The Effects of Allostatic Load on Comorbid Depression-Anxiety Symptoms in Adolescents With Low Family Socioeconomic Status: Evidence From a Cross-Sectional and Cross-Lagged Panel Network Analysis.

Abstract

Extant research has insufficiently examined how biological adaptations to chronic stress are associated with comorbid depression-anxiety among adolescents from low- and middle-income countries (LMICs). From a network perspective, comorbidity is conceptualized as symptom communities linked by bridge symptoms. Using this framework, we investigated whether allostatic load (AL), a composite indicator of cumulative physiological stress, was associated with differences in depression-anxiety symptom networks among Chinese adolescents with low family socioeconomic status (SES). A total of 485 adolescents with low family SES (54.0% girls; mean age = 12.06 years, SD = 1.67) completed AL assessments at baseline and self-report measures of depression and anxiety at two time points over 1 year. Participants were stratified into high- and low-AL groups based on biological parameters. Symptom networks were estimated using graphical Gaussian models and cross-lagged panel networks. While composite symptom scores showed minimal differences between the high-AL and low-AL groups, notable differences emerged in network structure. In both groups, depression symptoms exerted strong temporal influence within the comorbidity network. However, anxiety symptoms showed stronger prospective associations with depression symptoms in the high-AL group than in the low-AL group. Somatic symptoms, particularly gastrointestinal symptoms, exhibited greater prominence as bridge nodes in the high-AL network. These findings suggest that higher allostatic load is associated with altered patterns of symptom interrelation between depression and anxiety, particularly involving somatic bridge symptoms. Network analysis provides a fine-grained, descriptive characterization of how physiological stress burden relates to comorbid symptom dynamics without implying causal mechanisms.