The burden of depressive disorder among the global 10–24 age group and the construction of an early risk factors model

TL;DR

Depression rates are rising among 10–24-year-olds globally, and high levels of S100β, NSE, and PLT are linked to increased risk.

Contribution

This is the first study to combine GBD data with machine learning to model early risk factors for adolescent depression.

Findings

Depression incidence and DALYs increased globally among 10–24-year-olds from 1990 to 2021.

Random forest was the most reliable model for predicting depression risk.

Elevated S100β, NSE, and PLT are significant risk factors for depression.

Abstract

To understand the global trends in depression and identify potential early risk factors for its detection.

This study is the first to integrate the 2021 Global Burden of Disease (GBD) data with machine learning techniques to explore the risk factors of adolescent depression. A machine learning-based model was constructed, and SHAP (SHapley Additive exPlanations) plots were utilized for interpretive analysis.

From 1990 to 2021, the incidence and disability-adjusted life years (DALYs) of depression continued to rise globally among the 10–24 age group, particularly in high socio-demographic index(SDI) regions. Greenland, the United States of America, and Palestine had the highest rates of depression globally. Among the eight machine learning models evaluated, random forest (RF) proved to be the most reliable. SHAP analysis revealed that elevated levels of S100β (0.330), NSE (0.060), and PLT (0.031) significantly increased the risk of depression.

Our study shows an increasing trend of depression in the global 10–24 age group. Additionally, elevated S100β, NSE, and PLT are identified as key risk factors for depression.

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