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PURPOSE: Age-related muscle atrophy is a process that naturally occurs during life. Regenerating skeletal muscle to counteract this process can be achieved by resistance training (RT); however, the skeletal muscle hypertrophic response is highly variable across individuals. The molecular underpinnings of this heterogeneity are unclear but likely to include differential muscle gene expression. METHODS: This study explored transcriptional networks linked to RT-induced muscle hypertrophy classified as (1) predictive of hypertrophy, (2) responsive to RT independent of hypertrophy, or (3) plastic (or changes) with hypertrophy. Older adults (n=31, 18F/13M, 70±4y) performed 14-wk RT 3d/wk and muscle hypertrophy was assessed by change in: mid- thigh muscle cross-sectional area (CSA) [computed tomography (CT)], thigh lean mass [dual-energy x-ray absorptiometry (DXA)], and vastus lateralis myofiber CSA [histomorphometry]. Transcriptome-wide poly-A RNA-seq was performed on vastus lateralis tissue collected pre (n=31) and post-RT (n=22). Prediction networks (baseline only) were identified by Weighted Gene Correlation Network Analysis (WGCNA). To identify Plasticity networks, WGCNA change indices for paired samples were calculated and correlated to changes in muscle size outcomes. Pathway-Level Information ExtractoR (PLIER) was applied to identify response networks and link genes to biological annotation. RESULTS: Prediction networks (n=8) confirmed transcripts previously connected to resistance/aerobic training adaptations in the MetaMEx database while revealing novel genes that should fuel future research to understand the influence of baseline muscle gene expression on hypertrophy. Response networks (n=6) indicated RT-induced increase in aerobic metabolism and reduced expression of genes associated with spliceosome biology and type-I myofibers. Fewer Plasticity networks were identified (n=2). CONCLUSION: Findings suggest inter-individual differences in baseline gene expression may contribute more to muscle hypertrophic response heterogeneity than RT-induced changes. Investigation of factors (e.g., epigenomic) modulating baseline gene expression profiles are of great interest for future studies. Supported by: R01AG046920, U01AR071133, P2CHD086851, and F32AG062048. [ABSTRACT FROM AUTHOR]