Life-Course Trajectories of Chronic Non-Communicable Diseases and Social Inequalities: From Population Patterns to Individualized Prediction

Chronic non-communicable diseases (NCDs) emerge and progress through complex, long-term processes shaped by exposures accumulated across the life course. Increasing evidence shows that individuals follow heterogeneous disease development trajectories, and that these trajectories are socially patterned. Socioeconomic position, occupational histories, neighborhood contexts, migration, gender, and structural discrimination influence both upstream risks and downstream access to timely diagnosis and effective care. As longitudinal cohorts and real-world data resources expand, new analytic approaches, trajectory modeling, multi-state methods, joint modeling, and dynamic prediction, offer opportunities to map NCD natural history, quantify inequalities in progression, and support risk stratification for prevention and policy.This Research Topic aims to bring together life-course epidemiology, social epidemiology, and robust longitudinal methods to: (i) identify distinct NCD trajectory subgroups, (ii) clarify mechanisms linking social disadvantage to trajectory membership and transitions, and (iii) develop and validate individualized prediction models that are transparent, reproducible, and equity-aware. We welcome contributions that advance theory, methods, and translation, particularly work that evaluates model performance and calibration across social groups and discusses implications for equitable screening, prevention, and resource allocation.We welcome manuscripts addressing (but not limited to):• Life-course NCD trajectories and natural history (onset, progression, complications, remission, functional decline)• Multimorbidity pathways and clustering over time, including mental–physical comorbidity• Social inequalities in trajectories, including socioeconomic position across the life course, social mobility, migration, gender, and structural discrimination• Mechanisms and pathways (material, psychosocial, environmental, behavioral, healthcare access/quality)• Longitudinal methods: trajectory models, latent class approaches, multi-state models, joint models, time-varying exposures• Causal inference in life-course settings: mediation, interaction, selection bias, missingness, survivorship bias• Equity-aware risk stratification and dynamic prediction, including external validation and subgroup calibration• Policy and prevention implications: targeted screening, intervention timing, and equitable implementation Topic editors: Hao Chen, Jie Zhang, Yili Wu Source: https://www.frontiersin.org/research-topics/78321/life-course-trajectories-of-chronic-non-communicable-diseases-and-social-inequalities-from-population-patterns-to-individualized-prediction