Prediction of depressive symptoms severity based on sleep quality, anxiety, and brain: a machine learning approach across three cohorts
Authors: Olfati Mahnaz , Samea Fateme , Faghihroohi Shahrooz , Balajoo Somayeh Maleki , Küppers Vincent , Genon Sarah , Patil Kaustubh , Eickhoff Simon B. , Tahmasian Masoud 
Published: August 2023

Background

Depressive symptoms are rising in the general population, but their associated factors are unclear. Although the link between sleep disturbances and depressive symptoms severity (DSS) is reported, the predictive role of sleep on DSS and the impact of anxiety and the brain on their relationship remained obscure.

Method

Using three population-based datasets, we trained the machine learning models in the primary dataset (N = 1101) to assess the predictive role of sleep quality, anxiety, and brain structure and function measurements on DSS, then we tested our models’ performance in two independent datasets (N = 334, N = 378) to test the generalizability of our findings. Furthermore, we applied our machine learning model to a smaller longitudinal sample (N = 66). In addition, we performed a mediation analysis to identify the role of anxiety and brain measurements on the sleep quality-DSS link.

Findings

Sleep quality could predict individual DSS (r = 0.43, R2= 0.18, rMSE = 2.73), and adding anxiety, rather than brain measurements, strengthened its prediction performance (r = 0.67, R2= 0.45, rMSE = 2.25). Importantly, out-of-cohort validations in other cross-sectional datasets and a longitudinal sample provided robust results. Furthermore, anxiety scores (not brain measurements) mediated the association between sleep quality and DSS.

Interpretation

Poor sleep quality could predict DSS at the individual subject level across three cohorts. Anxiety symptoms not only increased the performance of the predictive model but also mediated the link between sleep and DSS.

Research in Context

Evidence before this study

Depressive symptoms are prevalent in modern societies, but their associated factors are less identified. Several studies suggested that sleep disturbance and anxiety are linked with depressive problems in the general population and patients with major depressive disorder. A few longitudinal studies and meta-analyses also suggested that sleep disturbance plays a key role in developing depressive problems and clinical depression. However, those original studies mainly used conventional group comparison statistical approaches, ignoring the inter-individual variability across participants. Moreover, their data were limited to a single cohort, limiting the generalizability of their findings in other samples. Thus, large-scale multi-cohort studies using machine learning predictive approaches are needed to identify the complex relationship between sleep quality, anxiety symptoms, and depressive symptoms at the individual subject level. We also focused on the neurobiological underpinning of their interplay.

Added value of this study

In this study, we used machine learning which enables individual-level predictions and can validate models on unseen data, thus providing a more robust analytical framework. This study used three independent cohorts, included a longitudinal sample, and performed careful complementary analyses to examine the robustness of our findings considering the impact of lifetime history of depression, effects of sleep-related questions of the depressive assessment, most important parameters of sleep quality in prediction of depressive symptoms severity, and testing the reverse direction i.e., predicting sleep quality based on depressive symptoms. We found that poor sleep quality could robustly predict depressive symptoms across three cohorts, but the reverse direction (prediction of sleep quality based on depressive symptoms) was less robust. Anxiety symptoms improved the performance of the predictive model and mediated the link between sleep and depressive symptoms. However, brain structure and function did not play an important role in their association. Our longitudinal data also highlighted the predictability of future depressive symptoms severity and the role of interventions (i.e., neurofeedback) in the prediction of future depressive symptoms based on sleep and anxiety.

Implications of all the available evidence

As depressive symptoms have a strong impact on public health, identifying their contributing factors such as poor sleep and anxiety is critical to decrease the burden of depressive symptoms and/or design better therapeutical approaches at the individual subject level.