Gene Age Gap Estimate (GAGE) for major depressive disorder: a penalized biological age model using gene expression

Abstract

Recent associations between Major Depressive Disorder (MDD) and measures of premature aging suggest accelerated biological aging as a potential biomarker for MDD susceptibility or MDD as a risk factor for age-related diseases. Residuals or "gaps" between the predicted biological age and chronological age have been used for statistical inference, such as testing whether an increased age gap is associated with a given disease state. Recently, a gene expression-based model of biological age showed a higher age gap for individuals with MDD compared to healthy controls (HC). In the current study, we propose an approach that simplifies gene selection using a least absolute shrinkage and selection operator (LASSO) penalty to construct an expression-based Gene Age Gap Estimate (GAGE) model. We train a LASSO gene age model on an RNA-Seq study of 78 unmedicated individuals with MDD and 79 HC, resulting in a model with 21 genes. The L-GAGE shows higher biological aging in MDD participants than HC, but the elevation is not statistically significant. However, when we dichotomize chronological age, the interaction between MDD status and age has a significant association with L-GAGE. This effect remains statistically significant even after adjusting for chronological age and sex. Using the 21 age genes, we find a statistically significant elevated biological age in MDD in an independent microarray gene expression dataset. We find functional enrichment of infectious disease and SARS-COV pathways using a broader feature selection of age related genes.