DCB Newsletter #3/25: Menstrual cycle and insulin requirements

by | Aug 5, 2025 | Linkedin

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DCB Newsletter #3/25: Menstrual cycle and insulin requirements

We are thrilled to present you with the next episode of our series “INSIDE DCB”!

Research at the Diabetes Centre Berne: Focus on the menstrual cycle and insulin requirements

The Diabetes Centre Berne (DCB) is investigating the influence of hormonal fluctuations, particularly the menstrual cycle, on diabetes management. Martina Rothenbühler and Stefanie Hossmann explain how they are working with the community to collect data and gain new insights to help those affected.

Martina Rothenbühler (MR) and Stefanie Hossmann (SH), what are your roles at the Diabetes Centre Berne (DCB)?

MR: My focus is on data analysis and developing research questions. I draw on my background in biostatistics and investigate, for example, how hormonal fluctuations influence insulin requirements.

SH: I am responsible for coordinating studies at the DCB and ensuring compliance with regulatory requirements. My background as a biologist enables me to tailor these studies to the specific needs of the participants.

How did you come up with the idea to research this topic?

SH: We learned from the diabetes community, from various people living with diabetes, that there are fluctuations in diabetes management in relation to the menstrual cycle. We then began to look into the topic in more detail. We found very little on this in the mainstream scientific journals, so we decided to investigate the topic more thoroughly. There are different phases in the menstrual cycle during which hormones increase or decrease. These include, for example, the follicular phase and, at the end, the luteal phase. Ovulation takes place in between. Since all of this is closely linked to hormones, we assumed that insulin requirements are also affected.

MR: I previously worked at Ava, a femtech start-up. There, we conducted research projects that showed that the menstrual cycle has many different effects, for example in the areas of sport and osteoporosis. This sparked my interest, as insulin sensitivity may also be related to this.

Why has this topic been largely unexplored until now?

MR: One of the reasons is the so-called gender health data gap, i.e. the lack of health data on women. For a long time, women were excluded from clinical studies to avoid risks, for example in the event of pregnancy. In addition, hormonal fluctuations were considered potential confounding factors that could make general statements difficult. These aspects are not yet sufficiently anchored in research.

SH: That’s really surprising. With almost every new research topic, you realise that this is not a given.

How does the diabetes community view your efforts to investigate the link between diabetes and the menstrual cycle?

MR: We have mainly received feedback from people who already considered the topic important or saw it as a personal problem. However, we do not know how many women did not feel affected by it. For some, enthusiasm for our research was greater than their willingness to actually participate in the study. Nevertheless, many found the topic interesting and important, even if participating in a study is a hurdle.

SH: We also often received feedback that this issue is seen as a relatively minor problem and that more important issues should be addressed first. However, we take a different view. Especially when diabetes management is already at a high level, a small improvement, such as five per cent more time in the target range (time in range), can bring about a noticeable improvement. This has also been confirmed by the community.

What further goals are you pursuing with your research in this area?

MR: Our aim is to bring more data from women and non-binary people into medical research and to raise awareness of these issues. The focus is not only on time-in-range, but also on how technology should be designed in this context. For example, more is already known about the influence of gender on sport and nutrition in relation to diabetes.

SH: However, less research has been done on hormonal transition phases such as pregnancy, breastfeeding or gender reassignment.

MR: Or even the entire puberty phase. All these gender-specific phases are related to diabetes, but they are still largely unexplored. We are currently working on an editorial that highlights precisely these areas.

Why is this topic particularly relevant to the Diabetes Centre Berne?

MR: What is special about diabetes and the menstrual cycle is that there is no specific industry behind it and therefore hardly any commercial interests at stake. Our research covers the entire field of diabetes technology and is not limited to a single manufacturer. However, this also means that there is often little research funding available and we are dependent on third-party funding. As a foundation, we are therefore in a good position to conduct this research together with people with diabetes.

What specific projects are you currently working on, and what are your main goals?

MR: What I find exciting is the fact that, although there are restrictions for certain groups, many of those affected have nevertheless found their own solutions to deal with the challenges. They have developed strategies that work for them personally.

SH: We are currently working closely with the community to understand exactly where the problems lie and what needs exist. We are collecting data on the extent of the burden and the strategies that those affected have developed to cope with it. So far, we have evaluated data from the past. In the future, we would like to collect data in a targeted and forward-looking manner to get even better answers to our questions.

Thanks so much for reading, and we’ll provide you with the next episode of this series soon!

This edition was authored by Sunjoy Mathieu, Communication Manager at DCB.

 

This post was previously published in Linkedin. Click here to see the original publication.

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But could the same data tell us much more about a person's health than we currently realise?

A new study published in npj Digital Medicine suggests that looking beyond individual glucose readings could reveal patterns associated with diabetes-related health risks. The findings point towards an interesting possibility: CGM technology may one day help us understand glucose levels, and also identify individual risk profiles and support more personalised diabetes care.

What did the researchers discover?

The researchers analysed CGM data from 90,757 participants in the iCanNet cohort, including people with normoglycemia, prediabetes, type 1 diabetes and type 2 diabetes. Rather than looking at individual glucose values alone, they examined 48 different CGM-derived metrics, including glucose variability and patterns over time. Their analysis revealed distinct glucose patterns across the different groups. Combining several metrics also improved the researchers' ability to distinguish between them.

Particularly interesting were the findings among people living with type 2 diabetes.

The researchers identified four distinct glycemic phenotypes – different patterns of glucose levels and fluctuations – associated with varying risks of diabetes-related complications.

For example:

  • Sustained high glucose levels were associated with a 59% higher relative risk of acute complications.
  • High glucose variability was associated with a 75% higher relative risk of acute complications and a 41% higher relative risk of chronic complications.

The researchers also developed a Digital Glycemic Risk Score (DGRS), combining multiple CGM metrics to identify people with potentially elevated risks.

Among participants with type 2 diabetes who had already achieved a Time in Range of at least 70%, the model reclassified 25% into a high-risk category for acute complications and 22.3% into a high-risk category for chronic complications. These were classifications generated by the model, not confirmed predictions of complications occurring in these individuals.

The findings suggest that combining different glucose patterns may reveal information about health risks that commonly used CGM metrics alone do not fully capture.

From CGM data to new insights. The study analysed 48 CGM-derived metrics from 90,757 participants and identified distinct glucose patterns across the diabetes spectrum. Among people with type 2 diabetes, four glycemic phenotypes were associated with

From CGM data to new insights. The study analysed 48 CGM-derived metrics from 90,757 participants and identified distinct glucose patterns across the diabetes spectrum. Among people with type 2 diabetes, four glycemic phenotypes were associated with

What could this mean for diabetes care?

Today, CGM systems primarily help people understand what is happening with their glucose levels and respond accordingly. But the enormous amount of data these devices generate may hold additional information that we are only beginning to explore.

By analysing glucose patterns over time, researchers may be able to develop digital biomarkers – measurable indicators derived from digital health data – that could help characterise individual metabolic profiles and identify potential health risks.

In the future, this could support healthcare professionals in recognising people who may benefit from closer monitoring or more personalised treatment approaches.

The Nordic region remains the global hotspot for childhood‑onset T1D. Finland has historically reported the highest incidence worldwide – around 60 per 100,000 children per year – although this rate appears to have stabilised or slightly declined since the early 2000s. Sweden, Norway and Denmark follow closely, and the IDF Diabetes Atlas 11 still places Finland, Sweden and Norway among the global top ten for published incidence.

A 2024 systematic review of European paediatric data (1994–2022) confirmed that the Nordic countries retain the highest absolute incidence in Europe, even as their rate of increase has slowed relative to the rapid rise in previously low‑incidence Central and Eastern European countries. A Swedish nationwide cohort study of 2.9 million children found that the heritability of childhood‑onset T1D has remained high and stable at around 0.83 over 30 years, implying that known environmental factors explain less than 10% of the observed rise.

In France, Santé publique France reports an annual T1D incidence increase of approximately 4%. Paediatric incidence rose from 15.4 to 19.1 per 100,000 person‑years between 2010 and 2015, with an overall rate of 19.5 per 100,000 in 2015–2017 and substantial regional variation. Sardinia (Italy) now reports the world’s highest published paediatric incidence at 76.3 per 100,000 (2022), having overtaken Finland, while the United Kingdom and Ireland also remain in the global top ten.

Projections to 2050

Two major modelling efforts suggest the same direction of travel. The GBD 2021 study projects that age‑standardised global type 1 diabetes prevalence will rise by about 23.9% between 2021 and 2050, a smaller relative increase than for type 2 diabetes, but a substantial absolute rise given the lifelong management burden of T1D. The IDF Diabetes Atlas 11 projects that overall adult diabetes prevalence (type 1 and type 2) will grow from 589 million in 2024 to around 853 million by 2050, a 46% absolute increase. While most of this growth is driven by type 2 diabetes, T1D contributes meaningfully through sustained or rising incidence in high‑income countries and improved survival, which increases prevalence even if incidence stabilises.

For the U.S., a modelling exercise based on SEARCH data suggests that, under a linear‑increase scenario, the number of young people with T1D could nearly triple by 2050, with the steepest relative growth among Hispanic and Black children.

Why is incidence rising?

T1D is a polygenic autoimmune disease in which environmental triggers act on a susceptible genetic background. Several, often overlapping, hypotheses are under investigation:

  • Reduced early‑life exposure to common infections (“hygiene hypothesis”), with mixed evidence.
  • Rising childhood adiposity and insulin resistance, which may accelerate the clinical presentation of existing islet autoimmunity (“accelerator hypothesis”).
  • Viral triggers such as enteroviruses and, more recently, SARS‑CoV‑2, where meta‑analyses report a 14–27% increase in paediatric T1D incidence during the pandemic.
  • Dietary and microbiome‑related factors, including early cow’s‑milk exposure, gluten timing and shifts in the infant gut microbiome.
  • Vitamin D status and latitude, endocrine‑disrupting chemicals, pollution and perinatal factors such as higher maternal age, higher birth weight and Caesarean delivery.

The Swedish cohort study mentioned above is a stark reminder of how much remains unexplained: even with population‑wide data on several environmental exposures, less than 10% of the rise in T1D incidence in Sweden could be attributed to known factors. This underscores the need for long‑term birth cohorts, population‑level islet‑autoantibody screening and mechanistic immunology.

Implications for the next decade

Three practical implications stand out for health systems, innovators and the diabetes community:

  • Health systems need to prepare for more people living with type 1 diabetes. Even under conservative assumptions, the number of people living with type 1 diabetes will continue to rise. This has practical consequences for insulin and sensor supply, reimbursement for diabetes technology, specialist care and clinical-trial infrastructure.
  • Early detection is becoming more relevant. Type 1 diabetes can often be detected before symptoms appear, through markers of early autoimmune activity. With the first therapy now approved to delay clinical onset in people at an early disease stage, screening is no longer only a research question. Programmes such as Fr1da in Germany and the D1Ce trial in Italy show how early identification could inform future intervention strategies.
  • We still need to understand why incidence is rising. Many environmental factors are being studied, but no single explanation is sufficient. Until we better understand the drivers behind the increase, true primary prevention will remain out of reach. This makes high-quality registries, long-term cohort studies and coordinated research more important than ever.

At DCB, we see these trends as both a warning and a call to action.

The rising incidence of type 1 diabetes will require sustained collaboration between clinicians, researchers, industry, policymakers and crucially people living with diabetes and their families.

Our work in innovation, clinical research infrastructure and community engagement aims to contribute to that collective effort.

    Selected sources

    GBD 2021 Diabetes Collaborators. Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050. The Lancet, 2023. https://www.thelancet.com/article/S0140-6736(23)01301-6/fulltext

    International Diabetes Federation. IDF Diabetes Atlas, 11th edition. 2025. https://diabetesatlas.org/

    Ogle GD, et al. Global type 1 diabetes prevalence, incidence, and mortality estimates 2025: Results from the IDF Atlas 11 and the T1D Index v3.0. Diabetes Research and Clinical Practice, 2025. https://www.sciencedirect.com/science/article/pii/S0168822725002918

    Stahl-Pehe A, et al. Wave in Pediatric Type 1 Diabetes Incidence After the Emergence of COVID-19: Peak and Trough Patterns in German Youth — DPV Registry. Diabetes Care, 2025. https://diabetesjournals.org/care/article/48/4/e47/157803/

    Buchmann M, et al. Type 1 diabetes incidence curves differ by age for girls and boys between 1996 and 2022: Results from the North Rhine-Westphalia Diabetes Registry. Diabetes Research and Clinical Practice, 2025. https://www.sciencedirect.com/science/article/pii/S0168822725000105

    Nagl K, et al. Ongoing Increase in Incidence of Diabetes in Austrian Children and Adolescents 1989–2021. Pediatric Diabetes, 2023. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12017070/

    Schoenle EJ, et al. Epidemiology of Type I diabetes mellitus in Switzerland: Steep rise in incidence in under-5 year old children in the past decade. Diabetologia, 2001. https://www.researchgate.net/publication/12017578

    Santé publique France. Diabetes — Data. Updated 2024. https://www.santepubliquefrance.fr/en/diabete/data

    Piffaretti C, et al. Trends in childhood type 1 diabetes incidence in France, 2010–2015. Diabetes Research and Clinical Practice, 2018. https://www.sciencedirect.com/science/article/abs/pii/S0168822718304662

    Ruiz-Grao MC, et al. Trends in the Incidence of Type 1 Diabetes in European Children and Adolescents from 1994 to 2022: A Systematic Review and Meta-Analysis. Pediatric Diabetes, 2024. https://onlinelibrary.wiley.com/doi/10.1155/2024/2338922

    Ji J, et al. Stable heritability of type 1 diabetes in a Swedish Nationwide Cohort Study. Nature Communications, 2024. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12174315/

    D’Souza D, et al. Incidence of Diabetes in Children and Adolescents During the COVID-19 Pandemic: A Systematic Review and Meta-Analysis. JAMA Network Open, 2023. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10314307/

    Quinn LM, et al. Environmental Determinants of Type 1 Diabetes: From Association to Proving Causality. Frontiers in Immunology, 2021. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8518604/

    Esposito S, et al. Changes in the Global Epidemiology of Type 1 Diabetes in an Evolving Landscape of Environmental Factors. International Journal of Molecular Sciences, 2023. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10141720/

    Rogers MAM, et al. Fluctuations in the incidence of type 1 diabetes in the United States from 2001 to 2015: a longitudinal study. BMC Medicine, 2017. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5688827/

    Imperatore G, et al. Projections of Type 1 and Type 2 Diabetes Burden in the U.S. Population Aged <20 Years Through 2050. Diabetes Care, 2012. https://pmc.ncbi.nlm.nih.gov/articles/PMC3507562/

    We hope this overview is helpful for your own work in diabetes care, research or advocacy. As always, we are grateful for your interest in and support of our activities and we look forward to continuing this conversation with you in future editions of our “DID YOU KNOW…” series.

    An earlier version of this article was originally published in the private newsletter of our CEO, Derek Brandt here: Diabetes Technology News.

    This post was previously published in Linkedin. Click here to see the original publication.

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    DCB Newsletter #2/25: DID YOU KNOW… that staying active with Type 1 Diabetes doesn’t have to be complicated?

    by | Jul 9, 2025 | Linkedin

    Back to News

    DCB Newsletter #2/25: DID YOU KNOW… that staying active with Type 1 Diabetes doesn’t have to be complicated?

    Dear Community,

    For many people living with Type 1 Diabetes (T1D), physical activity can bring unique challenges. Balancing glucose levels, managing insulin, and planning meals around exercise often require extra care and consideration.

    The new book “Empowered: The Science of Exercise with Type 1 Diabetes”, authored by Dr. Sam Scott, CEO and Co-Founder of Enhance-d, and Dr. Simon Helleputte, offers clear and accessible guidance to help you safely and effectively integrate physical activity into your life. Grounded in the latest research, this resource is designed to empower you, no matter your activity level or goals.

    This book is not just about exercise – it’s about empowering you to lead a more active, confident, and fulfilling life while managing your condition. Whether it’s adapting insulin for a morning walk or managing glucose during endurance events, the complexity of physical activity with T1D can feel overwhelming. Empowered bridges the gap between research and everyday life, providing tools and strategies to help you navigate exercise with confidence.

    Who is this book for?

    The e-book is designed to meet the diverse needs of:

    1. People living with Type 1 Diabetes: Whether you’re starting your fitness journey or are an experienced athlete, this guide is tailored to your needs.

    2. Healthcare professionals: Gain insights to better support people with Type 1 Diabetes in their physical activity goals.

    3. Caregivers and coaches: Learn how to provide practical support and encouragement.

    What’s Inside?

    Here’s a glimpse into the book’s ten chapters, crafted to address every key aspect of Type 1 Diabetes and exercise:

    1. Understanding Type 1 Diabetes and exercise: Gain a foundational understanding of how Type 1 Diabetes affects glucose levels during different types of physical activity, including aerobic and anaerobic exercise.

    2. The benefits of physical activity: Discover how regular exercise improves insulin sensitivity, supports cardiovascular health, and enhances overall wellbeing. Learn why even small steps, like reducing sedentary time, can make a big difference.

    3. Hormonal and metabolic responses to exercise: Explore how your body’s natural responses differ with Type 1 Diabetes and how to anticipate and manage fluctuations in glucose levels during and after activity.

    4. Practical exercise management Learn step-by-step approaches to:

    • Adjust insulin doses for various activities
    • Prepare for workouts with tailored nutrition strategies
    • Manage glucose levels during exercise with continuous glucose monitoring (CGM).

    5. Sports science management: For those pursuing endurance sports, delve into advanced topics like low-carbohydrate diets, post-workout recovery, and adaptations for extreme environments.

    And more about Metabolism and management, Sports science principles, Low-carbohydrate diets and Athletic performance in Type 1 Diabetes. Simon and Sam have meticulously compiled the most relevant research to help readers better understand and navigate exercise with Type 1 Diabetes.

    Why this book might be for you?

    1. Research Made Practical: The book translates decades of scientific studies into simple, actionable steps for your daily life.

    2. Real-Life Relevance: It was developed with input from people living with Type 1 Diabetes, ensuring the advice is applicable to real-world scenarios.

    3. Empowering Language: Written in an inclusive and uplifting tone, the book celebrates your strengths and provides solutions for overcoming challenges.

    Ready to Get Started?

    Exclusively for you, dear newsletter readers, we are happy to provide you with a free online version here:

    ➡️ https://book.enhance-d.com/5/empowered

    If you prefer a paperback version, you can get it here:

    ➡️ https://amazon.enhance-d.com/?asin=1068431717

    Join the Empowered Movement

    Connect with a community of people living with Type 1 Diabetes, healthcare professionals, and researchers dedicated to advancing understanding and support for exercise in Type 1 Diabetes management.

    Follow the authors and Enhance-d for updates:

    Key Takeaway

    Managing Type 1 Diabetes doesn’t mean giving up on your fitness goals – it means redefining them with knowledge, preparation, and the right tools. “Empowered” equips you with everything you need to move forward confidently, one step at a time.

    Thanks so much for reading!

    This post was previously published in Linkedin. Click here to see the original publication.

    More recent news

    Sign up for our newsletter
    to receive the latest news.

    * Pflichtfelder
    DSGVO *

    But could the same data tell us much more about a person's health than we currently realise?

    A new study published in npj Digital Medicine suggests that looking beyond individual glucose readings could reveal patterns associated with diabetes-related health risks. The findings point towards an interesting possibility: CGM technology may one day help us understand glucose levels, and also identify individual risk profiles and support more personalised diabetes care.

    What did the researchers discover?

    The researchers analysed CGM data from 90,757 participants in the iCanNet cohort, including people with normoglycemia, prediabetes, type 1 diabetes and type 2 diabetes. Rather than looking at individual glucose values alone, they examined 48 different CGM-derived metrics, including glucose variability and patterns over time. Their analysis revealed distinct glucose patterns across the different groups. Combining several metrics also improved the researchers' ability to distinguish between them.

    Particularly interesting were the findings among people living with type 2 diabetes.

    The researchers identified four distinct glycemic phenotypes – different patterns of glucose levels and fluctuations – associated with varying risks of diabetes-related complications.

    For example:

    • Sustained high glucose levels were associated with a 59% higher relative risk of acute complications.
    • High glucose variability was associated with a 75% higher relative risk of acute complications and a 41% higher relative risk of chronic complications.

    The researchers also developed a Digital Glycemic Risk Score (DGRS), combining multiple CGM metrics to identify people with potentially elevated risks.

    Among participants with type 2 diabetes who had already achieved a Time in Range of at least 70%, the model reclassified 25% into a high-risk category for acute complications and 22.3% into a high-risk category for chronic complications. These were classifications generated by the model, not confirmed predictions of complications occurring in these individuals.

    The findings suggest that combining different glucose patterns may reveal information about health risks that commonly used CGM metrics alone do not fully capture.

    From CGM data to new insights. The study analysed 48 CGM-derived metrics from 90,757 participants and identified distinct glucose patterns across the diabetes spectrum. Among people with type 2 diabetes, four glycemic phenotypes were associated with

    From CGM data to new insights. The study analysed 48 CGM-derived metrics from 90,757 participants and identified distinct glucose patterns across the diabetes spectrum. Among people with type 2 diabetes, four glycemic phenotypes were associated with

    What could this mean for diabetes care?

    Today, CGM systems primarily help people understand what is happening with their glucose levels and respond accordingly. But the enormous amount of data these devices generate may hold additional information that we are only beginning to explore.

    By analysing glucose patterns over time, researchers may be able to develop digital biomarkers – measurable indicators derived from digital health data – that could help characterise individual metabolic profiles and identify potential health risks.

    In the future, this could support healthcare professionals in recognising people who may benefit from closer monitoring or more personalised treatment approaches.

    The Nordic region remains the global hotspot for childhood‑onset T1D. Finland has historically reported the highest incidence worldwide – around 60 per 100,000 children per year – although this rate appears to have stabilised or slightly declined since the early 2000s. Sweden, Norway and Denmark follow closely, and the IDF Diabetes Atlas 11 still places Finland, Sweden and Norway among the global top ten for published incidence.

    A 2024 systematic review of European paediatric data (1994–2022) confirmed that the Nordic countries retain the highest absolute incidence in Europe, even as their rate of increase has slowed relative to the rapid rise in previously low‑incidence Central and Eastern European countries. A Swedish nationwide cohort study of 2.9 million children found that the heritability of childhood‑onset T1D has remained high and stable at around 0.83 over 30 years, implying that known environmental factors explain less than 10% of the observed rise.

    In France, Santé publique France reports an annual T1D incidence increase of approximately 4%. Paediatric incidence rose from 15.4 to 19.1 per 100,000 person‑years between 2010 and 2015, with an overall rate of 19.5 per 100,000 in 2015–2017 and substantial regional variation. Sardinia (Italy) now reports the world’s highest published paediatric incidence at 76.3 per 100,000 (2022), having overtaken Finland, while the United Kingdom and Ireland also remain in the global top ten.

    Projections to 2050

    Two major modelling efforts suggest the same direction of travel. The GBD 2021 study projects that age‑standardised global type 1 diabetes prevalence will rise by about 23.9% between 2021 and 2050, a smaller relative increase than for type 2 diabetes, but a substantial absolute rise given the lifelong management burden of T1D. The IDF Diabetes Atlas 11 projects that overall adult diabetes prevalence (type 1 and type 2) will grow from 589 million in 2024 to around 853 million by 2050, a 46% absolute increase. While most of this growth is driven by type 2 diabetes, T1D contributes meaningfully through sustained or rising incidence in high‑income countries and improved survival, which increases prevalence even if incidence stabilises.

    For the U.S., a modelling exercise based on SEARCH data suggests that, under a linear‑increase scenario, the number of young people with T1D could nearly triple by 2050, with the steepest relative growth among Hispanic and Black children.

    Why is incidence rising?

    T1D is a polygenic autoimmune disease in which environmental triggers act on a susceptible genetic background. Several, often overlapping, hypotheses are under investigation:

    • Reduced early‑life exposure to common infections (“hygiene hypothesis”), with mixed evidence.
    • Rising childhood adiposity and insulin resistance, which may accelerate the clinical presentation of existing islet autoimmunity (“accelerator hypothesis”).
    • Viral triggers such as enteroviruses and, more recently, SARS‑CoV‑2, where meta‑analyses report a 14–27% increase in paediatric T1D incidence during the pandemic.
    • Dietary and microbiome‑related factors, including early cow’s‑milk exposure, gluten timing and shifts in the infant gut microbiome.
    • Vitamin D status and latitude, endocrine‑disrupting chemicals, pollution and perinatal factors such as higher maternal age, higher birth weight and Caesarean delivery.

    The Swedish cohort study mentioned above is a stark reminder of how much remains unexplained: even with population‑wide data on several environmental exposures, less than 10% of the rise in T1D incidence in Sweden could be attributed to known factors. This underscores the need for long‑term birth cohorts, population‑level islet‑autoantibody screening and mechanistic immunology.

    Implications for the next decade

    Three practical implications stand out for health systems, innovators and the diabetes community:

    • Health systems need to prepare for more people living with type 1 diabetes. Even under conservative assumptions, the number of people living with type 1 diabetes will continue to rise. This has practical consequences for insulin and sensor supply, reimbursement for diabetes technology, specialist care and clinical-trial infrastructure.
    • Early detection is becoming more relevant. Type 1 diabetes can often be detected before symptoms appear, through markers of early autoimmune activity. With the first therapy now approved to delay clinical onset in people at an early disease stage, screening is no longer only a research question. Programmes such as Fr1da in Germany and the D1Ce trial in Italy show how early identification could inform future intervention strategies.
    • We still need to understand why incidence is rising. Many environmental factors are being studied, but no single explanation is sufficient. Until we better understand the drivers behind the increase, true primary prevention will remain out of reach. This makes high-quality registries, long-term cohort studies and coordinated research more important than ever.

    At DCB, we see these trends as both a warning and a call to action.

    The rising incidence of type 1 diabetes will require sustained collaboration between clinicians, researchers, industry, policymakers and crucially people living with diabetes and their families.

    Our work in innovation, clinical research infrastructure and community engagement aims to contribute to that collective effort.

      Selected sources

      GBD 2021 Diabetes Collaborators. Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050. The Lancet, 2023. https://www.thelancet.com/article/S0140-6736(23)01301-6/fulltext

      International Diabetes Federation. IDF Diabetes Atlas, 11th edition. 2025. https://diabetesatlas.org/

      Ogle GD, et al. Global type 1 diabetes prevalence, incidence, and mortality estimates 2025: Results from the IDF Atlas 11 and the T1D Index v3.0. Diabetes Research and Clinical Practice, 2025. https://www.sciencedirect.com/science/article/pii/S0168822725002918

      Stahl-Pehe A, et al. Wave in Pediatric Type 1 Diabetes Incidence After the Emergence of COVID-19: Peak and Trough Patterns in German Youth — DPV Registry. Diabetes Care, 2025. https://diabetesjournals.org/care/article/48/4/e47/157803/

      Buchmann M, et al. Type 1 diabetes incidence curves differ by age for girls and boys between 1996 and 2022: Results from the North Rhine-Westphalia Diabetes Registry. Diabetes Research and Clinical Practice, 2025. https://www.sciencedirect.com/science/article/pii/S0168822725000105

      Nagl K, et al. Ongoing Increase in Incidence of Diabetes in Austrian Children and Adolescents 1989–2021. Pediatric Diabetes, 2023. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12017070/

      Schoenle EJ, et al. Epidemiology of Type I diabetes mellitus in Switzerland: Steep rise in incidence in under-5 year old children in the past decade. Diabetologia, 2001. https://www.researchgate.net/publication/12017578

      Santé publique France. Diabetes — Data. Updated 2024. https://www.santepubliquefrance.fr/en/diabete/data

      Piffaretti C, et al. Trends in childhood type 1 diabetes incidence in France, 2010–2015. Diabetes Research and Clinical Practice, 2018. https://www.sciencedirect.com/science/article/abs/pii/S0168822718304662

      Ruiz-Grao MC, et al. Trends in the Incidence of Type 1 Diabetes in European Children and Adolescents from 1994 to 2022: A Systematic Review and Meta-Analysis. Pediatric Diabetes, 2024. https://onlinelibrary.wiley.com/doi/10.1155/2024/2338922

      Ji J, et al. Stable heritability of type 1 diabetes in a Swedish Nationwide Cohort Study. Nature Communications, 2024. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12174315/

      D’Souza D, et al. Incidence of Diabetes in Children and Adolescents During the COVID-19 Pandemic: A Systematic Review and Meta-Analysis. JAMA Network Open, 2023. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10314307/

      Quinn LM, et al. Environmental Determinants of Type 1 Diabetes: From Association to Proving Causality. Frontiers in Immunology, 2021. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8518604/

      Esposito S, et al. Changes in the Global Epidemiology of Type 1 Diabetes in an Evolving Landscape of Environmental Factors. International Journal of Molecular Sciences, 2023. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10141720/

      Rogers MAM, et al. Fluctuations in the incidence of type 1 diabetes in the United States from 2001 to 2015: a longitudinal study. BMC Medicine, 2017. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5688827/

      Imperatore G, et al. Projections of Type 1 and Type 2 Diabetes Burden in the U.S. Population Aged <20 Years Through 2050. Diabetes Care, 2012. https://pmc.ncbi.nlm.nih.gov/articles/PMC3507562/

      We hope this overview is helpful for your own work in diabetes care, research or advocacy. As always, we are grateful for your interest in and support of our activities and we look forward to continuing this conversation with you in future editions of our “DID YOU KNOW…” series.

      An earlier version of this article was originally published in the private newsletter of our CEO, Derek Brandt here: Diabetes Technology News.

      This post was previously published in Linkedin. Click here to see the original publication.

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      3010 Bern
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      info@dcberne.com

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      DCB Newsletter #1/25: INSIDE DCB – Interview with Lisa Koch

      by | Feb 11, 2025 | Linkedin

      Back to News

      DCB Newsletter #1/25: INSIDE DCB – Interview with Lisa Koch

      Dear Community,

      We are thrilled to present you with the next episode of our series “INSIDE DCB” – this time, with an interview with Lisa Koch. Enjoy the read!

      Since April 2024, Lisa Koch has been an assistant professor at the University and University Hospital of Bern as well as the DCB (Diabetes Center Berne). Using artificial intelligence, she and her team are dedicated to developing demonstrably safe, reliable, and effective data-driven tools to shape the future of diabetes care.

      Prof Dr Koch, what is your research about?

      My research is concerned with the development of artificial intelligence (AI) methods for medicine. I am particularly interested in how artificial intelligence can be used safely and reliably. For example: How can we explain in an understandable way how an AI makes important decisions? How do we ensure that AI performs just as well for real people as it does in studies? And could it be that the AI is penalising certain groups? If so, how can we recognise and improve this?

      What motivates you to work in the field of diabetes?

      Diabetes research offers enormous potential to improve the lives of millions of people. The use of artificial intelligence in diabetes technology makes it possible to develop personalised therapies that respond to individual needs. The field of diabetes is also of scientific interest to me because there are many different applications for artificial intelligence. These range, for example, from blood glucose management using wearable devices (wearables) to the detection of secondary diseases of diabetes, such as the early detection of diabetic retinopathy based on images of the back of the eye. There are still many unanswered questions in each of these areas.

      What is your greatest challenge as a professor?

      My daily life as a professor is very diverse. Even the actual research itself involves a variety of different tasks: I continuously develop my short-, medium- and long-term research vision, supervise students, read and write a lot, and exchange ideas in my international environment. This also includes responsibilities in academic self-governance and teaching. In addition, there are countless other exciting tasks that are important to me: I help organise scientific conferences and am regularly invited as an expert to review the work of other professionals (peer review), participate in panel discussions, or give lectures. I would say my biggest challenge is prioritising and managing my limited time. Fortunately, since I have a great deal of independence in organising my time, the responsibility lies entirely with me: I need to learn to occasionally say no to exciting opportunities.

      What goals do you aim to achieve with your team and your lab, the MLM-Lab (Machine Learning in Medicine Lab)?

      I have several goals that I would like to pursue with my team. On one hand, my core research objectives are close to my heart: I aim to make artificial intelligence effective and safe for use in medicine. Ultimately, I want to contribute to improved patient care, both generally and specifically for people with diabetes. It’s important to me that, within this domain, my team and I work on projects that we find personally exciting and that we are methodologically passionate about. Apart from the research content itself, I aim to promote skills in my team and thus offer my team members good prospects for a successful career in research or the private sector. For example, we invest a lot of time in scientific communication. It is also very important to me to create a healthy and positive environment in which all team members can also prioritise their private lives.

      What projects are you currently researching and how can they make life easier for people with diabetes?

      We are working on various methods to automatically analyse medical images and data from wearable devices such as blood glucose meters and fitness trackers. For example, we use machine learning to tailor physiological glucose models to individuals, and investigate how we can recognise and correct inaccuracies in AI models. These technologies are designed to support people with diabetes by enabling safe, personalised treatment.

      How do you work with the DCB? 

      Our offices are located at the DCB, which leads to regular dialogue. In particular, my team benefits from the interdisciplinary research environment that the DCB and the University of Bern have created with their endowed professorships. The DCB also offers us active access to the network in the field of diabetes technology.

      Where do you hope your field of research will be in five to ten years’ time? What is your vision? 

      Artificial intelligence will have a fundamental impact on many areas of our society in the coming years. My vision is that this change in medicine will be positive. With my research, I want to provide trustworthy technologies to facilitate this.

      About Lisa Koch

      Lisa Koch is an assistant professor and research group leader at the University and Inselspital Bern. Together with her team at the DCB, she researches and develops trustworthy diabetes care supported by artificial intelligence that brings real benefits to patients and the medical profession.

      After completing a bachelor’s degree in electrical engineering (BSc) and a master’s degree in biomedical engineering (MSc) at ETH Zurich, Lisa Koch completed a PhD in machine learning for medical image analysis at Imperial College London. After a postdoctoral position at ETH Zurich, she joined the Swiss wearable medical device start-up Ava, where she eventually became head of the data science team. In this position, she recognised the need for demonstrably secure machine learning in healthcare. In 2021, she returned to academic research to conduct research on this topic as a group leader for machine learning in medical diagnostics in the Berens Lab at the Hertie Institute for AI in Brain Research at the University of Tübingen, Germany.

      Thanks so much for reading and we’ll provide you with the next episode of this series soon!

      This edition was authored by Sunjoy Mathieu , Communication Manager at DCB, and originally published on https://www.d-journal.ch in German and French.

      This post was previously published in Linkedin. Click here to see the original publication.

      More recent news

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      But could the same data tell us much more about a person's health than we currently realise?

      A new study published in npj Digital Medicine suggests that looking beyond individual glucose readings could reveal patterns associated with diabetes-related health risks. The findings point towards an interesting possibility: CGM technology may one day help us understand glucose levels, and also identify individual risk profiles and support more personalised diabetes care.

      What did the researchers discover?

      The researchers analysed CGM data from 90,757 participants in the iCanNet cohort, including people with normoglycemia, prediabetes, type 1 diabetes and type 2 diabetes. Rather than looking at individual glucose values alone, they examined 48 different CGM-derived metrics, including glucose variability and patterns over time. Their analysis revealed distinct glucose patterns across the different groups. Combining several metrics also improved the researchers' ability to distinguish between them.

      Particularly interesting were the findings among people living with type 2 diabetes.

      The researchers identified four distinct glycemic phenotypes – different patterns of glucose levels and fluctuations – associated with varying risks of diabetes-related complications.

      For example:

      • Sustained high glucose levels were associated with a 59% higher relative risk of acute complications.
      • High glucose variability was associated with a 75% higher relative risk of acute complications and a 41% higher relative risk of chronic complications.

      The researchers also developed a Digital Glycemic Risk Score (DGRS), combining multiple CGM metrics to identify people with potentially elevated risks.

      Among participants with type 2 diabetes who had already achieved a Time in Range of at least 70%, the model reclassified 25% into a high-risk category for acute complications and 22.3% into a high-risk category for chronic complications. These were classifications generated by the model, not confirmed predictions of complications occurring in these individuals.

      The findings suggest that combining different glucose patterns may reveal information about health risks that commonly used CGM metrics alone do not fully capture.

      From CGM data to new insights. The study analysed 48 CGM-derived metrics from 90,757 participants and identified distinct glucose patterns across the diabetes spectrum. Among people with type 2 diabetes, four glycemic phenotypes were associated with

      From CGM data to new insights. The study analysed 48 CGM-derived metrics from 90,757 participants and identified distinct glucose patterns across the diabetes spectrum. Among people with type 2 diabetes, four glycemic phenotypes were associated with

      What could this mean for diabetes care?

      Today, CGM systems primarily help people understand what is happening with their glucose levels and respond accordingly. But the enormous amount of data these devices generate may hold additional information that we are only beginning to explore.

      By analysing glucose patterns over time, researchers may be able to develop digital biomarkers – measurable indicators derived from digital health data – that could help characterise individual metabolic profiles and identify potential health risks.

      In the future, this could support healthcare professionals in recognising people who may benefit from closer monitoring or more personalised treatment approaches.

      The Nordic region remains the global hotspot for childhood‑onset T1D. Finland has historically reported the highest incidence worldwide – around 60 per 100,000 children per year – although this rate appears to have stabilised or slightly declined since the early 2000s. Sweden, Norway and Denmark follow closely, and the IDF Diabetes Atlas 11 still places Finland, Sweden and Norway among the global top ten for published incidence.

      A 2024 systematic review of European paediatric data (1994–2022) confirmed that the Nordic countries retain the highest absolute incidence in Europe, even as their rate of increase has slowed relative to the rapid rise in previously low‑incidence Central and Eastern European countries. A Swedish nationwide cohort study of 2.9 million children found that the heritability of childhood‑onset T1D has remained high and stable at around 0.83 over 30 years, implying that known environmental factors explain less than 10% of the observed rise.

      In France, Santé publique France reports an annual T1D incidence increase of approximately 4%. Paediatric incidence rose from 15.4 to 19.1 per 100,000 person‑years between 2010 and 2015, with an overall rate of 19.5 per 100,000 in 2015–2017 and substantial regional variation. Sardinia (Italy) now reports the world’s highest published paediatric incidence at 76.3 per 100,000 (2022), having overtaken Finland, while the United Kingdom and Ireland also remain in the global top ten.

      Projections to 2050

      Two major modelling efforts suggest the same direction of travel. The GBD 2021 study projects that age‑standardised global type 1 diabetes prevalence will rise by about 23.9% between 2021 and 2050, a smaller relative increase than for type 2 diabetes, but a substantial absolute rise given the lifelong management burden of T1D. The IDF Diabetes Atlas 11 projects that overall adult diabetes prevalence (type 1 and type 2) will grow from 589 million in 2024 to around 853 million by 2050, a 46% absolute increase. While most of this growth is driven by type 2 diabetes, T1D contributes meaningfully through sustained or rising incidence in high‑income countries and improved survival, which increases prevalence even if incidence stabilises.

      For the U.S., a modelling exercise based on SEARCH data suggests that, under a linear‑increase scenario, the number of young people with T1D could nearly triple by 2050, with the steepest relative growth among Hispanic and Black children.

      Why is incidence rising?

      T1D is a polygenic autoimmune disease in which environmental triggers act on a susceptible genetic background. Several, often overlapping, hypotheses are under investigation:

      • Reduced early‑life exposure to common infections (“hygiene hypothesis”), with mixed evidence.
      • Rising childhood adiposity and insulin resistance, which may accelerate the clinical presentation of existing islet autoimmunity (“accelerator hypothesis”).
      • Viral triggers such as enteroviruses and, more recently, SARS‑CoV‑2, where meta‑analyses report a 14–27% increase in paediatric T1D incidence during the pandemic.
      • Dietary and microbiome‑related factors, including early cow’s‑milk exposure, gluten timing and shifts in the infant gut microbiome.
      • Vitamin D status and latitude, endocrine‑disrupting chemicals, pollution and perinatal factors such as higher maternal age, higher birth weight and Caesarean delivery.

      The Swedish cohort study mentioned above is a stark reminder of how much remains unexplained: even with population‑wide data on several environmental exposures, less than 10% of the rise in T1D incidence in Sweden could be attributed to known factors. This underscores the need for long‑term birth cohorts, population‑level islet‑autoantibody screening and mechanistic immunology.

      Implications for the next decade

      Three practical implications stand out for health systems, innovators and the diabetes community:

      • Health systems need to prepare for more people living with type 1 diabetes. Even under conservative assumptions, the number of people living with type 1 diabetes will continue to rise. This has practical consequences for insulin and sensor supply, reimbursement for diabetes technology, specialist care and clinical-trial infrastructure.
      • Early detection is becoming more relevant. Type 1 diabetes can often be detected before symptoms appear, through markers of early autoimmune activity. With the first therapy now approved to delay clinical onset in people at an early disease stage, screening is no longer only a research question. Programmes such as Fr1da in Germany and the D1Ce trial in Italy show how early identification could inform future intervention strategies.
      • We still need to understand why incidence is rising. Many environmental factors are being studied, but no single explanation is sufficient. Until we better understand the drivers behind the increase, true primary prevention will remain out of reach. This makes high-quality registries, long-term cohort studies and coordinated research more important than ever.

      At DCB, we see these trends as both a warning and a call to action.

      The rising incidence of type 1 diabetes will require sustained collaboration between clinicians, researchers, industry, policymakers and crucially people living with diabetes and their families.

      Our work in innovation, clinical research infrastructure and community engagement aims to contribute to that collective effort.

        Selected sources

        GBD 2021 Diabetes Collaborators. Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050. The Lancet, 2023. https://www.thelancet.com/article/S0140-6736(23)01301-6/fulltext

        International Diabetes Federation. IDF Diabetes Atlas, 11th edition. 2025. https://diabetesatlas.org/

        Ogle GD, et al. Global type 1 diabetes prevalence, incidence, and mortality estimates 2025: Results from the IDF Atlas 11 and the T1D Index v3.0. Diabetes Research and Clinical Practice, 2025. https://www.sciencedirect.com/science/article/pii/S0168822725002918

        Stahl-Pehe A, et al. Wave in Pediatric Type 1 Diabetes Incidence After the Emergence of COVID-19: Peak and Trough Patterns in German Youth — DPV Registry. Diabetes Care, 2025. https://diabetesjournals.org/care/article/48/4/e47/157803/

        Buchmann M, et al. Type 1 diabetes incidence curves differ by age for girls and boys between 1996 and 2022: Results from the North Rhine-Westphalia Diabetes Registry. Diabetes Research and Clinical Practice, 2025. https://www.sciencedirect.com/science/article/pii/S0168822725000105

        Nagl K, et al. Ongoing Increase in Incidence of Diabetes in Austrian Children and Adolescents 1989–2021. Pediatric Diabetes, 2023. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12017070/

        Schoenle EJ, et al. Epidemiology of Type I diabetes mellitus in Switzerland: Steep rise in incidence in under-5 year old children in the past decade. Diabetologia, 2001. https://www.researchgate.net/publication/12017578

        Santé publique France. Diabetes — Data. Updated 2024. https://www.santepubliquefrance.fr/en/diabete/data

        Piffaretti C, et al. Trends in childhood type 1 diabetes incidence in France, 2010–2015. Diabetes Research and Clinical Practice, 2018. https://www.sciencedirect.com/science/article/abs/pii/S0168822718304662

        Ruiz-Grao MC, et al. Trends in the Incidence of Type 1 Diabetes in European Children and Adolescents from 1994 to 2022: A Systematic Review and Meta-Analysis. Pediatric Diabetes, 2024. https://onlinelibrary.wiley.com/doi/10.1155/2024/2338922

        Ji J, et al. Stable heritability of type 1 diabetes in a Swedish Nationwide Cohort Study. Nature Communications, 2024. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12174315/

        D’Souza D, et al. Incidence of Diabetes in Children and Adolescents During the COVID-19 Pandemic: A Systematic Review and Meta-Analysis. JAMA Network Open, 2023. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10314307/

        Quinn LM, et al. Environmental Determinants of Type 1 Diabetes: From Association to Proving Causality. Frontiers in Immunology, 2021. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8518604/

        Esposito S, et al. Changes in the Global Epidemiology of Type 1 Diabetes in an Evolving Landscape of Environmental Factors. International Journal of Molecular Sciences, 2023. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10141720/

        Rogers MAM, et al. Fluctuations in the incidence of type 1 diabetes in the United States from 2001 to 2015: a longitudinal study. BMC Medicine, 2017. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5688827/

        Imperatore G, et al. Projections of Type 1 and Type 2 Diabetes Burden in the U.S. Population Aged <20 Years Through 2050. Diabetes Care, 2012. https://pmc.ncbi.nlm.nih.gov/articles/PMC3507562/

        We hope this overview is helpful for your own work in diabetes care, research or advocacy. As always, we are grateful for your interest in and support of our activities and we look forward to continuing this conversation with you in future editions of our “DID YOU KNOW…” series.

        An earlier version of this article was originally published in the private newsletter of our CEO, Derek Brandt here: Diabetes Technology News.

        This post was previously published in Linkedin. Click here to see the original publication.

        DCB Research AG

        Freiburgstrasse 3
        3010 Bern
        Schweiz

        info@dcberne.com

        Legal Notice

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        DCB Newsletter #5/24: INSIDE DCB – Our Milestones in 2024

        by | Dec 19, 2024 | Linkedin

        Back to News

        DCB Newsletter #5/24: INSIDE DCB – Our Milestones in 2024

        Dear DCB community – as we approach the end of this year, we want to take the opportunity to look back on our team’s achievements in 2024 – the seventh year of the existence of DCB.

        A lot has been going on and the list keeps getting longer! But see for yourself:

        Operations

        👥 Inside DCB: We welcomed the fourth professorship, Lisa Koch, to our premises. In two retreats, we refined our vision, defined our goals, and worked on team effectiveness and collaboration.

        ✅ Fully certified: We further developed our Management System according to ISO 9001, defined and integrated clinical processes and passed the SQS recertification audit with flying colours.

        ⛑️ Safety first: Several studies were successfully initiated and conducted at the facility. There were no safety-related incidents.

        Innovation & Business Development

        🌐 Global Impact: Our team evaluated over 300 ideas, proposals, and applications, propelling the global innovation ecosystem forward. We identified and supported several dozen projects and startups, providing them with unparalleled expertise, access to our extensive network, cutting-edge facilities, and crucial funding.

        🏆 DCB Open Innovation Challenge 2024: The DCB Open Innovation Challenge once again emerged as a beacon of success. We are very proud of the number of start-ups this year and of our collaboration with the Diabetes Technology Society from the U.S. We are particularly pleased to have received a total of 100 new and innovative ideas from 40 different countries. Six exceptional projects from USA, Spain, Switzerland, Sweden, and the UK took center stage, culminating in a thrilling Award Night attended by 240 enthusiasts from around the globe.  This year’s winner, IMS, impressed the jury with a revolutionary, particularly small, and minimally invasive device for continuous glucose monitoring (CGM). The world’s first fully integrated single-chip device monitors blood glucose using multiple sensors simultaneously and can also record other physiological parameters such as tissue temperature. It is also able to measure various physiological parameters such as glucose and ketones – all with just one device.

        The other amazing finalists were:

        Genomelink – Yuta Matsuda
        Beep Insights – Christine Brännvall
        Innodose – Sufyan Hussain
        Neuria – Frédérik Plourde
        Amic Health – Jorge Bondia

        🚀Tailored Workshops for Start-ups: Multiple workshops were organised for various start-ups, covering different key topics. They ranged from a few hours virtually to multiple days in person. The workshops addressed everything from clinical insights, data management, project management, communication, to branding. A key point noticed was that investor decks were often too detailed and overloaded with content. Top tip: Investors typically spend only 2.5 to 3 minutes reading them!

        📊DT-report: Together with diabetesschweiz, the Swiss Society for Endocrinology and Diabetology (SGED | SSED), and partners in Germany and Austria, we are conducting a representative survey on the topic of diabetes technology for the DT-Report. About 200 people with diabetes and 100 healthcare professionals have already filled out the questionnaire.

        🎉 DCB project Beyond Diagnostics receives Innosuisse grant: Dr. Maren Schinz has secured an innovation grant from Innosuisse to advance her research on insulin inactivity with Beyond Diagnostics, a DCB spin-off. Congratulations!

        📚Diabetes Training for Pharmacists: The Amavita pharmacies from Galenicare in Switzerland organized 5 half-day workshops focused on everything related to diabetes. In these sessions, we provided a comprehensive training for pharmacists to enhance their knowledge and skills in managing diabetes care.

        👥Conferences: EASD conference, hosted the Innovation Zone in Madrid and brought almost 20 startups who had the chance to present their ideas to the audience. Our booth was well visited, and we were happy with the results.

        Communications

        👋 Growing our community: We’ve reached 4,800 (almost 5000!) followers on LinkedIn and more than 700 followers on Instagram in 2025. We’re so excited our community is ever-growing and appreciate every single one of you, joining us on our journey of making life better for people with diabetes!

        🔍 Visibility boost: DCB hosted the speech of Viola Amherd, President of the Swiss Confederation, on occasion of the 85th anniversary of the oldest Swiss disease awareness day, ‘Tag der Kranken’, on 3 March 2024, which resulted in strong media coverage for DCB.

        💻 Website updates: Our website is now enriched with additional content in various sections and is running even faster than before. We want to thank HYVE for the valued and long-lasting collaboration!

        🗣️ Valuing Lived Experience: People living with diabetes are at the core of all that we do at DCB. We are so grateful for our continually growing community of Lived Experience Experts – currently 18 people in 14 different countries all over the world!

        Clinical & Data

        🔬 Innosuisse: We are recognised by Innosuisse as a Research Partner and submissions in collaboration with industry partners are on-going.

        🩸 Menstrual cycle project: We are about to complete the menstrual cycle project which was conducted in close collaboration with Tidepool. The aim of this research project is to collect data on diabetes management among menstruating people living with type 1 diabetes to evaluate changes in insulin sensitivity across the menstrual cycle. We have received data from 435 cycles of 76 women and look forward to presenting the first results soon. 

        🔎 Clinical Study FibreGum on-going: Under the supervision of our assistant professor Maria Luisa Balmer, the FibreGum study is running at the Children Clinic Berne, assessing the effect of an additive in a chewing gum on body weight loss. 64 adolescents and young adults have been included so far. The participation of the Eastern Swiss children’s hospital in St. Gallen is under evaluation at the competent Ethics Committee. The study will be conducted also there starting 1st quarter 2025. An equivalent study in adults -FaibaGo – is under preparation.

        🔎 Clinical Study GlucoSet: The DCB is contributing to the first-in-human study where a novel intravascular glucose sensor in an intensive care unit setting is tested. Results are intended to supplement data obtained from laboratory studies, animal studies and an earlier first-in-man study.

        🔎 Clinical Study AID-JUNCT will soon recruit participants: the supervision of our assistant professor José Garcia Tirado, the AID-JUNCT will be conducted at the Cantonal Hospital in Olten. It will evaluate the efficacy of tirzepatide (Mounjaro®) as adjunctive therapy to automated insulin delivery on percent time in range . 42 T1D adults will be included.

        📥 Clinical Study SPN-005 successfully conducted: At the University Clinic for Diabetology, Endocrinology, Nutritional Medicine & Metabolism (UDEM) in Berne, the study aims to assess the feasibility of a noninvasive medical device, developed by the company Liom, to detect, and track glucose changes transcutaneously in defined and dynamic states of glycemia. 20 T1D adults were included over 6 months and the study results will be available at the beginning of 2025.

        🤝 First participant recruited for the Evo Study (Study Sponsor: BODYSense SAS): The study will examine associations between glycaemic states such as stable (hypoglycaemia, euglycemia, and hyperglycaemia), increasing, and decreasing blood glucose, and the VOC (volatile organic compound) spectra in the breath measured with a Gas Chromatograph-Ion Mobility Spectrometer (GC-IMS) device as a reference and a portable device prototype to measure VOCs. The device prototype has been developed by BODYSense with the purpose to allow blood glucose monitoring through non-invasive breath analyses.

        👥 Study conduct network extension: The Eastern Swiss children’s hospital in St. Gallen, the Cantonal Hospital in Olten and the Division of Endocrinology and Diabetology of the Medical University of Graz (Austria) are now qualified as study sites and are allowed to conduct studies under our support / responsibility.

        📖 Contribution to publications: Using data from our previous VAARA study, the publication led by Prof. Lilian Witthauer titled “Detection of Hypoglycaemia in Type 1 Diabetes Through Breath Volatile Organic Compound Profiling Using Gas Chromatography-Ion Mobility Spectrometry” was featured in Diabetes, Obesity and Metabolism.

        A preprint authored by Liom titled “A Prospective Pilot Study Demonstrating Non-Invasive Calibration-Free Glucose Measurement” was published on medRxiv.

        DCB also featured numerous abstracts presented at major diabetes conferences, including EASD and ATTD.

        📊 Electronic Data Capture System: REDCap® has undergone significant upgrades and enhancements through the integration of external modules and services. These improvements ensure the platform continues to provide a secure environment, uphold strict quality control standards, and offer user-friendly interfaces. Additionally, REDCap® promotes participant engagement and supports efficient operational management.

        Thank you, dear readers for your loyalty and interest throughout the year! We will continue to provide you with new series, inside stories and regular updates in 2025. All the best and happy holidays!🎄 🎆

        This post was previously published in Linkedin. Click here to see the original publication.

        Sign up for our newsletter
        to receive the latest news.

        * Pflichtfelder
        DSGVO *

        But could the same data tell us much more about a person's health than we currently realise?

        A new study published in npj Digital Medicine suggests that looking beyond individual glucose readings could reveal patterns associated with diabetes-related health risks. The findings point towards an interesting possibility: CGM technology may one day help us understand glucose levels, and also identify individual risk profiles and support more personalised diabetes care.

        What did the researchers discover?

        The researchers analysed CGM data from 90,757 participants in the iCanNet cohort, including people with normoglycemia, prediabetes, type 1 diabetes and type 2 diabetes. Rather than looking at individual glucose values alone, they examined 48 different CGM-derived metrics, including glucose variability and patterns over time. Their analysis revealed distinct glucose patterns across the different groups. Combining several metrics also improved the researchers' ability to distinguish between them.

        Particularly interesting were the findings among people living with type 2 diabetes.

        The researchers identified four distinct glycemic phenotypes – different patterns of glucose levels and fluctuations – associated with varying risks of diabetes-related complications.

        For example:

        • Sustained high glucose levels were associated with a 59% higher relative risk of acute complications.
        • High glucose variability was associated with a 75% higher relative risk of acute complications and a 41% higher relative risk of chronic complications.

        The researchers also developed a Digital Glycemic Risk Score (DGRS), combining multiple CGM metrics to identify people with potentially elevated risks.

        Among participants with type 2 diabetes who had already achieved a Time in Range of at least 70%, the model reclassified 25% into a high-risk category for acute complications and 22.3% into a high-risk category for chronic complications. These were classifications generated by the model, not confirmed predictions of complications occurring in these individuals.

        The findings suggest that combining different glucose patterns may reveal information about health risks that commonly used CGM metrics alone do not fully capture.

        From CGM data to new insights. The study analysed 48 CGM-derived metrics from 90,757 participants and identified distinct glucose patterns across the diabetes spectrum. Among people with type 2 diabetes, four glycemic phenotypes were associated with

        From CGM data to new insights. The study analysed 48 CGM-derived metrics from 90,757 participants and identified distinct glucose patterns across the diabetes spectrum. Among people with type 2 diabetes, four glycemic phenotypes were associated with

        What could this mean for diabetes care?

        Today, CGM systems primarily help people understand what is happening with their glucose levels and respond accordingly. But the enormous amount of data these devices generate may hold additional information that we are only beginning to explore.

        By analysing glucose patterns over time, researchers may be able to develop digital biomarkers – measurable indicators derived from digital health data – that could help characterise individual metabolic profiles and identify potential health risks.

        In the future, this could support healthcare professionals in recognising people who may benefit from closer monitoring or more personalised treatment approaches.

        The Nordic region remains the global hotspot for childhood‑onset T1D. Finland has historically reported the highest incidence worldwide – around 60 per 100,000 children per year – although this rate appears to have stabilised or slightly declined since the early 2000s. Sweden, Norway and Denmark follow closely, and the IDF Diabetes Atlas 11 still places Finland, Sweden and Norway among the global top ten for published incidence.

        A 2024 systematic review of European paediatric data (1994–2022) confirmed that the Nordic countries retain the highest absolute incidence in Europe, even as their rate of increase has slowed relative to the rapid rise in previously low‑incidence Central and Eastern European countries. A Swedish nationwide cohort study of 2.9 million children found that the heritability of childhood‑onset T1D has remained high and stable at around 0.83 over 30 years, implying that known environmental factors explain less than 10% of the observed rise.

        In France, Santé publique France reports an annual T1D incidence increase of approximately 4%. Paediatric incidence rose from 15.4 to 19.1 per 100,000 person‑years between 2010 and 2015, with an overall rate of 19.5 per 100,000 in 2015–2017 and substantial regional variation. Sardinia (Italy) now reports the world’s highest published paediatric incidence at 76.3 per 100,000 (2022), having overtaken Finland, while the United Kingdom and Ireland also remain in the global top ten.

        Projections to 2050

        Two major modelling efforts suggest the same direction of travel. The GBD 2021 study projects that age‑standardised global type 1 diabetes prevalence will rise by about 23.9% between 2021 and 2050, a smaller relative increase than for type 2 diabetes, but a substantial absolute rise given the lifelong management burden of T1D. The IDF Diabetes Atlas 11 projects that overall adult diabetes prevalence (type 1 and type 2) will grow from 589 million in 2024 to around 853 million by 2050, a 46% absolute increase. While most of this growth is driven by type 2 diabetes, T1D contributes meaningfully through sustained or rising incidence in high‑income countries and improved survival, which increases prevalence even if incidence stabilises.

        For the U.S., a modelling exercise based on SEARCH data suggests that, under a linear‑increase scenario, the number of young people with T1D could nearly triple by 2050, with the steepest relative growth among Hispanic and Black children.

        Why is incidence rising?

        T1D is a polygenic autoimmune disease in which environmental triggers act on a susceptible genetic background. Several, often overlapping, hypotheses are under investigation:

        • Reduced early‑life exposure to common infections (“hygiene hypothesis”), with mixed evidence.
        • Rising childhood adiposity and insulin resistance, which may accelerate the clinical presentation of existing islet autoimmunity (“accelerator hypothesis”).
        • Viral triggers such as enteroviruses and, more recently, SARS‑CoV‑2, where meta‑analyses report a 14–27% increase in paediatric T1D incidence during the pandemic.
        • Dietary and microbiome‑related factors, including early cow’s‑milk exposure, gluten timing and shifts in the infant gut microbiome.
        • Vitamin D status and latitude, endocrine‑disrupting chemicals, pollution and perinatal factors such as higher maternal age, higher birth weight and Caesarean delivery.

        The Swedish cohort study mentioned above is a stark reminder of how much remains unexplained: even with population‑wide data on several environmental exposures, less than 10% of the rise in T1D incidence in Sweden could be attributed to known factors. This underscores the need for long‑term birth cohorts, population‑level islet‑autoantibody screening and mechanistic immunology.

        Implications for the next decade

        Three practical implications stand out for health systems, innovators and the diabetes community:

        • Health systems need to prepare for more people living with type 1 diabetes. Even under conservative assumptions, the number of people living with type 1 diabetes will continue to rise. This has practical consequences for insulin and sensor supply, reimbursement for diabetes technology, specialist care and clinical-trial infrastructure.
        • Early detection is becoming more relevant. Type 1 diabetes can often be detected before symptoms appear, through markers of early autoimmune activity. With the first therapy now approved to delay clinical onset in people at an early disease stage, screening is no longer only a research question. Programmes such as Fr1da in Germany and the D1Ce trial in Italy show how early identification could inform future intervention strategies.
        • We still need to understand why incidence is rising. Many environmental factors are being studied, but no single explanation is sufficient. Until we better understand the drivers behind the increase, true primary prevention will remain out of reach. This makes high-quality registries, long-term cohort studies and coordinated research more important than ever.

        At DCB, we see these trends as both a warning and a call to action.

        The rising incidence of type 1 diabetes will require sustained collaboration between clinicians, researchers, industry, policymakers and crucially people living with diabetes and their families.

        Our work in innovation, clinical research infrastructure and community engagement aims to contribute to that collective effort.

          Selected sources

          GBD 2021 Diabetes Collaborators. Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050. The Lancet, 2023. https://www.thelancet.com/article/S0140-6736(23)01301-6/fulltext

          International Diabetes Federation. IDF Diabetes Atlas, 11th edition. 2025. https://diabetesatlas.org/

          Ogle GD, et al. Global type 1 diabetes prevalence, incidence, and mortality estimates 2025: Results from the IDF Atlas 11 and the T1D Index v3.0. Diabetes Research and Clinical Practice, 2025. https://www.sciencedirect.com/science/article/pii/S0168822725002918

          Stahl-Pehe A, et al. Wave in Pediatric Type 1 Diabetes Incidence After the Emergence of COVID-19: Peak and Trough Patterns in German Youth — DPV Registry. Diabetes Care, 2025. https://diabetesjournals.org/care/article/48/4/e47/157803/

          Buchmann M, et al. Type 1 diabetes incidence curves differ by age for girls and boys between 1996 and 2022: Results from the North Rhine-Westphalia Diabetes Registry. Diabetes Research and Clinical Practice, 2025. https://www.sciencedirect.com/science/article/pii/S0168822725000105

          Nagl K, et al. Ongoing Increase in Incidence of Diabetes in Austrian Children and Adolescents 1989–2021. Pediatric Diabetes, 2023. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12017070/

          Schoenle EJ, et al. Epidemiology of Type I diabetes mellitus in Switzerland: Steep rise in incidence in under-5 year old children in the past decade. Diabetologia, 2001. https://www.researchgate.net/publication/12017578

          Santé publique France. Diabetes — Data. Updated 2024. https://www.santepubliquefrance.fr/en/diabete/data

          Piffaretti C, et al. Trends in childhood type 1 diabetes incidence in France, 2010–2015. Diabetes Research and Clinical Practice, 2018. https://www.sciencedirect.com/science/article/abs/pii/S0168822718304662

          Ruiz-Grao MC, et al. Trends in the Incidence of Type 1 Diabetes in European Children and Adolescents from 1994 to 2022: A Systematic Review and Meta-Analysis. Pediatric Diabetes, 2024. https://onlinelibrary.wiley.com/doi/10.1155/2024/2338922

          Ji J, et al. Stable heritability of type 1 diabetes in a Swedish Nationwide Cohort Study. Nature Communications, 2024. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12174315/

          D’Souza D, et al. Incidence of Diabetes in Children and Adolescents During the COVID-19 Pandemic: A Systematic Review and Meta-Analysis. JAMA Network Open, 2023. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10314307/

          Quinn LM, et al. Environmental Determinants of Type 1 Diabetes: From Association to Proving Causality. Frontiers in Immunology, 2021. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8518604/

          Esposito S, et al. Changes in the Global Epidemiology of Type 1 Diabetes in an Evolving Landscape of Environmental Factors. International Journal of Molecular Sciences, 2023. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10141720/

          Rogers MAM, et al. Fluctuations in the incidence of type 1 diabetes in the United States from 2001 to 2015: a longitudinal study. BMC Medicine, 2017. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5688827/

          Imperatore G, et al. Projections of Type 1 and Type 2 Diabetes Burden in the U.S. Population Aged <20 Years Through 2050. Diabetes Care, 2012. https://pmc.ncbi.nlm.nih.gov/articles/PMC3507562/

          We hope this overview is helpful for your own work in diabetes care, research or advocacy. As always, we are grateful for your interest in and support of our activities and we look forward to continuing this conversation with you in future editions of our “DID YOU KNOW…” series.

          An earlier version of this article was originally published in the private newsletter of our CEO, Derek Brandt here: Diabetes Technology News.

          This post was previously published in Linkedin. Click here to see the original publication.

          DCB Research AG

          Freiburgstrasse 3
          3010 Bern
          Schweiz

          info@dcberne.com

          Legal Notice

          Privacy Policy

          DCB Newsletter #4/24: IT’S A WRAP! A Recap of the 2024 DCB Open Innovation Challenge

          by | Nov 21, 2024 | Linkedin

          Back to News

          DCB Newsletter #4/24: IT’S A WRAP! A Recap of the 2024 DCB Open Innovation Challenge

          Dear community – long time no see! At the height of Diabetes Awareness Month Novemver, we are happy to now provide you with a new episode of our newsletter series and give you some insights into this year’s  DCB Innovation Challenge.

          Enjoy the read!

          The DCB Open Innovation Challenge

          The DCB Open Innovation Challenge is an opportunity for anyone with an innovative solution to change diabetes management. It only takes an idea to participate for anyone – whether a healthcare professional, a start-up, a person living with diabetes or others. Those who share their ideas receive sparring and feedback from experts. It all culminates in the DCB Start-Up night, where we award the winners for both categories. The DCB Open Innovation Challenge award is one of the world’s most generous and renowned diabetes-related awards.

          This year, incredible 100 ideas were submitted to the challenge from all over the world, and we rang in a new collaboration with the Diabetes Technology Society from the US. Because of this, we had 2 finalists from the US and 4 finalists from the rest of the world. They pitched their projects live on stage in Bern in front of 300 guests and an international jury, who then chose the winner of each category.

          But how exactly did we get there and what did the DCB Open Innovation Challenge look like?

          Read all about the entire journey below!

          Our Top 20

          Before we were able to select our top six finalists to travel to our bootcamp and the much-anticipated DCB Start-Up Night, we selected our Top 20.

          The Top 20 of received mentoring from chosen experts of our network on various topics, ranging from co-creation experts to regulatory specialists. Additionally, we hosted a Pitch Training session before their big Pitch Day, where they could network and learn from our experts as well as their peers about how to improve their pitch and convince our jury.

          The Innovation Bootcamp – 4 intense days of learning and networking in the Swiss Alps

          To get ready for the DCB Start-Up Night with pitches and winners’ celebrations, we took all six finalists to a bootcamp in the Swiss Alps. Over the course of four days, we spent our time with a stunning view and mentoring, pitch training, 1:1 coaching, and of course, with many fruitful discussions and lots of networking.

          But see for yourself! Our team on-site documented their journey and experience in a short video.

          The Winners of 2024

          On October 3, this year’s DCB Start-Up Night and Award Ceremony of the Open Innovation Challenge 2023 took place. After an exciting evening of pitching, an international jury chose the winners of the fourth edition of the Open Innovation Challenge. The first place and with it 100k CHF in funding and in-kind support went to Muhammad Mujeeb-U-Rahman and his company IMS, for their product CGM+!

          IMS (Integrated Medical Sensors) impressed the expert jury with a revolutionary, particularly small, and minimally invasive device for continuous glucose monitoring (CGM). The world’s first fully integrated single-chip device monitors blood glucose using multiple sensors simultaneously and can also record other physiological parameters such as tissue temperature. It is also able to measure various physiological parameters such as glucose and ketones – all with just one device.

          “The DCB team did an outstanding job. This achievement will provide us with the resources and network needed to gain traction towards our goals, making a significant impact on our company’s journey”,

          shares Muhammad Mujeeb-U-Rahman, CEO of Integrated Medical Sensors.

          The start-up Beep Insights was honoured with the Public Choice Award by the audience thanks to its Beep app, which estimates insulin resistance using lactate measurement and other data such as activity and nutrition.

           

          We thank all finalists for bringing their innovative ideas to our big stage – AMIC Health, Innodose, Genomelink and Neuria also did an amazing job and we are excitied to continue working with all of them. Diabetes technology thrives on innovation, and we are very grateful to be working alongside brilliant innovators to make life better for people with diabetes.

          “We are very proud of the number of start-ups this year and of our collaboration with the Diabetes Technology Society from the US. We are particularly pleased to have received a total of 100 new and innovative ideas from start-ups. Together with the top six start-ups, we look forward to working on our shared mission to improve the lives of people with diabetes,”

          says Ema Grabenweger, Innovation Manager DCB and responsible for the Open Innovation Challenge 2024.

           

          Sad you missed out and couldn’t be in Bern for the big night? No need to worry! Watch our highlight video now to get a glimpse behind the scenes:

          “The DCB Open Innovation Challenge is our annual highlight where we promote the best start-ups in diabetes technology. With a wide range of services from DCB and sustainable networks, we look forward to continuing our collaboration with all finalists and achieving great things together.”

          concludes Hanne Ballhausen, Project Manager Innovation and organiser of the DCB Bootcamp.

          It’s been a wonderful few months with the cohort of this year’s DCB Open Innovation Challenge and we are already eagerly awaiting next year’s! 

          Now it’s your turn – it’s never too early to start brainstorming ideas for the next challenge. We’re excited to see what you come up with fort he Innovation Challenge 2025!

          Thanks so much for reading and we’ll provide you with the next episode of this series soon!

          This post was previously published in Linkedin. Click here to see the original publication.

          Sign up for our newsletter
          to receive the latest news.

          * Pflichtfelder
          DSGVO *

          But could the same data tell us much more about a person's health than we currently realise?

          A new study published in npj Digital Medicine suggests that looking beyond individual glucose readings could reveal patterns associated with diabetes-related health risks. The findings point towards an interesting possibility: CGM technology may one day help us understand glucose levels, and also identify individual risk profiles and support more personalised diabetes care.

          What did the researchers discover?

          The researchers analysed CGM data from 90,757 participants in the iCanNet cohort, including people with normoglycemia, prediabetes, type 1 diabetes and type 2 diabetes. Rather than looking at individual glucose values alone, they examined 48 different CGM-derived metrics, including glucose variability and patterns over time. Their analysis revealed distinct glucose patterns across the different groups. Combining several metrics also improved the researchers' ability to distinguish between them.

          Particularly interesting were the findings among people living with type 2 diabetes.

          The researchers identified four distinct glycemic phenotypes – different patterns of glucose levels and fluctuations – associated with varying risks of diabetes-related complications.

          For example:

          • Sustained high glucose levels were associated with a 59% higher relative risk of acute complications.
          • High glucose variability was associated with a 75% higher relative risk of acute complications and a 41% higher relative risk of chronic complications.

          The researchers also developed a Digital Glycemic Risk Score (DGRS), combining multiple CGM metrics to identify people with potentially elevated risks.

          Among participants with type 2 diabetes who had already achieved a Time in Range of at least 70%, the model reclassified 25% into a high-risk category for acute complications and 22.3% into a high-risk category for chronic complications. These were classifications generated by the model, not confirmed predictions of complications occurring in these individuals.

          The findings suggest that combining different glucose patterns may reveal information about health risks that commonly used CGM metrics alone do not fully capture.

          From CGM data to new insights. The study analysed 48 CGM-derived metrics from 90,757 participants and identified distinct glucose patterns across the diabetes spectrum. Among people with type 2 diabetes, four glycemic phenotypes were associated with

          From CGM data to new insights. The study analysed 48 CGM-derived metrics from 90,757 participants and identified distinct glucose patterns across the diabetes spectrum. Among people with type 2 diabetes, four glycemic phenotypes were associated with

          What could this mean for diabetes care?

          Today, CGM systems primarily help people understand what is happening with their glucose levels and respond accordingly. But the enormous amount of data these devices generate may hold additional information that we are only beginning to explore.

          By analysing glucose patterns over time, researchers may be able to develop digital biomarkers – measurable indicators derived from digital health data – that could help characterise individual metabolic profiles and identify potential health risks.

          In the future, this could support healthcare professionals in recognising people who may benefit from closer monitoring or more personalised treatment approaches.

          The Nordic region remains the global hotspot for childhood‑onset T1D. Finland has historically reported the highest incidence worldwide – around 60 per 100,000 children per year – although this rate appears to have stabilised or slightly declined since the early 2000s. Sweden, Norway and Denmark follow closely, and the IDF Diabetes Atlas 11 still places Finland, Sweden and Norway among the global top ten for published incidence.

          A 2024 systematic review of European paediatric data (1994–2022) confirmed that the Nordic countries retain the highest absolute incidence in Europe, even as their rate of increase has slowed relative to the rapid rise in previously low‑incidence Central and Eastern European countries. A Swedish nationwide cohort study of 2.9 million children found that the heritability of childhood‑onset T1D has remained high and stable at around 0.83 over 30 years, implying that known environmental factors explain less than 10% of the observed rise.

          In France, Santé publique France reports an annual T1D incidence increase of approximately 4%. Paediatric incidence rose from 15.4 to 19.1 per 100,000 person‑years between 2010 and 2015, with an overall rate of 19.5 per 100,000 in 2015–2017 and substantial regional variation. Sardinia (Italy) now reports the world’s highest published paediatric incidence at 76.3 per 100,000 (2022), having overtaken Finland, while the United Kingdom and Ireland also remain in the global top ten.

          Projections to 2050

          Two major modelling efforts suggest the same direction of travel. The GBD 2021 study projects that age‑standardised global type 1 diabetes prevalence will rise by about 23.9% between 2021 and 2050, a smaller relative increase than for type 2 diabetes, but a substantial absolute rise given the lifelong management burden of T1D. The IDF Diabetes Atlas 11 projects that overall adult diabetes prevalence (type 1 and type 2) will grow from 589 million in 2024 to around 853 million by 2050, a 46% absolute increase. While most of this growth is driven by type 2 diabetes, T1D contributes meaningfully through sustained or rising incidence in high‑income countries and improved survival, which increases prevalence even if incidence stabilises.

          For the U.S., a modelling exercise based on SEARCH data suggests that, under a linear‑increase scenario, the number of young people with T1D could nearly triple by 2050, with the steepest relative growth among Hispanic and Black children.

          Why is incidence rising?

          T1D is a polygenic autoimmune disease in which environmental triggers act on a susceptible genetic background. Several, often overlapping, hypotheses are under investigation:

          • Reduced early‑life exposure to common infections (“hygiene hypothesis”), with mixed evidence.
          • Rising childhood adiposity and insulin resistance, which may accelerate the clinical presentation of existing islet autoimmunity (“accelerator hypothesis”).
          • Viral triggers such as enteroviruses and, more recently, SARS‑CoV‑2, where meta‑analyses report a 14–27% increase in paediatric T1D incidence during the pandemic.
          • Dietary and microbiome‑related factors, including early cow’s‑milk exposure, gluten timing and shifts in the infant gut microbiome.
          • Vitamin D status and latitude, endocrine‑disrupting chemicals, pollution and perinatal factors such as higher maternal age, higher birth weight and Caesarean delivery.

          The Swedish cohort study mentioned above is a stark reminder of how much remains unexplained: even with population‑wide data on several environmental exposures, less than 10% of the rise in T1D incidence in Sweden could be attributed to known factors. This underscores the need for long‑term birth cohorts, population‑level islet‑autoantibody screening and mechanistic immunology.

          Implications for the next decade

          Three practical implications stand out for health systems, innovators and the diabetes community:

          • Health systems need to prepare for more people living with type 1 diabetes. Even under conservative assumptions, the number of people living with type 1 diabetes will continue to rise. This has practical consequences for insulin and sensor supply, reimbursement for diabetes technology, specialist care and clinical-trial infrastructure.
          • Early detection is becoming more relevant. Type 1 diabetes can often be detected before symptoms appear, through markers of early autoimmune activity. With the first therapy now approved to delay clinical onset in people at an early disease stage, screening is no longer only a research question. Programmes such as Fr1da in Germany and the D1Ce trial in Italy show how early identification could inform future intervention strategies.
          • We still need to understand why incidence is rising. Many environmental factors are being studied, but no single explanation is sufficient. Until we better understand the drivers behind the increase, true primary prevention will remain out of reach. This makes high-quality registries, long-term cohort studies and coordinated research more important than ever.

          At DCB, we see these trends as both a warning and a call to action.

          The rising incidence of type 1 diabetes will require sustained collaboration between clinicians, researchers, industry, policymakers and crucially people living with diabetes and their families.

          Our work in innovation, clinical research infrastructure and community engagement aims to contribute to that collective effort.

            Selected sources

            GBD 2021 Diabetes Collaborators. Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050. The Lancet, 2023. https://www.thelancet.com/article/S0140-6736(23)01301-6/fulltext

            International Diabetes Federation. IDF Diabetes Atlas, 11th edition. 2025. https://diabetesatlas.org/

            Ogle GD, et al. Global type 1 diabetes prevalence, incidence, and mortality estimates 2025: Results from the IDF Atlas 11 and the T1D Index v3.0. Diabetes Research and Clinical Practice, 2025. https://www.sciencedirect.com/science/article/pii/S0168822725002918

            Stahl-Pehe A, et al. Wave in Pediatric Type 1 Diabetes Incidence After the Emergence of COVID-19: Peak and Trough Patterns in German Youth — DPV Registry. Diabetes Care, 2025. https://diabetesjournals.org/care/article/48/4/e47/157803/

            Buchmann M, et al. Type 1 diabetes incidence curves differ by age for girls and boys between 1996 and 2022: Results from the North Rhine-Westphalia Diabetes Registry. Diabetes Research and Clinical Practice, 2025. https://www.sciencedirect.com/science/article/pii/S0168822725000105

            Nagl K, et al. Ongoing Increase in Incidence of Diabetes in Austrian Children and Adolescents 1989–2021. Pediatric Diabetes, 2023. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12017070/

            Schoenle EJ, et al. Epidemiology of Type I diabetes mellitus in Switzerland: Steep rise in incidence in under-5 year old children in the past decade. Diabetologia, 2001. https://www.researchgate.net/publication/12017578

            Santé publique France. Diabetes — Data. Updated 2024. https://www.santepubliquefrance.fr/en/diabete/data

            Piffaretti C, et al. Trends in childhood type 1 diabetes incidence in France, 2010–2015. Diabetes Research and Clinical Practice, 2018. https://www.sciencedirect.com/science/article/abs/pii/S0168822718304662

            Ruiz-Grao MC, et al. Trends in the Incidence of Type 1 Diabetes in European Children and Adolescents from 1994 to 2022: A Systematic Review and Meta-Analysis. Pediatric Diabetes, 2024. https://onlinelibrary.wiley.com/doi/10.1155/2024/2338922

            Ji J, et al. Stable heritability of type 1 diabetes in a Swedish Nationwide Cohort Study. Nature Communications, 2024. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12174315/

            D’Souza D, et al. Incidence of Diabetes in Children and Adolescents During the COVID-19 Pandemic: A Systematic Review and Meta-Analysis. JAMA Network Open, 2023. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10314307/

            Quinn LM, et al. Environmental Determinants of Type 1 Diabetes: From Association to Proving Causality. Frontiers in Immunology, 2021. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8518604/

            Esposito S, et al. Changes in the Global Epidemiology of Type 1 Diabetes in an Evolving Landscape of Environmental Factors. International Journal of Molecular Sciences, 2023. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10141720/

            Rogers MAM, et al. Fluctuations in the incidence of type 1 diabetes in the United States from 2001 to 2015: a longitudinal study. BMC Medicine, 2017. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5688827/

            Imperatore G, et al. Projections of Type 1 and Type 2 Diabetes Burden in the U.S. Population Aged <20 Years Through 2050. Diabetes Care, 2012. https://pmc.ncbi.nlm.nih.gov/articles/PMC3507562/

            We hope this overview is helpful for your own work in diabetes care, research or advocacy. As always, we are grateful for your interest in and support of our activities and we look forward to continuing this conversation with you in future editions of our “DID YOU KNOW…” series.

            An earlier version of this article was originally published in the private newsletter of our CEO, Derek Brandt here: Diabetes Technology News.

            This post was previously published in Linkedin. Click here to see the original publication.

            DCB Research AG

            Freiburgstrasse 3
            3010 Bern
            Schweiz

            info@dcberne.com

            Legal Notice

            Privacy Policy

            DCB Newsletter #3/24: The DCB Open Innovation Challenge is back!

            by | Apr 19, 2024 | Linkedin

            Back to News

            DCB Newsletter #3/24: The DCB Open Innovation Challenge is back!

            Dear community,

            it’s that time of the year again…the DCB Open Innovation Challenge is back – and this time, it’s stronger than ever! Do you have an innovative idea to improve diabetes management with diabetes technology? Then continue reading and learn more about the challenge, important deadlines and what’s in it for you! 🚀

            Can your idea change diabetes management forever?

            Do you have an innovative idea to facilitate change diabetes management forever? Want to see how your idea becomes reality? Do you have valuable insights to help co-create the diabetes technology of tomorrow? Do you have feedback to share with others in the community?

            We are looking for innovators, co-creators, scientists and diabetes supporters from around the world to join our open idea call and be part of an innovative community at the forefront of diabetes technology.

            Now you might be thinking, “That’s great, but what does it have to do with diabetes?”. Most people would perhaps consider diabetes as the opposite of a rare disease when about 537 million people worldwide live with the disease, making it a prevalence of 1 in 10 [3].

            However, most outlets usually only cover the most common type 1, type 2 and gestational diabetes. In reality, there are many other types which are considered rare types, and some of them are results of rare diseases. In this newsletter, we want to draw your attention to some of them, to honor all people living with diabetes – no matter how rare the type!

            What’s in it for you?

            • the chance to win USD 100,000 in funding and in-kind support
            • a chance to participate in our one-week bootcamp with mentoring & co-learning, and a pitch training worth CHF 5,000
            • access to a network of experts from the Diabetes Center Berne, @Diabetes Technology Society, and others
            • a kick-start to your business with first class coaching and training
            • a safe harbour where your idea remains entirely yours

            Everything at a glance – this is the timeline of our 2024 Innovation Challenge!

            Ema Grabenweger, Innovation Manager at DCB and responsible for the challenge, says: “Our challenge is an excellent opportunity for anyone who would like to get expert guidance on their idea in diabetes technology. Through a carefully designed process we provide a compass for innovators on all levels to efficiently & successfully navigate the complex landscape of developing a medical product or solution to improve the lives of PwD. We do that by offering an open innovation platform for exchange with the community, connection to international experts and mentors who can support to shape the ideas, exposure to venture capitalists and experienced start ups, world-class bootcamp training in the beautiful Swiss Alps tailored to innovator needs. Additionally, the top 6 finalists get to present in front of >300 people in Switzerland and have the opportunity to win an award of USD 100,000 in cash and in-kind service. All of that while having fun! Join our community and make your idea dreams come true!”

            Ready to become part of diabetes innovation?

            All you have to do is register on our Innovation Platform at https://innovation.dcberne.com/ and submit your idea! All former participants (excluding the finalists) of the past years can also apply again, given their idea has been developed further since the last participation.

            Idea submission is still open until April 30 – get the word out and apply! You can find more information on https://www.dcberne.com/en/innovation-challenge/

            Whether you are a start-up, a healthcare professional, researcher or an individual, if you have an idea or insight on how to improve the lives of people with diabetes, we want to hear from you! Thank you so much for reading and see you soon for the next edition of the DCB Newsletter!

            This post was previously published in Linkedin. Click here to see the original publication.

            Sign up for our newsletter
            to receive the latest news.

            * Pflichtfelder
            DSGVO *

            But could the same data tell us much more about a person's health than we currently realise?

            A new study published in npj Digital Medicine suggests that looking beyond individual glucose readings could reveal patterns associated with diabetes-related health risks. The findings point towards an interesting possibility: CGM technology may one day help us understand glucose levels, and also identify individual risk profiles and support more personalised diabetes care.

            What did the researchers discover?

            The researchers analysed CGM data from 90,757 participants in the iCanNet cohort, including people with normoglycemia, prediabetes, type 1 diabetes and type 2 diabetes. Rather than looking at individual glucose values alone, they examined 48 different CGM-derived metrics, including glucose variability and patterns over time. Their analysis revealed distinct glucose patterns across the different groups. Combining several metrics also improved the researchers' ability to distinguish between them.

            Particularly interesting were the findings among people living with type 2 diabetes.

            The researchers identified four distinct glycemic phenotypes – different patterns of glucose levels and fluctuations – associated with varying risks of diabetes-related complications.

            For example:

            • Sustained high glucose levels were associated with a 59% higher relative risk of acute complications.
            • High glucose variability was associated with a 75% higher relative risk of acute complications and a 41% higher relative risk of chronic complications.

            The researchers also developed a Digital Glycemic Risk Score (DGRS), combining multiple CGM metrics to identify people with potentially elevated risks.

            Among participants with type 2 diabetes who had already achieved a Time in Range of at least 70%, the model reclassified 25% into a high-risk category for acute complications and 22.3% into a high-risk category for chronic complications. These were classifications generated by the model, not confirmed predictions of complications occurring in these individuals.

            The findings suggest that combining different glucose patterns may reveal information about health risks that commonly used CGM metrics alone do not fully capture.

            From CGM data to new insights. The study analysed 48 CGM-derived metrics from 90,757 participants and identified distinct glucose patterns across the diabetes spectrum. Among people with type 2 diabetes, four glycemic phenotypes were associated with

            From CGM data to new insights. The study analysed 48 CGM-derived metrics from 90,757 participants and identified distinct glucose patterns across the diabetes spectrum. Among people with type 2 diabetes, four glycemic phenotypes were associated with

            What could this mean for diabetes care?

            Today, CGM systems primarily help people understand what is happening with their glucose levels and respond accordingly. But the enormous amount of data these devices generate may hold additional information that we are only beginning to explore.

            By analysing glucose patterns over time, researchers may be able to develop digital biomarkers – measurable indicators derived from digital health data – that could help characterise individual metabolic profiles and identify potential health risks.

            In the future, this could support healthcare professionals in recognising people who may benefit from closer monitoring or more personalised treatment approaches.

            The Nordic region remains the global hotspot for childhood‑onset T1D. Finland has historically reported the highest incidence worldwide – around 60 per 100,000 children per year – although this rate appears to have stabilised or slightly declined since the early 2000s. Sweden, Norway and Denmark follow closely, and the IDF Diabetes Atlas 11 still places Finland, Sweden and Norway among the global top ten for published incidence.

            A 2024 systematic review of European paediatric data (1994–2022) confirmed that the Nordic countries retain the highest absolute incidence in Europe, even as their rate of increase has slowed relative to the rapid rise in previously low‑incidence Central and Eastern European countries. A Swedish nationwide cohort study of 2.9 million children found that the heritability of childhood‑onset T1D has remained high and stable at around 0.83 over 30 years, implying that known environmental factors explain less than 10% of the observed rise.

            In France, Santé publique France reports an annual T1D incidence increase of approximately 4%. Paediatric incidence rose from 15.4 to 19.1 per 100,000 person‑years between 2010 and 2015, with an overall rate of 19.5 per 100,000 in 2015–2017 and substantial regional variation. Sardinia (Italy) now reports the world’s highest published paediatric incidence at 76.3 per 100,000 (2022), having overtaken Finland, while the United Kingdom and Ireland also remain in the global top ten.

            Projections to 2050

            Two major modelling efforts suggest the same direction of travel. The GBD 2021 study projects that age‑standardised global type 1 diabetes prevalence will rise by about 23.9% between 2021 and 2050, a smaller relative increase than for type 2 diabetes, but a substantial absolute rise given the lifelong management burden of T1D. The IDF Diabetes Atlas 11 projects that overall adult diabetes prevalence (type 1 and type 2) will grow from 589 million in 2024 to around 853 million by 2050, a 46% absolute increase. While most of this growth is driven by type 2 diabetes, T1D contributes meaningfully through sustained or rising incidence in high‑income countries and improved survival, which increases prevalence even if incidence stabilises.

            For the U.S., a modelling exercise based on SEARCH data suggests that, under a linear‑increase scenario, the number of young people with T1D could nearly triple by 2050, with the steepest relative growth among Hispanic and Black children.

            Why is incidence rising?

            T1D is a polygenic autoimmune disease in which environmental triggers act on a susceptible genetic background. Several, often overlapping, hypotheses are under investigation:

            • Reduced early‑life exposure to common infections (“hygiene hypothesis”), with mixed evidence.
            • Rising childhood adiposity and insulin resistance, which may accelerate the clinical presentation of existing islet autoimmunity (“accelerator hypothesis”).
            • Viral triggers such as enteroviruses and, more recently, SARS‑CoV‑2, where meta‑analyses report a 14–27% increase in paediatric T1D incidence during the pandemic.
            • Dietary and microbiome‑related factors, including early cow’s‑milk exposure, gluten timing and shifts in the infant gut microbiome.
            • Vitamin D status and latitude, endocrine‑disrupting chemicals, pollution and perinatal factors such as higher maternal age, higher birth weight and Caesarean delivery.

            The Swedish cohort study mentioned above is a stark reminder of how much remains unexplained: even with population‑wide data on several environmental exposures, less than 10% of the rise in T1D incidence in Sweden could be attributed to known factors. This underscores the need for long‑term birth cohorts, population‑level islet‑autoantibody screening and mechanistic immunology.

            Implications for the next decade

            Three practical implications stand out for health systems, innovators and the diabetes community:

            • Health systems need to prepare for more people living with type 1 diabetes. Even under conservative assumptions, the number of people living with type 1 diabetes will continue to rise. This has practical consequences for insulin and sensor supply, reimbursement for diabetes technology, specialist care and clinical-trial infrastructure.
            • Early detection is becoming more relevant. Type 1 diabetes can often be detected before symptoms appear, through markers of early autoimmune activity. With the first therapy now approved to delay clinical onset in people at an early disease stage, screening is no longer only a research question. Programmes such as Fr1da in Germany and the D1Ce trial in Italy show how early identification could inform future intervention strategies.
            • We still need to understand why incidence is rising. Many environmental factors are being studied, but no single explanation is sufficient. Until we better understand the drivers behind the increase, true primary prevention will remain out of reach. This makes high-quality registries, long-term cohort studies and coordinated research more important than ever.

            At DCB, we see these trends as both a warning and a call to action.

            The rising incidence of type 1 diabetes will require sustained collaboration between clinicians, researchers, industry, policymakers and crucially people living with diabetes and their families.

            Our work in innovation, clinical research infrastructure and community engagement aims to contribute to that collective effort.

              Selected sources

              GBD 2021 Diabetes Collaborators. Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050. The Lancet, 2023. https://www.thelancet.com/article/S0140-6736(23)01301-6/fulltext

              International Diabetes Federation. IDF Diabetes Atlas, 11th edition. 2025. https://diabetesatlas.org/

              Ogle GD, et al. Global type 1 diabetes prevalence, incidence, and mortality estimates 2025: Results from the IDF Atlas 11 and the T1D Index v3.0. Diabetes Research and Clinical Practice, 2025. https://www.sciencedirect.com/science/article/pii/S0168822725002918

              Stahl-Pehe A, et al. Wave in Pediatric Type 1 Diabetes Incidence After the Emergence of COVID-19: Peak and Trough Patterns in German Youth — DPV Registry. Diabetes Care, 2025. https://diabetesjournals.org/care/article/48/4/e47/157803/

              Buchmann M, et al. Type 1 diabetes incidence curves differ by age for girls and boys between 1996 and 2022: Results from the North Rhine-Westphalia Diabetes Registry. Diabetes Research and Clinical Practice, 2025. https://www.sciencedirect.com/science/article/pii/S0168822725000105

              Nagl K, et al. Ongoing Increase in Incidence of Diabetes in Austrian Children and Adolescents 1989–2021. Pediatric Diabetes, 2023. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12017070/

              Schoenle EJ, et al. Epidemiology of Type I diabetes mellitus in Switzerland: Steep rise in incidence in under-5 year old children in the past decade. Diabetologia, 2001. https://www.researchgate.net/publication/12017578

              Santé publique France. Diabetes — Data. Updated 2024. https://www.santepubliquefrance.fr/en/diabete/data

              Piffaretti C, et al. Trends in childhood type 1 diabetes incidence in France, 2010–2015. Diabetes Research and Clinical Practice, 2018. https://www.sciencedirect.com/science/article/abs/pii/S0168822718304662

              Ruiz-Grao MC, et al. Trends in the Incidence of Type 1 Diabetes in European Children and Adolescents from 1994 to 2022: A Systematic Review and Meta-Analysis. Pediatric Diabetes, 2024. https://onlinelibrary.wiley.com/doi/10.1155/2024/2338922

              Ji J, et al. Stable heritability of type 1 diabetes in a Swedish Nationwide Cohort Study. Nature Communications, 2024. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12174315/

              D’Souza D, et al. Incidence of Diabetes in Children and Adolescents During the COVID-19 Pandemic: A Systematic Review and Meta-Analysis. JAMA Network Open, 2023. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10314307/

              Quinn LM, et al. Environmental Determinants of Type 1 Diabetes: From Association to Proving Causality. Frontiers in Immunology, 2021. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8518604/

              Esposito S, et al. Changes in the Global Epidemiology of Type 1 Diabetes in an Evolving Landscape of Environmental Factors. International Journal of Molecular Sciences, 2023. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10141720/

              Rogers MAM, et al. Fluctuations in the incidence of type 1 diabetes in the United States from 2001 to 2015: a longitudinal study. BMC Medicine, 2017. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5688827/

              Imperatore G, et al. Projections of Type 1 and Type 2 Diabetes Burden in the U.S. Population Aged <20 Years Through 2050. Diabetes Care, 2012. https://pmc.ncbi.nlm.nih.gov/articles/PMC3507562/

              We hope this overview is helpful for your own work in diabetes care, research or advocacy. As always, we are grateful for your interest in and support of our activities and we look forward to continuing this conversation with you in future editions of our “DID YOU KNOW…” series.

              An earlier version of this article was originally published in the private newsletter of our CEO, Derek Brandt here: Diabetes Technology News.

              This post was previously published in Linkedin. Click here to see the original publication.

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