运动诱发骨骼肌肥大的全基因组关联研究及预测模型的构建

IF 2.5 4区 生物学 Q3 CELL BIOLOGY
Physiological genomics Pub Date : 2024-08-01 Epub Date: 2024-06-17 DOI:10.1152/physiolgenomics.00019.2024
Xiaolin Yang, Yanchun Li, Tao Mei, Jiayan Duan, Xu Yan, Lars Robert McNaughton, Zihong He
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引用次数: 0

摘要

目的:研究12周阻力训练(RT)或高强度间歇训练(HIIT)后股直肌(MTRF)肌肉厚度的个体间差异,以探索骨骼肌肥大的遗传结构并构建预测模型。我们对 440 名缺乏运动的成年人在 12 周运动期后的 MTRF 反应进行了肌肉骨骼超声波评估。我们采用了全基因组关联研究(GWAS)来识别与 MTRF 反应相关的变体,分别针对 RT 和 HIIT。利用多基因预测得分(PPS),我们估算了运动诱导肥大的遗传贡献。我们采用随机森林(RF)、支持向量Mac(SVM)和广义线性模型(GLM)等10种交叉验证方法构建了MTRF反应的预测模型。结果表明,两种 RT 后 MTRF 均明显增加(8.8%,P-5.0)。PPS 与 MTRF 反应相关,解释了 47.2% 的 RT 反应变化和 38.3% 的 HIIT 反应变化。值得注意的是,与 RF 模型相比,GLM 和 SVM 预测模型表现出更优越的性能(P-5)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Genome-wide association study of exercise-induced skeletal muscle hypertrophy and the construction of predictive model.

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.

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来源期刊
Physiological genomics
Physiological genomics 生物-生理学
CiteScore
6.10
自引率
0.00%
发文量
46
审稿时长
4-8 weeks
期刊介绍: The Physiological Genomics publishes original papers, reviews and rapid reports in a wide area of research focused on uncovering the links between genes and physiology at all levels of biological organization. Articles on topics ranging from single genes to the whole genome and their links to the physiology of humans, any model organism, organ, tissue or cell are welcome. Areas of interest include complex polygenic traits preferably of importance to human health and gene-function relationships of disease processes. Specifically, the Journal has dedicated Sections focused on genome-wide association studies (GWAS) to function, cardiovascular, renal, metabolic and neurological systems, exercise physiology, pharmacogenomics, clinical, translational and genomics for precision medicine, comparative and statistical genomics and databases. For further details on research themes covered within these Sections, please refer to the descriptions given under each Section.
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