DCB Newsletter #4/23: INSIDE DCB – Interview with Prof. Lilian Witthauer
Dear Community,
We’re thrilled to present you with the next episode or our series “INSIDE DCB” – this time, an interview with Prof. Lilian Witthauer. Enjoy the read!
Since 2021, Lilian Witthauer has been a professor specialising in sensor technology at the University Department of Diabetology, Endocrinology, Nutritional Medicine and Metabolism (UDEM) and at the DCB (Diabetes Center Berne). Together with her team, she researches innovative sensor technologies to improve the daily lives of people with diabetes.
Lilian, what is your research about?
My field of research is very interdisciplinary and includes both the scientific fundamentals of sensor technology and the clinical aspects of diabetes research. In particular, I focus on the development and improvement of sensors that enable people with diabetes to continuously measure blood glucose levels. An important aspect of my work is to make these sensors more accurate and reliable to enable automated insulin delivery. To achieve this goal, we redesign sensors, use different materials and optimise insulin delivery.
What motivates you to work in the field of diabetes technology? How did you get into it as a physicist?
What motivates me most is that people with diabetes can live better lives by having access to advanced sensor technologies that improve their healthcare and promote self-control and responsibility. I am involved in the field of diabetes technology as a physicist because I have always been interested in the application of physical principles and methods in medicine. As part of a changing society, diabetes is an important topic where there are still many opportunities for development.
What is your biggest challenge as a professor?
My biggest challenge as a professor is to find a balance between research, teaching and administration and to ensure that my team and I achieve our goals effectively and efficiently. This is not always easy, as I only have a short time for each individual task. It’s like I’m strategically managing a company, and at the same time I’m responsible for the operational implementation, while also taking care of the finances and IT.
Professor Dr. Lilian Witthauer. Assistant Professor with Tenure Track in Diabetes Technology. Photo credit: Sandra Blaser
What goals do you want to achieve with your team and your laboratory, the samlab (Sensing and Monitoring Lab)?
In collaboration with my team and samlab, I am pursuing the goal of developing innovative sensor technologies that make everyday life easier for people with diabetes and improve their quality of life. In addition, we want to promote interdisciplinarity and closely link our research results with practice.
What projects are you currently researching and how can they make life easier for people with diabetes?
One of my current projects deals with the development of a sensor that measures the glucose level in the blood continuously and without delay. The instantaneous measurement is of special interest when the blood glucose level changes rapidly, during meals, sports, or stress. The glucose sensor is based on a light-based measurement method and therefore differs from existing sensors on the market.
Such a sensor could be worn longer and would allow for completely automated insulin delivery. This would reduce the number of decisions a person with diabetes has to make every day and thus improve the quality of life.
How do you work with the DCB?
The DCB offers a unique environment where I work closely with project managers and experts from the hospital, statistics and business environment. We have regular meetings and interdisciplinary working groups to coordinate our work and drive the development of innovative solutions. In addition, the DCB connects me with industry partners and start-ups, which is very important in my research field.
Where do you hope your research field will be in 5-10 years? What is your vision?
My vision is that in 5-10 years, a wide range of innovative sensor technologies will be available to make the daily lives of people with diabetes easier and improve their healthcare.
I hope that our research will help to make monitoring blood glucose levels easier and more accurate, and that people with diabetes will be able to make better decisions about their diet, exercise and insulin therapy based on real-time data.
About Lilian Witthauer
Lilian Witthauer is Assistant Professor of Diabetes Technology at the University of Bern and the Diabetes Center Berne (DCB). She studied physics in Basel, completed a Master of Advanced Studies in medical physics at ETH Zurich and graduated with a PhD in nuclear and particle physics from the University of Basel in 2015. Lilian Witthauer has done postdoctoral research in the development of optical sensors for navigation during surgery at the Department of Biomedical Engineering at the University of Basel and on oxygen sensors at Massachusetts General Hospital and Harvard Medical School in Boston (USA).
More about samlab: https://samlab.org
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
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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