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Abstract：
BACKGROUND: Sarcopenia, characterized by a progressive decline in muscle mass, strength, and function, is primarily attributable to aging. DNA methylation, influenced by both genetic predispositions and environmental exposures, plays a significant role in sarcopenia occurrence. This study employed machine learning (ML) methods to identify differentially methylated probes (DMPs) capable of diagnosing sarcopenia in middle-aged individuals. We also investigated the relationship between muscle strength, muscle mass, age, and sarcopenia risk as reflected in methylation profiles.
METHODS: Data from 509 male participants in the urban cohort of the Korean Genome Epidemiology Study_Health Examinee study were categorized into quartile groups based on the sarcopenia criteria for appendicular skeletal muscle index (ASMI) and handgrip strength (HG). To identify diagnostic biomarkers for sarcopenia, we used recursive feature elimination with cross validation (RFECV), to pinpoint DMPs significantly associated with sarcopenia. An ensemble model, leveraging majority voting, was utilized for evaluation. Furthermore, a methylation risk score (MRS) was calculated, and its correlation with muscle strength, function, and age was assessed using likelihood ratio analysis and multinomial logistic regression.
RESULTS: Participants were classified into two groups based on quartile thresholds: sarcopenia (n = 37) with ASMI and HG in the lowest quartile, and normal ranges (n = 48) in the highest. In total, 238 DMPs were identified and eight probes were selected using RFECV. These DMPs were used to build an ensemble model with robust diagnostic capabilities for sarcopenia, as evidenced by an area under the receiver operating characteristic curve of 0.94. Based on eight probes, the MRS was calculated and then validated by analyzing age, HG, and ASMI among the control group (n = 424). Age was positively correlated with high MRS (coefficient, 1.2494; odds ratio [OR], 3.4882), whereas ASMI and HG were negatively correlated with high MRS (ASMI coefficient, -0.4275; OR, 0.6521; HG coefficient, -0.3116; OR, 0.7323).
CONCLUSIONS: Overall, this study identified key epigenetic markers of sarcopenia in Korean males and developed a ML model with high diagnostic accuracy for sarcopenia. The MRS also revealed significant correlations between these markers and age, HG, and ASMI. These findings suggest that both diagnostic models and the MRS can play an important role in managing sarcopenia in middle-aged populations.
METHODS: Data from 509 male participants in the urban cohort of the Korean Genome Epidemiology Study_Health Examinee study were categorized into quartile groups based on the sarcopenia criteria for appendicular skeletal muscle index (ASMI) and handgrip strength (HG). To identify diagnostic biomarkers for sarcopenia, we used recursive feature elimination with cross validation (RFECV), to pinpoint DMPs significantly associated with sarcopenia. An ensemble model, leveraging majority voting, was utilized for evaluation. Furthermore, a methylation risk score (MRS) was calculated, and its correlation with muscle strength, function, and age was assessed using likelihood ratio analysis and multinomial logistic regression.
RESULTS: Participants were classified into two groups based on quartile thresholds: sarcopenia (n = 37) with ASMI and HG in the lowest quartile, and normal ranges (n = 48) in the highest. In total, 238 DMPs were identified and eight probes were selected using RFECV. These DMPs were used to build an ensemble model with robust diagnostic capabilities for sarcopenia, as evidenced by an area under the receiver operating characteristic curve of 0.94. Based on eight probes, the MRS was calculated and then validated by analyzing age, HG, and ASMI among the control group (n = 424). Age was positively correlated with high MRS (coefficient, 1.2494; odds ratio [OR], 3.4882), whereas ASMI and HG were negatively correlated with high MRS (ASMI coefficient, -0.4275; OR, 0.6521; HG coefficient, -0.3116; OR, 0.7323).
CONCLUSIONS: Overall, this study identified key epigenetic markers of sarcopenia in Korean males and developed a ML model with high diagnostic accuracy for sarcopenia. The MRS also revealed significant correlations between these markers and age, HG, and ASMI. These findings suggest that both diagnostic models and the MRS can play an important role in managing sarcopenia in middle-aged populations.
摘要：
背景：肌肉减少症，以肌肉质量逐渐下降为特征，力量,和功能，主要归因于衰老。DNA甲基化，受遗传易感性和环境暴露的影响，在肌肉减少症的发生中起重要作用。这项研究采用机器学习（ML）方法来识别能够诊断中年人肌肉减少症的差异甲基化探针（DMP）。我们还调查了肌肉力量之间的关系，肌肉质量，年龄,和肌肉减少症风险反映在甲基化谱中。
方法：根据阑尾骨骼肌指数（ASMI）和握力（HG）的肌肉减少症标准，将来自韩国基因组流行病学研究_健康人研究城市队列中509名男性参与者的数据分为四分位数组。为了确定肌少症的诊断生物标志物，我们使用递归特征消除与交叉验证(RFECV)，确定与肌肉减少症显著相关的DMPs。合奏模型，利用多数投票，用于评估。此外，计算甲基化风险评分（MRS），以及它与肌肉力量的相关性，函数,使用似然比分析和多项逻辑回归评估年龄。
结果：根据四分位数阈值将参与者分为两组：肌肉减少症（n=37），ASMI和HG位于最低四分位数，和正常范围（n=48）最高。总的来说,鉴定了238个DMP,并使用RFECV选择了8个探针。这些DMPs被用来建立一个具有强大诊断能力的集成模型，接收器工作特性曲线下的面积为0.94。基于八个探测器,计算MRS，然后通过分析年龄来验证，HG,对照组和ASMI(n=424)。年龄与高MRS呈正相关(系数，1.2494；赔率比[OR]，3.4882),而ASMI和HG与高MRS呈负相关(ASMI系数，-0.4275;或,0.6521；HG系数，-0.116；或，0.7323)。
结论：总体而言，这项研究确定了韩国男性肌肉减少症的关键表观遗传标记,并建立了一种诊断肌肉减少症的高准确率ML模型.MRS还揭示了这些标记与年龄之间的显着相关性，HG,ASMI。这些发现表明，诊断模型和MRS都可以在中年人群的肌肉减少症中发挥重要作用。
方法：根据阑尾骨骼肌指数（ASMI）和握力（HG）的肌肉减少症标准，将来自韩国基因组流行病学研究_健康人研究城市队列中509名男性参与者的数据分为四分位数组。为了确定肌少症的诊断生物标志物，我们使用递归特征消除与交叉验证(RFECV)，确定与肌肉减少症显著相关的DMPs。合奏模型，利用多数投票，用于评估。此外，计算甲基化风险评分（MRS），以及它与肌肉力量的相关性，函数,使用似然比分析和多项逻辑回归评估年龄。
结果：根据四分位数阈值将参与者分为两组：肌肉减少症（n=37），ASMI和HG位于最低四分位数，和正常范围（n=48）最高。总的来说,鉴定了238个DMP,并使用RFECV选择了8个探针。这些DMPs被用来建立一个具有强大诊断能力的集成模型，接收器工作特性曲线下的面积为0.94。基于八个探测器,计算MRS，然后通过分析年龄来验证，HG,对照组和ASMI(n=424)。年龄与高MRS呈正相关(系数，1.2494；赔率比[OR]，3.4882),而ASMI和HG与高MRS呈负相关(ASMI系数，-0.4275;或,0.6521；HG系数，-0.116；或，0.7323)。
结论：总体而言，这项研究确定了韩国男性肌肉减少症的关键表观遗传标记,并建立了一种诊断肌肉减少症的高准确率ML模型.MRS还揭示了这些标记与年龄之间的显着相关性，HG,ASMI。这些发现表明，诊断模型和MRS都可以在中年人群的肌肉减少症中发挥重要作用。
