New proposed center aims to hack the diabetes code

Diabetes is one of the world’s fastest-growing health challenges and remains a leading cause of illness and premature death.

According to the World Health Organization, around 830 million people, roughly ten percent of the global population, were living with some form of diabetes in 2025.

Diabetes is a chronic condition in which the body either does not produce enough insulin or cannot use insulin effectively, resulting in elevated blood sugar levels. Over time, this can damage nerves, blood vessels, and organs such as the heart, kidneys and eyes.

Disease in young people

Despite significant advances in our understanding of diabetes and its subtypes, key questions remain unanswered. One of the most pressing is how the disease develops in early life.

“One of our primary objectives is to uncover mechanisms that will lead to diabetes and complications in young people”, says professor Pål Rasmus Njølstad from the Department of Clinical Science, University of Bergen. 

Recent research published in The Lancet estimates that nearly one in three children worldwide will be overweight within the next 25 years. In addition, one in six young people is expected to develop obesity, a major risk factor for type 2 diabetes and a growing public health concern.

Njølstad points out that while projections on future diabetes prevalence are well documented, the underlying causes of the disease in children and young adults are still poorly understood.

A butterfly effect

To explain the ambition of their initiative, the researchers often refer to the butterfly effect – the idea that small changes can trigger profound, long-term consequences.

Professor Simona Chera, Principal Investigator and Co-Director in the team, explains that the roots of diabetes may be laid far earlier than previously thought.

“We believe that people who develop diabetes are, to some extent, pre-programmed already before birth,” she says.

“Our goal is to identify those individuals early and intervene. It really is a butterfly or domino effect – a small difference, a slight biological misalignment – with dramatic consequences. We want to hack the diabetes code – and beat the disease.” “

Professor Marc Vaudel, Principal Investigator and Co-Director in the team, underlines the unique focus of the proposed center on genetics and mechanisms during pregnancy and early childhood.

“Large-scale studies and advanced data models will provide actionable measures and robust predictions. When we identify early triggers of the disease, we give clinical teams the opportunity to act before the disease develops", he points out.   

If the center-idea should get the necessary funding, it would mean a shift from reactive to early, mechanism-based prevention and treatment, with long-term benefits for individuals, the health system, and society.  

A core vision is to move diabetes care from symptom-based classification to pathophysiology-based subtypes, grounded in genetics, early-life biology, and mechanisms.

Potential consequences for society could be:

• More accurate diagnoses earlier in life.
• Reduced misdiagnosis and trial-and-error treatment.
• Better understanding for patients and families of why disease develops.

Unique data resources

The iDIA researchers will have access to some of the world’s most comprehensive health registries, including the Norwegian Mother, Father and Child Cohort Study (MoBa). These datasets can be linked with childhood and adult diabetes registries with genetic, clinical and biological data collected over more than 20 years.

The proposed centre brings together six internationally recognised researchers at the University of Bergen with complementary skills and outstanding track records.

“We have assembled a dream team to tackle these questions”, Njølstad concludes.

The iDIA initiative is built around three main objectives: 

1. Identify and validate risk and protective factors for diabetes and related conditions by integrating large biobanks and clinical registries with detailed genetic and protein data.
    
2. Unravel the biological mechanisms that predispose individuals to diabetes or drive disease progression during early life.
    
3. Develop actionable biological and computational models of diabetes and its complications to enable innovative, personalised treatment strategies early in life.