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表观遗传衰老中的精准营养:SHAP优化的机器学习识别出ω-3成分与衰老生物标志物的特定关联。

Precision nutrition in epigenetic aging: SHAP-optimized machine learning identifies omega-3 constituent-specific associations with aging biomarkers.

作者信息

Yan Zhaoqi, Xu Yifeng, Peng Ting, Du Xiufan

机构信息

Department of Rehabilitation Medicine, The Third Hospital of Nanchang, Nanchang People's Hospital, 240 Zhanqian West Road, Xihu District, Nanchang City, 330009, Jiangxi Province, China.

Cardiovascular Department, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, No.5 North Line Pavilion, Xicheng District, Beijing, 100053, China.

出版信息

Biogerontology. 2025 Jul 24;26(4):148. doi: 10.1007/s10522-025-10294-z.

Abstract

This cross-sectional investigation seeks to examine the association between dietary omega-3 fatty acids (including α-linolenic acid [ALA], eicosapentaenoic acid [EPA], and docosahexaenoic acid [DHA]) and biomarkers of cellular aging, specifically DNA methylation age (Horvathage) and telomere length (Horvathtelo), in older adults. Our analysis leveraged nationally representative data from 2,136 participants aged ≥ 50 years in the 1999-2002 NHANES cycles. Multivariable linear regression models with survey weights were constructed to evaluate dose-response relationships, complemented by restricted cubic splines (RCS) for nonlinearity detection. Machine learning optimization included comparative evaluation of nine algorithms through five-fold cross-validation, with model interpretability enhanced via SHapley Additive exPlanations (SHAP) analysis. Higher omega-3 intake (Tertile 3 [T3] vs Tertile 1 [T1]) demonstrated inverse associations with HorvathAge (β = -1.07), particularly for ALA intake (T3 ≥ 1.512 g/d: β = -1.11). Contrastingly, moderate-to-high omega-3 intake (T2 ≥ 0.917 g/d: β = 0.04; T3: β = 0.04) and individual components (ALA_T3: β = 0.04; DHA_T3 ≥ 0.041 g/d: β = 0.05; EPA_T3 ≥ 0.011 g/d: β = 0.03) exhibited positive correlations with HorvathTelo. RCS modeling revealed distinct patterns: linear inverse correlation for HorvathAge versus nonlinear J-shaped association with Horvathtelo. Among ML models, Linear Support Vector Machines achieved superior predictive performance. SHAP feature importance analysis consistently ranked omega-3 composite measures highest, followed by constituent components (ALA > DHA > EPA). Our findings suggest a potential dual role of omega-3 in biological aging modulation: higher intake associates with decelerated epigenetic aging while maintaining telomere length homeostasis. These observations underscore the importance of considering both composite measures and individual components in nutritional gerontology research.

摘要

这项横断面调查旨在研究老年人饮食中的ω-3脂肪酸(包括α-亚麻酸[ALA]、二十碳五烯酸[EPA]和二十二碳六烯酸[DHA])与细胞衰老生物标志物之间的关联,具体为DNA甲基化年龄(Horvath年龄)和端粒长度(Horvath端粒)。我们的分析利用了1999 - 2002年美国国家健康与营养检查调查(NHANES)周期中2136名年龄≥50岁参与者的全国代表性数据。构建了带有调查权重的多变量线性回归模型来评估剂量反应关系,并辅以受限立方样条(RCS)进行非线性检测。机器学习优化包括通过五折交叉验证对九种算法进行比较评估,并通过夏普利值附加解释(SHAP)分析增强模型可解释性。较高的ω-3摄入量(第三分位数[T3]与第一分位数[T1]相比)与Horvath年龄呈负相关(β = -1.07),特别是ALA摄入量(T3≥1.512克/天:β = -1.11)。相反,中度至高度的ω-3摄入量(T2≥0.917克/天:β = 0.04;T3:β = 0.04)以及各成分(ALA_T3:β = 0.04;DHA_T3≥0.041克/天:β = 0.05;EPA_T3≥0.011克/天:β = 0.03)与Horvath端粒呈正相关。RCS建模揭示了不同模式:Horvath年龄呈线性负相关,而与Horvath端粒呈非线性J形关联。在机器学习模型中,线性支持向量机表现出卓越的预测性能。SHAP特征重要性分析始终将ω-3综合指标排在首位,其次是各组成成分(ALA>DHA>EPA)。我们的研究结果表明,ω-3在生物衰老调节中可能具有双重作用:较高的摄入量与表观遗传衰老减缓相关,同时维持端粒长度的稳态。这些观察结果强调了在营养老年学研究中考虑综合指标和各组成成分的重要性。

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