Our research

From idea to implementation: DCB supports and funds project activities and innovations with partners and researchers that are blazing new trails in the area of diabetes.

Publications

2026

The Need for Medical Device Batteries to Be Designed to Be Removable

Brandt et al.

| DOI: 10.1177/19322968251409200
Abstract
We advocate for policies by the government and practices by manufacturers and users of medical batteries for medical device design to incorporate removable batteries. Implementing new regulations that mandate the removability of batteries from medical devices after usage addresses critical safety, environmental, and biohazard risks and simplifies end-of-life management....

Radar-Based Monitoring for Non-Contact Detection of Nocturnal Hypoglycemia in Diabetes: A Review

Ravaioli et al.

| DOI: 10.1109/JBHI.2026.3663452
Abstract
Radar technology enables contactless sleep monitoring of respiration, heart rate, movement, and sleep staging with over 90% accuracy across 78 studies. These physiological signals change during nocturnal hypoglycemia, positioning radar as a promising indirect detection tool — particularly where wearables are impractical. Clinical validation pairing radar with glucose measurement is now needed.

The dt-Report: A Novel Real-World Data Approach to Understanding the Use and Impact of Diabetes Technology in European Countries

Brandt et al.

| DOI 10.1177/19322968261436830
Abstract
Real-world use of diabetes technologies, such as continuous glucose monitoring, automated insulin delivery, smart pens, and green diabetes approaches, confirms and extends RCT evidence. Survey-based data from Germany, Austria, and Switzerland highlight benefits, dropout reasons, age and type 2–specific issues, and system-level barriers, guiding targeted improvements in diabetes care.

Accuracy of Continuous Glucose Monitoring in Adults With Postbariatric Hypoglycemia After Roux-en-Y Gastric Bypass Surgery Under Stable and Dynamic Glucose Conditions

Lizoain et. al.

| DOI: 10.2337/dc25-2724
Abstract
A study of 70 gastric bypass patients found that Dexcom G6 CGMs are less accurate during rapid post-meal glucose drops – common in this population. With a ~10-minute lag and elevated readings at glucose lows, CGMs should be interpreted cautiously for diagnosis and management.

Attitudes and Barriers Toward Automated Insulin Delivery Systems Among Health Care Providers in Germany: A Network Analysis From the dt-Report

Dominic Ehrmann et. al.

| DOI: 10.1177/19322968261442286
Abstract
This German survey of 594 healthcare providers found that only 11% of type 1 diabetes patients used automated insulin delivery (AID) systems in 2022, despite clear benefits. Key barriers included lack of training materials, body image concerns, and insufficient team education. Most providers anticipated growing educational demands as AID adoption increases.

Diabetes Technology Across the Lifespan of People With Diabetes (Ages 2-88 Years): Insights From the dt-Report on Usage Trends Across all Age Groups

Hermanns et. al.

| DOI: 10.1177/19322968261444045
Abstract
Analyzing 2,056 people with diabetes (ages 2–88) across Germany, Austria, and Switzerland, this real-world study found diabetes technologies are widely used across all age groups. CGM use was consistently high in type 1 diabetes, AID use declined after age 70, yet older adults showed comparable glycemic outcomes and high satisfaction.

Automated Insulin Delivery Systems: Impact on Clinical Practice, Discontinuation, Rejection, and Reasons for Not Managing Well

Timm Roos et. al.

| DOI: 10.1177/19322968261441314
Abstract
This multinational survey of 1,294 healthcare providers and 2,535 people with diabetes across four European countries found high overall satisfaction with AID systems, yet 22% of eligible patients rejected them and 8% discontinued use. Unrealistic expectations, lack of trust, and technical issues were the strongest predictors of poor outcomes.

Metabolic Activity of Insulin and Temperature Exposure in the Real World: Challenges for People Living With Diabetes

Heinemann et. al.

| DOI: 10.1177/19322968261423208
Abstract
replace em with en dash11:18 AMClaude responded: Insulin must stay cold to remain effective, but real-world storage conditions – especially in warmer climates with limited cold chain access – can compromise i…Insulin must stay cold to remain effective, but real-world storage conditions – especially in warmer climates with limited cold chain access – can compromise its biological activity. Despite growing climate and equity concerns, evidence on how everyday storage affects insulin's potency remains scarce. The authors call for holistic, real-world research.

Smart Pens: Expectations Regarding Use and Functionality From Health Care Providers

Mader et. al.

| DOI: 10.1177/19322968261448215
Abstract
Smart pens remain significantly underutilized despite strong HCP support: only 4–15% of type 1 and 3–6% of type 2 patients currently use them, versus indicated rates of 51–73%. Top valued features are missed-dose reminders and bolus suggestions. Main barriers: preference for disposable pens, limited device options, and cost.

Defined Microbiota Modulates Host Metabolome and Skeletal Adaptation to Diet- Induced Obesity

Scalise et. al.

| DOI: 10.1096/fj.202600564RR
Abstract
Gut microbiota shapes skeletal adaptation to diet-induced obesity: gnotobiotic mice colonized with Oligo-MM12 developed increased cortical thickness and trabecular area under high-fat diet, while germ-free mice did not, despite similar weight gain. Distinct metabolomic signatures and reduced β-CTX in colonized mice suggest microbiota-driven metabolic reprogramming suppresses bone resorption, linking gut microbes to the gut–bone axis.

Effects of Menstrual Cycle on Insulin Sensitivity in Type 1 Diabetes: An Observational Study of Individuals Using an Automated Insulin Delivery System

Hossmann et. al.

| DOI: 10.2337/figshare.32732970
Abstract
In menstruating individuals with type 1 diabetes, hormonal fluctuations across the menstrual cycle may alter insulin sensitivity and glycemia, despite advances in automated insulin delivery (AID) systems. This study aimed to characterize glycemic patterns, insulin requirements, and insulin sensitivity across menstrual cycle phases.

2025

Performance of Three Continuous Glucose Monitoring Systems in Adults With Type 1 Diabetes

Eichenlaub et al.

| DOI: 10.1177/19322968251315459
Abstract
The performance of continuous glucose monitoring (CGM) systems is difficult to compare due to different study designs and a lack of head-to-head studies. This study evaluated the performance of FreeStyle Libre 3 (FL3), Dexcom G7 (DG7), and Medtronic Simplera (MSP) against different comparator methods and during clinically relevant glycemic scenarios.

Feasibility of a Glucose Manipulation Procedure for the Standardized Performance Evaluation of Continuous Glucose Monitoring Systems

Link et al.

| DOI: 10.1177/19322968251317526
Abstract
In continuous glucose monitoring (CGM) system performance studies, it is common to implement specific procedures for manipulating the participants’ blood glucose (BG) levels during the collection of comparator BG measurements. Recently, such a procedure was proposed by a group of experts, and this study assessed its ability to produce combinations of BG levels and rates of change (RoCs) with certain characteristics.

A prospective pilot study demonstrating non-invasive calibration-free glucose measurement

Rothenbühler et al.

| DOI: 10.1101/2024.08.17.24312052
Abstract
Glucose is an essential molecule in energy metabolism. Dysregulated glucose metabolism, the defining feature of diabetes, requires active monitoring to prevent significant morbidity and mortality. Current technologies for intermittent and continuous glucose measurement are invasive. Non-invasive glucose measurement would eliminate this barrier towards making glucose monitoring more accessible, extending the benefits from people living with diabetes to prediabetes and the healthy. We developed and investigated a spectroscopy-based system for measuring glucose non-invasively and without per-person calibration.

Toward Detection of Nocturnal Hypoglycemia in People With Diabetes Using Consumer-Grade Smartwatches and a Machine Learning Approach

Mendez et al.

| DOI: 10.1177/19322968251319800
Abstract
Nocturnal hypoglycemia poses significant risks to individuals with insulin-treated diabetes, impacting health and quality of life. Although continuous glucose monitoring (CGM) systems reduce these risks, their poor accuracy at low glucose levels, high cost, and availability limit their use. This study examined physiological biomarkers associated with nocturnal hypoglycemia and evaluated the use of machine learning (ML) to detect hypoglycemia during nighttime sleep using data from consumer-grade smartwatches.

Cost-Effective Quality of Life Improvement While Reducing Health Care Professional Burnout With an AI-Driven Intervention for Personalized Medicine

Kelly et al.

| DOI: 10.1177/19322968241310879
Abstract
Burnout affects >50% of physicians and nurses. Spotlight-AQ is a personalized digital health platform designed to improve routine diabetes visits. We assessed cost-effectiveness, visit length, and association with health care professional (HCP) burnout.

A Comparative Analysis of Glycemic Metrics Derived From Three Continuous Glucose Monitoring Systems

Freckmann et al.

| DOI: 10.2337/dc25-0129
Abstract
This study analyzed the differences in continuous glucose monitoring (CGM)-derived metrics among three current-generation systems and evaluated their impact on therapeutic decision-making.

One Size Does Not Fit All: The Need for Sex-Specific Precision Medicine in Diabetes Technology

Hossmann et al.

| DOI: 10.1177/19322968251340673
Abstract
Incorporating sex-specific factors in diabetes research and treatment is essential for advancing precision medicine. There are critical gaps in understanding and applying sex-related differences. Female-specific diabetes pathophysiology manifests in three major areas: life cycle phases (including puberty, pregnancy, and menopause), lifestyle factors (such as responses to nutrition and physical activity), and insulin pharmacology. These elements significantly affect insulin sensitivity and glycemic control in women, yet are frequently underrepresented or ignored in both research and clinical practice. Greater research and clinical focus across these domains is needed to better understand and address sex-based differences in diabetes. Identifying and filling evidence gaps will support more systematic and effective care.

Rahmenbedingungen der künstlichen Intelligenz in der Diabetologie

Hossmann et al.

| DOI: 10.1007/s11428-025-01356-4
Abstract
Der Einsatz von KI (künstliche Intelligenz) in der Diabetologie ist an komplexe rechtliche, regulatorische und ethische Rahmenbedingungen gebunden. Im europäischen Kontext sind dabei die Medizinprodukte- (MDR) und die KI-Verordnung besonders wichtig, ergänzt durch internationale Normen, nationale Gesetze und Datenschutzvorgaben wie die Datenschutz-Grundverordnung (DSGVO). Für eine verantwortungsvolle Implementierung sind zudem ethische Leitlinien, wissenschaftliche Positionspapiere sowie die Perspektiven der Nutzer:innen zentral.

Discrepancies between current displayed and auto-logged glucose values in FreeStyle Libre 3: Implications for clinical interpretation

Witthauer PhD et al.

| DOI: 10.1111/dom.70140
Abstract
The FreeStyle Libre 3 (FSL3) continuous glucose monitoring (CGM) system provides both auto-logged glucose values (AL) and current displayed glucose values (CUR). These values are often assumed to be interchangeable; however, discrepancies and their clinical relevance remain underexplored.

A digital twin-enhanced decision support system improves time-in-range in type 1 diabetes: a randomized clinical trial

Builes-Montaño et al.

| DOI: 10.1038/s41598-025-23165-x
Abstract
Most individuals with type 1 diabetes (T1D) worldwide continue to be managed with multiple daily injections or sensor-augmented pumps. Decision-support systems (DSSs) have emerged as cost-effective tools to enhance treatment adherence and glucose control. We conducted a randomized, open-label, parallel-group study to evaluate STUDIA, a DSS incorporating a digital twin-enabled simulation-assisted bolus calculator. Twenty-eight participants with T1D used either the simulation-assisted calculator or traditional carbohydrate counting for prandial insulin dosing, with glucose monitored using Freestyle Libre.

Clinical assessment and acceptance criteria for continuous glucose monitoring (CGM) system performance: A proposed guideline by the IFCC Working Group on CGM

Pleus et al.

| DOI: 10.1016/j.cca.2025.120728
Abstract
Continuous glucose monitoring (CGM) systems are essential tools for modern diabetes care. However, the assessment of their analytical performance has been hindered by a lack of standardization. This poses challenges for the field, as the choice of study procedures and evaluation methods can influence the observed performance, further complicating the comparisons between CGM systems and confounding the interpretation and use of CGM metrics as research outcomes. To address this issue, the Working Group on CGM of the International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) has developed a comprehensive guideline for the clinical assessment of CGM systems. The guideline defines requirements for study design and procedures, characteristics of comparator measurements, minimum requirements for accuracy, and performance characterization.

Real-World Experience With Insulin Activity Among People With Type 1 Diabetes: Results of a Multinational Survey

Ballhausen et. al.

| DOI: 10.1177/19322968251403527
Abstract
This multinational survey of people with type 1 diabetes found that 32% struggle with insulin storage during travel and 28% recall heat-related incidents. Furthermore, 10% reported unexplained hyperglycemia despite proper use, often linking variability to stress or medications.

2024

Patientenperspektive auf moderne Diabetestechnologien

Roos et al.

| DOI: 10.1007/s11428-024-01243-4
Abstract
The treatment of diabetes has changed fundamentally, with patients now actively involved in their self-management. Digitalization plays a central role in this, from glucose measurement to automated insulin delivery. These offer not only informative but also motivational benefits for those affected. Online communities, influencers, and apps promote exchange and support diabetes self-management. Social media plays an important role in the exchange of information and experiences. Those affected can share their thoughts, experiences, and emotions, which leads to increased self-efficacy and motivation. The increasing importance of the patient perspective is reflected in studies that consider person-reported outcomes (PROs) such as quality of life and diabetes distress.

The Diabetes Technology Society Error Grid and Trend Accuracy Matrix for Glucose Monitors

Klonoff et al.

| DOI: 10.1177/19322968241275701
Abstract
An error grid compares measured versus reference glucose concentrations to assign clinical risk values to observed errors. Widely used error grids for blood glucose monitors (BGMs) have limited value because they do not also reflect clinical accuracy of continuous glucose monitors (CGMs).

Sex-specific differences in physiological parameters related to SARS-CoV-2 infections among a national cohort (COVI-GAPP study)

Grossmann et al.

| DOI: 10.1371/journal.pone.0292203
Abstract
Considering sex as a biological variable in modern digital health solutions, we investigated sex-specific differences in the trajectory of four physiological parameters across a COVID-19 infection.

Pitfalls and Challenges for Diabetes Technology Start-Ups

Brandt et al.

| DOI: 10.1177/19322968241233606
Abstract
When dealing with start-ups, you keep seeing the same “mistakes,” especially those that do not know how to develop a medical device and document it accordingly. The topic of ISO 13485:2016 and/or FDA 21 CFR Part 820 is often not known; however, it is not that difficult if one understands the logic behind it.

Detection of hypoglycaemia in type 1 diabetes through breath volatile organic compound profiling using gas chromatography-ion mobility spectrometry

Nicolier et al.

| DOI: 10.1111/dom.15944
Abstract
To evaluate the relationship between breath volatile organic compounds (VOCs) and glycaemic states in individuals with type 1 diabetes (T1D), focusing on identifying specific VOCs as biomarkers for hypoglycaemia to offer a non-invasive diabetes-monitoring method.

Comparator Data Characteristics and Testing Procedures for the Clinical Performance Evaluation of Continuous Glucose Monitoring Systems

Eichenlaub et al.

| DOI: 10.1089/dia.2023.0465
Abstract
Comparing the performance of different continuous glucose monitoring (CGM) systems is challenging due to the lack of comprehensive guidelines for clinical study design. In particular, the absence of concise requirements for the distribution of comparator (reference) blood glucose (BG) concentrations and their rate of change (RoC) that are used to evaluate CGM performance, impairs comparability. For this article, several experts in the field of CGM performance testing have collaborated to propose characteristics of the distribution of comparator measurements that should be collected during CGM performance testing.

2023

The Role of Dietary Fibre in Enteral Nutrition in Sepsis Prevention and Therapy: A Narrative Review

Huwiler et al.

| DOI: 10.3390/nu15112489
Abstract
This narrative review summarises the current evidence on the role of dietary fibre in enteral nutrition in the prevention and therapy of sepsis, with a focus on critically ill patients. The aim is to discuss the implications for clinical practice and identify future directions for policy and research.

Real-Time Volatile Metabolomics Analysis of Dendritic Cells

Arnold et al.

| DOI: 10.1021/acs.analchem.3c00516
Abstract
Dendritic cells (DCs) actively sample and present antigen to cells of the adaptive immune system and are thus vital for successful immune control and memory formation. Immune cell metabolism and function are tightly interlinked, and a better understanding of this interaction offers potential to develop immunomodulatory strategies. However, current approaches for assessing the immune cell metabolome are often limited by end-point measurements, may involve laborious sample preparation, and may lack unbiased, temporal resolution of the metabolome.

Untangling the bloody mess of menstrual cycle effects on T1D management: A systematic review

Burlando et al.

Abstract

Changes in Heart Rate, Heart Rate Variability, Breathing Rate, and Skin Temperature throughout Pregnancy and the Impact of Emotions—A Longitudinal Evaluation Using a Sensor Bracelet

Bossung et al.

| DOI: 10.3390/s23146620
Abstract
Basic vital signs change during normal pregnancy as they reflect the adaptation of maternal physiology. Electronic wearables like fitness bracelets have the potential to provide vital signs continuously in the home environment of pregnant women.

Can Wrist-Worn Medical Devices Correctly Identify Ovulation?

Niggli et al.

| DOI: 10.3390/s23249730
Abstract
Hormonal fluctuations across the menstrual cycle lead to multiple changes in physiological parameters such as body temperature, cardiovascular function, respiratory rate and perfusion. Electronic wearables analyzing those parameters might present a convenient alternative to urinary ovulation tests for predicting the fertile window.

A Statistical Approach for Assessing the Compliance of Integrated Continuous Glucose Monitoring Systems with Food and Drug Administration Accuracy Requirements

Stephan et al.

| DOI: 10.1089/dia.2022.0331
Abstract
To assess the compliance of “integrated” continuous glucose monitoring (CGM) systems with U.S. Food and Drug Administration requirements, the calculation of confidence intervals (CIs) on agreement rates (ARs), that is, the percentage of CGM measurements lying within a certain deviation of a comparator method, is stipulated. However, despite the existence of numerous approaches that could yield different results, a specific procedure for calculating CIs is not described anywhere. This report, therefore, proposes a suitable statistical procedure to allow transparency and comparability between CGM systems.

2022

Magnesium sensing via LFA-1 regulates CD8+ T cell effector function

Lötscher et al.

| DOI: 10.1016/j.cell.2021.12.039
Abstract
The relevance of extracellular magnesium in cellular immunity remains largely unknown. Here, we show that the co-stimulatory cell-surface molecule LFA-1 requires magnesium to adopt its active conformation on CD8+ T cells, thereby augmenting calcium flux, signal transduction, metabolic reprogramming, immune synapse formation, and, as a consequence, specific cytotoxicity. Accordingly, magnesium-sufficiency sensed via LFA-1 translated to the superior performance of pathogen- and tumor-specific T cells, enhanced effectiveness of bi-specific T cell engaging antibodies, and improved CAR T cell function.

Epithelial GPR35 protects from Citrobacter rodentium infection by preserving goblet cells and mucosal barrier integrity

Melhem et al.

| DOI: 10.1038/s41385-022-00494-y
Abstract
Goblet cells secrete mucin to create a protective mucus layer against invasive bacterial infection and are therefore essential for maintaining intestinal health. However, the molecular pathways that regulate goblet cell function remain largely unknown. Although GPR35 is highly expressed in colonic epithelial cells, its importance in promoting the epithelial barrier is unclear.

Continuous Glucose Deviation Interval and Variability Analysis (CG-DIVA): A Novel Approach for the Statistical Accuracy Assessment of Continuous Glucose Monitoring Systems

Eichenlaub et al.

| DOI: 10.1177/19322968221134639
Abstract
The accuracy of continuous glucose monitoring (CGM) systems is crucial for the management of glucose levels in individuals with diabetes mellitus. However, the discussion of CGM accuracy is challenged by an abundance of parameters and assessment methods. The aim of this article is to introduce the Continuous Glucose Deviation Interval and Variability Analysis (CG-DIVA), a new approach for a comprehensive characterization of CGM point accuracy which is based on the U.S. Food and Drug Administration requirements for “integrated” CGM systems.

Improving the Bias of Comparator Methods in Analytical Performance Assessments Through Recalibration

Pleus

| DOI: 10.1177/19322968221133107
Abstract
In analytical performance studies, the choice of comparator method plays an important role, as studies have shown that there exist relevant systematic differences (bias) between laboratory analyzers. The feasibility of retrospective recalibration of measurement results through comparison with methods or materials of higher metrological order to minimize bias was therefore assessed.

2021

Sorry, no publications available yet.

2020

Memory CD8+ T Cells Balance Pro- and Anti-inflammatory Activity by Reprogramming Cellular Acetate Handling at Sites of Infection

Balmer et al.

| DOI: 10.1016/j.cmet.2020.07.004
Abstract
Serum acetate increases upon systemic infection. Acutely, assimilation of acetate expands the capacity of memory CD8+ T cells to produce IFN-γ. Whether acetate modulates memory CD8+ T cell metabolism and function during pathogen re-encounter remains unexplored. Here we show that at sites of infection, high acetate concentrations are being reached, yet memory CD8+ T cells shut down the acetate assimilating enzymes ACSS1 and ACSS2.

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