Genetic prediction of lower limb isometric strength changes after 12 weeks of resistance training.

This study aimed to identify genetic variations associated with changes in isometric strength following resistance training and develop a predictive model for understanding training effects, providing insights for tailored fitness guidance. A 12-week resistance training program, consisting of traditional squats and bench press exercises, was completed by 193 healthy Chinese adults, with isometric strength assessed before and after the intervention. DNA was extracted for whole-genome genotyping, followed by genomewide association analysis using PLINK 1.9. Lasso regression was used to screen variables, and predictive models for training effectiveness were established using logistic regression, Nomogram, and stepwise linear regression. Following the training, participants showed an increased isometric strength (Δ = 8.69%, p = 0.08, ES = 0.07), ranging from -55.78% to 133.47%. Nineteen lead SNPs were significantly associated with improvements in lower limb isometric strength (p < 1 × 10-5), with 8 SNPs showing nominal associations (p < 1 × 10-5), and rs4623258 was the only SNP with genome-wide significance (p < 5 × 10-8). Stepwise linear regression identified several factors that impact training effects: baseline isometric strength, lower limb muscle mass, right rectus femoris length, rs4623258, rs344843, rs112298078, rs200507975, rs559077, rs8008364, rs6837485, rs4712860, rs76521421, rs1965365, and rs2746086 (adjusted R2 = 0.704). Logistic regression identified isometric strength, lean body mass, trunk fat, right rectus femoris length, rs139338397, rs76521421, rs8008364, and rs6579275 as significant factors influencing the outcome. (AUC = 0.875, p < 0.001). These findings show that predictive models can accurately predict changes in lower limb isometric strength after resistance training in Chinese subjects. However, applicability is primarily confined to East Asians, necessitating further studies in diverse populations to validate broader relevance.