Dear DCB Community,

Continuous glucose monitoring (CGM) has changed the way people manage diabetes. Glucose readings, trends and alerts provide valuable information that helps people with diabetes and healthcare professionals make informed decisions every day.

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 goal should not be to generate more data, but to turn existing data into meaningful information that makes diabetes management easier and supports better decisions.

However, the study is an important step in research, not proof that these approaches are ready for everyday clinical use.

The findings still need to be validated in other populations and tested prospectively. Further research must also determine whether using these risk profiles to guide clinical decisions actually improves health outcomes.

Looking ahead: The future of CGM technology

For years, advances in CGM technology have focused on improving sensor accuracy, wear time, comfort and connectivity. These developments remain essential.

But this study highlights another promising direction: the value of CGM technology may increasingly depend on what we can learn from the data it generates.

As data analysis and algorithms advance, glucose sensors could potentially become part of a broader approach to understanding metabolic health and supporting more personalised care.

This also creates new opportunities for researchers, healthcare professionals and technology developers. The challenge will be to translate complex data into reliable, clinically meaningful insights, without adding unnecessary complexity to diabetes management.

Why is this relevant to DCB?

At the Diabetes Center Berne, we focus on advancing diabetes technology through research, clinical development, data science and innovation. Understanding how technology can better address the needs of people living with diabetes is central to our work.

Research like this highlights why innovation in diabetes technology about developing better devices as well as making better use of the information these devices already provide.

Combining advances in sensor technology with intelligent data analysis could open up new opportunities for personalised diabetes care. But for these approaches to make a meaningful difference, they need to be scientifically validated, clinically useful and designed around the needs of the people who will use them.

Thanks for reading and stay tuned for more insights from our “DID YOU KNOW…” series!

Original research

Lu, J., Ding, Y., Zhao, B. et al. (2026). Multidimensional continuous glucose monitoring metrics enable digital risk stratification across the diabetes spectrum. npj Digital Medicine. Published 13 October 2026.

https://doi.org/10.1038/s41746-026-03368-w

This article is adapted from an original contribution to the newsletter of Derek Brandt, CEO of the Diabetes Center Berne.

Dear DCB Community,

We often talk about diabetes management in terms of glucose levels, insulin dosing, carbohydrates, physical activity, sleep, stress and technology. But there is another factor that can influence glucose patterns and insulin needs for many women and people who menstruate, month after month: the menstrual cycle.

In the latest episode of loop&life, the DCB Diabetes Tech Talk, host Greta Ehlers speaks with Martina Rothenbühler, Scientific Program Manager at the Diabetes Center Berne , and Laura Burlando, who lives with type 1 diabetes, about a topic that is still too often overlooked in diabetes care, research and technology development: diabetes and the menstrual cycle.

“No one ever talked to me about it.”

One of the most powerful moments in the episode comes from Laura Burlando. Laura has lived with type 1 diabetes for 18 years. She has also had a menstrual cycle for almost the same amount of time. Yet, as she explains in the episode, she was not really aware of cyclical changes in her own diabetes management until she started researching the topic herself during her master’s thesis at the Diabetes Center Berne.

“And then suddenly a light went off in my head,” she says. “I started noticing certain patterns in my own diabetes management.”

When Greta asks whether this was something Laura had already realised in her personal life with diabetes, Laura answers:

“But no one ever talked to me about it.”

This sentence captures the problem very clearly. Many people living with diabetes are used to looking for explanations. Was it the meal? Stress? Sleep? Exercise? A wrong estimate? A device issue? But if the menstrual cycle is not discussed in routine diabetes care, many people are left to discover these patterns on their own, be it through tracking, trial and error, conversations with peers or through social media. That is a challenge that should not remain with the individual alone.

A recurring factor, but still a research gap

From a research perspective, this gap is striking. As Martina Rothenbühler explains in the episode, the team expected to find more evidence. After all, roughly half of people living with diabetes are women, and the menstrual cycle is not a rare or exceptional event. It is a recurring physiological process. Yet the scientific evidence is still limited.

Existing studies suggest that glucose levels and insulin needs may change across different phases of the menstrual cycle. For example, some data indicate a tendency towards lower glucose levels or more hypoglycaemia around the beginning of the cycle, and higher glucose levels or reduced insulin sensitivity in the luteal phase, shortly before the next period starts. But it is not that simple because patterns vary from person to person. They can also vary from one cycle to the next in the same person. Some may notice changes around ovulation. Others may experience changes before or during their period. Some may not see a clear pattern at all.

This variability matters and makes the topic harder to translate into general guidance and harder to implement in diabetes technology.

But at the same time, this also makes the topic even more relevant, because if diabetes care and technology aim to support real life, they need to account for real-life variability.

Diabetes technology is not yet built for this

The episode also raises an important technology question. Current diabetes technology has become increasingly sophisticated. Many people now use CGMs, insulin pumps and automated insulin delivery systems. These tools can reduce burden and improve glucose management for many users. Still, there are limitations: current AID systems are not designed to meaningfully adapt insulin delivery based on menstrual cycle-related hormonal changes.

For people who do notice cycle-related patterns, this can mean manual work. They may create different pump profiles, adjust insulin settings, track their cycle in a separate app, or try to interpret recurring changes themselves.

Laura describes how research helped her better understand her own patterns and create pump profiles depending on her menstrual phase. But she also points out that it is not the same every month. Even with a relatively regular cycle, the diabetes impact can still vary and this is exactly where better evidence is needed: to understand possible patterns, differences and influencing factors more clearly.

The TIMES study: DCB’s contribution to closing the gap

At the Diabetes Center Berne, this question is not only being discussed, but also being researched.

The ongoing TIMES study aims to gain a better understanding of how insulin sensitivity changes over the course of the menstrual cycle in women with type 1 diabetes. The goal is to identify patterns in insulin requirements and glucose levels, with the hope of supporting more personalised diabetes care in the future.

The study is multinational and decentralised, meaning that participants contribute real-world data from their daily lives across several countries. This allows the study to capture more diverse and realistic patterns than a single-site study could. It collects data from automated insulin delivery systems, including glucose levels, insulin doses and carbohydrate intake. This is complemented by information entered by participants themselves, such as menstrual cycle data, symptoms and lifestyle factors.

The study also looks at possible confounders, including physical activity, because the menstrual cycle is not the only factor that may influence insulin needs.

This approach was chosen because it reflects everyday life more closely than a highly controlled setting. Diabetes does not happen in isolation, it happens during work, sleep, exercise, stress, travel, hormonal changes and everything else that shapes daily life.

The TIMES study is designed to help generate better evidence from that reality.

Why lived experience matters

A key strength of this episode is that it brings research and lived experience together.

Laura’s perspective shows what it can feel like when an important part of diabetes management is not named or explained. Martina’s perspective shows why this topic is scientifically complex and why better evidence is needed.

Together, their conversation highlights an important point: lived experience can help reveal where research, care and technology are still missing something.

What should change?

There is no single answer, but several needs are clear, as the podcast episode highlights:

• People living with diabetes should receive better information about the possible connection between the menstrual cycle and glucose management.
• Healthcare professionals should feel better equipped to discuss this topic in routine care.
• Diabetes technology should become more responsive to physiological changes that are part of real life.
• Research should continue to generate evidence that reflects the diversity of individual experiences.

As Greta says in the episode, it should not depend on someone doing their own research to discover that there may be a connection between their menstrual cycle and diabetes management.

This topic deserves more attention: in research, in clinical care, in technology development and in everyday conversations with people living with diabetes.

Listen to the full episode

In this episode of loop&life, Greta Ehlers speaks with Martina Rothenbühler and Laura Burlando about diabetes, the menstrual cycle, lived experience, research gaps, technology limitations and the ongoing TIMES study at DCB.

Listen or watch the full episode here:

YouTube: https://youtu.be/Th1L-lN9nCs

Spotify: https://open.spotify.com/episode/0pXQeasczpCIAeNAGt9WIZ

Apple Podcasts: https://podcasts.apple.com/ch/podcast/diabetes-and-the-menstrual-cycle-what-we-know-what/id1885831147?i=1000773847130

Learn more about the TIMES study on the DCB website: https://www.dcberne.com/en/times-study/