medRxiv : the preprint server for health sciences · 2026
Exposome factors predict the association between sleep health and depression.
СтатьяПрепринтНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Liu Wen, Küppers Vincent, Bi Hanwen, Mahdipour Mostafa, Wu Jianxiao, Samea Fateme, Hoffstaedter Felix, Wolf Kathrin, von Gall Charlotte, Ibanez Agustin, Eickhoff Simon B, Genon Sarah, Balajoo Somayeh Maleki, Tahmasian Masoud
Аннотация
BACKGROUND: Sleep health and depression are multidimensional constructs, yet their shared determinants remain obscure. We aim to identify the key exposome factors in predicting sleep-related depression (SRD).
METHODS: We integrated regularized canonical correlation analysis with machine-learning models within a nested cross-validation to identify a shared phenotype (i.e., sleep-related depression (SRD)) and to predict SRD from exposome factors in the UK Biobank (n = 64,781). The best-performing model was validated in an independent subsample at baseline and follow-up (n = 8,139) and in a clinical depression subsample (n = 3,046) to assess generalizability. Subsequently, we conducted complementary analyses to assess the exposome's role in other latent phenotypes of sleep and depression.
FINDINGS: We identified a robust multivariate association between sleep and depression in GP1 (canonical r = 0.42). Exposome factors moderately predicted SRD (r = 0.25; 95% CI [0.25, 0.26]; R2 = 0.06; 95% CI [0.06, 0.07]; rMSE = 1.11; 95% CI [1.10, 1.11]). The top predictors were less frequency of confiding in others, less vigorous physical activity, passive smoking exposure, and more sedentary television viewing. 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 less walking frequency and less time spent outdoors in winter predicted poor sleep-related profiles.
INTERPRETATION: 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.
METHODS: We integrated regularized canonical correlation analysis with machine-learning models within a nested cross-validation to identify a shared phenotype (i.e., sleep-related depression (SRD)) and to predict SRD from exposome factors in the UK Biobank (n = 64,781). The best-performing model was validated in an independent subsample at baseline and follow-up (n = 8,139) and in a clinical depression subsample (n = 3,046) to assess generalizability. Subsequently, we conducted complementary analyses to assess the exposome's role in other latent phenotypes of sleep and depression.
FINDINGS: We identified a robust multivariate association between sleep and depression in GP1 (canonical r = 0.42). Exposome factors moderately predicted SRD (r = 0.25; 95% CI [0.25, 0.26]; R2 = 0.06; 95% CI [0.06, 0.07]; rMSE = 1.11; 95% CI [1.10, 1.11]). The top predictors were less frequency of confiding in others, less vigorous physical activity, passive smoking exposure, and more sedentary television viewing. 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 less walking frequency and less time spent outdoors in winter predicted poor sleep-related profiles.
INTERPRETATION: 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.