研究12周阻力训练(RT)或高强度间歇训练(HIIT)后股直肌肌层厚度(MTRF)的个体差异,探讨骨骼肌肥大的遗传结构并构建预测模型。在12周的运动期后,我们对440名不运动的成年人的MTRF反应进行了肌肉骨骼超声评估。全基因组关联研究(GWAS)用于鉴定与MTRF反应相关的变异,分别用于RT和HIIT。利用多基因预测评分(PPS),我们估计了运动诱导的肥大的遗传贡献。使用随机森林(RF)构建了MTRF响应的预测模型,支持向量Mac(SVM),和广义线性模型(GLM)的10种交叉验证方法。MTRF在两次RT后显著增加(8.8%,P<0.05)和HIIT(5.3%,P<0.05),但具有相当大的个体差异(RT:-13.5~38.4%,HIIT:-14.2%~30.7%)。在P<1×10-5的显著性水平上鉴定出RT中的11个前导SNP和HIIT中的8个前导SNP。PPS与MTRF反应相关,解释了47.2%的RT响应变异和38.3%的HIIT响应变异。值得注意的是,与RF模型相比,GLM和SVM预测模型表现出优异的性能(p<0.05),并且GLM表现出最佳性能,AUC为0.809(95CI:0.669-0.949)。PPS等因素,MTRF基线,运动方案对MTRF对运动的反应产生了影响,PPS是主要贡献者。GLM和SVM预测模型,结合遗传和表型因素,成为预测运动引起的骨骼肌肥大的有希望的工具。
The aim of the current study was to investigate interindividual differences in muscle thickness of the rectus femoris (MTRF) following 12 wk of resistance training (RT) or high-intensity interval training (HIIT) to explore the genetic architecture underlying skeletal muscle hypertrophy and to construct predictive models. We conducted musculoskeletal ultrasound assessments of the MTRF response in 440 physically inactive adults after the 12-wk exercise period. A genome-wide association study was used to identify variants associated with the MTRF response, separately for RT and HIIT. Using the polygenic predictor score (PPS), we estimated the genetic contribution to exercise-induced hypertrophy. Predictive models for the MTRF response were constructed using random forest (RF), support vector mac (SVM), and generalized linear model (GLM) in 10 cross-validated approaches. MTRF increased significantly after both RT (8.8%, P < 0.05) and HIIT (5.3%, P < 0.05), but with considerable interindividual differences (RT: -13.5 to 38.4%, HIIT: -14.2 to 30.7%). Eleven lead single-nucleotide polymorphisms in RT and eight lead single-nucleotide polymorphisms in HIIT were identified at a significance level of P < 1 × 10-5. The PPS was associated with the MTRF response, explaining 47.2% of the variation in response to RT and 38.3% of the variation in response to HIIT. Notably, the GLM and SVM predictive models exhibited superior performance compared with RF models (P < 0.05), and the GLM demonstrated optimal performance with an area under curve of 0.809 (95% confidence interval: 0.669-0.949). Factors such as PPS, baseline MTRF, and exercise protocol exerted influence on the MTRF response to exercise, with PPS being the primary contributor. The GLM and SVM predictive model, incorporating both genetic and phenotypic factors, emerged as promising tools for predicting exercise-induced skeletal muscle hypertrophy.NEW & NOTEWORTHY The interindividual variability induced muscle hypertrophy by resistance training (RT) or high-intensity interval training (HIIT) and the associated genetic architecture remain uncertain. We identified genetic variants that underlie RT- or HIIT-induced muscle hypertrophy and established them as pivotal factors influencing the response regardless of the training type. The genetic-phenotype predictive model developed has the potential to identify nonresponders or individuals with low responsiveness before engaging in exercise training.