Diabetes mellitus continues to impose a substantial and growing global health burden, with prevalence rising across countries, age groups, and socioeconomic contexts. This increase reflects a complex combination of demographic change, lifestyle transitions, and widening exposure to metabolic and environmental risk factors. Despite improvements in clinical management, diabetes remains a major cause of cardiovascular disease, kidney failure, vision impairment, and premature mortality, highlighting the need for ongoing advances in both population-level understanding and biological investigation.

Modern epidemiology provides essential insight into the distribution and determinants of diabetes across diverse populations. Contemporary studies increasingly integrate social determinants of health, life-course exposures, and high-resolution behavioural and environmental data to refine our understanding of risk. These approaches illuminate how structural factors, early-life influences, and behavioural patterns contribute to disease onset and progression, informing opportunities for prevention at both individual and population scales.

In parallel, rapid advances in molecular biology, immunometabolism, genomics, and systems science have transformed our understanding of the mechanistic pathways underlying diabetes. Research into β-cell dysfunction, insulin resistance, inflammation, metabolic tissue cross-talk, and gene–environment interactions continues to reveal the biological complexity of the disease. These mechanistic insights are key to identifying new therapeutic targets and improving strategies for early detection, risk stratification, and personalised intervention.

This special issue brings together cutting-edge research spanning population science and molecular mechanisms. By integrating these perspectives, the issue aims to advance understanding of diabetes aetiology, highlight emerging opportunities for prevention and treatment, and stimulate interdisciplinary dialogue across the diabetes research community.

Keywords: Diabetes, epidemiology, biomarkers, pathogenesis, precision medicine, artificial intelligence, complications