 indicates severe difficulty. The score of the seven components are then added together, and one ‘global’ score is obtained that ranges from 0–21; here, 0 indicates no difficulty, and 21 indicates severe difficulties in all areas. A global score of 5 or more indicates poor sleep quality.

Triglyceride Glucose Index

The TyG index was calculated as the natural logarithm of the product of plasma glucose and triglycerides using the formula: natural logarithm (trigylcerides [mg/dL]×glucose [mg/ dL]/2). It is a reliable and valid surrogate marker of insulin resistance.25

Data Analysis

The data was analysed in SPSS version 27 (IBM, Armonk, New York, USA). χ2 and Pearson’s correlation tests were applied.

Ethical Approval

The study was approved by the Institutional Review Board of the Department of Foods and Nutrition, Faculty of Family and Community Sciences, The Maharaja Sayajirao University of Baroda, Vadodara, Gujarat, India. The ethical approval number of the study is IECHR/ FCSC/2020/46.

RESULTS A total of 227 participants (35–60 years; 108 female) participated in the study. The study was carried out in two private diabetes clinics and one diabetes clinic in a tertiary private hospital in an urban area of Vadodara district, Gujarat, India. Around 53% and 31% of male and female participants have indicated graduation as their highest education qualification, respectively.

None of them were illiterate. Around 88% of female participants were homemakers. Around 60% of male participants were retired. Around 73%, 22%, and 5% live in a nuclear family,

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Table 1: Anthropometric parameters of the participants (n=227).

Cut-off

Mean±SD

%

Cut-off

Male (119)

Mean±SD

%

Female (108)

Waist circumference Normal

≤85

83.3±2.1

3.4

≤80

79.0

0.9

High

>85

102.8±8.5

96.6

>80

96.1±8.7

99.1

Normal

≤0.88

0.8±0.0

5.8

≤0.81

0.7±0.0

7.4

High

>0.88

0.9±0.1

94.1

>0.88

0.9±0.1

92.6

Underweight

<18.5

N/A

N/A

<18.5

N/A

N/A

Normal

18.5–22.9

21.6±1.1

5.0

18.5–22.9

21.1±1.4

6.5

Overweight

23.0–24.9

24.3±0.5

7.6

23.0–24.9

24.2±0.6

12.9

Obese

>25.0

29.0±2.9

87.4

>25.0

29.1±3.7

80.6

Waist–hip ratio

BMI

N/A: not applicable; SD: standard deviation.

Table 2: Association of behavioural indicators of chrono-nutrition with glycaemic control.

HbA1c (%) <5.7

5.7– 6.4

6.5– 7.9

8.0– 8.9

FBS (mg/dL) ≥9.0

<100

100–125

PPBS (mg/dL) ≥126

<140

≥140

Association between the time of eating last meal with glycaemic control in people with diabetes <8:00 p.m.

2%

0%

9%

2%

1%

3%

4%

8%

5%

9%

8:00 p.m.–11:59 p.m.

0%

7%

28%

14%

15%

6%

9%

50%

13%

51%

≥11:00 p.m.

0%

1%

13%

4%

4%

0%

2%

19%

2%

19%

X2(8,N=227)=40.0; p=0.001*

X2(4,N=227)=14.5; p=0.006*

X2(2,N=227)=8.0; p=0.010†

r=0.129; n=227; p=0.050†

r=0.244; n=227; p=0.001*

r=0.185; n=227; p=0.005*

Association of timing of low GI dinner with glycaemic control in people with diabetes Low GI dinner+early dinner (<8 p.m.)

1%

1%

6%

0%

1%

3%

4%

1%

6%

3%

Low GI dinner+late dinner (8 p.m.–12 a.m.)

0%

1%

43%

23%

23%

0%

0%

91%

0%

91%

X2(4,N=162)=30.3; p=0.001*

X2(2,N=162)=137.0; p=0.001*

X2(1,N=162)=100.0; p=0.001*

r=0.303; n=162; p=0.001*

r=0.865; n=162; p=0.001*

r=0.789; n=162; p=0.001*

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Table 2 continued.

HbA1c (%) <5.7

5.7– 6.4

6.5– 7.9

FBS (mg/dL)

8.0– 8.9

≥9.0

<100

100–125

PPBS (mg/dL) ≥126

<140

≥140

Association between time of eating largest meal with glycaemic control in people with diabetes Breakfast

0%

0%

22%

7%

6%

3%

7%

26%

7%

28%

Lunch

2%

8%

1%

0%

0%

6%

5%

1%

11%

1%

Dinner

0%

1%

26%

12%

14%

0%

3%

50%

3%

50%

X2(8,N=227)=175.0; p=0.001*

X2(4,N=227)=106.0; p=0.001*

X2(2,N=227)=92.0; p=0.002*

r=0.114; n=227; p=0.080*

r=0.204; n=227; p=0.002*

r=0.241; n=227; p=0.001*

Association between evening latency with glycaemic control in people with diabetes >6 hours

0%

0%

0%

0%

0%

0%

0%

0%

0%

0%

2–6 hours

2%

4%

20%

7%

5%

7%

7%

25%

13%

26%

<2 hours

0%

4%

29%

13%

15%

3%

7%

52%

8%

53%

X2(4,N=227)=12.0; p=0.010†

X2(2,N=227)=13.0; p=0.001*

X (1,N=227)=13.0; p=0.001*

r=0.204; n=227; p=0.002*

r=0.246; n=227; p=0.001*

r=13; n=227; p=0.001*

2

Association between eating window with glycaemic control in people with diabetes <12 hours

2%

4%

17%

5%

4%

5%

9%

19%

11%

22%

12–14 hours

0%

4%

32%

13%

15%

4%

5%

56%

9%

56%

>14 hours

0%

0%

0%

1%

1%

0%

0%

2%

1%

1%

X2(8,N=227)=14.0; p=0.060

X2(4,N=227)=22.0; p=0.001*

X (2,N=227)=11.0; p=0.001*

r=0.233; n=227; p=0.001*

r=0.073; n=227; p=0.001*

r=0.166; n=227; p=0.010†

2

Association between frequencies of eating during midnight hours with glycaemic control in people with diabetes 1 day/week or less

2%

3%

19%

4%

4%

5%

7%

10%

10%

21%

2–3 days/week

0%

4%

7%

3%

3%

3%

4%

10%

7%

10%

≥4 days/week

0%

2%

24%

12%

14%

1%

3%

48%

4%

48%

X2(8,N=227)=24.8; p=0.002*

X2(2,N=227)=23.6; p=0.006*

X (8,N=227)=24.8; p=0.002*

r=0.258; n=227; p=0.001*

r=0.330; n=227; p=0.001*

r=0.290; n=227; p=0.001*

2

*Highly significant Significant

†

A p-value less than or equal to the significance level (≤0.050) is statistically significant, while less than or equal to the significance level (≤0.005) is statistically highly significant.

FBS: fasting blood sugar; GI: glycaemic index; PPBS: post-prandial blood sugar; r: Pearson’s correlation.

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extended family, and joint family, respectively.

Most of the male participants (37.8%) and female participants (25.9%) are from the age group 55–60 years. The median duration of diabetes is 2 years. BMI, waist circumference, and waist to hip ratio are given in Table 1. Around 85% of the total male participants have more than 25% of fat. Around 65.0% of the total female participants had 30–35% of total body fat; 26.9% had more than 35% of total body fat, while only 4.6% had <20% of total body fat. Around 47% of total male participants and 31% of total female participants have visceral fat more than 15%. Most of the male (91%) and female (80%) participants have low total body skeletal muscle percentage of less than 24% of total body weight.

Chrono-nutrition status was assessed using six behavioural indicators that are likely to influence metabolic health: breakfast skipping, largest meal of the day, evening eating hours, evening latency, midnight eating, and eating window.

There was a significant association between the early eating hours of the last meal, eating window, breakfast timing, and evening latency with blood glucose level (Table 2). There was a significant association between having a low glycaemic index (GI) dinner early with blood glucose levels (Table 2). There was a significant association between the greater frequencies of eating at midnight with poor glucose control (Table 2). The surrogate marker of insulin resistance TyG index correlated positively with the largest meal in the resting phase (r=0.209, where r is Pearson’s correlation; p=0.002).

No one skipped breakfast more than once a week in the present study.

A longer eating window negatively affected sleep (r=-0.123; p=0.05). There was a significant association between the longer evening latency with glucose homeostasis. Early dinner and longer evening latency aid in the glycaemic control of people with diabetes. Evening latency

Table 3: Association between chronotype with chrono-nutrition components in people with diabetes, and association between chronotype with dietary data in people with diabetes.

Behavioural Indicators

Morning Chronotype

Evening Chronotype

p

Eating window <12 hours

31%

1%

12–14 hours

14%

51%

>14 hours

0%

2%

<7:00 p.m.

13%

0%

7 p.m.–9:59 p.m.

31%

34%

≥10:00 p.m.

1%

20%

>6 hours

0%

0%

2–6 hours

39%

0%

<2 hours

7%

54%

1 day/week or less

29%

2%

2–3 days/week

17%

0%

≥4 days/week

0%

52%

Breakfast

10%

0%

Lunch

35%

2%

Dinner

1%

52%

X2(2,N=227)=111.0; p=0.001*

Evening eating X2(2,N=227)=66.0; p=0.001*

Evening latency X2(1,N=227)=169.0; p=0.001*

Midnight cravings X2(2,N=227)=208.0; p=0.001*

Largest meals

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X2(3,N=227)=202.0; p=0.001*

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Table 3 continued.

Behavioural Indicators

Morning Chronotype

Evening Chronotype

p

≤Once a week

46%

54%

N/A

2–3 times/week

0%

0%

>3 times/week

0%

0%

400 g

7%

0%

300–399 g

37%

0%

200–299 g

2%

54%

<200 g

0%

0%

≤25 g/d

0%

0%

26–49 g/d

14%

12%

≥50 g/d

32%

42%

1 cup

10%

0%

2 cups

36%

2%

3 cups

0%

34%

4 cups

0%

18%

0 serving/day

44%

2%

1 serving/day

2%

52%

≥2 servings/day

0%

0%

Breakfast skipping

Vegetable and fruits intake X2(2,N=227)=207.0; p=0.001*

Sugar X2(1,N=227)=1.8; p=0.17

Caffeinated beverages X2(3,N=227)=208.0; p=0.001*

Sweets X2(3,N=227)=202.0; p=0.001*

*Highly significant: A p-value less than or equal to the significance level (≤0.005) is statistically highly significant.

N/A: not applicable.

also affected sleep. Evening latency and sleep were significantly associated (X2[2,N=227]=8.7; p=0.01). The shorter the evening latency, the poorer the quality of sleep (r=0.185; p=0.005).

There was a significant association between having largest meal during the day than night, with better glucose control.

Chronotype of the participants: 47.1% of males and 44.4% of females had the morning chronotype, while 52.9% of males and 55.6% of females had the evening chronotype. There was a significant association between morningness, with greater servings of vegetables, lesser sugar servings, and lesser servings of caffeinated

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beverages and sweets (Table 3). There was a significant association between morningness, with early dining, shorter eating windows, having the largest meal in the day, and lesser midnight cravings (Table 3). Participants with the evening chronotype ate almost twice the amount of carbohydrates and fat at dinner than at breakfast.

DISCUSSION Late-night eating has deleterious effects on insulin sensitivity.26 As reported in a few studies, glucose outcomes were better in the

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morning hours (8 a.m.) than in the evening (8 p.m.) for an identical composition of meal.27,28 The offset reduction in insulin levels, and the decreased β-cell sensitivity to glucose occur in the evening.28,29 Hence, the consumption of carbohydrate-rich meals in the evening would not be processed efficiently, and will further reduce insulin sensitivity. Eating dinner early is crucial for metabolic health, especially due to insulin and leptin. Food intake decreases and lipolysis increases when leptin peaks at approximately 7 p.m., and later declines. Leptin also inhibits fat accumulation.30 The resting phase begins when melatonin production starts, which reduces glucose tolerance.31 The nutrients from the food get effectively digested and metabolised when the food is consumed earlier in the day, rather than during the resting phase. Hence, dinner should be eaten early, irrespective of the GI of the meal.32,33 An increase in melatonin levels suppresses insulin sensitivity, resulting in impaired glucose tolerance in the midnight hours; hence, late-night eating should be avoided. There was a significant association between the shorter eating window with controlled fasting and post-prandial blood glucose. The production of fibroblast growth factor 21 and adiponectin, which encourage fatty acid oxidation, glycolysis, and limit fat formation, occurs between 8 a.m.–4 p.m.34 Food consumed during the insulin peak, i.e., between 2 p.m.–6 p.m., promotes fat accumulation.35 A peak release in the leptin hormone, which decreases appetite, occurs around midnight, while a trough occurs at noon,36 when the sun is highest in the sky in India, and the body is becoming active again. This explains why the largest meal should be had at noon, contrary to popular opinion; this needs to be explored.

The zenith of the sun varies by latitude and season of the year.

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International Diabetes Federation (IDF). IDF diabetes atlas: ninth edition. 2019. Available at: https://www.diabetesatlas. org/upload/resources/ material/20200302_133351_ IDFATLAS9e-final-web.pdf.

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The level of cortisol in the blood, urine, and saliva increased by 50–75% between 30–60 minutes post-awakening, and is lowest from midday in both sexes on all days.37,38 Eating a large meal is not advisable when cortisol peaks. It is important to practice meditation, walk in nature, and eliminate waste, rather than indulging in a large breakfast when the cortisol peaks. A recent study reported that 32% of the total daily intake of calories was consumed in the late evening between 7 p.m.–11 p.m. by healthy individuals in Indian, with the median dinnertime being after 10 p.m.39 Dinner is the most important meal in family environments, especially in South Asia, and not restricted to weekends.

Commensality, the act of eating together provides opportunities for social integration, and facilitates communication. These findings are likely to have public health implications, as calorie-dense meals are often consumed during late evenings, which can desynchronise circadian rhythms. Intermittent fasting does not have any standardised protocol. The effects of people following late intermittent fasting patterns on metabolic health, in comparison to early eating patterns should be studied.

The assembly of the presented results suggests that the associations between chronotype, chrono-nutrition, and glucose outcomes is significant. Eating meals as per the circadian rhythm could be one of the non-pharmacological strategies that favours better blood glucose outcomes. As interventions of meal timings and their effects are limited to animals, studies should be designed with the human population, in order to clearly establish time-restricted feeding and fasting protocols, which can vary by latitude and season of the year.

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19. Reutrakul S et al. The relationship between breakfast skipping, chronotype, and glycemic control in type 2 diabetes. Chronobiol Int. 2014;31(1):64-71.

20. Ma Y et al. Association between eating patterns and obesity in a free-living US adult population. Am J Epidemiol. 2003;158(1):85-92.

29. Van Cauter E et al. Roles of circadian rhythmicity and sleep in human glucose regulation. Endocr Rev. 1997;18(5):716-38.

30. Gavrila A et al. Diurnal and ultradian dynamics of serum adiponectin in healthy men: comparison with leptin, circulating soluble leptin receptor, and cortisol patterns. J Clin Endocrinol Metab.

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31. Gamble KL et al. Circadian clock control of endocrine factors. Nat Rev Endocrinol. 2014;10(8):466-75.

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34. Fisher FM, Maratos-Flier E. Understanding the physiology of FGF21. Annu Rev Physiol.

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13. Papantoniou K et al. Circadian variation of melatonin, light exposure, and diurnal preference in day and night shift workers of both sexes. Cancer Epidemiol Biomarkers Prev. 2014;23(7): 1176-86.

14. Dumont M, Paquet J. Progressive decrease of melatonin production over consecutive days of simulated night work. Chronobiol Int. 2014;31(10):1231-8.

15. Wehr TA. Melatonin and seasonal rhythms. J Biol Rhythms.

1997;12(6):518-27.

16. Hatori M et al. Time-restricted feeding without reducing caloric intake prevents metabolic diseases in mice fed a high-fat diet. Cell Metab. 2012;15(6):848-60.

17. Chaix A et al. Time-restricted feeding is a preventative and therapeutic intervention against diverse nutritional challenges. Cell Metab. 2014;20(6):991-1005.

24. Kahleova H et al. Meal frequency and timing are associated with changes in body mass index in adventist health study 2. J Nutr.

2017;147(9):1722-8.

25. Selvi NMK et al. Association of triglyceride-glucose index (TyG index) with HbA1c and insulin resistance in type 2 diabetes mellitus. Maedica (Bucur).

2021;16(3):375-81.

26. Song X et al. Association of timeof-day energy intake patterns with nutrient intakes, diet quality, and insulin resistance. Nutrients.

2021;13(3):725.

27. Morris CJ et al. Endogenous circadian system and circadian misalignment impact glucose tolerance via separate mechanisms in humans. Proc Natl Acad Sci U S A. 2015;112(17):E2225-34.

35. Cacan R. Régulation métabolique - Gènes, enzymes, hormones et nutriments (2008) Paris: Ellipses.

36. Pan W, Kastin AJ. Leptin: a biomarker for sleep disorders?

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37. Pruessner JC et al. Free cortisol levels after awakening: a reliable biological marker for the assessment of adrenocortical activity. Life Sci. 1997;61(26): 2539-49.

38. Bowles NP et al. The circadian system modulates the cortisol awakening response in humans.

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39. Gupta NJ et al. A camera-phone based study reveals erratic eating pattern and disrupted daily eatingfasting cycle among adults in India.

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2016;27(5):282-93.

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Satisfaction with Home Healthcare Provider Service in Adults with Type 1 Diabetes Using a Hybrid Closed Loop System: The SATURN Study Authors:

*Hélène Hanaire1, Nicolas Naiditch,2 Vincent Melki,1 Pierre Morcel,3 Nelly Puech,1 Emeric Scharbarg,3 Raquel TiradoGodas,1 Guila Lancman,4 Manuella Schaller,4 Cécile Delval,4 Lucy Chaillous3 1. Department of Diabetology, Metabolic Diseases and Nutrition, University Hospital of Toulouse, France 2. Fédération Française des Diabétiques (FFD), Diabète LAB, Paris, France 3. Endocrinology-Diabetology-Nutrition, Institut du Thorax, University Hospital of Nantes, France 4. Air Liquide Santé International, Bagneux, France *Correspondence to hanaire.h@chu-toulouse.fr

Disclosure:

Hanaire has received consulting and lecturer fees, and financial support for meetings from Insulet, Abbott, AstraZeneca, Novo Nordisk, Eli Lilly and Company, Sanofi, Isis, Medtronic; and research grants from Novo Nordisk and Abbott. Melki has received consulting and lecturer fees, and financial support for meetings from Medtrum, Sanofi, DIABSANTE, and Asten Santé; and research grants from Medtrum, Sanofi, Medtronic, and Novo Nordisk. Morcel has received financial support for meetings from Société Francophone du Diabète (SFD). Lancman, Schaller, and Delval are shareholders and are employed by Air Liquide Santé International. Chaillous has received lecturer fees and financial support for meetings from Vitalaire, Asten Santé, Eli Lilly and Company, Novo Nordisk, and Sanofi. Naiditch, Puech, Scharbarg, and Tirado-Godas have no conflicts of interest to declare.

Acknowledgements:

The authors would like to thank the home healthcare provider nurses from LVL and Dinno Santé (HHP Subsidiaries of Air Liquide); Marie Danielle Milcent, Air Liquide Santé International, for her involvement as Clinical Project Manager; Aurélie Formentin and Sylvie Blais, on behalf of Air Liquide Santé International for their involvement as respectively Clinical Study Manager and Clinical Data Manager; and the clinical research organisation team of ICTA. Katrin Spiegel provided writing assistance on behalf of Air Liquide. Author contribution statement: Hanaire: conceptualisation, methodology, validation, investigation, writing, review, and editing; Naiditch: conceptualisation, methodology, validation, review, and editing; Melki: investigation; Morcel: investigation; Puech: investigation; Scharbarg: investigation; Tirado-Godas: investigation; Lancman: conceptualisation, methodology, validation, writing, review, editing, project administration, and funding acquisition; Schaller: methodology and formal analysis; Delval: conceptualisation, methodology, validation, writing, review, editing, project administration, and funding acquisition; and Chaillous: conceptualisation, methodology, validation, investigation, writing, review, editing.

Support:

The study was supported by Air Liquide Santé International.

Received:

05.09.23

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Accepted:

03.10.23

Keywords:

Home healthcare provider (HHP), hybrid closed loop (HCL) system, patient education, patient satisfaction, Type 1 diabetes (T1D).

Citation:

EMJ Diabet. 2023;11[1]:57-67.

DOI/10.33590/emjdiabet/10303508. https://doi.org/10.33590/emjdiabet/10303508.

Abstract Introduction: Hybrid closed loop (HCL) systems have the potential to improve glycaemic control in people with Type 1 diabetes (T1D). In France, patient technical education and assistance for HCL users is provided by trained nurses from home healthcare providers (HHP). The objective of this study was to evaluate satisfaction of people with T1D with HHP services.

Methods: In total, 35 participants with T1D and a prescription for an HCL system were studied during 3 months after HCL initiation in two French hospitals. A series of questionnaires were completed by participants. The number of planned (per protocol) and unplanned HHP interactions was monitored. Glycaemic control at inclusion and Day 90 was compared; formal statistical testing was carried out post-hoc.

Results: Client Satisfaction Questionnaire (CSQ-8) with HHP service was high both at Day 30 (mean CSQ-8 score: 28.9; 95% confidence interval [CI]: 28.0; 29.9) and at Day 90 (29.0; 95% CI: 27.9; 30.0). Hypoglycemia Fear Survey-II (HFS-II) score (standard deviation) decreased from 31.2 (±15.7) at inclusion to 23.1 (±16.8) at Day 90.

Participants had a median number of four home visits and two phone calls, but important differences were observed between participants: total interactions with HHP nurses ranged between five and 12 contacts, and 45.7% of participants requested unplanned interactions.

Glycaemic control improved significantly: mean time in range increased from 57.0% (±13.3) at inclusion to 71.4% (±9.4) at Day 90 (p<0.001).

Conclusion: HHP services for early phase HCL implementation were met with high client satisfaction levels. Study results emphasise the need for a personalised HHP approach.

INTRODUCTION HCL systems have the potential to improve glycaemic control in people living with T1D.1-4 HCLs combine continuous glucose monitoring (CGM) and algorithm-directed insulin pump delivery, such that glucose levels are continuously controlled and corrected.5 Despite increased use of insulin pumps and CGM devices, glycaemic targets are often not achieved among adults with T1D. Approximately 20% of patients achieve HbA1c target levels.6,7

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The benefits of HCL systems were confirmed in observational studies in real-life settings.8-11 Time in range (TIR; blood glucose: 70–180 mg/ dL) was 70–80% in HCL users, and increased by approximately 10% compared with pre-HCL values.8-11 HCL may increase independence, alleviate the burden of insulin management and glucose monitoring, reduce the risk of hypoglycaemia, and improve quality of life.2,9,12 Patients with T1D who start using an HCL device will need technical and educational assistance to become proficient in the safe handling of the

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HCL system and its components. Assistance is especially important in the early phase of using an HCL system. The key to successful use of HCL devices lies in patient motivation, knowledge, and building trust in the system.13 Lack of either element may lead to early discontinuation or reduced health benefit.14 In France, nurses from HHPs have a specific role in the management of patients using insulin pumps, CGM devices, and, more recently, HCL systems. HCL setup and installation are carried out at hospital specialised centres, with knowledge and skills in intensive diabetes management with assistance from HHP nurses.

Once discharged from hospital, HHP nurses allow for a smooth transition from hospital to home. They supply devices to patients’ home, participate in their installation, replace equipment when necessary, provide initial and continuous technical training, register patients on the remote monitoring platform, and support and ensure information exchange between patients and clinicians.15-17 HHPs may be of particular value during the first few months to reduce the risk of early discontinuation. The need for HHP support may gradually decrease as patients get more proficient in the use of their HCL system.13 There is little information about the optimal organisation in providing initial support to patients initiating on an HCL in France, and globally.

The primary objective of this study was to evaluate patient satisfaction with the HHP service before, during, and after installation of an HCL system under real-world conditions.

Additional objectives were to assess hypoglycaemia-related constraints, knowledge acquisition, number of HHP interactions, glycaemic outcomes, and safety.

Home Healthcare Provider in France In France, ‘prestataire de santé à domicile’ (HHP) is an integral part of the French healthcare system. The first HHP services in France appeared in the 1980s to provide O2 therapy to patients at home, thereby avoiding hospitalisation. Since then, the range of services has been continuously

extended to cover other therapeutic domains, including sleep apnoea, respiratory insufficiency, and insulin-therapy. Today, HHP nurses provide technical assistance and equipment to approximately 3.5 million patients in France each year.18 HHP nurses receive training in both the specific therapeutic area by expert clinicians and on the technical aspects of the HCL system by the device manufacturer. HHP personnel constitute an important link between patients, their treating physician, and medical care centres/hospitals through constant communication and feedback.

Interactions with HHP nurses can also take place at patients’ homes, providing additional comfort, especially for people with reduced mobility and in rural areas.

HHP services are reimbursed by the French healthcare system.

METHODS This was a multicentre, longitudinal, noncomparative, interventional study in people with T1D initiating on an HCL system (SATURN, NCT04635280).19 The study was conducted in two hospitals in France. HHP nurses for this study were part of Air Liquide subsidiaries (Air Liquide, Paris, France). The study was approved by an independent ethics committee and carried out in respect of good clinical practice guidelines, the Declaration of Helsinki, and French law.

Participants aged ≥18 years were eligible if they had a history of T1D of at least 2 years, and used an insulin pump for at least 6 months. HbA1c levels had to be <10% during the previous 4 months. The participating centres pre-selected patients with T1D based on their medical file.

Patients who met the inclusion/exclusion criteria were invited to participate in the study and underwent the baseline visit to confirm eligibility.

All participants received the t:slim X2™ HCL pump (Tandem Diabetes Care, San Diego, USA) with integrated Control-IQ technology (Tandem Diabetes Care), associated with the Dexcom G6 CGM (Dexcom, San Diego, USA).

The study was divided into pre-installation, installation, and 3-month follow-up phases. The main goals of the HHP service were to provide

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training for the HCL system, ascertain proper functioning and completeness of the equipment, supply and replace equipment, provide technical on call assistance, and liaise with clinicians throughout the study. The number of visits and contacts were adapted based on participants’ needs, as well as investigator and HHP staff judgement. Investigators were responsible for verifying inclusion/exclusion criteria, informed consent, initiating the HCL system, biological tests, evaluating the patient’s metabolic parameters transmitted via the online monitoring platform, decision regarding continuation of HCL system, and collection of adverse events.

The primary endpoint of this study was participants’ satisfaction with the HHP service as determined via the CSQ-8.20,21 Additional endpoints included the HFS-II score,22 knowledge acquisition evaluated through a tailor-made questionnaire, the number of contacts between HHP nurses and participants, and glycaemic control (HbA1c and glucose metrics obtained by CGM). The different questionnaires used in this study are described below.

Client Satisfaction Questionnaire

The CSQ-8 is a validated questionnaire, which has been translated to several languages and applied to a range of healthcare settings, including childbirth and mental health.23 The CSQ-8 consists of eight questions, each of which has four possible answers. The questions assess the quality of the service, whether the service corresponds to participants’ expectations, whether needs are met, and problems solved. Participants rate overall satisfaction and whether they would recommend the service to a friend or use the same service again. The maximum total CSQ-8 score is 32, with higher scores indicating greater client satisfaction.

Hypoglycemia Fear Survey-II

Hypoglycaemia can be a life-threatening event.

The constant fear of experiencing hypoglycaemic events can be stressful to patients with T1D, and reduce their quality of life.24 To avoid such events, patients may restrict social interactions, keep higher glucose levels, avoid activity, and recur to frequent blood sugar measurements.

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The HFS-II is a validated questionnaire, translated into several languages, consisting of two subscales for behaviour (HFS-B) and worries (HFS-W). The HFS-B consists of 15 questions, each with five possible answers, with focus on specific behaviours to avoid hypoglycaemia. Typical items are ‘limit physical activity’, ‘eat large snacks’, or ‘keep blood glucose >15 mmol/L’. The HFS-W assesses the worries related to hypoglycaemic events and includes 18 questions (each with five possible answers); for example ‘not realising having low blood sugar’, ‘low blood sugar interfering with important things’, or ‘becoming hypoglycaemic during sleep’. Responses add up to subscales and total scores with maxima of 60 (HFS-B), 72 (HFS-W), and 132 (total HFS-II). Higher scores indicate that patients are more affected by hypoglycaemia events.

Knowledge Acquisition

The knowledge acquisition questionnaire is a tailor-made tool that was developed by HHPs, not validated psychometrically, to evaluate patients’ knowledge regarding HCL use over time. The questionnaire is divided into basic and advanced functions, and comprises 15 categories; for example: ‘knowledge on the use of the pump’, ‘knowledge on the control module menu’, ‘knowledge on the precautions for use of the components’, or ‘programming specific boluses’. The HHP assesses the knowledge acquisition for all items in each category using a 4-point Likert scale (‘not addressed’, ‘not acquired’, ‘in progress’, and ‘acquired’).

This questionnaire allows HHPs to follow their patients’ progress and to deliver patient-centred training, which focuses on those areas where patients present knowledge gaps.

In this study, knowledge was considered acquired if the response to a specific item was either ‘acquired’ or ‘in progress’. The percentage of participants with knowledge acquired at installation, Day 30, and Day 90 was summarised using descriptive statistics.

Participants completed the CSQ-8 and the HFS-II at the beginning of the study, and 1 and 3 months after HCL installation. Participant satisfaction was considered achieved if the CSQ-8 score at Day 30 was ≥20 points and

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Table 1: Overview of Client Satisfaction Questionnaire and Hypoglycemia Fear Survey results.

Inclusion (N=35)

Day 30 (N=35)

Day 90 (N=35)

Mean±SD

N/A

28.9±2.8

29.0±3.1

95% CI

N/A

28.0; 29.9

27.9; 30.0

Median

N/A

30.0

30.0

Min; max

N/A

22; 32

20; 32

Mean±SD

N/A

N/A

0.1±2.4

95% CI

N/A

N/A

-0.8; 0.9

Median

N/A

N/A

0.0

Min; max

N/A

N/A

-5; 4

Satisfaction, n (%)†

N/A

35 (100.0)

35 (100.0)

CSQ-8 Summary Scores CSQ-8 score*

Change from Day 30 to Day 90

Maintenance of satisfaction at Day 90, n (%)‡ Yes

N/A

N/A

33 (94.3)

No

N/A

N/A

2 (5.7)

Mean±SD

31.2±5.7

25.7±20.1

23.1±16.8

Median

29.0

22.0

21.0

Min; max

10; 68

1; 105

0; 65

HFS-II HFS-II total scores

Absolute change with respect to inclusion in HFS-II Mean±SD

N/A

-5.5±21.5

-8.0±18.7

Median

N/A

-6.0

-6.0

Min; max

N/A

-46; 85

-54; 50

Mean±SD

15.4±7.3

12.8±9.6

11.6±8.6

Median

15.0

11.0

10.0

Min; max

2; 37

1; 42

0; 34

Behaviour

Absolute change with respect to inclusion in HFS-B Mean±SD

N/A

-2.7±8.0

-3.8±7.9

Median

N/A

-3.0

-4.0

Min; max

N/A

-15; 27

-19; 21

Mean±SD

15.7±11.4

12.9±11.5

11.5±9.2

Median

14.0

12.0

11.0

Min; max

3; 54

0; 63

0; 34

Worries

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Table 1 continued.

Inclusion (N=35)

Day 30 (N=35)

Day 90 (N=35)

Absolute change with respect to inclusion in HFS-W Mean±SD

N/A

-2.8±15.1

-4.2±12.9

Median

N/A

-3.0

-3.0

Min; max

N/A

-35; 58

-45; 29

*Score ranges from 8–32, with higher values indicating higher satisfaction. †Score ≥20. ‡Score at Day 90 ≥20 that did not decrease by more than four points compared with Day 30.

CI: confidence interval; CSQ-8: Client Satisfaction Questionnaire; HFS: Hypoglycemia Fear Survey; HFS-B: Hypoglycemia Fear Survey – Behavior; HFS-W: Hypoglycemia Fear Survey – Worries; max: maximum; min; minimum; N/A: not applicable; SD: standard deviation.

the CSQ-8 score at Day 90 was ≥20 points and not decreasing more than four points with respect to the Day 30 evaluation. HFS-II scores, knowledge acquisition, number of HHP contacts, and glycaemic outcomes were described using descriptive statistics. Post hoc analyses using formal statistical comparison between glycaemic parameters at inclusion and Day 90 were carried out, using a paired t-test or McNemar test. A sample size of 32 participants was considered sufficient to estimate the percentage of participants satisfied at Day 90 with a precision of ≤12.4%. All analyses were carried out with the software package SAS version 9.4 (SAS Institute, Cary, North Carolina, USA) or higher.

RESULTS In total, 35 people with T1D were enrolled between May–September 2021, including 20 at site one and 15 at site two. All participants completed the study. Mean age was 42.4 (±11.5) years, and 21 (60%) participants were male.

The mean time since T1D diagnosis was 23.3 (±12.4) years, ranging between 4–61 years. Of these participants, 24 (68.6%) had at least one comorbidity at inclusion; the most common comorbidities were metabolism and nutrition disorders, reported in 14 (40%) participants. At inclusion, 14 (40%) participants had at least one diabetes-related complication.

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All participants had prior use of an insulin pump and CGM device, including intermittently scanned CGMs. The mean time since first installation of insulin pump was 10.9 (±6.9) years.

Seven (20%) participants had a history of HCL system use.

Primary Endpoint: Client Satisfaction Questionnaire

The overall CSQ-8 score was high. At Day 30, mean CSQ-8 score was 28.9 (95% CI: 28.0; 29.9), and at Day 90 it was 29.0 (95% CI: 27.9; 30.0; Table 1). The mean change in CSQ-8 score between Day 30 and Day 90 was 0.1 (95% CI: -0.8; 0.9), suggesting that satisfaction levels were maintained during the study period.

All participants rated the quality of HHP service as either ‘good’ or ‘excellent’. All participants were ‘mostly’ or ‘very satisfied’ with the help they received and HHP nurses were able to deal with problems (‘somewhat’ or ‘a great deal’) according to participants’ perception.

Hypoglycemia Fear Survey

The overall mean (standard deviation) HFS-II score decreased from 31.2 (±15.7) at inclusion to 23.1 (±16.8) at Day 90 (Table 1). A decrease in scores was seen in both the behaviour and worries subscales. The mean HFS-B score decreased from 15.4 (±7.3) at inclusion to

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Table 2: Metabolic control at inclusion, Day 30, and Day 90 (intention-to-treat population).

Parameter/Statistics

Inclusion (N=35)

Day 30 (N=35)

Day 90 (N=35)

p*

HbA1c (%)

<0.001

Mean±SD

7.4±0.8

N/A

6.7±0.6

Median

7.5

N/A

6.8

Min; max

5.9; 8.8

N/A

5.1; 7.6

HbA1c by class, n (%)

35

<7

10 (28.6)

N/A

22 (62.9)

[7–8]

18 (51.4)

N/A

13 (37.1)

[8–10]

7 (20.0)

N/A

0

<0.001

Average daily glucose level (mg/dL)

<0.001

Mean±SD

165.4±22.4

152.5±11.9

153.1±13.7

Median

165.5

152.0

152.0

Min; max

127; 219

12; 179

131; 187

Missing values

1

0

0

Coefficient of variation for glucose level (%)

<0.001

Mean±SD

38.7±5.8

34.3±5.5

34.1±5.0

Median

39.0

34.0

34.0

Min; max

25; 50

23; 48

24; 46

Missing values

7

0

0

Time spent in the glycaemic target (70–180 mg/dL; %)

<0.001

Mean±SD

57.0±13.3

72.2±8.3

71.4±9.4

Median

56.0

73.0

73.0

Min; max

24; 78

53; 87

51; 89

Missing values

1

0

0

Time spent above range (>180 mg/dL; %)

<0.001

Mean±SD

37.8±14.9

25.5±8.0

26.4±9.5

Median

38.0

25.0

24.0

Min; max

14; 76

8; 42

10; 48

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Table 2 continued.

Parameter/Statistics

Inclusion (N=35)

Day 30 (N=35)

Day 90 (N=35)

Time spent above range (>250 mg/dL; %)

p* <0.001

Mean±SD

13.2±8.3

5.9±4.9

6.1±4.8

Median

12.0

5.0

5.0

Min; max

1; 30

0; 21

0; 23

Missing values

8

N/A

N/A

Time spent below range (<54 mg/dL; %)

0.047

Mean±SD

1.2±2.2

0.3±0.5

0.4±0.6

Median

0.5

0.0

0.0

Min; max

0; 10

0; 1

0; 2

Missing values

7

0

0

*Paired t-test or McNemar test to compare values at Day 90 to values at inclusion.

Max: maximum; min; minimum; N/A: not applicable; SD: standard deviation.

11.6 (±8.6) at Day 90. The mean HFS-W score decreased from 15.7 (±11.4) at inclusion to 11.5 (±9.2) at Day 90.

Home Healthcare Provider Interactions

HHP interactions were planned at specific time points per protocol, namely during HCL installation, early follow-up, at Day 30, and Day 90. All other visits were considered as unplanned.

Most participants had four home visits and two phone calls, but important differences were observed between participants; total HHP interactions ranged between five and 12 contacts.

Overall, there were 7.3 (±1.5) HHP interactions on average, resulting in a mean cumulative time of approximately 8 hours during the 3-month study period. Approximately half of participants (45.7%) required additional unplanned contacts, with the majority being at the patient’s request. Reasons for these unplanned interactions were additional training needs, device-related incidents, sensor installation support, support for data upload,

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replenishment needs, management of alarms, motivational support, or need of reassurance.

Knowledge Acquisition

At the early follow-up visit (Day 1/3), 74.3% of participants had acquired the knowledge provided during their initial training. Areas that needed to be revisited most often, i.e., knowledge not yet acquired in ≥4 participants at Day 1/3, included re-sugaring, sensor start-up, start-up in closed loop mode, status bar and icons on the terminal, difference between self-calibration and calibration mode, and safety mode. Participants still in the learning process had achieved 94.1% (±3.6%) of required knowledge at Day 1/3. At Day 90, all but two participants had fully acquired the required knowledge, while the remaining participants achieved 98.4% of required knowledge.

Glycaemic Control

Glycaemic parameters consistently improved during the study period (Table 2). Mean HbA1c

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value was 7.4% (±0.8%) versus 6.7% (±0.6%) at inclusion and Day 90, respectively (p<0.001).

The proportion of participants achieving HbA1c values <7% increased from 28.6% at inclusion to 62.9% at Day 90. At the same time, the proportion of participants with an HbA1c ≥7 and <8 decreased from 51.4% to 37.1% while no participant had HbA1c values ≥8 at Day 90 compared with 20% at inclusion.

Mean time spent in glucose range, defined as ≥70 to <180 mg/dL, was 57.0% (±13.3%) at inclusion and 71.4% (±9.4%) at Day 90 (p<0.001). Mean percentage time spent below range (<70 mg/ dL) decreased from 4.7% (±4.6%) at inclusion to 2.1% (±1.7%) at Day 90 (p<0.001) and mean percentage time above range (>180 mg/dL) decreased from 37.8% (±14.9%) at inclusion to 26.4% (±9.4%) at Day 90 (p<0.001).

All participants continued their treatment by HCL after the end of the study.

Safety

Twenty participants experienced a total of 38 adverse events (AE), either mild or moderate. There were no serious AEs. Three participants (8.6%) had a total of four events of hyperglycaemia with ketosis, and 12 AEs were related to the device, mainly hyperglycaemia and/ or ketosis and catheter site-related AEs. Overall, 25 (71.4%) participants reported a total of 55 incidents with the HCL system, mainly related to catheter issues. No severe hypoglycaemia events were reported.

DISCUSSION In this study, participants with a prescription for an HCL system received educational and technical support from HHP nurses for 3 months.

The authors assessed participants’ satisfaction with the HHP service using a validated questionnaire: the CSQ-8. The overall satisfaction level was high and constant over the study period. Most participants responded that they were satisfied with the quality of the service and the help received.

The goal of the HHP service is to ensure that patients acquire the knowledge and proficiency required to use the HCL system, and to develop

trust in the system and its algorithm. Trust and knowledge are paramount in order for patients to ‘let go’ and ‘hand over control’ to the system.

HHPs also provide motivational support and reassurance such that patients may draw full benefit of their HCL system, e.g., improved glycaemic control and fewer diabetes-related concerns, such as hypoglycaemic events.

Qualitative research with clinical study participants receiving an HCL device highlighted that participants had ambivalent feelings at the beginning of the study.13 Building trust and confidence in the HCL system required several weeks during which participants scrutinised the algorithm and the functioning of the HCL device.

Once they understood the workings and realised that the device made sensible adjustments, they were able to step back and let the system do its job. Participants emphasised the benefit of tailored training and the opportunity to ask indepth questions in the early phases of HCL use.

Also, memorising all features at once is illusionary and certain functionalities will only be acquired over time, making additional training sessions essential.

Results from the authors’ study agree with qualitative research and emphasise the need for a personalised service. Not all participants had acquired the necessary knowledge at the early follow-up visit, but the percentage of knowledge acquisition increased with subsequent HHP interactions. There were variations in the number of planned and unplanned interactions, highlighting the need for a personalised approach. A patient-centred service may provide ad hoc assistance, reduce the risk of early HCL discontinuation, and improve the health benefits of HCL devices. This echoes the viewpoint of clinicians who highlight the importance of highquality training to new HCL users both in clinical research and real-life setting.14 In a longitudinal study in children and young adults with T1D, 28 out of 91 participants (30%) discontinued HCL within 6 months.25 Major hurdles and reasons for discontinuations were ‘technical issues/problems’, ‘too much work to maintain HCL’, ‘expense/ reimbursement’, and ‘alarms’.25 Thus, technical assistance is equally important for trouble shooting and resolving technical issues that otherwise could lead to frustration.

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Metabolic balance improved during the study with an increase in TIR of 10 percentage points, which is considered clinically meaningful. TIR is strongly associated with microvascular complications; a decrease in TIR by 10 percentage points was shown to increase the probability of developing retinopathies by 64% and that of kidney disease by 40%.26 No severe hypoglycaemia events were reported during the study period.

There are several limitations to this study: the research was conceived as a feasibility study to evaluate satisfaction with HHP services before the introduction of this new technology on the market. Therefore, the sample size was relatively small, and the study duration was limited to 3 months. In this study, all participants continued to use the HCL device after study completion, but larger studies with a longer follow-up period are needed to determine the discontinuation rate of HCL users who receive assistance from HHP services. Patients with HbA1c ≥10% were excluded in this study. Patients with poorly controlled glycaemia may benefit most from the HCL system. The value of HHP services in this patient group deserves further attention, especially in light of higher HCL discontinuation rates observed among patients with higher HbA1c values at baseline.25 The HHP service is a patientcentred approach with the objective of ensuring patient satisfaction, lowering discontinuation rates, and improving health benefits, including quality of life. Such objectives are consistent with the notion of value-based healthcare, i.e., optimising health outcomes from a patient perspective while minimising costs to the healthcare system.27

References 1.

2.

3.

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Weisman A et al. Effect of artificial pancreas systems on glycaemic control in patients with type 1 diabetes: a systematic review and meta-analysis of outpatient randomised controlled trials.

Lancet Diabetes Endocrinol.

2017;5(7):501-12.

Pease A et al. Time in range for multiple technologies in type 1 diabetes: a systematic review and network meta-analysis. Diabetes Care. 2020;43(8):1967-75.

Jiao X et al. Better TIR, HbA1c, and less hypoglycemia in closed-loop

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No comparator arm was included in this study as there was no ‘standard of care’ for HCL assistance in France at the time of the study.

Therefore, it is not possible quantify the contribution of HHP services to the observed improvements in glycaemic control and HFS-II values. Technical assistance for insulin pumps is historically provided by HHP services in France, and this service could be naturally extended to HCL systems. All participants received the same HCL system in this study because only one HCL system with Conformité Européenne (CE) mark was available during the recruitment period; however, HHP nurses are trained on different models and their assistance is not specific to a single model. The French HHP model may alleviate the burden on healthcare professionals who are often under time pressure, by delivering educational and technical assistance to patients with an HCL system.28

CONCLUSIONS The HHP service as implemented in this study reached high satisfaction levels among participants. HHP service represents an important step towards a value-based healthcare system, providing patient-centred and personalised support. Improvements in glycaemic control and HFS-II scores need to be viewed in the context of study limitations, i.e., small sample size and short follow-up period. Studies with longer duration are needed to determine discontinuation rates and estimate the contribution of HHP services to optimise health benefits and quality of life of patients, while lowering the burden to the healthcare system.

insulin system in patients with type 1 diabetes: a meta-analysis.

BMJ Open Diabetes Res Care.

2022;10(2):e002633.

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Bekiari E et al. Artificial pancreas treatment for outpatients with type 1 diabetes: systematic review and meta-analysis. BMJ. 2018;361:k1310.

5.

Boughton CK, Hovorka R. New closed-loop insulin systems.

Diabetologia. 2021;64(5):1007-15.

6.

Foster NC et al. State of type 1 diabetes management and outcomes from the T1D Exchange in 2016-2018. Diabetes Technol

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Ther. 2019;21(2):66-72.

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Renard E et al. The SAGE study: global observational analysis of glycaemic control, hypoglycaemia and diabetes management in T1DM. Diabetes Metab Res Rev. 2021;37(7):e3430.

8.

Forlenza GP et al. Real-world evidence supporting Tandem Control-IQ hybrid closed-loop success in the Medicare and Medicaid type 1 and type 2 diabetes populations. Diabetes Technol Ther. 2022;24(11):814-23.

9.

Beato-Vibora PI et al. Amelioration of user experiences and glycaemic

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outcomes with an advanced hybrid closed loop system in a real-world clinical setting. Diabetes Res Clin Pract. 2021;178:108986.

10. DuBose SN et al.; HYCLO Study Group. Patient-reported and clinic data from individuals with type 1 diabetes using the MiniMed 670G hybrid closed-loop system. Diabetes Technol Ther.

2021;23(12):791-8.

11. Da Silva J et al. Real-world performance of the MiniMed 780G system: first report of outcomes from 4120 users. Diabetes Technol Ther. 2022;24(2):113-9.

12. Barnard KD et al. Closing the loop in adults, children and adolescents with suboptimally controlled type 1 diabetes under free living conditions: a psychosocial substudy. J Diabetes Sci Technol.

2017;11(6):1080-8.

13. Boughton CK et al. Training and support for hybrid closed-loop therapy. J Diabetes Sci Technol.

2022;16(1):218-23.

14. Lawton J et al. Participants' experiences of, and views about, daytime use of a day-and-night hybrid closed-loop system in real life settings: longitudinal qualitative study. Diabetes Technol Ther.

2019;21(3):119-27.

15. Tubiana-Rufi N et al.; SFD SFD Paramedical SFE SFEDP AJD FFD FENAREDIAM and CNPEDN. Practical implementation of automated closed-loop insulin delivery: a French position statement. Diabetes Metab.

2021;47(3):101206.

16. Société Francophone du Diabète

(SFD). [Implementation of automated insulin therapy through closed loop system: a position paper by French experts]. 2020.

Available at: https://www.sfdiabete. org/sites/www.sfdiabete.org/ files/files/ressources/mmm_ bouclefermee_sept2020.pdf. Last accessed: 23 August 2023. (In French).

17. Naïditch N et al. [Pump support study. Quantitative study on the satisfaction and needs of diabetic patients using an insulin pump regarding their home health care provider]. Médecine des maladies Métaboliques. 2022;16(4):359-64.

(In French).

18. Fédération des prestataires de santé à domicile (PSAD). Key figures. 2023. Available at: https:// www.fedepsad.fr/chiffrescles.php.

Last accessed: 23 August 2023.

19. Air Liquide Santé International.

Evaluation of satisfaction regarding home healthcare provider (HHP) management of type 1 diabetic patients using a closed-loop automatic insulin delivery system (SATURN). NCT04635280. https:// https://classic.clinicaltrials.gov/ct2/ show/NCT04635280.

20. Attkisson CC, Zwick R. The client satisfaction questionnaire.

Psychometric properties and correlations with service utilization and psychotherapy outcome. Eval Program Plann. 1982;5(3):233-7.

21. Attkisson CC, Greenfield T., “The UCSF client satisfaction scales: I. The UCSF client satisfaction questionnaire-8,” Maurish ME (eds.) The Use of Psychological Testing for Treatment Planning and Outcome Assessment (2004)

3rd edition, New York: Lawrence Erlbaum Associates Publishers, pp.799-811.

22. Gonder-Frederick LA et al.

Psychometric properties of the hypoglycemia fear survey-II for adults with type 1 diabetes.

Diabetes Care. 2011;34(4):801-6.

23. Matsubara C et al. Reliability tests and validation tests of the client satisfaction questionnaire (CSQ8) as an index of satisfaction with childbirth-related care among Filipino women. BMC Pregnancy Childbirth. 2013;13:235.

24. Chatwin H et al. 'Never again will I be carefree': a qualitative study of the impact of hypoglycemia on quality of life among adults with type 1 diabetes. BMJ Open Diabetes Res Care. 2021;9(1):e002322.

25. Messer LH et al. Real world hybrid closed-loop discontinuation: predictors and perceptions of youth discontinuing the 670G system in the first 6 months. Pediatr Diabetes.

2020;21(2):319-27.

26. Beck RW et al. Validation of time in range as an outcome measure for diabetes clinical trials. Diabetes Care. 2019;42(3):400-5.

27. Porter ME. What is value in health care? N Engl J Med. 2010;363:2477-81.

28. Kimbell B et al. What training, support, and resourcing do health professionals need to support people using a closed-loop system?

A qualitative interview study with health professionals involved in the Closed Loop from Onset in Type 1 Diabetes (CLOuD) trial. Diabetes Technol Ther. 2020;22(6):468-75.

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Type 1 Diabetes and Health Information Systems: Lessons from Denmark Authors:

*David Beran,1,2 Kurt Højlund,3 Anders Green3 1. Division of Tropical and Humanitarian Medicine, Faculty of Medicine, University of Geneva, Switzerland 2. Geneva University Hospitals, Switzerland 3. Steno Diabetes Center Odense, Odense University Hospital, Denmark *Correspondence to david.beran@unige.ch

Disclosure:

Beran reports that this paper is part of a Visiting Professor Grant by the Danish Diabetes Academy. The authors declare no conflicts of interest.

Acknowledgements:

Many thanks to all the colleagues at Steno Diabetes Center Odense, experts who participated in two workshops held in November 2022, and different key opinion leaders who gave their time and insight for this project. Special thanks to Tanja Thybo from the Danish Diabetes Association and Jan Erik Henriksen from the Steno Diabetes Center Odense, Odense University Hospital, Denmark, for their support and insight.

Received:

20.07.23

Accepted:

14.08.23

Keywords:

Data collection, diabetes, health systems, registers.

Citation:

EMJ Diabet. 2023; DOI/10.33590/emjdiabet/10300830. https://doi.org/10.33590/emjdiabet/10300830.

Abstract One of the components of a health system as defined by the World Health Organization (WHO) is data that the health system collects, generates, and uses to improve the health of its population. The aim of this paper is to describe key lessons from Denmark in planning, implementing, and using health system-generated data, using Type 1 diabetes as a tracer. A broad literature search was complemented with a review of grey literature, two series of interviews with Danish experts, and feedback from a presentation of these findings to two expert meetings. Denmark, through its unique identifier and data environment, enables the health system to collect a variety of data on Type 1 diabetes. Facilitators and challenges exist for data collection, data aggregation, use of data, communication of data and results, and intangible factors. For each of these, the environment, infrastructure, health system, and Danish society act as facilitators. Barriers relate to data being collected primarily for clinical and administrative purposes, and not necessarily for research, planning, policymaking, or advocacy. Fora are lacking to facilitate the communication and presentation of these results. An intangible element is the trust Danes have in their system, which is hard to replicate. As shown in the Danish setting, contextual factors cannot be negated in developing and implementing data-related solutions at a population level. The lessons from Denmark show that there is the need to conceive and act on all aspects of the data from its collection, aggregation, use, and communication. The last step of interaction between science and policy and practice requires a range of factors, including networks and knowledge brokers.

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Key Points 1. Data is an integral part of the health system. For diabetes, this data can be used for a variety of elements, from describing epidemiology, to ensuring appropriate follow-up of an individual. Denmark provides an example of an environment that collects and uses data for a variety of purposes.

2. This paper describes the Danish Type 1 diabetes environment, as well as the facilitators and barriers that exist. Facilitators relate to the environment, infrastructure, health system, and society. Barriers are that the data is primarily collected for clinical and administrative purposes, not for research, planning, policymaking, or advocacy. Another barrier is the means to communicate such results.

3. The lessons from Denmark show that there is the need to conceive and act on all aspects of the data, from its collection, aggregation, use, and communication. The last step of interaction between science and policy and practice requires a range of factors, including networks and knowledge brokers.

INTRODUCTION The World Health Organization (WHO) defines six key components of a health system.1 This includes data that the health system collects, generates, and uses to improve its management and care of the population. Data in the context of health can include patient records, diseasebased or facility-based registers, sub-national and national registers and clinical databases.

The aggregation of this data can help the health system in monitoring population health (incidence, prevalence, morbidity, and mortality), planning (medicines, supplies, human resources, etc.), as well as for quality and monitoring and evaluation purposes.2,3 Policies also require data in their formulation, implementation, and monitoring, and, therefore, the health system also needs to generate information that can be used by policymakers.4 In 1989, a meeting of diabetes associations, the WHO, and representatives of Ministries of Health in Europe developed the St. Vincent Declaration on diabetes care and research in Europe.5 Included in this declaration was the need to collect data to ensure improvements in the management of diabetes and the prevention of complications. More recently in Europe, in 2022, the European Union (EU) Parliament adopted a resolution on diabetes which states: “The Commission should follow through with the Healthier Together – EU Non-Communicable Disease Initiative, inter alia by collaborating with Member States in developing and implementing common, standardised criteria, and methods for data collection on diabetes, and in

collecting, registering, monitoring, and managing comprehensive epidemiological data on diabetes, as well as economic data on the cost of diabetes prevention and management in the EU, including patient preferences and patient-generated data.”6 Similar messages on the need to strengthen health information systems for diabetes have also been affirmed by the World Health Assembly (WHA) in 2021, with a resolution entitled ‘Reducing the burden of noncommunicable diseases through strengthening prevention and control of diabetes’.7 Bak et al.8 state that diabetes registers allow for a better understanding of diagnosis, complications, and management. However, their impact policy is less clear. Khunti et al.9 propose recommendations for the development of Type 2 diabetes registries in Europe. They highlight the need for buy-in from multiple levels of the health system, including policymakers and frontline health professionals who are responsible for entering the data. Denmark can serve as a model for other countries in its experience of using registers and the collection of diabetesrelated information.10 Using Type 1 diabetes as a tracer,11 the aim of this paper is to describe some key lessons from Denmark for other settings in planning, implementing, and using health system-generated data for Type 1 diabetes.

METHODS Context

Denmark has a population of 5.8 million (2020), with a life expectancy for females of 83.6 years,

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and 79.5 years for males (2020)12 and a gross domestic product per capita of 60,909 USD (at current conversion rate in 2020.)13 Healthcare is provided for free, with over 10% of gross domestic product being spent on health.14

Approach

The focus of this paper is to document the data environment for Type 1 diabetes in Denmark.

This was done by a broad literature search on PubMed using the following search terms: “Denmark,” “diabetes,” “Type 1 diabetes,” “registers,” and “epidemiology.” The search was carried out from January 2011–August 2021, and then updated until March 2022. Reports and publications from the Ministry of Health, Regional Authorities, Danish Diabetes Association (Diabetesforeningen), WHO, and other international organisations were also included as part of this literature review. Bibliographies of articles and documents were used to identify further resources.

This review was complemented by two series of interviews with Danish experts. One set of interviews was with key opinion leaders in the field of diabetes to learn more about clinical practice, the use of data, and gain further insight into the literature from a practical angle. The other was carried out with a purposive sample of experts playing different roles within the Danish Health System, to learn about how data is used to inform policy and practice. These interviews were carried out using a discussion guide that used ‘Grand Tour’ questions15 to gain a ‘verbal tour’ of different areas of the collection, use, and dissemination of data in Denmark. The interviews also used prompts to delve into certain areas mentioned by the experts. Interviews were recorded, anonymised, and analysed thematically.

The final component was to present the results of this work at two meetings held in November 2022. One meeting included Danish diabetes experts, and the other a panel of international diabetes experts. Feedback was provided on the initial results of the lessons learnt from Denmark to further refine these and develop the material presented in this paper.

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RESULTS The Danish Data Environment

The Danish Civil Registration System provides each person residing in Denmark with a unique 10-digit identifier.16 All registers in Denmark use this unique identifier allowing for linkages to be made between different data sources for administrative and research purposes. The Danish Health Data Authority (DHDA) has the responsibility for managing the Danish health registers, and the use of this data for research is regulated by law.17 Data is available to authorised users through a centrally protected environment.18 Individual data can be accessed by researchers, but these need to be published in an aggregate way to ensure that individuals cannot be identified.18 Within the health data environment, there is the Danish National Patient Register, which includes individual data on diagnosis, treatments, etc., for inpatient, outpatient, and emergency visits.19 Reporting to this register is mandated by law, and each outpatient visit or hospital discharge needs to be documented. In Denmark, other registers are also present. The National Health Service Register20 includes information from primary healthcare, and is used for the payment of services. For individual prescriptions, the Danish National Prescription Register21 collects this information. Reporting of all deaths, including information on causes of death, is part of the Danish Register of Causes of Death.

The Danish Diabetes Data Environment

In 1996, the Danish Childhood Diabetes Registry was established. This registry was set up to collect data from paediatric facilities caring for children aged 0–15 with Type 1 diabetes,22 for both epidemiological and monitoring purposes.23 This register collects laboratory, clinical, treatment, and demographics for each individual. The National Diabetes Register, established in 2006 by the Danish National Board of Health (now DHDA), was created as a tool for “monitoring the clinical course in patients with diabetes.”24 This ceased to operate in 2012,25 and was replaced by the Register of Selected Chronic Disease and Severe Mental Disorders (RUKS).26 Specific diabetes research databases have also been established using data from the central

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Danish health registers mentioned above.27 In addition, a Nationwide Clinical Quality Database for diabetes combines information from different sources, to assess quality for a series of core indicators defined by the health system.28

Lessons from Denmark

In Denmark, there are different facilitators and barriers which are present at national, health system, and population level. These exist for data collection, aggregation, use, and communication.

In addition, intangible factors are present.

Data collection

Experts describe a variety of factors that facilitate data collection at a national level. This includes the tradition of the data environment in Denmark, governance of the health system, digital infrastructure present, and a high level of digitalisation in Danish society. Data collection is part and parcel of the daily activity of the health system, with this data being used for improving care and the system. Interviewees also expressed that people entering the data felt a sense of responsibility for doing this. From the population’s perspective, experts stated that there was underlying trust in the government, and that the data would be used properly.

The only barrier mentioned at the level of the government was regarding the General Data and Protection Regulation of the EU, that Denmark, as a member of the EU, had to follow. Barriers at health system and research levels were related to three elements. Firstly, that the data did not currently capture all aspects of laboratory tests, and did not include patient-centred and reported outcomes. Another factor was that the onus of data collection was on clinicians. Finally, the data can have many uses for clinical, administrative, and research purposes, but limitations exist for each of these.

Data aggregation

Data aggregation is a key function of the Danish Diabetes Data environment that allows for individual patient data to be combined at clinic, facility, regional, and national levels.

Being able to aggregate this data at these levels allows for comparisons, quality assessments, development of national reports, and research to be carried out. A key component that allows for data aggregation is the standardisation of data

collection. The unique identifier was also a tool to aggregate data across different data sources.

The challenges related to data aggregation is that, as with data collection, the primary reason these data are collected is not for research purposes.

Therefore, there is the need for validation of the clinical data, and if the codes used are relevant for research purposes. Interviewees also relate that capturing complexity is hard with the existing data. The challenge is that if researchers are interested in looking at multiple factors, e.g., pump use and hypoglycaemia, or mental health and Type 1 diabetes, the aggregation of the different data points can be difficult. Experts also highlighted two other challenges. The first challenge is that the use of the register data requires both clinical and epidemiological knowledge to contextualise and navigate the data. The other is that with the increase of user generated data in Type 1 diabetes with continuous glucose monitors, there is no way to integrate this in the existing data capture.

Use of data

Facilitating the use of diabetes-related data in Denmark is also the unique diabetes environment. This includes academia and the different Steno Diabetes Centers established in each region of the country. These centres of excellence facilitate the interaction between researchers and clinicians for the data to be used in different ways.

The challenges reported for the use of diabetes data was that the data is complex to use, and it requires significant investment to present the data in formats that are relevant for different stakeholders. Key informants described how policy and decision-makers could access data generated by the health system, directly from the system itself, researchers, or advocates, but that often knowledge brokers played a key role as intermediaries in this process. The Danish Diabetes Association was one example of a knowledge broker that was mentioned, in that it had the capacity to use existing data from the system, as well as collecting its own information and use these for advocacy purposes. Other examples were different key opinion leaders who were either clinicians or researchers, who also had administrative roles within the health system, and used their knowledge of the diabetes data in non-scientific settings. Interviewees also

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state that just because the data is collected and aggregated, this does not mean it will be used, and this can depend on interest of individuals to carry out research in a specific area.

Communication of data and results

Interviewees highlight a variety of factors that facilitate the communication of data and results in Denmark. This includes that Denmark is a small country, the existence of diabetes-related networks, and an active interest in Type 1 diabetes. Beyond this, many experts state that beyond the communication of the results, there is a need to “translate” the data and findings so that these can be useful, and integrated into policy and practice by different stakeholders. In this process, knowledge brokers were mentioned as a key component. Those interviewed highlighted the need for a two-stage process in generating “useable” information, and then being able to provide this in a format that was adequate for the given audience, be it at clinic, facility, regional, or national levels, or for non-expert audiences.

Different formats are needed, as well as platforms for communicating these results.

Intangible factors

The interviews also highlighted the importance of intangible factors present in Denmark that facilitated the unique data environment. A recurring theme was the element of trust that Danes have in their government and the health system. The other point was that people were glad to give their data in exchange for the variety of health and social benefits they receive for free. Transparency was another intangible factor, in that the system allows individuals to access their own data. Two elements that could possibly be challenges to the intangible factors in the future were mentioned. These were the fear of health data being shared in a post-COVID-19 society, as well as the increase in 'big data' from Google (Mountain View, California, USA), Apple (Cupertino, California, USA), Facebook (Meta, Menlo Park, California, USA), and Amazon (Seattle, Washington, USA).

The barriers and facilitators for each of these components is presented in Table 1.

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DISCUSSION For data collection, aggregation, use, and communication, certain lessons can be learnt from Denmark that might be of use for other contexts, and enable the operationalisation of some of the statements and recommendations being made to improve data on diabetes in Europe and beyond. For data collection, the lesson is that although the data collected allows for a variety of information to be generated, fundamentally the data is collected for clinical and administrative purposes. Therefore, the data system needs to focus on the clinical interaction, and be integrated into clinical practice. The system needs to start simply, and collect data that is useable for the clinician, health system, and decision-maker, in that order of priority.

The cost of setting up the system should not be minimised, and, therefore, there is the need to focus on what is essential in addition to having a system that can evolve, as retrofitting is complex and costly. Data aggregation needs to start, with the data being used to improve clinical and administrative outcomes and processes. This focus impacts the use of this data for research purposes, as researchers are then reliant on what and how data has been collected within the registry’s infrastructure. A balance needs to be found between clinical and research results, requiring partnerships to be established between clinicians, administrators, and researchers, to make the most out of aggregating existing data.

For the use of the data, those using the data need to be aware of their responsibility in its proper use, which needs to align with Danes willing to share their data as part of their social contribution to maintain their trust. Leadership is needed from the system and user boards, control mechanisms, and who and how research projects are designed should be included as procedures to be put in place. Finally, lessons on the communication of data and results show that there is the need to understand the results that are produced by the data, and then be able to present this in a clear and comprehensive way to different audiences using knowledge brokers.

Overall lessons are summarised in Table 2.

Beyond these lessons, which can be taken and applied to other settings, the intangible factors present in Denmark cannot be ignored, as the unique historical, political, and social context cannot necessarily be replicated, but must be considered.

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Table 1: Facilitators and barriers for different elements of the Danish Diabetes Data environment.

Data collection

• Facilitators: historical context of the data environment; governance and infrastructure; Danish welfare model; unique identifier for each individual living in Denmark; level of digitalisation in Denmark; data needs to be collected for clinical and administrative purposes; use of the data within the system; improvements versus control; data environment and infrastructure; sense of responsibility for those entering, managing, and analysing data; trust of the population, and giving back.

• Barriers: supra national rules (General Data Protection Regulation); onus of data entry on clinicians; codes used for clinical versus research purposes; system adapted for its different uses (clinical versus administrative versus research); patient-centred and reported outcomes missing.

• Facilitators: population-based registers; linkages between different data sources; allows for studies to look at wide range of health aspects of diabetes.

Data aggregation

Use of data

• Barriers: primary aim of data is not research; challenges in capturing complexity and addressing cross-cutting issues; increase in user-generated data, and how to integrate this; knowledge needed to navigate data; codes used for administrative purposes and their relevance for research.

• Facilitators: diabetes environment is quite unique in Denmark; linkages between clinicians and researchers; data used in quality processes; use of the data within the system; unique data means a variety of possible studies.

• Barriers: complexity of data; integration of data into practice; time to access, read, and use data for policy decisions.

Communication of data

Intangible factors

• Facilitators: small country with existing networks; unique diabetes environment; active interest in Type 1 diabetes.

• Barriers: complexity of data; integration of data into practice for different audiences; time to access, read, and use data for policy; complexity of data for non-expert audiences.

• Facilitators: trust; population accustomed to data being used; people 'giving back' to the system; transparency of the system.

• Barriers: increase in fear of data in a post-COVID-19 society; distrust of 'big data' from Google (Mountain View, California, USA), Apple (Cupertino, California, USA), Facebook (Meta, Menlo Park, California, USA), and Amazon (Seattle, Washington, USA).

Another key element from Denmark is that Danish data informs Danish practice. In many other contexts, countries are reliant on global estimates or modelling. Different estimates exist for Type 1 diabetes,29-33 but these show significant variation in the numbers they present.34 Critiques have been raised about these approaches to

defining the burden of disease where there are ethical issues in using such estimates, issues of transparency in how these estimates are developed, their trustworthiness, and who owns them.35 In order to improve the data for diabetes globally, a proposed roadmap might include global, national, and facility-based investments.34

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Table 2: Lessons learnt on data collection, data aggregation, data use, and communication of data and results.

Data collection

• Primary role of the data collection • Focus on the clinical interaction • Data environment needs to be part of daily clinical life • Who manages the system and acts as a gatekeeper?

• Start simple, but build foundations º Collection of usable data º Cost of setting up the system º What is essential? º How can system evolve?

• Retrofitting is complex and costly • Balance of clinical versus administrative versus research purposes of data collection

Data aggregation

• Keep in mind that data should start with the clinical and administrative focus • Reliance on registry data º What is coded and entered is the data available º Balance clinical and research interpretation of results • Partnerships needed between clinicians, administrators and researchers

Use of data

• Responsibility of those using data • Multiple uses º Clinical interaction º Quality reviews º Reporting (institutional, governmental) º Research • Leadership needed º User boards º 'Control mechanisms' º Who and how are research projects designed?

• Need competence and knowledge to understand the data • How to translate data into useable information Communication • Information formats for different audiences, especially people with diabetes of data • Role of knowledge brokers • Needs mechanisms for communication of results

Previously, global and European-based registry studies have existed, namely the WHO Diabetes Mondiale (DIAMOND)36 study, and the EURODIAB register.37 These studies would need to expand, as previously they only collected childhood data, and estimates now show that in many contexts, the largest number of prevalent cases is in the adult population.29 Globally, guidance and protocols, such as those developed by the International Diabetes Federation (IDF),38 could be used to develop tools and targeted studies to fill existing gaps in knowledge. At a national level, clearly the investments and infrastructure needed to develop a system like Denmark’s will be challenging, but new technologies might be an opportunity to build less onerous data collection environments. Beyond

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ongoing data collection and the establishment of registries, other ways of documenting diabetes could be employed. This could be in the form of a formal research study, or the use of existing data from published and unpublished data, presented as report cards, dashboards, or White Books.

Ultimately, diabetes registers, or comprehensive data collection, will be needed to both ensure improved clinical outcomes and overall disease burden, as well as measurement of the attainment of the WHO’s first global coverage targets for diabetes.39 These targets include the aim of “100% of people with Type 1 diabetes having access to affordable insulin and blood glucose selfmonitoring.” To achieve these goals, knowledge of Type 1 diabetes needs to be improved.

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There are some limitations to this work.

Firstly, only PubMed was used in the literature review, and any limitations in the search strategy may have had an impact. This limitation also applies to non-peer reviewed material. As many documents were in Danish, online translation tools were used, resulting in possible misinterpretations.

These limitations were addressed by working closely with Danish experts, as well as having all the material reviewed during two expert meetings.

The choice and participants at this meeting may have also impacted their feedback, and thus the overall work. Limitations are also present with the interviews of key opinion leaders, as their selection was done purposively. Interviewer and interviewee bias, as well as the analysis being done by only one person, need to be considered.

CONCLUSION

As shown in the Danish setting, contextual factors cannot be negated in developing and implementing data-related solutions at a population level. The lessons from Denmark show that there is the need to conceive and act on all aspects of the data, from its collection, aggregation, use, and communication. The last step of interaction between science and policy and practice requires a range of factors, including networks and knowledge brokers. Margaret Chan, former Director-General of the WHO, is often quoted as having said during her tenure that: “What gets measured, gets done.” With the importance of diabetes being highlighted at national, continental, and global levels, approaches need to be developed to measure different elements of diabetes, to ensure improved health systems and policy responses for the benefit of people with diabetes.

Even in well-resourced health systems, to create a positive data environment for diabetes as it exists in Denmark will require substantial investments.

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11. Beran D. Health systems and the management of chronic diseases: lessons from type 1

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14. World Bank. Current health expenditure (% of GDP) - Denmark Washington DC. 2020. Available at: https://data.worldbank. org/indicator/SH.XPD.CHEX. GD.ZS?locations=DK. Last accessed: 22 July 2021.

15. Leech BL. Asking questions: techniques for semistructured interviews. Polit Sci Polit.

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16. Bøcker Pedersen C et al. The Danish Civil Registration System A cohort of eight million persons.

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17. Danish Ministry of Justice. Act on supplementary provisions to the regulation on the protection of natural persons with regard to the processing of personal data and

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21. Kildemoes HW et al. The Danish national prescription registry. Scand J Public Health.

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22. Svensson J et al; Danish Childhood Diabetes Registry. Long-term trends in the incidence of type 1 diabetes in Denmark: the seasonal variation changes over time.

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24. Green A et al. Validation of the Danish national diabetes register.

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26. eSundhed. Udvalgte kroniske sygdomme og svære psykiske lidelser. Sygdomsforekomst for udvalgte kroniske sygdomme og psykiske lidelser. 2021. Available at: https://www.esundhed.dk/ Emner/Operationer-og-diagnoser/ Udvalgte-kroniske-sygdommeog-svaere-psykiske-lidelser. Last accessed: 12 August 2021.

27. Carstensen B et al. Components of diabetes prevalence in Denmark 1996-2016 and future trends until 2030. BMJ Open Diabetes Res Care. 2020;8(1):e001064.

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34. Beran D et al. A plan to improve global type 1 diabetes epidemiology data.

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35. AbouZahr C et al. Global estimates of country health indicators: useful, unnecessary, inevitable?

Glob Health Action. 2017;10(Suppl 1):1290370.

28. Andersen N et al. The Danish registry of diabetic retinopathy.

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36. DIAMOND Project Group. Incidence and trends of childhood Type 1 diabetes worldwide 1990-1999.

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29. Green A et al. Type 1 diabetes in 2017: global estimates of incident and prevalent cases in children and adults. Diabetologia.

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37. Green A et al. Incidence of childhood-onset insulindependent diabetes mellitus: the EURODIAB ACE Study. Lancet.

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30. Gregory GA et al. Global incidence, prevalence, and mortality of type 1 diabetes in 2021 with projection to 2040: a modelling study. Lancet Diabetes Endocrinol.

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38. International Diabetes Federation (IDF). IDF guide for diabetes epidemiology studies.

Brussels: International Diabetes Federation. 2022. Available at https://diabetesatlas.org/ idf-guide-for-epidemiologystudies/#:~:text=The%20 IDF%20Guide%20for%20 Diabetes,studies%20that%20 generate%20robust%20data.

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Last accessed: 30 August 2022.

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Hidden Complication of Obesity and Diabetes: Is It Time to Put More Focus on Fatty Liver?

Authors:

*James Woijoo Kim,1 Giada Sebastiani2 1. University of Calgary, Alberta, Canada 2. McGill University, Montréal, Quebec, Canada *Correspondence to jkim@vineyardmedicalclinic.com

Disclosure:

Kim has received research funding from Novo Nordisk; and has acted as a speaker and served as an advisory board member for Abbott, AbbVie, AstraZeneca, Bayer, Boehringer-Ingelheim, Eisai, Eli Lilly, embecta, GlaxoSmithKline (GSK), Janssen, Miravo Pharm, Moderna, Novartis, Novo Nordisk, Otsuka, Pfizer, Sanofi, Takeda, Teva, ALK, and Valeo. Sebastiani is supported by a Senior Salary Award from FRQS (#296306); has acted as speaker for Merck, Gilead, AbbVie, Novo Nordisk, and Pfizer; served as an advisory board member for Pfizer, Merck, Novo Nordisk, Gilead, and Intercept; and has received unrestricted research funding from Theratechnologies Inc. These are in no way affiliated with the research in this article.

Received:

18.08.23

Accepted:

31.10.23

Keywords:

Cardiovascular complications, diabetes complications, metabolic dysfunction-associated liver disease, metabolic dysfunctionassociated steatotic liver disease, non-alcoholic fatty liver disease (NAFLD).

Citation:

EMJ Diabet. 2023;11[1]:77-84.

DOI/10.33590/emjdiabet/10307262. https://doi.org/10.33590/emjdiabet/10307262.

Abstract Since the release of the EMPA-REG trial in 2015, the focus in diabetes management has been shifted from a glucocentric approach to a more organ-protective approach.

Much of the focus has been on cardiorenal protections, thanks to the numerous landmark trials being published in recent years. However, the significance of what seems to be an innocuous fatty deposition in the liver has received less attention than it deserves for many years, especially in people living with diabetes and obesity, but its impact on health has slowly been highlighted more in the last decade. Bodies of research are now suggesting that non-alcoholic fatty liver disease (NAFLD) is a significant independent risk factor for cardiovascular disease, including myocardial infarction, heart failure, and atrial fibrillation, while it carries the increased risk of cirrhosis, hepatocellular carcinoma, and extrahepatic cancers.

Numerous organisations have begun to publish guidelines focusing on screening and treating NAFLD in recent years in an effort to combat this underappreciated, underdiagnosed, and undertreated complication of diabetes and obesity. This review paper will provide an overview of NAFLD, highlighting the argument that NAFLD is indeed an independent cardiovascular risk factor, discussing the proposed pathophysiology of NAFLD being a cardiovascular risk factor, and suggesting a highly validated hepatic fibrosis screening tool, which is a simple, easy-to-use tool to screen for hepatic fibrosis, and can be used in primary care offices.

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Key Points 1. Since the EMPA-REG trial in 2015, there has been a shift in diabetes management from a glucosecentred approach to one focused on organ protection, especially cardiorenal protection. The focus has shifted towards addressing complications that are often overlooked, such as non-alcoholic fatty liver disease (NAFLD).

2. NAFLD is not just a liver condition but is also an independent risk factor for cardiovascular disease (CVD). People with NAFLD, especially those with diabetes and obesity, are at an increased risk of various CVD outcomes, including myocardial infarction, heart failure, and atrial fibrillation.

3. Despite its prevalence and significance as a CVD risk factor, NAFLD is often underdetected, underappreciated, and undertreated. Healthcare providers, especially primary care providers, need more education and guidelines to improve the screening, detection, and management of NAFLD.

INTRODUCTION Since the release of the EMPA-REG trial, in which for the first time an antihyperglycaemic agent demonstrated a reduction in major adverse cardiovascular events, the world of diabetes management has shifted from its glucocentric mentality into putting more emphasis on organ protection without discounting the importance of glucose management. After all, the most common cause of death amongst people living with Type 2 diabetes (T2D) is cardiovascular disease (CVD).1 Starting with EMPA-REG, numerous research has been conducted and demonstrated that certain antihyperglycaemic agents are able to reduce cardiorenal outcomes, further providing cardiorenal protection. This includes the reduction in hard cardiovascular outcomes, such as cardiovascular death; hospitalisations for heart failure, as seen in EMPA-REG; and hard renal outcomes, as seen in CREDENCE. However, it seems that the diabetes world has been putting so much focus on cardiac and renal protection, that another metabolic condition, possibly a hidden complication of diabetes playing a significant role in CVD, has been almost forgotten and has not received the attention that it deserves. That is non-alcoholic fatty liver disease (NAFLD).

gloomy prediction that the prevalence of global obesity will reach 1 billion, with close to 40% of Canadians being obese by 2030.3 Given that the prevalence of NAFLD in the obese population is 75%,4 it is unsurprising that the prevalence of NAFLD is expected to rise up to 35% globally.5 One Canadian modelling study projected that by 2030, there would be an increase in the prevalence of NAFLD by 20% from 2019.6 The same study forecasted a significant increased prevalence of advanced and end-stage NAFLD (i.e., decompensated cirrhosis, hepatocellular carcinoma) by 95%, while doubling of NAFLDrelated deaths and need for liver transplantation would occur at the same time.6 What seemed to be an innocuous fatty deposition in the liver, may result in hepatic cirrhosis and hepatocellular carcinoma in 10–20% of the individuals living with NAFLD.2 NAFLD has now become the number one cause of liver transplant in the USA,2 and is predicted to be the leading cause for liver transplant in Canada by 2025.6 The condition is also associated with extrahepatic cancers.

A meta-analysis by Mantovani et al.7 in 2022 demonstrated a 1.5–2.0-fold increase in risk of developing gastrointestinal cancers, such as oesophageal, stomach, pancreas, or colorectal cancer; and 1.2–1.5-fold increase in developing lung, breast, urinary, or gynaecological cancers.7

Global prevalence of NAFLD is 30%,2 but the prevalence dramatically increases to 55% in patients with T2D.2 This pandemic seems to be driven by the rise in T2D and obesity globally, including Canada. As matter of fact, the World Health Organization (WHO) made a

Perhaps more importantly, NAFLD has demonstrated to be a significant cardiovascular risk factor by itself, as cardiovascular mortality is the leading cause of death in people living with NAFLD.2,5 Despite NAFLD being common in the general population and carrying a significant

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cardiovascular risk, it is underdetected, underappreciated, and, therefore, undertreated.

This paper aims to address NAFLD as an independent cardiovascular risk factor, arguing that NAFLD should be viewed as one of the complications of T2D. The authors will also provide a brief overview of the pathophysiological link between NAFLD and CVD, and discuss ways to improve the detection of this frequent condition.

NON-ALCOHOLIC FATTY LIVER DISEASE-RELATED DEFINITIONS AND DISEASE PROGRESSION NAFLD is defined as the presence of hepatic steatosis in more than 5% of the hepatocytes, with no other causes, including absence of excessive ongoing or recent alcohol consumption (i.e., <20 g/day for females and <30 g/day for males) without secondary causes of hepatic steatosis.8,9 This terminology encompasses a disease spectrum from a simple state of hepatic steatosis without hepatocellular injury or fibrosis, known as non-alcoholic fatty liver, to non-alcoholic steatohepatitis (NASH), and finally cirrhosis.8 NASH refers to the presence of >5% of hepatic steatosis with inflammation and hepatocyte injury (i.e., hepatocyte ballooning), with or without fibrosis, which then is further classified into Stages 1–4. Stage 4 fibrosis due to NASH corresponds to cirrhosis of the liver.3,8 There have been a number of terminology changes of NAFLD in recent years. In 2020, an international expert consensus decided to change the terminology from NAFLD to metabolic dysfunction-associated fatty liver disease, to demonstrate its clinical outcomes and its close association with metabolic syndrome.10 More recently, a multi-society nomenclature change was agreed, where NAFLD and metabolic dysfunction-associated fatty liver disease were to be changed to metabolic dysfunctionassociated steatotic liver disease.11 However, for the purpose of this article, the older and more familiar terminology NAFLD will be used.

In general, the actual pathogenesis of NAFLD is not fully understood, as various hypotheses have been proposed. Multiple factors seem to occur simultaneously in the pathogenesis of NAFLD, and this has led to the development of

multiple parallel hit hypotheses approximately a decade ago.12 However, it is widely accepted that both hepatic and systemic insulin resistance play a central role in the development of NAFLD. Therefore, it is not surprising to find high prevalence of NAFLD in individuals living with obesity and diabetes.13 Increase in a hepatic de novo lipogenesis, suboptimal uptake of circulating lipid, poor enhancement of compensatory fatty acid oxidation, and inadequate export of triglyceride-rich very low-density lipoprotein are the main pathways resulting in the imbalance between the lipid acquisition and lipid disposal, which, in turn, leads to hepatic fat accumulation and ultimately results in hepatic insulin resistance.14 Ectopic fat accumulation also occurs in the pancreas, which is associated with systemic insulin resistance and β-cell dysfunction. This, in turn, will lead to the abnormal glucose metabolism and compensatory hyperinsulinaemia, which elevates the free fatty acid, and increases hepatic gluconeogenesis.

NAFLD interestingly causes a reduction in hepatic insulin clearance, which will accentuate these effects.14 The usual endocrine effect of the adipose tissues, including these ectopic adipose tissues, results in elevated adiponectin, which inhibits lipid accumulation, and regulates fatty acid oxidation, glucose homeostasis, and hepatic insulin sensitivity. Adiponectin has demonstrated anti-fibrotic and anti-inflammatory effects as well. Interestingly, patients with NAFLD or T2D have reduced adiponectin level, which will impair the fatty acid metabolism while promoting chronic inflammation of the liver.15 Fatty acid level decreases with the antilipolytic action and with the release of insulin.

However, due to the insulin resistance in people with NAFLD, the level of circulating fatty acid tends to remain higher than usual.

This results in increased uptake of fatty acid by the liver. Through complex processes, the liver is subjected to proinflammatory state, and profibrotic pathways are activated, which causes the progression of NASH.16 Numerous innate immune cells, such as neutrophils, Kupffer cells, dendritic cells, natural killer cells, and mast cells, are involved in developing NAFLD by producing proinflammatory cytokines, which ultimately progresses to fibrosis, cirrhosis, and hepatocellular carcinoma.17

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Other factors, such as the alteration of the gut microbiome, are also believed to be main drivers in the pathogenesis of NAFLD. More recently, genetics and dietary components have been added to the list of contributors to NAFLD development.12

NON-ALCOHOLIC FATTY LIVER DISEASE AS AN INDEPENDENT CARDIOVASCULAR DISEASE RISK FACTOR Firstly, the leading cause of death in people living with NAFLD is CVD,2 and this should not come as a surprise since the typical person living with NAFLD frequently meets the diagnostic criteria for metabolic syndrome (i.e., obesity, diabetes, dyslipidaemia, hypertension), which are established risk factors for CVD.18 Hypertension and NAFLD seem to have a bidirectional relationship.19 A prospective study from Germany with 3,191 patients demonstrated prevalence of hypertension in NAFLD cohorts were three-fold higher (odds ratio [OR]: 3.1; 95% confidence interval [CI]: 1.7–5.8) than the cohorts without NAFLD.20 Another prospective cohort study from Korea demonstrated that the incidence rate of hypertension was increased with increased severity of NAFLD (i.e., mild: 21.8%; moderateto-severe: 30.1%).21 An increased risk of CVD in people with NAFLD was evident in a recent large meta-analysis (hazard ratio [HR]: 1.45; 95% CI: 1.31–1.61).22 The same meta-analysis further demonstrated the higher risk of NAFLD in non-fatal CVD (pooled random-effects HR: 1.40; 95% CI: 1.20–1.64), and fatal CVD (pooled random-effects HR: 1.30; 95% CI: 1.08–1.56).22 When examining the various risk factors for CVD in people with NAFLD, diabetes was found to be the most important (OR: 2.18; 95% CI: 1.580– 3.000; p=0.0004), followed by hypertension (OR: 1.67; 95% CI: 1.500–1.860; p<0.0001), and age (β: 0.100; 95% CI: 0.0576–0.142; p<0.0001).23 Multiple studies have demonstrated the relationship between higher CVD risk and worsening fibrosis, regardless of how the fibrosis was measured or even estimated. In a large meta-analysis, worsening fibrosis increased the risk of CVD 2.5 times (95% CI: 1.68–3.72).22 Two validated fibrosis biomarkers using standard laboratory parameters, namely the Fibrosis-4 (FIB-4) index, which is a simple, highly validated

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non-invasive test calculated using age, alanine transaminase, aspartate aminotransferase (AST), and platelet; and NAFLD Fibrosis Score (NFS), were used in a prospective study by Baratta et al.24 An NFS of >0.676 demonstrated a significant increase in CVD events after adjustment for comorbidities (HR: 2.29; 95% CI: 1.17–4.47; p=0.016). A FIB-4 score of >2.67 presented with a substantial increase in CVD events (HR: 4.57; 95% CI: 1.61–12.98; p=0.004).24 Two other studies from Asia using the FIB-4 score demonstrated a high degree of association between advanced fibrosis (evaluated by a high FIB-4 score of >2.67) and CVD events.25,26 As matter of fact, simple hepatic steatosis alone is also associated with CVD risk. In 6,340 patients from PREVEND who had hepatic steatosis (i.e., Fatty Liver Index [FLI] >60) but without pre-existing CVD, hepatic steatosis was found to be a CVD risk factor after adjusting for traditional CVD risk factors.27 Along the same lines, a population study from the UK also reported similar findings, where elevated FLI resulted in higher CVD events.28 When examining the newly diagnosed patients with T2D with hepatic steatosis, once again defined

by FLI ≥60, a Korean population study reported increased risk of myocardial infarction (HR: 1.28; 95% CI: 1.14–1.44), heart failure (HR: 1.17; 95% CI: 1.07–1.26), stroke (HR: 1.41; 95% CI: 1.25–1.56), and mortality (HR: 1.41; 95% CI: 1.32–1.51), even when adjusting for traditional CVD risk factors.29 Ultrasonography was used to determine the presence of hepatic steatosis in a nested cohort study by Meyersohn et al.30 Presence of hepatic steatosis resulted in an increased risk of CVD events compared with people without hepatic steatosis (HR: 1.69; 95% CI: 1.16–2.48; p=0.006), even after adjusting for the traditional cardiovascular risk factors, such as atherosclerotic CVD scores, metabolic syndrome, significant steatosis, or obesity.30 A Swedish nationwide cohort study involving over 10,000 individuals with biopsy-proven NAFLD were matched to 50,000 people for controls, demonstrating that NAFLD may be a risk factor for developing new onset of heart failure.31 The risk of developing heart failure increased with worsening of NAFLD.32 Valbusa et al.33 reported that advanced liver fibrosis, calculated by liver fibrosis scores (i.e., FIB-4 and AST-to-Platelet Ratio Index [APRI]), was

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associated with higher risk of hospital and post-discharge mortality in an elderly population admitted for acute heart failure.33 A review paper by Alkagiet et al.34 examined the risk of ischaemic stroke in people with NAFLD. It was fascinating to note that NAFLD did not only increase the risk of ischaemic stroke, but also increased the severity of ischaemic stroke with worse clinical outcomes, concluding that NAFLD is an independent risk factor for ischaemic stroke, and suggesting that people with ischaemic stroke should be screened for NAFLD and treated aggressively.34 Large bodies of evidence demonstrate the relationship between NAFLD and atrial fibrillation.

A recent meta-analysis by Jaiswal et al.35 included 12 cohort studies with over 18 million patients and showed a significant increased likelihood of developing atrial fibrillation (relative risk: 1.42; 95% CI: 1.19–1.68) in people with NAFLD compared with the ones without NAFLD.35 A population study from South Korea examined over 8 million people with NAFLD, and demonstrated the association between worsening hepatic steatosis with higher risk of developing atrial fibrillation.36

BRIEF OVERVIEW ON PATHOPHYSIOLOGY OF NAFLD’S EFFECT ON CARDIOVASCULAR DISEASE A detailed overview of the pathophysiology on how NAFLD affects CVD is beyond the scope of this paper, and it is not fully understood.

Therefore, a brief overview on pathophysiology of NAFLD’s effect on CVD will be discussed.

NAFLD seems to induce metabolic impairment, which is then also associated with systemic inflammation and increase in oxidative stress.

Oxidative stress itself induces inflammation via endothelial dysfunction, which will predispose people with NAFLD to CVD events, resulting in impaired cardiac function and atherosclerosis.37 As obesity is closely associated with NAFLD, adiposity can trigger pro-inflammation through a number of mechanisms negatively impacting the production of anti-inflammatory cytokines while increasing the expression of pro-inflammatory cytokines such as IL-1β, IL-6, and TNF-α. This

systemic inflammation arising from adipose tissue inflammation can lead to worsening of insulin resistance and CVD, which can result in worsening of NAFLD.38 Inflammation of the liver leads to the progression from simple hepatosteatosis (i.e., NAFLD) to NASH, and eventually to cirrhosis. During this process, systemic inflammation ensues at the same time. Hepatic necro-inflammation is thought to be an independent proatherogenic mechanism.38 Some of the major hallmarks of NAFLD are abnormal glucose metabolism and hepatic insulin resistance. Abnormal glucose metabolism can contribute to systemic inflammation and increased weight gain, including visceral obesity, leading to the accumulation of dysfunctional ectopic fatty tissue. These fatty tissues can accumulate in the pancreas, resulting in insulin resistance and β-cell dysfunction. Furthermore, insulin resistance leading to the negative metabolic status of a patient is also likely to be one of the main contributors to the development of CVD.14 Ectopic fat can be deposited into the myocardium and surround the heart, which can be further classified into epicardial fat and pericardial fat. Epicardial fat plays a critical role in cardiac metabolism. The previously mentioned adiponectin, which is a protective hormone that mediates anti-fibrotic, anti-atherogenic, antiinflammatory, and antioxidant cardioprotective effects, declines with NAFLD, while more proinflammatory cytokines are synthesised, including IL-6, IL-1β, and TNF-α. These changes lead to the promotion of atherosclerosis of coronary arteries and structural changes in the adjacent myocardium, including myocardial fibrosis, which ultimately leads to cardiac dysfunction.14 Electrical and autonomic remodelling of the heart occurs, which increases the susceptibility to atrial fibrillation, ventricular arrhythmia, and prolongation of the QT interval.37 It is likely that the epicardial adipose tissue may have placed itself at the centre of the pathophysiological link between NAFLD and CVD.14 Other hypotheses have been implicated on the pathophysiology of NAFLD’s effect of CVD. Numerous genes have been identified which increase the risk of developing NAFLD. Plaque formation increases due to increased activity of the pro-coagulation factors. Altered, dysregulated gut microbiome, which leads to the

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production of certain metabolites, resulting in systemic inflammation, is also implicated in CVD development.14

SUGGESTED APPROACH TO NON-ALCOHOLIC FATTY LIVER DISEASE IN PRIMARY CARE CLINIC A cross-sectional study by Islam et al.39 revealed that primary care providers (PCP) are aware of the importance of fibrosis in NAFLD. Although time constraint was not found to be a barrier, lack of knowledge in diagnosis and management of NAFLD was a main barrier.39 A similar survey was carried out in Canada by one of the authors, which demonstrated that a significant proportion of Canadian physicians and nurses were not familiar with this disease. The survey concluded that lack of educational programmes and national guidelines on NAFLD may be contributing to these results.40 Several guidelines have been created to aid the healthcare providers in improving screening and management of NAFLD. The Calgary NAFLD pathway (based on the use of shear wave elastography) has shown that 92% of the referred patients for NAFLD were at low risk for fibrosis and were able to be managed within primary care. The implementation of such a pathway has reduced the NAFLD waitlist by 84%.41 The Camden and Islington NAFLD pathways, which utilise sequential use of FIB-4 and the patent serum biomarker, Enhanced Liver Fibrosis (ELF) marker, recommended to screen for NAFLD in people with elevated liver enzymes.42 However, this could still result in a significant number of people with NAFLD remaining undetected. A cross-sectional study conducted in 561 patients with T2D demonstrated that the proportion of people with hepatosteatosis with fibrosis having AST >40 units/L and alanine transaminase >40 units/L was only 6% and 10%, respectively.43 The American Association of Clinical Endocrinology (AACE), American Association for the Study of Liver Diseases (AASLD), and American Gastroenterology Association (AGA) have similar guidelines and pathways which heavily rely on FIB-4 score. This non-invasive test has been highly validated is several clinical settings.4,44,45 Most of these lab test results are readily available and the calculation is easy, providing risk stratification of the individual probability and severity of hepatic fibrosis status.

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DISCUSSION The prevalence of NAFLD has been underestimated and its sequelae have been largely underappreciated by PCPs. Many PCPs may have considered NAFLD to affect the liver alone, and yet, the leading cause of morbidity and mortality for people living with NAFLD remains CVD, just like any other typical metabolic condition. Seemingly, an innocuous fatty deposition in the liver can negatively affect the metabolic status, which can lead to various CVD events. Risk factors have been identified and although not fully understood, numerous pathophysiological links between NAFLD and CVD have been hypothesised. Treatments of NAFLD include non-pharmacological therapy such as weight loss, and reduction of cardiovascular risk factors. However, concrete and feasible evidence on pharmacotherapy targeting NASH and liver fibrosis for NAFLD treatment is still lacking. NAFLD in patients with T2D has been making enormous inroads in management of diabetes, where number of newer classes of antihyperglycaemic agents such as sodium-glucose cotransporter 2 inhibitors and especially glucagon-like peptide-1 receptor agonists are being researched vigorously with promising outcomes. Even older antihyperglycaemic agents, such as pioglitazone, have shown a positive therapeutic effect in NAFLD. Therefore, several societies have recommended the use of glucagon-like peptide-1 receptor agonists in treatment of T2D in people with NAFLD in their guidelines, and pioglitazone in people with or without NAFLD.4,44 Anti-fibrotic medications are being developed currently and there will be more options in the treatment of NAFLD. It is beyond the scope of this paper to describe all the anti-fibrotic medications that are in development, but it is worth noting a few of them. Farnesoid X receptor (FXR) is one of the nuclear receptors which plays a crucial role in metabolic pathways, such as glucose homeostasis, and inflammatory and fibrogenic processes. Activating FXR has positive impacts on the hepatic lipogenesis, glucose homeostasis, and production of bile acid, while protecting the hepatocytes against the bile acid-induced cytotoxicity.46 In a rodent study by Fiorucci et al.,47 FXR agonists promoted the reduction of hepatic fibrosis. Therefore, there are number

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of FXR agonists being developed, such as tropifexor.47 Certain fibroblast growth factors (FGF), such as FGF19, FGF21, and FGF23, are signalling proteins that regulate the metabolism of lipid, bile acid, and carbohydrates. Several studies have demonstrated histological improvement (i.e., fibrosis regression) and reduction in hepatosteatosis with FGF agonists.45 Peroxisome proliferator-activated receptors are the nuclear receptors, playing an important role in regulating various metabolic processes, such as lipid transportation. Lanifibranor (Inventiva Pharma, Daix, France) and Saroglitazar (Zydus Cadila Healthcare Ltd, Ahmedabad, India) are two peroxisome proliferator-activated receptor agonists that are in development. Their Phase II studies have shown significant reduction in fat content, and, in some cases, resolution of NASH was noted in the interventional group.47,48 Since the discovery of the link between hypothyroidism and NASH, many researches were dedicated to find the potential therapy to target this pathway, and it has led to the development of resmetirom, which is a thyroid hormone β-receptor agonist.

A recent Phase III randomised control study demonstrated significant NASH resolution and improvement of hepatic fibrosis.49

References

It is important to note that several guidelines, such as those of the AASLD, strongly recommend statins to be used with people living with NAFLD for CVD risk reduction. The same guideline also emphasises the safety of statin in NAFLD disease spectrum, including compensated cirrhosis.50

CONCLUSION NAFLD has been a hidden complication of obesity and diabetes, which has gone unnoticed for so long and is now affecting 30% of the general population. More educational programmes are required to inform the PCPs on this CVD equivalent condition. NAFLD algorithms created or endorsed by national organisations are crucial in improving the screening, detection rate, and management of patients with NAFLD and are urgently needed. This may be the first step towards addressing the silent pandemic of NAFLD. The diabetes world has made a huge step forward in cardiorenal protections. Now, it is time, although belated, to focus on NAFLD, which is evidently an independent CVD risk factor.

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36. Lee SR et al. Nonalcoholic fatty liver disease and the risk of atrial fibrillation stratified by body mass index: a nationwide population-based study. Sci Rep. 2021;11(1);3737.

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43. Lomonaco R et al. Advanced liver fibrosis is common in patients with type 2 diabetes followed in the outpatient setting: the need for systematic screening. Diabetes Care. 2021;44(2):399-406.

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45. Tian H et al. Fibroblast growth factors for nonalcoholic fatty liver disease: opportunities and challenges. Int J Mol Sci. 2023;24(5):4583.

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Mean Platelet Volume in Type 2 Diabetes: Correlation with Poor Glycaemic Control Authors:

*Avarna Agarwal,1 Anita Arya,1 Rita Singh Saxena,1 Simmi Dube1 1. Department of Medicine; Gandhi Medical College and Associated Hamidia Hospital, Bhopal, India *Correspondence to avarna.agarwal28@gmail.com

Disclosure:

The authors have declared no conflicts of interest.

Received:

18.05.23

Accepted:

09.10.23

Keywords:

Mean platelet volume (MPV), Type 2 Diabetes (T2D).

Citation:

EMJ Diabet. 2023;11[1]:85-91.

DOI/10.33590 emjdiabet/10309803. https://doi.org/10.33590/emjdiabet/10309803.

Abstract Background: Diabetes is a global pandemic. Mean platelet volume (MPV) is an indicator of increased platelet activity, which is considered to play a role in the development of vascular complications in diabetes. Platelet volume is strongly and independently related with glycaemic control in Type 2 diabetes (T2D).

Aim: To study the MPV in patients with T2D, and its correlation with HbA1c, duration of T2D, and microvascular complications.

Methodology: This was a cross-sectional, observational study conducted at the Department of Medicine, Gandhi Medical College, Bhopal, India, during a period of 18 months on 300 patients with T2D. Blood glucose, HbA1c, MPV, fundoscopy, and 24-hour urine protein were done. Data were represented as mean+/-standard deviation and statistical analysis was done using SPSS software (IBM, Armonk, New York, USA).

Results: The mean age of patients was 56.64±8.69 years, with 52% of the patients being females. Of these patients, 18.3% had HbA1c between 6.5–8.0%, 51.7% between 8.1–10.0% and the rest above 10.0%. A total of 51% of the patients had diabetes for 6–10 years of duration and 30% for more than 10 years. Patients with higher HbA1c level and prolonged duration of diabetes had higher MPV (p<0.001).

Patients with advanced diabetic retinopathy changes and nephropathy also had higher MPV (p<0.05).

Conclusion: Measurement of MPV in patients with T2D can be a useful and easily available prognostic marker, and hence assist clinicians to anticipate the occurrence of microvascular complications associated with the disease, especially in resourcepoor settings, and reduce the burden of cost for the patient.

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Key Points 1. Mean platelet volume (MPV) in Type 2 diabetes (T2D) is an indicator of poor glycaemic control.

MPV is available as a part of the complete blood count report and, therefore, can be used to help indicate the occurrence of diabetes-related complications, which is especially useful in resourcelimited settings.

2. This cross-sectional, observational study evaluated MPV as an indicator of glycaemic control in patients with T2D. The findings revealed a positive correlation between MPV and HbA1c, and MPV and microvascular complications, including diabetic nephropathy and retinopathy.

3. MPV is an accessible biomarker that can be used in clinical practice to assess glycaemic control in patients with T2D, help clinicians predict morbidity and mortality, and allow for earlier interventions to reduce the risk of developing associated microvascular complications.

INTRODUCTION Diabetes is emerging as an epidemic worldwide and is a global public health problem. The International Diabetes Federation (IDF) has predicted that 463 million individuals had diabetes in the year 2019, and this figure is projected to increase to 578 million by the year 2030, and 700 million by the year 2045.1 Diabetes affects 77 million adults in India, making it the country with the second highest prevalence of the disease worldwide. Prevalence of diabetes is highest among adults between the ages of 50–70 years old, compared with all other age categories. With an estimated 1 million fatalities a year attributed to diabetes and its associated complications, India was the country that had the greatest impact on the overall diabetes mortality rate.1 Type 2 diabetes (T2D) is the most prevalent form of the disease, accounting for approximately 90% of diabetes cases globally. Hyperglycaemia defines T2D, which is characterised by insulin resistance. Whenever a person has insulin resistance, the hormone is rendered ineffective, which, in due time, causes an increase in insulin production. Because the pancreatic β-cells are not able to keep up with the demand for insulin over time, an inadequate amount of insulin production might develop. Most people who develop T2D are adults in their later years; however, the disease is becoming more prevalent in children and younger adults as a result of rising rates of obesity, physical inactivity, and improper diet.2

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Patients with diabetes have been found to have altered platelet morphology and function, and the mean platelet volume (MPV) has been found to be significantly higher in patients with diabetes compared with the normal population.3,4 Individuals with uncontrolled hyperglycaemia and longer duration of diabetes are more prone to develop diabetic microvascular problems.5 Diabetes is associated with an increased risk of microvascular complications, and the increase in platelet size may be one component that contributes to this increased risk. As a result, MPV has the potential to serve as a valuable predictor of vascular problems in diabetes. It is believed that osmotic swelling as a result of hyperglycaemia is the cause of greater MPV in people with diabetes, despite the fact that the underlying mechanism responsible for this is not yet fully known.6 Alternately, a larger platelet size could indicate the existence of a high platelet turnover rate as well as younger platelets, which will reflect as higher MPV.7 The link between MPV and diabetes was clearest in those individuals who had the least control of their glucose levels.8 As a result, the purpose of the current study was to investigate the impact of HbA1c on platelet activity as measured by MPV, as well as the correlation of MPV with glucose control, duration of diabetes, and microvascular complications.

METHODOLOGY The present study was conducted as a crosssectional, observational study in the Department

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of Medicine, Gandhi Medical College and associated Hamidia Hospital, Bhopal, India, during the study period of 18 months (from January 2021–June 2022). All the patients diagnosed as per American Diabetes Association (ADA) criteria, over 18 years, with HbA1c >6.5%, fasting plasma glucose >126 mg/dL, post-2 hour blood glucose (after 75 g glucose) >200 mg/dL, and random blood glucose >200 mg/dL were included. Individuals with abnormal platelet count <1.5 lacs/µL or >4 lacs/µL; with known bleeding/ clotting disorders; taking drugs affecting platelet function like antiplatelet drugs, statins, anticoagulants, etc.; with chronic liver disease; any malignancy; and pregnant females were excluded from the study.

RESULTS

The study was initiated after obtaining ethical clearance from the Institute’s ethical committee.

A total of 300 cases were enrolled. All the patients were then subjected to detailed history and clinical examination. Plasma glucose levels were calculated using glucose oxidase method, and HbA1c using high performance liquid chromatography. MPV was measured using automated haematology blood counter from Mindray (Mindray Global, Shenzhen, China).

Based on the HbA1c levels, patients were categorised in three groups: 6.5–8.0%, 8.1–10.0%, and >10.0%. Fundoscopy was done to look for retinopathy. Apart from this, urine routine microscopy and 24-hour urine protein was also done to look for nephropathy.

On comparing the MPV level with the glycaemic control, it was revealed that those with higher HbA1c level had higher MPV (p<0.001).

Similarly, MPV was significantly higher in patients with prolonged duration of diabetes (p<0.050; Table 2).

STATISTICAL ANALYSIS

In the authors’ study, patients with advanced diabetic retinopathy and with significant proteinuria (nephropathy) had higher MPV than those with early diabetic changes and those with <150 mg/dL proteinuria (p<0.05; Table 3).

Data was recorded in Microsoft Excel (Microsoft, Redmond, Washington, USA) and statistical analysis was performed by the SPSS program for Windows, version 25 (IBM, Armonk, New York, USA). Continuous variables were presented as mean±standard deviation, and categorical variables were presented as absolute numbers and percentage. Categorical variables were analysed using either the χ2 test or Fisher’s exact test. Continuous variables were assessed using analysis of variance or independent sample t-test. Pearson correlation (r) was performed to establish the correlation between different parameters. P<0.05 was considered statistically significant.

The study was conducted on a total of 300 patients with T2D with a mean age of 56.6±8.6 years.

The majority of patients with T2D belonged to age group 51–60 years (47%), followed by 61–70 years (22.7%). Approximately 52% were females and 50.3% of the participants had hypertension.

The majority (68%) of the participants had BMI between 18.5–24.9 kg/m2 and mean BMI was 21.79 kg/m2. The majority of the patients with T2D had HbA1c between 8.1–10.0% (51.7%) and 30.0% of the patients had HbA1c above 10.0% (Table 1).

On comparing diabetes duration among HbA1c category for MPV, it was observed that patients with diabetes duration between 1–5 years, and HbA1c level >10 had higher MPV than the lower HbA1c values. A similar trend was observed for 6–10 and >10 years of diabetes duration where MPV increased with increasing HbA1c level (p<0.05). However, there was no significant difference in MPV in different duration of diabetes with similar HbA1c.

Pearson correlation revealed a significant positive correlation of fasting blood sugar (r=0.804), postprandial blood sugar (r=0.745), random blood sugar (r=0.799), HbA1c (r=0.860), duration of diabetes (r=0.174), proteinuria (r=0.651), and abnormal fundus findings (r=0.335) with MPV (p<0.05).

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Table 1: Distribution of cases according to baseline variables.

Baseline variables

Category

Frequency (n=300)

%

Age (years)

≤40

6

2.0

41–50

67

22.3

51–60

141

47.0

61–70

68

22.7

>70

18

6.0

Female

156

52.0

Male

144

48.0

<18.5

39

13.0

18.5–24.9

204

68.0

25.0–29.9

57

19.0

>30.0

0

0.0

Yes

151

50.3

No

149

49.6

6.5–8.0

55

18.3

8.1–10.0

155

51.7

>10.0

90

30.0

Sex BMI (kg/m2)

Hypertension HbA1c (%)

DISCUSSION MPV is an efficient, easily available, and cost-effective marker, which can be used in monitoring glycaemic status in patients with T2D. This was an observational study on 300 patients with diabetes with mean age of 56.64±8.69 years. The majority of individuals in this study who had T2D had HbA1c that ranged between 8.1–10.0% (51.7%). When the MPV level was compared with the glycaemic control, it was discovered that those with a higher HbA1c level had a greater MPV (p<0.001). This provides evidence that an increasing MPV can be a sign of poor glycaemic control.

Swaminathan et al.9 also discovered that the difference between the MPV in patients with diabetes and without diabetes was statistically significant. In patients with diabetes, the MPV was found to be 10.200±0.770, whereas in those without diabetes, the MPV was 9.816±0.400.

According to Kodiatte et al.,10 the mean HbA1c level in the diabetes group was 9.130±2.530%,

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while the level in the non-diabetic group was 5.950±0.723% (p<0.001). MPV was significantly greater in the patients with diabetes (8.290±0.735 fL) compared with the non-diabetic group (7.470±0.726 fL), which was statistically significant (p<0.001).

Saluja et al.11 reported that among the subjects with diabetes, a positive statistical Pearson correlation was seen between MPV and HbA1c levels and fasting blood sugar levels.11 In the current study, increased duration of diabetes also correlated with increasing MPV. Also in the same group of duration MPV showed a positive correlation with increasing HbA1c.

For duration of 1–5 years, 6–10 years, and >10 years, the MPV increased with increasing HbA1c levels. Patients with durations ranging from 1–5 years, 6–10 years, and >10 years had respective MPV values of 10.503±1.040, 10.790±0.934, and 10.979±1.428. It can be seen here that there is a statistically significant upward trend in the proportion of MPV with increasing duration of diabetes. Platelet parameters between people

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Table 2: Association of mean platelet volume with glycaemic control, duration of diabetes, and microvascular complications.

Variables

MPV

HbA1c (%)

Duration of diabetes (years)

Mean

SD

6.5–8.0

9.513

0.422

8.1–10.0

10.353

0.486

>10.0

11.950

0.660

1–5

10.503

1.040

6–10

10.790

0.934

>10

10.979

1.428

Microvascular complications

Fundus

Proteinuria

p 0.001

0.005

MPV

p

Mean

SD

Advanced diabetic changes (proliferative)

11.148

1.215

Early diabetic changes (preproliferative)

10.934

0.999

Normal

10.262

0.858

Absent

10.088

0.491

Present (>150 mg/ dL)

10.096

0.643

Traces (<150 mg/dL) 11.671

0.829

0.001

0.001

HbA1c: haemoglobin A1c; MPV: mean platelet volume; SD: standard deviation.

Table 3: Comparing diabetes duration among HbA1c category for mean platelet volume.

Diabetes duration

HbA1c groups 6.5-8.0%

p 8.1-10.0%

>10.0%

Mean

SD

Mean

SD

Mean

SD

1–5 years (n=144)

9.497

0.4382

10.320

0.3760

12.157

0.7709

6–10 years (n=137)

9.750

0.2710

10.444

0.5132

11.860

0.6224

>10 years (n=19)

9.050

0.1225

9.000

0.3464

11.780

0.2741

0.005

HbA1c: haemoglobin A1c; MPV: mean platelet volume; SD: standard deviation.

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with diabetes with diabetic duration <10 years and those with diabetic duration >10 years showed that MPV was significantly increased in people with diabetes with duration of disease >10 years, as reported by Swaminathan et al.9 in their study. However, in the study by Kodiatte et al.,10 they came to the conclusion that there was no correlation between MPV and the length of diabetes, or the existence of complications.

It was stated by Saluja et al.11 that the mean HbA1c in patients who had diabetes for more than 5 years was 8.62±0.96, while among patients who had diabetes for less than 5 years it was 8.51±1.09 (p=0.49). According to the findings of Saluja et al.,11 there is not a statistically significant link between MPV and the length of diabetes (p=0.50).

In the current study, an independent association between the risk of microvascular problems and the MPV was found in patients with T2D. When the MPV level was compared with the presence of proteinuria (which is suggestive of diabetic nephropathy), it was found that those with the presence of proteinuria had a higher MPV than those without proteinuria. On the other hand, the MPV was significantly higher in the group that contained less than 150 mg/dL of protein (MPV: 11.670), in contrast to the group that had more than 150 mg/dL of protein (MPV: 10.096), which suggested that MPV did not increase with increase in proteinuria.

In the current study, the MPV was found to be significantly higher in the group that had advanced diabetic retinopathy (11.148), compared with the group that had early diabetic changes (10.934). This difference was

References 1.

2.

90

Aschner P. New IDF clinical practice recommendations for managing type 2 diabetes in primary care. Diabetes Res Clin Pract. 2017;132:169-70.

Nathan DM et al. Medical management of hyperglycemia in type 2 diabetes: a consensus algorithm for the initiation and adjustment of therapy: a consensus statement of the American Diabetes Association and the European Association for the Study of Diabetes. Diabetes Care. 2009;32(1):193-203.

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statistically significant (p<0.001), which also suggested a positive correlation between MPV and diabetic retinopathy. Saluja et al.11 reported that the MPV in subjects with complications (13.12±1.40 fL) was higher than the MPV in subjects without complications (12.80±1.21 fL), despite the fact that an independent student t-test did not show any statistical significance (p=0.13).

CONCLUSION Patients with diabetes who have higher HbA1c have higher values of MPV compared with those who have lower HbA1c, which is a marker for diabetes control. Also, MPV correlates positively with duration of diabetes and with presence of microvascular complications in the authors’ study. Hence, the measurement of MPV in patients with T2D can be a good marker of control of diabetes and of probability of microvascular complications. Therefore, it can assist clinicians to anticipate morbidity and mortality associated with the disease. As MPV is widely available as a part of the complete blood count report, it can be easily used, especially in resource-limited settings.

LIMITATION Absence of a healthy control group is a limitation of the study. Macrovascular complications were not taken into consideration as most patients with cardiovascular diseases were on antiplatelets, which would alter the MPV values and hence they were excluded from the study.

3.

Papanas N et al. Mean platelet volume in patients with type 2 diabetes mellitus. Platelets.

2004;15(8):475-8.

4.

Hekimsoy Z et al. Mean platelet volume in type 2 diabetic patients.

J Diabetes Complications.

2004;18(3):173-6.

5.

Shera AS. Prevalence of chronic complications and associated factors in type 2 diabetes. J Pak Med Assoc. 2004;54(1):54-9.

6.

Martyn CN et al. Effects of sorbinil treatment on erythrocytes and platelets of persons with diabetes.

Diabetes Care. 1986;9(1):36-9.

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7.

Guthikonda S et al. Role of reticulated platelets and platelet size heterogeneity on platelet activity after dual antiplatelet therapy with aspirin and clopidogrel in patients with stable coronary artery disease. J Am Coll of Cardiol. 2008;52(9):743-9.

8.

Shah B et al. The relationship between diabetes, metabolic syndrome, and platelet activity as measured by mean platelet volume: the National Health And Nutrition Examination Survey, 1999–2004. Diabetes Care.

2012;35(5):1074-8.

9.

Swaminathan A et al. Evaluation

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of mean platelet volume and other platelet parameters in subjects with type-2 diabetes mellitus.

Natl J PHysiol Pharm Pharmacol.

2017;7(1):51-4.

10. Kodiatte TA et al. Mean platelet volume in type 2 diabetes mellitus.

J Lab Physicians. 2012;4(1):5-9.

11. Saluja M et al. Study of Impact of

glycemic status (HbA1c) on platelet activity measured by mean platelet volume & vascular complications in diabetics. J Assoc Physicians India.

2019;67(4):26-9.

FOR REPRINT QUERIES PLEASE CONTACT: INFO@EMJREVIEWS.COM

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Preconception Care for Individuals with Diabetes Authors:

*S. Mimi Mukherjee,1 Aimee Dawson,1 Katherine M. Carey1 1. Department of Pharmacy Practice, Massachusetts College of Pharmacy and Health Sciences, Worcester, USA *Correspondence to mimi.mukherjee@mcphs.edu

Disclosure:

The authors have declared no conflicts of interest.

Received:

29.09.23

Accepted:

27.10.23

Keywords:

Contraception, diabetes, dyslipidaemia, hypertension, preconception, pregnancy, weight.

Citation:

EMJ Diabet. 2023;11[1]:92-101.

DOI/10.33590/emjdiabet/10305435. https://doi.org/10.33590/emjdiabet/10305435.

Abstract The incidence of diabetes has been increasing and, in parallel, so has the incidence of females in their childbearing years with diabetes. Preconception care is important in females with diabetes due many factors related to fertility, blood glucose control, and complications. For example, many individuals with Type 2 diabetes are obese, which can affect contraceptive efficacy, fertility, and fetal growth. Additionally, patients with all types of diabetes are at risk for disordered eating, which can be harmful to a developing fetus. Both hypoglycaemia and hyperglycaemia are known to increase the risk of adverse maternal and neonatal outcomes, including during the first trimester, when many females are not aware of pregnancy. Additionally, individuals with diabetes are at risk for complications, such as atherosclerotic cardiovascular disease, nephropathy, retinopathy, and neuropathy, that can lead to complicated pregnancies. Importantly, several of the medications used to control blood glucose, and manage diabetes complications, are not recommended for use during pregnancy due to potential fetal harm. For these reasons, females with diabetes in their childbearing years are encouraged to utilise reliable contraception, so that pregnancies can be planned, or should be treated with medications with low teratogenicity potential. Thus, the preconception care of females with diabetes is complex, and the increasing prevalence of this patient population warrants greater awareness among clinicians. This narrative review summarises the current standard of preconception care for individuals with diabetes, including the management of contraception, weight, blood glucose, hypertension, and dyslipidaemia.

Key Points 1. The incidence of individuals with diabetes in their childbearing years has been increasing.

Preconception care in these individuals is complex due to factors related to fertility, blood glucose control, and complications. This narrative review summarises the current standard of preconception care for individuals with diabetes, including the management of contraception, weight, blood glucose, hypertension, and dyslipidaemia.

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2. Family planning is important for individuals with diabetes to minimise the risk of pregnancy complications. The use of reliable contraception is important, such that first-line medications to prevent diabetes complications can be safely used.

3. Once a decision to attempt pregnancy has been made, individuals should be transitioned to treatment that is safer during gestation. Patients can be shifted to safer medications for blood pressure, medications for dyslipidaemia can be temporarily stopped, and insulin can be initiated if needed, to reach blood glucose goals. The involvement of a multidisciplinary healthcare team is recommended.

INTRODUCTION According to the World Health Organization (WHO), the number of people with diabetes has quadrupled since the 1980s, with estimates of over 420 million people diagnosed.1 The International Diabetes Federation (IDF) estimates that over 530 million adults have diabetes, with many individuals unaware that they have it.2 The percentage of individuals with Type 1 diabetes has remained stable, but there are greater numbers of individuals with Type 2 diabetes secondary to an ageing population, sedentary lifestyle, and increasing worldwide obesity.2 Similarly, there are more individuals with diabetes in their childbearing years worldwide, due to more individuals with Type 2 diabetes, and advances in reproductive technology leading to higher maternal ages, since the incidence of diabetes increases with age.3 Within this review, the term female will be used to discuss all individuals who are potentially able to become pregnant, and is intended to be inclusive of gender-diverse individuals with diabetes in their childbearing years. The term male will be used to discuss individuals who produce sperm, and is intended to be inclusive of gender-diverse individuals that may utilise other terms to describe themselves.

Considerations of preconception care encompass decades of a female’s life, since many pregnancies are unplanned, and contraception is not 100% reliable. A United Nations Population Fund (UNFPA) report estimates that close to half of all pregnancies worldwide are not planned.4 Therefore, anyone of childbearing age (including pre-teens and adolescents) should regularly receive age-appropriate counselling regarding pregnancy planning. In individuals with diabetes, the complexity of care increases multifold. Access to counselling, as well as the involvement of a multidisciplinary team that may

include a primary care provider, endocrinologist, obstetrician/gynaecologist, registered dietician, pharmacist, and diabetes care and education specialist, is recommended.5 General guidelines for preconception care that apply to individuals without diabetes should be followed for individuals with diabetes (Table 1).5,6

CONTRACEPTION AND FERTILITY Family planning is important for patients with diabetes due to the associated increased risk of pregnancy complications.7,8 Planning pregnancies reduces the risk of complications during pregnancy and peripartum in patients with diabetes.5 If pregnancy is not desired at the current time, patients should use reliable contraception when engaging in sexual activity with reproductive potential.5 If pregnancy is desired, patients should work with their care team to optimise medications, lifestyle, and health outcomes, including reaching an optimal HbA1c, before conception.

Choosing contraception should be done in a patient-specific manner, with considerations for efficacy rates with typical use; safety, including precautions and contraindications; and the individual’s preferred dosage form.7,8 Long-acting reversible contraception, which includes intrauterine devices (IUD) and subcutaneous progestin implants, should be considered whenever practical, due to their high efficacy rating, limited potential for nonadherence, and relative safety profiles.5,7 Patients should remain on contraception until they are ready to conceive.

The UK Medical Eligibility Criteria (UKMEC) and United States Medical Eligibility Criteria (USMEC) ranks the safety of contraceptive products on a 4-point scale.7,8 A category of 1 represents no

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known restrictions on the use of a contraceptive product in designated populations.7,8 A ranking of 4 means the use of that contraceptive product would pose an unacceptable health risk to the user, or is contraindicated.7,8 Scores of 1 or 2 indicate the contraceptive product is typically recommended in that population, and the advantages outweigh any theoretical or proven risks.7,8 For individuals with a history of gestational diabetes, all contraceptive products are ranked as category 1, and are safe to use.7,8 Additionally, copper-containing IUDs are ranked as category 1 for patients with diabetes, regardless of the type of diabetes or the presence of complications.7,8 All other contraception, including progestin-containing IUDs, implants, depot medroxyprogesterone acetate (DMPA), progestin-only pills, and combined hormonal contraceptive (CHC) products (pill, ring, or patch) are ranked as category 2, with two notable exceptions. CHCs are ranked as a category 3 in the UKMEC, and category 3 or 4 (depending on the severity of complications) in the USMEC for patients with microvascular complications of diabetes (nephropathy, retinopathy, or neuropathy), diabetes with another vascular disease, or diabetes of greater than 20 years duration, due to the higher risk of thrombosis. The other notable exception is that, for this same patient population, UKMEC ranks DMPA as a category 2, whereas the USMEC ranks it as a category 3 due to its metabolic adverse effects. Therefore, the use of CHCs and DMPA should generally be avoided in patients with microvascular complications, vascular disease, or greater than 20 year duration of diabetes. An IUD, implant, or progestin only pill would be preferred for this population. Non-pharmacologic methods of contraception, like barrier methods including condoms, could also be considered for family planning, although the efficacy with typical use is less than the pharmacologic methods.7 Preconception counselling for patients with diabetes should include a discussion about the expected time to conception, and when to consider screening for infertility. DMPA users should be aware of a delayed return to fertility of up to 1 year after discontinuation, although this can vary for each patient.9 Females with diabetes may also struggle with infertility, and need specialist support.10 Polycystic ovarian syndrome shares a common pathology with

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Type 2 diabetes, so the two conditions often coexist.11 Prior to conception, optimising glycaemic control and obtaining and maintaining a healthy weight may improve fertility, but may not resolve all of the underlying causes.10 Although the focus of this review is primarily females with diabetes, males with diabetes who are planning for conception should also be counselled on the importance of controlling blood sugar to maintain fertility. A retrospective analysis of 110 males with diabetes referred to a urology clinic found that approximately 50% of male patients with diabetes presented with some degree of subfertility or infertility.12 Hyperglycaemia may be associated with erectile dysfunction, altered hormone levels, and decreased sperm function.13 Male patients with diabetes may need a referral to a urologist for evaluation of fertility concerns.14

WEIGHT MANAGEMENT Achieving a healthy body weight prior to conception is recommended, as being overweight or underweight can be harmful to the developing fetus.15 Reproductive risks of obesity include infertility, miscarriage, birth defects, and preterm delivery.16 Low BMI during pregnancy has been associated with low birth weight.17 Medical nutritionists can provide individuals with counselling and lifestyle recommendations during the preconception period. Behavioural therapy to support lifestyle changes may be indicated.

As much as is possible, medications for diabetes that are associated with weight gain (e.g., insulin, sulfonylureas, and thiazolidinediones) should be minimised, although intensification of previous pharmacotherapy may be needed to achieve goal HbA1c.18 The lifestyle recommendations mentioned in Table 1 help to support attaining a BMI between 20.0–24.9. For individuals needing to lose weight, it is recommended to maintain a 500 to 750 kilocalorie per day energy deficit.19 With increased public interest regarding medications such as semaglutide, dulaglutide, and tirzepatide, patients may enquire about these options for diabetes and weight loss.

Because the full effects of these agents on the developing fetus are not yet known, it is recommended that glucagon-like peptide 1 agonists and tirzepatide be discontinued prior to

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Table 1: General preconception care recommendations.5,6

• Verify vaccines or confirm immunity: hepatitis B, rubella, varicella, influenza, COVID-19; administer live vaccines such as measles, mumps and rubella (MMR) vaccine, and varicella vaccine at least 1 month prior to pregnancy.

• Protect against sexually transmitted infections, including HIV. • Take daily prenatal vitamin with 400–800 µg of folic acid.

• Consume potassium iodide 150 µg daily.

• Stop unhealthy substances, such as tobacco, alcohol, vaping, marijuana, and recreational drugs.

• Try to reach a healthy BMI. • Exercise regularly (150 minutes of moderate physical activity per week, plus muscle-strengthening activities on 2 days or more a week).

• Treat oral health abnormalities, such as dental caries and gum disease.

• Avoid hyperthermia (e.g., secondary to hot tubs and saunas).

conceiving.20 For patients in the preconception period who are on contraception, and not yet ready to conceive, it should be noted that tirzepatide is associated with reduced efficacy of oral hormonal contraception. Patients should be advised to use non-oral hormonal contraception or barrier contraceptive methods for 4 weeks after initiation of tirzepatide, and after each dose increase.21 Medications for weight loss are not recommended at conception.22

BLOOD GLUCOSE CONTROL A taskforce convened by the International Endocrine Society published guidelines on treating diabetes and pregnancy in 2013.

Based on observational research, suggesting that increased risk of fetal complications has been associated with HbA1c levels greater than 6.4%, they recommend that females with diabetes attempt to achieve blood glucose levels as close to normal as possible, while avoiding hypoglycaemia.23 Both the UK’s National Institute for Heath and Care Excellence (NICE) and the American Diabetes Association (ADA) recommend a goal HbA1c of less than 6.5% in the preconception phase to reduce risk of preeclampsia, preterm birth, macrosomia, congenital abnormalities, and complications of pregnancy.5,6 To decrease the risk of congenital abnormalities from hyperglycaemia, contraception can be prescribed until medications and HbA1c are optimised for pregnancy.5,6,15 Following

conception, the goal HbA1c intensifies to less than 6.0%, as long as hypoglycaemia can be avoided.24-26 Because the rate of red blood cell turnover increases during pregnancy, the correlation between HbA1c and glucose levels changes. During pregnancy, the HbA1c will be lower relative to blood glucose levels, and blood glucose monitoring will be a more accurate measure of glycaemic control. The NICE guidelines for diabetes and pregnancy specifically recommend that females with HbA1c levels greater than 10.0% avoid trying to conceive until their HbA1c is lower, due to the associated risks.6 This recommendation is based on limited data that indicates a first trimester HbA1c greater than 10.0% is associated with congenital malformation rates as high as 20–30%.27,28 As access to continuous glucose monitoring (CGM) increases, more patients will have the ability to actively monitor their blood glucose, and avoid potentially harmful hypoglycaemia episodes. The CONCEPTT study evaluated use of CGM in pregnant patients with Type 1 diabetes, and found that use of CGM was associated with decreased HbA1c, increased time in glucose target, and decreased incidence of large birthweight infants.29 It is reasonable to extrapolate that use of CGM during the preconception period will benefit patients interested in this technology.

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Pharmacotherapy for Blood Glucose Control

In females with Type 1 and 2 diabetes, once the decision has been taken to attempt pregnancy, guidelines recommend utilising insulin.5 For females with Type 1 diabetes, the majority are already solely on multidose insulin. If they have achieved tight control with their current insulin regimen, due to the risks of uncontrolled blood glucose and the limited differences among available insulins, they should keep it as is.

For females with Type 2 diabetes, who may or may not be on insulin prior to pregnancy, insulin lispro, insulin aspart, and insulin detemir are preferred, due to having the most evidence with regards to safety.30 Although insulin regular and neutral protamine Hagedorn insulin are safe as far as congenital malformation risk, they are associated with a higher risk of hypoglycaemia in comparison to insulin analogues.30 Recent open label trials indicate that insulin degludec is safe and comparable to insulin detemir.31 Most of the data on insulin glargine are based on retrospective studies.32 Nonetheless, it is also considered safe. Metformin could be considered as an adjunct or alternative to insulin during preconception in patients with Type 2 diabetes.6 There are data to support its safe use in pregnancy, although insulin is still preferred.5 The long term effects of metformin use during pregnancy have not been established.30 Females who are on reliable contraception and regularly access healthcare may be on any available diabetes medications, and should be switched to insulin when a decision to attempt pregnancy is made. For females who are not planning pregnancy, but are on less reliable contraception, metformin is recommended as a first-line medication due to demonstrated safety during pregnancy.30 In patients on unreliable contraception, other first-line medications for diabetes (e.g., sodium-glucose cotransporter-2 inhibitors and glucagon-like peptide 1 agonists) should generally not be withheld based on limited data in animals during pregnancy.33 The benefits of these medications to prevent maternal diabetes complications, such as atherosclerotic cardiovascular disease (ASCVD), outweigh the potential risk of early gestational exposure in an unplanned pregnancy. In this scenario, it is imperative that patients are counselled to contact providers if they believe they are pregnant, such that their medications can be changed as soon as possible.

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Preconception Counselling Regarding Blood Glucose Management During Pregnancy

Although guidelines recommend insulin for preconception care, the transition from oral therapy to insulin can be difficult for many patients. As optimising glycaemic control during the preconception window is important in reducing fetal complications, clinicians should consider a patient specific approach when determining appropriate preconception therapy.

Patients should be aware that insulin titration is a multistep process, and ideally should plan, so there is sufficient time to achieve glycaemic control prior to conception. Therefore, patients should be encouraged to discuss their current reproductive goals and desired timeline with their healthcare team at each visit.6 Patients should be encouraged to make their healthcare team aware of a pregnancy as soon as possible.

Additionally, during preconception counselling, providers should educate patients about the changes to blood glucose monitoring and insulin doses that are typically needed during pregnancy. Once pregnancy is achieved, quick dose adjustments and frequent monitoring to achieve glycaemic control will be required to prevent fetal harm and maternal complications.34 Insulin doses are anticipated to be increased throughout pregnancy, as insulin resistance is known to increase in the second and third trimesters.34 Patients should be counselled so they do not see this as their failure, but simply as an anticipated dose adjustment. If a patient is utilising metformin during the preconception period, or during pregnancy, they should be aware that in a large clinical trial investigating metformin use for gestational diabetes, metformin failed to achieve glycaemic control for 46% of patients, requiring a transition to insulin.35 This awareness is important, so that patients have realistic expectations, and can prepare themselves for a potential shift to insulin injections.

HYPERTENSION Hypertensive disorders, including pre-existing hypertension, gestational hypertension (developing after 20 weeks gestation), and preeclampsia, are the primary cause of maternal morbidity and mortality worldwide.36

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Pre-existing diabetes and chronic hypertension both increase the risk for developing preeclampsia.37 The American College of Obstetricians and Gynecologists (ACOG) recommends utilising a threshold of 140/90 mmHg to start antihypertensive therapy during pregnancy, rather than the previous recommendation of 160/110 mmHg.38 This is primarily based on the results of the CHAP trial, which demonstrated that targeting blood pressure less than 140/90 mmHg reduced the risk of severe pre-eclampsia and preterm birth.39 First-line medications used in the study included labetalol and extended-release nifedipine, with amlodipine and methyldopa used if preferred by the patient. The International Society for the Study of Hypertension in Pregnancy (ISSHP) recommends using antihypertensives to maintain blood pressure in the range of 110–140/80–85 mmHg during pregnancy.40 The Endocrine Society recommends achieving goal blood pressure of less than 130/80 mmHg prior trying to conceive.23 During preconception counselling, patients’ medication lists should be evaluated to identify any potentially harmful therapies, including angiotensin converting enzyme (ACE) inhibitors, angiotensin receptor blockers (ARB), and spironolactone.5 These agents have been associated with complications, including fetal renal dysplasia, oligohydramnios, pulmonary hypoplasia, and intrauterine growth restriction. They should be stopped prior to conception, especially in patients not using reliable contraception. In those with persistent albuminuria or decreased estimated glomerular filtration rate, the risk of using an ACE inhibitor or ARB must be weighed carefully against the benefits of slowing progression to end-stage renal disease. As the pre-pregnancy severity of chronic kidney disease (CKD) increases, the risk of pregnancy-related complications increases, as well as worsening of the CKD itself during pregnancy.41 Therefore, it is reasonable to use an ACE inhibitor or ARB in a patient with CKD, as long as they are on reliable contraception.

The adverse effects associated with these medications occur primarily during the second and third trimesters; the ACE inhibitor and ARB should be stopped once a decision to attempt pregnancy is made, or pregnancy has occurred.42 Acceptable pharmacologic agents for pregnant individuals include labetalol, methyldopa,

nifedipine, and diltiazem.40 Prazosin and hydralazine may be used as second- or third-line agents. Home blood pressure monitoring is useful to encourage patient involvement in care, as well as for screening for white coat hypertension.

Non-pharmacologic therapy to reduce blood pressure includes all lifestyle recommendations in Table 1, as well as restricting dietary sodium to less than 2.3 g per day.43

DYSLIPIDAEMIA Patients with diabetes should be screened for dyslipidaemia during an initial preconception health assessment.5 The majority of individuals who are pregnant should not be on lipidlowering treatment during pregnancy. Selected individuals with familial hypercholesterolaemia or pre-existing ASCVD may require treatment.44 There is controversy regarding the teratogenicity of statins. There has been a long-held belief that statins cause fetal abnormalities, and therefore would be contraindicated in pregnancy; for this reason, bile acid sequestrants were used, despite limited data indicating ASCVD protection. Observational data do not support this finding, and multiple large-scale studies have demonstrated no increased risk of fetal abnormalities seen in patients who became pregnant while on a statin.45 These data are helpful in reassuring patients who may experience an unintended pregnancy while on a statin. However, observational data are not enough to rule out all fetal harm, and the increased risk of spontaneous pregnancy loss. A study found a correlation with statin use and an increased risk of miscarriage in patients using statins 3 months before conception, or during pregnancy.46 Therefore, it is still not recommended to use statins during preconception or pregnancy in most patients.

There may be a small subset of patients where the benefits of statin treatment, including reduction in mortality for those at very high-risk of an ASCVD event, may outweigh the potential risk of harm during pregnancy.45 The ADA and NICE guidelines recommend discontinuing statins prior to conception, and avoiding their use in patients with childbearing potential that are not on a reliable form of contraception.5,6

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DIABETES COMPLICATIONS All individuals with diabetes should be regularly evaluated for diabetes complications, such as retinopathy, neuropathy, nephropathy, and ASCVD. During preconception planning, these evaluations are even more important, since they potentially worsen during pregnancy, and can increase maternal morbidity. For example, individuals with proliferative retinopathy should be treated with laser photocoagulation surgery prior to conception to minimise worsening.47 Antivascular endothelial growth factor medications can be used preconception, but should not be used during pregnancy, due to concerns regarding impact on the blood vessels of the

fetus.47 Also, in individuals with retinopathy, blood glucose lowering should be gradual, since rapid lowering is associated with further damage to the ocular blood vessels.47 Gastrointestinal neuropathy can affect nutrition, and is associated with extreme fluctuations in blood glucose, increased risk of diabetic ketoacidosis, and hospitalisations.48 Diabetic peripheral neuropathy remains stable in most females during pregnancy, but medications used to treat neuropathic pain may be harmful to the fetus. Topical treatments like capsaicin are the safest medications to use for treatment of neuropathy during pregnancy, and if oral treatment is needed, gabapentin, based on limited data, is likely safe, although it does cross the placenta.33

Table 2: Preconception care for females with pre-existing diabetes attempting to become pregnant, or planning pregnancy in the imminent future.

Consideration

Recommendation

Reproductive timeline

Discuss current reproductive desires and anticipated timeline.

Educate on the impact diabetes may have on fertility. Encourage patient to make healthcare team aware of pregnancy as soon as feasible.

General preconception care

Educate and implement recommendations outlined in Table 1.

Weight management

Set goal of achieving a healthy body weight prior to conception to minimise fetal and maternal risks during pregnancy, and meet diabetes treatment goals.

Blood glucose control

Set goal of achieving HbA1c <6.5% prior to conception, with plan for HbA1c goal to reduce to <6.0% during pregnancy to minimise fetal and maternal risks.

Pharmacotherapy for blood glucose control

Switch to insulin monotherapy (preferably insulin lispro or insulin aspart if prandial insulin is needed, and insulin detemir if basal insulin is needed; if on insulin prior to pregnancy, may continue previous insulin regimen). In individuals with Type 2 diabetes, may consider utilising metformin as an adjunct or alternative therapy to insulin during preconception period. Per guidelines, consider discontinuing metformin when pregnancy is achieved.

Pharmacotherapy for hypertension

Discontinue angiotensin converting enzyme inhibitors, angiotensin receptor blockers, and spironolactone. Switch to antihypertensive medications to those safe in pregnancy, including labetalol, methyldopa, nifedipine, and diltiazem.

Pharmacotherapy of dyslipidaemia

Avoid lipid-lowering treatment for the majority of patients.

Rarely, the benefits of a statin or bile acid sequestrant may necessitate their use in pregnancy and preconception (e.g., familial hypercholesterolemia or pre-existing atherosclerotic cardiovascular disease).

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Table 3: Preconception care of females with pre-existing diabetes not currently planning for pregnancy.

Consideration

Recommendation if at risk of pregnancy (e.g., using unreliable contraceptive methods, and engaging in sexual behaviours with pregnancy potential)

Recommendation if at low risk of pregnancy (e.g., on reliable contraceptive methods, or not engaging in sexual behaviours with pregnancy potential)

Reproductive timeline

Discuss current reproductive desires and anticipated timeline. Encourage patient to make healthcare team aware of pregnancy as soon as feasible. Discuss if patient is open to more reliable forms of contraception if pregnancy is not desired at present time.

Discuss current reproductive desires and anticipated timeline. Encourage patient to make healthcare team aware of pregnancy as soon as feasible.

General preconception care

Educate and consider implementing recommendations outlined in Table 1.

Educate on recommendations outlined in Table 1 so patient is aware when pregnancy is desired.

Weight management

Set goal of achieving a healthy body weight to meet diabetes treatment goals and minimise risk to a potential pregnancy.

Set goal of achieving a healthy body weight to meet diabetes treatment goals.

Blood glucose control

Achieve patient specific HbA1c goal, typically <6.5–7.0%, to meet diabetes treatment goals. Patient should be aware of more intensive goals if pregnancy was to occur.

Achieve patient specific HbA1c goal, typically <6.5–7.0%, to meet diabetes treatment goals.

Pharmacotherapy for blood glucose control

Consider utilising metformin as first-line therapy for Type 2 diabetes. If glycaemic control is not achieved with metformin alone, use any other medications, as recommended by guidelines. Encourage utilisation of more reliable contraception. If pregnancy should occur, patient should be counselled to notify provider as soon as feasible for evaluation. Avoid noninsulin medications in patients with Type 1 diabetes.

Follow diabetes treatment guidelines without regard to pregnancy potential.

If pregnancy should occur, notify provider as soon as feasible for evaluation.

Pharmacotherapy for hypertension

Avoid angiotensin converting enzyme inhibitors, angiotensin receptor blockers, and spironolactone. Consider using antihypertensive medications safe in pregnancy, including labetalol, methyldopa, nifedipine, and diltiazem. If pregnancy should occur, notify provider as soon as feasible for evaluation.

Follow diabetes and hypertension treatment guidelines without regard to pregnancy potential. If pregnancy should occur, notify provider as soon as feasible for evaluation.

Pharmacotherapy of dyslipidaemia

Weigh risks and benefits of lipid lowering treatment against risk of potential pregnancy. If patient has multiple risk factors for atherosclerotic cardiovascular disease, encourage utilisation of more reliable contraception, such that a statin can be safely used.

Follow diabetes and dyslipidaemia treatment guidelines without regard to pregnancy potential. If pregnancy should occur, notify provider as soon as feasible for evaluation.

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Renal insufficiency generally does not progress during pregnancy, but is correlated with an increased risk of pregnancy complications, such as pre-eclampsia and premature delivery.49 A history of ASCVD during pregnancy is associated with increased maternal mortality; females who are older than age 35, with evidence of microvascular disease or with signs of possible cardiovascular disease, should be screened with an electrocardiogram.49

CONCLUSION Despite the lack of a cure for diabetes at this time, the treatment of diabetes has progressed tremendously over the past decade, with greater knowledge, technology, and medication options, allowing for fewer patients who experience diabetes complications. Tables 2 and 3 summarise the recommendations set forth in this review for patients in different stages of preconception. With greater numbers of individuals in their childbearing years with diabetes, the benefits of the treatments must be weighed carefully against the potential harms to a fetus. The guidelines are clearer when the preconception timeframe is short, and pregnancy is planned. In these circumstances, patients can

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be switched to safer blood pressure medications, medications for dyslipidaemia can be temporarily stopped, and insulin can be initiated if pharmacotherapy is needed to reach blood glucose goals. The more challenging situation is when the preconception period may be longer than anticipated, in which case, the risk of diabetes complications must be considered, since maternal diabetes complications increase pregnancy-related maternal and fetal morbidity and mortality. For patients with Type 2 diabetes, reducing diabetes complications, reaching blood glucose goals, and achieving a healthy weight may be easier to accomplish with newer medications, for which we have less information regarding safety during pregnancy.

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