Screening and Risk Assessment for Type 2 Diabetes

Type 2 diabetes represents a growing global health burden, with early detection and risk stratification pivotal to reducing complications and healthcare costs. Screening programmes employ a range of approaches, from simple questionnaires and risk scores to biochemical assays such as fasting plasma glucose, oral glucose tolerance tests and haemoglobin A_1c measurement. These methods identify individuals at high risk of developing overt disease or with undiagnosed diabetes, enabling timely lifestyle or pharmacological intervention. Risk assessment integrates demographic factors (age, ethnicity, family history), clinical parameters (body mass index, blood pressure) and laboratory biomarkers to generate personalised risk estimates. Emerging technologies harness electronic health records, radiographic imaging and machine-learning algorithms to enhance opportunistic detection. Economic evaluations guide the selection of cost-effective strategies, while implementation science addresses barriers to uptake, including accessibility, acceptability and potential psychosocial harms. Integration of diabetes screening into routine primary care and community settings ensures broad reach, with particular attention to underserved or high-risk populations. By combining robust risk assessment with targeted interventions, health systems can mitigate the long-term cardiovascular, renal and retinopathic sequelae of type 2 diabetes.

Research from Nature Portfolio

Deep-learning models applied to routinely acquired frontal chest radiographs, in conjunction with electronic health-record data, have demonstrated strong performance in detecting undiagnosed type 2 diabetes. One study achieved an area under the receiver operating characteristic curve of 0.84 in internal testing and 0.77 on external validation, flagging subtle radiographic correlates of adiposity that augment conventional screening pathways. In parallel, cohort analyses of diabetic retinopathy have revealed that the clinical course of diagnosis—including health-check participation and prompt initiation of antidiabetic therapy—profoundly influences the cumulative incidence of vision-threatening retinopathy. Stratifying patients by their diagnostic trajectory offers a refined approach to risk assessment, complementing glycaemic measures for tailored surveillance and early ophthalmic referral.

Screening and Risk Assessment for Type 2 Diabetes publication trend

The graph below shows the total number of articles in screening and risk assessment for type 2 diabetes across all publications each year (not limited to Nature Index journals).

Technical terms

Haemoglobin A_1c (HbA_1c): A marker of average blood glucose over two to three months, used for diagnosing and monitoring diabetes.

Fasting plasma glucose: Concentration of glucose in the blood after an overnight fast, employed as a diagnostic criterion for diabetes.

Oral glucose tolerance test (OGTT): Assessment of the body’s response to a standard oral glucose load, measuring glucose at defined intervals to detect impaired glucose regulation.

Receiver operating characteristic curve (ROC curve): A plot characterising the performance of a binary classifier system; the area under the curve indicates overall accuracy.

Deep learning: A subset of machine-learning techniques using neural networks to model complex patterns, applied here to integrate imaging and health-record data for disease detection.

References

1. Opportunistic detection of type 2 diabetes using deep learning from frontal chest radiographs. Nature Communications (2023).
2. Association of prior outpatient diabetes screening with cardiovascular events and mortality among people with incident diabetes: a population-based cohort study. Cardiovascular Diabetology (2023).
3. Screening for Type 2 Diabetes Mellitus: A Systematic Review of Recent Economic Evaluations. Value in Health (2025).
4. Different incidences of diabetic retinopathy requiring treatment since diagnosis according to the course of diabetes diagnosis: a retrospective cohort study. Scientific Reports (2023).