DCB Newsletter #1/24: DID YOU KNOW… that there are Rare Types of Diabetes?
Dear Community,
we are happy to present you with the next episode of our series “DID YOU KNOW” in which we publish interesting facts about life with diabetes that you might not know yet. In this edition, on the special occasion of Rare Disease Day, we want to focus on the rare and lesser known types of diabetes. Enjoy the read!
Today is 29 February – the rarest day of the year, only occurring every 4 years during a leap year. As rare as this day – or most of the times even rarer – are many diseases which qualify as “rare diseases”. Between 7,000 and 8,000 rare diseases have been identified worldwide and around 300 million people live with at least one [1, 2]. Rare Disease Day was brought to life by advocacy organisations as an opportunity to raise awareness for rare diseases and to call for a human rights priority at local, national and international level.
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!
Secondary Diabetes
Rare forms of diabetes are often classified as “secondary diabetes”, meaning that the manifestation of diabetes is the result of another disease or medication [5]. There are different types, induced by either genetic mutations, different syndromes or medications such as steroids [5].
MODY (Maturity Onset Diabetes of the Young)
MODY is a rare form of monogenic diabetes, caused by a mutation in a single gene. If a parent has it, children have a 50% chance of inheriting it. With only 1-2% of all people with diabetes living with MODY, it is very rare compared to the common types. Because of its rare character, it is estimated that 90% of people are misdiagnosed with either type 1 or type 2 diabetes at first [4]. MODY typically manifests under the age of 25 and treatment does not necessarily require insulin. There are many different subtypes, depending on which gene the mutation occurs in [4].
Cystic Fibrosis Diabetes
As the name suggests, this type of diabetes is a result of the genetic condition Cystic Fibrosis. While people living with Cystic Fibrosis can also develop type 1 or type 2 diabetes, Cystic Fibrosis Diabetes is considered a separate type. It is caused by sticky mucus typical in Cystic Fibrosis scarring the pancreas and damaging the parts responsible for insulin production [6].
Type 3c Diabetes
Type 3c Diabetes is often also referred to as “pancreatogenic diabetes” – it is caused by damage to the pancreas, or its removal [7]. Reasons for the development can thus be an acute or chronic inflammation of the pancreas, pancreatic cancer or other conditions. Type 3c Diabetes can be managed with medication or insulin injection, depending on the extent of pancreatic damage [7].
Alström Syndrome
Alström syndrome is an extremely rare disease, estimated to affect less than 1 in a million [8]. It is a genetically inherited syndrome which affects eyesight, hearing and other parts of the body. As a result of Alström Syndrome, people often develop an insulin resistance and a type 2 diabetes [9].
Wolfram Syndrome
Wolfram Syndrome is a very rare genetic disorder estimated to affect 1 in 160,000–770,000 people [11]. It is often also referred to as “DIDMOAD syndrome” – an acronym for its four most common features Diabetes Insipidus, Diabetes Mellitus, Optic Atrophy and Deafness [10]. The Diabetes Mellitus which comes with this syndrome is different to other types of diabetes and usually manifests during childhood [11]. It is commonly treated with insulin, similar to type 1 diabetes [10].
There are many more subcategories and other genetic disorders that can cause the onset of type 2 or type 1 diabetes, or classify as an entirely different type. To mark Rare Disease Day, we want to hold space for all of these types and the medical diversity within Diabetes Mellitus. Whereas at DCB, we commonly interact with the common types of diabetes, diabetes technologies can benefit all people living with diabetes, regardless of type and pathophysiology. Our aim is to make life easier for all people living with diabetes – including extremely rare forms.
Thanks so much for reading and we’ll provide you with the next episode of this series soon!
[1] Federal Office of Public Health Switzerland: Numerous Rare Diseases and Many People Affected.
[2] Rare Disease Day: What is a Rare Disease?
[4] Diabetes UK: MODY
[5] Nomiyama T, Yanase T. [Secondary diabetes]. Nihon Rinsho. 2015 Dec;73(12):2008-12. Japanese. PMID: 26666145.
[6] Diabetes UK: Cystic Fibrosis Diabetes
[7] Marshall JD, Maffei P, Collin GB, Naggert JK. Alström syndrome: genetics and clinical overview. Curr Genomics. 2011 May;12(3): 225-35. doi: 10.2174/138920211795677912. PMID: 22043170; PMCID: PMC3137007.
[8] Diabetes UK: Type 3c Diabetes
[9] Alström Syndrome UK: Alström Syndrome
[10] Diabetes UK: Wolfram Syndrome
[11] Urano F. Wolfram Syndrome: Diagnosis, Management, and Treatment. Curr Diab Rep. 2016 Jan;16(1):6. doi: 10.1007/s11892-015-0702-6. PMID: 26742931; PMCID: PMC4705145.
This edition was authored by DCB Digital Communications Specialist Svea Krutisch.
This post was previously published in Linkedin. Click here to see the original publication.
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
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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