Prediction of depression onset risk among middle-aged and elderly adults using machine learning and Canadian Longitudinal Study on Aging cohort

Background: Early identification of the middle-aged and elderly people with high risk of developing depression disorder in the future and the full characterization of the associated risk factors are crucial for early interventions to prevent depression among the aging population.Methods: Canadian Longitudinal Study on Aging (CLSA) has collected comprehensive information, including psychological scales and other non-psychological measures, i.e., socioeconomic, environmental, health, lifestyle, cognitive function, personality, about its participants (30,097 subjects aged from 45 to 85) at baseline phase in 2012-2015. We applied machine learning models for the prediction of these participants' risk of depression onset approximately three years later using information collected at baseline phase.Results: Individual-level risk for future depression onset among CLSA participants can be accurately predicted, with an area under receiver operating characteristic curve (AUC) 0.791 & PLUSMN; 0.016, using all baseline information. We also found the 10-item Center for Epidemiological Studies Depression Scale coupled with age and sex information could achieve similar performance (AUC 0.764 & PLUSMN; 0.016). Furthermore, we identified existing subthreshold depression symptoms, emotional instability, low levels of life satisfaction, perceived health, and social support, and nutrition risk as the most important predictors for depression onset independent from psychological scales. Limitations: Depression was based on self-reported doctor diagnosis and depression screening tool.Conclusions: The identified risk factors will further improve our understanding of the depression onset among middle-aged and elderly population and the early identification of high-risk subjects is the first step for successful early interventions.

Automated summary

Based on the Canadian Longitudinal Study on Aging, machine learning models can accurately predict the risk of future depression among middle-aged and elderly individuals. The identified risk factors include subthreshold depression symptoms, emotional instability, low life satisfaction, perceived health, social support, and nutrition risk.