Fu Grace, Rushing Blake R, Graves Lee, Nieman David C, Pellegrini Matteo, Soldano Matthew, Thompson Michael J, Sakaguchi Camila A, Pathmasiri Wimal, Sumner Susan J
Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.
Nutrition Research Institute, University of North Carolina at Chapel Hill, Kannapolis, NC, 28081, USA.
Hum Genomics. 2025 Aug 30;19(1):101. doi: 10.1186/s40246-025-00817-7.
This multi-omics cross-sectional study investigated differences in metabolomics, proteomics, and epigenomics profiles between two groups of adults matched for age but differing in lifestyle factors such as body composition, diet, and physical activity patterns. Data from prior studies were utilized for a comprehensive integrative analysis. The study included 52 participants in the lifestyle group (LIFE) (28 males, 24 females) and 52 in the control group (CON) (27 males, 25 females). Using multi-omics integration software (OmicsNet and Pathview), 96 significantly (p < 0.05) enriched pathways were identified that differentiated the LIFE and CON groups. Top pathways significantly (p < 2.63 × 10) influenced by group status included fatty acid degradation, fatty acid elongation, glutathione metabolism, Parkinson disease, and central carbon metabolism in cancer. This study identified a distinct metabolic signature comprised of metabolites, proteins, and gene methylation sites associated with a healthy lifestyle. These findings provide unique, but complementary, results to previous single-omics analyses using metabolomics and proteomics procedures which showed that the LIFE group exhibited lower plasma bile acid levels, higher levels of beneficial fatty acids, reduced innate immune activation, enhanced lipoprotein metabolism, and increased HDL remodeling. The current multi-omics analysis builds on these previous results by providing a more holistic view of how metabolites, proteins, and methylation sites associated with a healthy lifestyle, providing a larger, more comprehensive list of altered pathways. Additionally, the integrated analysis revealed connections between lifestyle factors and conditions such as cancer and insulin resistance beyond what identified in the single-omics approaches, highlighting the broader metabolic impact of lifestyle on health. Overall, the signatures identified by this multi-omics approach provide a basis for developing more translational biomarkers, such as those that defined the cancer and insulin resistance pathways that can be used to assess one's state of health and provide guidance on behavior modifications that should be taken to lower disease risk.
这项多组学横断面研究调查了两组年龄匹配但在身体组成、饮食和身体活动模式等生活方式因素上存在差异的成年人在代谢组学、蛋白质组学和表观基因组学谱方面的差异。利用先前研究的数据进行全面的综合分析。该研究包括52名生活方式组（LIFE）参与者（28名男性，24名女性）和52名对照组（CON）参与者（27名男性，25名女性）。使用多组学整合软件（OmicsNet和Pathview），鉴定出96条显著（p < 0.05）富集的通路，这些通路区分了LIFE组和CON组。受组状态显著（p < 2.63 × 10）影响的顶级通路包括脂肪酸降解、脂肪酸延长、谷胱甘肽代谢、帕金森病和癌症中的中心碳代谢。本研究确定了一个由与健康生活方式相关的代谢物、蛋白质和基因甲基化位点组成的独特代谢特征。这些发现为先前使用代谢组学和蛋白质组学程序进行的单组学分析提供了独特但互补的结果，这些分析表明LIFE组的血浆胆汁酸水平较低、有益脂肪酸水平较高、先天免疫激活降低、脂蛋白代谢增强以及高密度脂蛋白重塑增加。当前的多组学分析在这些先前结果的基础上，通过提供更全面的视角来展示与健康生活方式相关的代谢物、蛋白质和甲基化位点，提供了一份更大、更全面的改变通路清单。此外，综合分析揭示了生活方式因素与癌症和胰岛素抵抗等状况之间的联系，超出了单组学方法所确定的范围，突出了生活方式对健康更广泛的代谢影响。总体而言，这种多组学方法确定的特征为开发更多可转化的生物标志物提供了基础，例如那些定义了癌症和胰岛素抵抗通路的生物标志物，可用于评估一个人的健康状况，并为降低疾病风险应采取的行为改变提供指导。
