To systematically identify and prioritize age- and sex-specific modifiable risk factors for incident depression, and to characterize integrated risk patterns to pinpoint high-risk populations across different life stages.

Using data from 428,078 participants in a cohort, incident depression was identified using hospital records. Cox proportional hazards models were applied to assess age- (the middle-aged, quinquagenarian and the elderly) and sex-specific (females and males) associations between modifiable risk factors and depression incidence. Population-attributable fractions (PAFs) prioritized predominant factors, and self-organizing maps (SOMs) visualized integrated risk patterns. Mendelian randomization (MR) analyses examined potential causal associations between top-ranked factors and depression risk.

During a mean follow-up of 13.11 years, 15,106 (3.53%) participants developed depression. Hazard ratios for modifiable factors were similar between sexes but varied by age. Generally, the overall PAFs decreased with age (PAF of 83.47% in the middle-aged, 82.96% in the quinquagenarian and 76.77% in the elderly). Health-related factors consistently showed the highest PAFs (33.46–38.12%) across all age subgroups and females, driven by weak grip strength (12.60–18.09%) and unhealthy body mass index (6.94–7.80%). Socioeconomic factors ranked first in males (24.55%), with unemployment contributing the most (9.27%). Lifestyle factors ranked second in younger groups and females, led by poor sleep quality and smoking. Environmental exposure and social support were the last two risk profiles across all subgroups, jointly contributing to about 12% of PAF. SOM integrated patterns predominated by less social support and low socioeconomic status increased depression risk up to 4.37-fold. Potential causal associations between the top five risk factors and depression were observed by MR analyses.

Health condition, socioeconomic status and lifestyle factors demonstrated stronger associations and greater PAFs for depression among relatively younger adults, pinpointing the importance of targeting these impactful factors from an earlier age.