Aging reshapes the architecture of human skeletal muscle, yet objective tissue-level markers that capture this process remain limited. We combined large-scale histology with deep learning to identify reproducible features of muscle aging and to test their biological relevance. We analyzed 974 hematoxylin-eosin whole-slide images from a population resource using a dual-attention convolutional neural network and an independent Mask R-CNN model to quantify nuclear size and density, verified by manual review. The classifier distinguished young from aged muscle with high accuracy (AUC 0.91; accuracy 86.2%), and attention maps consistently highlighted nuclear enlargement and spatial disorganization as salient features. Nuclear diameter increased with age (Spearman's ρ = 0.71, p < 0.0001) across automated and manual measurements. Transcriptomes matched to the same donors showed that samples with larger nuclei were enriched for pathways related to chromatin remodeling, proteostasis, cellular senescence, mitochondrial activity, and telomere regulation, whereas smaller nuclei aligned with anti-inflammatory and DNA repair programs. External pediatric inflammatory myopathies exhibited nuclear enlargement comparable to aged muscle, suggesting inflammation-related premature histologic aging. These findings identify nuclear enlargement as a robust, quantifiable feature that integrates structural and molecular signatures of muscle aging. The proposed deep learning-based nuclear morphometry provides a scalable framework for tissue-level aging biomarkers and suggests a potential "muscle aging clock" applicable to both physiological aging and disease states.
