Socioeconomic and lifestyle factors predict the association between sleep health and depression
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Objective
Sleep health and depression are interconnected multidimensional constructs, yet their shared determinants remain obscure. Understanding the role of socioeconomic/lifestyle factors in predicting sleep-related depression (SRD) is critical for preventive strategies. This study aimed to identify the key socioeconomic/lifestyle predictors of SRD in the general population and patients with clinical depression.
Methods
To characterize SRD, we performed regularized canonical correlation analysis between sleep and depression to identify latent phenotypes of SRD in a general population subsample (GP1; n□=□87,405) from the UK Biobank. Subsequently, machine-learning predictive models were developed in GP1 to predict SRD using socioeconomic/lifestyle factors. The best-performing predictive model was subsequently validated in GP2 at both baseline and follow-up (GP2; n□=□5,187), and in clinical depression (n□=□7,454) to assess its generalizability. Complementary analyses were conducted to assess other latent phenotypes (i.e., depression-related sleep, non-SRD, non-depression-related sleep, overall sleep health, and overall depression).
Results
A robust multivariate association was identified between sleep and depression in GP1 (canonical r = 0.42, P FDR < 0.001). Socioeconomic/lifestyle factors moderately predicted SRD (r = 0.25; 95% CI: [0.24, 0.25]; R² = 0.06; 95% CI: [0.06, 0.06]; rMSE = 1.08; 95% CI: [1.08, 1.09]). The top predictors were less frequency of confiding in others, more sedentary television viewing, less vigorous physical activity, and passive smoking exposure. Out-of-sample validation of the predictive model showed similar patterns in GP2 at baseline, at follow-up, and in clinical depression subsamples. Similarly, less frequency of confiding in others and greater sedentary television viewing were the main predictors of other depression-related profiles, whereas more alcohol consumption frequency, less walking frequency, and less time spent outdoors in winter predicted poor sleep-related profiles.
Conclusions
Our generalizable predictive model identifies critical modifiable predictors of the association between sleep health and depression that could serve as potential targets for personalized interventions.
