A new study using an advanced "digital twin" artificial intelligence model has found that factors such as loneliness, insomnia and poor mental health substantially raise a person's future risk of developing type 2 diabetes.
The research, led by Anglia Ruskin University (ARU) in collaboration with Cranfield University, the University of Portsmouth, and Intelligent Omics Ltd, and published in Frontiers in Digital Health, used lifestyle and health data from 19,774 UK adults in the UK Biobank, tracked for up to 17 years. Unlike traditional prediction tools, the new model focuses entirely on behavioural, lifestyle and psychosocial information rather than blood tests or wearable devices.
The digital twin model system, developed by ARU, simulated how changes in people's day‑to‑day lives could alter long-term diabetes risk. It found that loneliness, insomnia and poor mental health were each associated with an estimated 35‑percentage‑point rise in risk, under AI‑modelled assumptions.
When all three of these factors occurred together, the model predicted a 78‑percentage‑point increase in absolute risk and is a more accurate predictor of type 2 diabetes risk than diet alone, the study found.
Researchers note these effects are likely linked to the body's response to long-term stress, which raises stress hormones, triggers inflammation and disrupts how the body manages blood sugar.
The study also uncovered strong links between stress-related factors and dietary habits, including higher consumption of salt, sugary cereals and processed meats, which are all associated with increased risk of developing type 2 diabetes. Even small dietary shifts reinforced risk levels, the model suggested. It also suggested cheese may have protective qualities, but this reduced in significance when mental health issues were present.
The digital twin model system also highlighted significant ethnic disparities, with South Asian, African and Caribbean participants showing markedly higher estimated risk than White participants, echoing long‑established NHS and Public Health England findings.
Because the model does not rely on medical tests, researchers say it could help health services identify high‑risk individuals earlier and design affordable, targeted prevention programmes.
Type 2 diabetes affects more than 500 million people and remains one of the world's most pressing public health challenges, driven largely by preventable factors. It differs from type 1 diabetes, which is an autoimmune condition not linked to lifestyle.
Healthcare professionals have historically struggled to predict who will develop type 2 diabetes early enough to intervene effectively.
Co-author Professor Barbara Pierscionek, Deputy Dean for Research and Innovation in the Faculty of Health, Medicine and Social Care at Anglia Ruskin University (ARU), said: "Type 2 diabetes is a rising global health concern which we know is heavily influenced by lifestyle. However, current risk prediction models rely on BMI, age and blood pressure, which over-simplify this disease and overlook the more complex interconnected behavioural and emotional factors that precede and shape the onset of the condition.
"Digital twin model systems replicate an individual's health profiles, enabling us to test 'what-if' scenarios and tailor care to individual needs. However, most of these existing models rely on real-time data from wearable devices, which can be a barrier for settings lacking in technical infrastructure or underserved communities that struggle with costs.
"Digital Twin model systems present a viable cost-effective way of diagnosis, testing and treatment for a number of conditions."
Dr Mahreen Kiran, lead author and postgraduate researcher at ARU, said: "This study shows the importance of including behavioural and psychosocial variables such as loneliness, sleep disruption and mental health history within health datasets used for risk prediction.
"These factors are often overlooked, yet they provide meaningful signals about future disease risk. Incorporating them into digital twin models and other AI based approaches can support more accurate and equitable prevention strategies."
Dr Nasreen Anjum, of the University of Portsmouth, said: "A key strength of this work is the use of transparent modelling and causal simulation techniques that help explain how behavioural factors interact over time. This improves confidence in how AI tools can support decision making in preventive healthcare."
