1 Research Center, CHU Sainte-Justine, Montréal, Québec, Canada.
2 Centre for Structural and Functional Genomics, Biology Department, Concordia University, Montréal, Québec, Canada.
3 Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, MA, USA.
4 Lady Davis Institute for Medical Research, Jewish General Hospital, Montréal, Québec, Canada.
5 Gerald Bronfman Department of Oncology, McGill University, Montréal, Québec, Canada.
6 Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, Québec, Canada.
7 Department of Paediatrics, Université de Montréal, Montréal, Québec, Canada.
8 Department of Social and Preventive Medicine, School of Public Health, Université de Montréal, Montréal, Québec, Canada.
9 Ingram School of Nursing, Faculty of Medicine and Health Science, McGill University, Montréal, Québec, Canada.
10 Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK.
11 Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
12 Research Center, CHU Sainte-Justine, Montréal, Québec, Canada. despina.manousaki@umontreal.ca.
13 Department of Paediatrics, Université de Montréal, Montréal, Québec, Canada. despina.manousaki@umontreal.ca.
14 Department of Biochemistry and Molecular Medicine, Université de Montréal, Montréal, Québec, Canada. despina.manousaki@umontreal.ca.

Aims/hypothesis: Dysglycaemia in youth results from complex interactions between genetic and environmental factors, yet their individual and combined contributions remain unclear. We aimed to: (1) evaluate the predictive performance of polygenic risk scores (PRSs) for glycaemic traits from childhood to early adulthood; (2) identify gene-environment interactions shaping glucose homeostasis trajectories; and (3) explore underlying mechanisms using pathway-specific PRSs.

Methods: Data from 8783 participants (aged 7-24 years) from the Avon Longitudinal Study of Parents and Children (ALSPAC) were used to compute 12 PRSs for type 2 diabetes, fasting glucose, insulin and BMI. Glycaemic outcomes, including insulin resistance, prediabetes (impaired fasting glucose) and type 2 diabetes, were assessed using fasting glucose and insulin (ages 7, 15, 18 and 24 years) and HbA_1c (age 9 years). We evaluated whether PRSs could distinguish between transient (resolved by adulthood) and persistent glycaemic abnormalities using multinomial regression. We performed univariate and multivariate regressions, incorporating environmental factors (lifestyle, diet and maternal characteristics), and evaluated model performance using R² and AUC. We tested interactions between PRS quintiles and environmental factors and explored pathophysiological mechanisms using pathway-specific PRSs.

Results: Prediabetes prevalence was up to 24% at age 24 years. PRS-enhanced models outperformed those using environmental factors alone (e.g. AUC for dysglycaemia at age 15 improved by 0.12, reaching 0.78). Fasting glucose PRS showed moderate ability to differentiate transient from persistent prediabetes (AUC=0.70). Gene-environment analyses revealed that high genetic risk combined with increased screen time or younger maternal age increased insulin resistance risk. Physical activity and health awareness attenuated this risk. Pathway analyses indicated insulin secretion as a key mechanism earlier in life, with insulin resistance emerging later.

Conclusions/interpretation: Genes interact with environment to define glucose homeostasis in youth, highlighting modifiable factors as actionable targets for early prevention.