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BACKGROUND

Phenotypic age (PhenoAge), a widely used marker of biological aging, has been shown to be a robust predictor of all-cause mortality and morbidity in different populations. Existing studies on biological aging have primarily focused on individual domains, resulting in a lack of a comprehensive understanding of the multi-systemic dysregulation that occurs in aging.

METHODS

PhenoAge was evaluated based on a linear combination of chronological age (CA) and 9 clinical biomarkers in 952 multi-ethnic Asian women of reproductive age. Phenotypic age acceleration (PhenoAgeAccel), an aging biomarker, represents PhenoAge after adjusting for CA. This study conducts an in-depth association analysis of PhenoAgeAccel with clinical, nutritional, lipidomic, gut microbiome, and genetic factors.

RESULTS

Higher adiposity, glycaemia, plasma saturated fatty acids, kynurenine pathway metabolites, GlycA, riboflavin, nicotinamide, and insulin-like growth factor binding proteins were positively associated with PhenoAgeAccel. Conversely, a healthier diet and higher levels of pyridoxal phosphate, all-trans retinol, betaine, tryptophan, glutamine, histidine, apolipoprotein B, and insulin-like growth factors were inversely associated with PhenoAgeAccel. Lipidomic analysis found 132 lipid species linked to PhenoAgeAccel, with PC(O-36:0) showing the strongest positive association and CE(24:5) demonstrating the strongest inverse association. A genome-wide association study identified rs9864994 as the top genetic variant (P = 5.69E-07) from the ZDHHC19 gene. Gut microbiome analysis revealed that Erysipelotrichaceae UCG-003 and Bacteroides vulgatus were inversely associated with PhenoAgeAccel. Integrative network analysis of aging-related factors underscored the intricate links among clinical, nutritional and lipidomic variables, such as positive associations between kynurenine pathway metabolites, amino acids, adiposity, and insulin resistance. Furthermore, potential mediation effects of blood biomarkers related to inflammation, immune response, and nutritional and energy metabolism were observed in the associations of diet, adiposity, genetic variants, and gut microbial species with PhenoAgeAccel.

CONCLUSIONS

Our findings provide a comprehensive analysis of aging-related factors across multiple platforms, delineating their complex interconnections. This study is the first to report novel signatures in lipidomics, gut microbiome and blood biomarkers specifically associated with PhenoAgeAccel. These insights are invaluable in understanding the molecular and metabolic mechanisms underlying biological aging and shed light on potential interventions to mitigate accelerated biological aging by targeting modifiable factors.

背景

表型年龄（PhenoAge）是一种广泛用于衡量生物年龄的标志物，已被证明是不同人群全因死亡率和发病率的强有力预测因子。现有的生物学衰老研究主要集中在单个领域，因此对衰老过程中多系统失调的认识还不够全面。

方法

本研究基于 952 名多民族亚洲育龄女性的线性组合，对表型年龄（PhenoAge）进行了评估。表型年龄加速（PhenoAgeAccel）是一种衰老标志物，代表调整了年龄（CA）后的表型年龄。本研究对 PhenoAgeAccel 与临床、营养、脂质组学、肠道微生物组和遗传因素进行了深入的关联分析。

结果

更高的体脂率、血糖、血浆饱和脂肪酸、犬尿氨酸途径代谢物、GlycA、核黄素、烟酰胺和胰岛素样生长因子结合蛋白与 PhenoAgeAccel 呈正相关。相反，更健康的饮食和更高水平的吡哆醛磷酸、全反式视黄醇、甜菜碱、色氨酸、谷氨酰胺、组氨酸、载脂蛋白 B 和胰岛素样生长因子与 PhenoAgeAccel 呈负相关。脂质组学分析发现了 132 种与 PhenoAgeAccel 相关的脂质，其中 PC（O-36:0）表现出最强的正相关，CE（24:5）表现出最强的负相关。全基因组关联研究确定了 ZDHHC19 基因中的 rs9864994 为最佳遗传变异（P = 5.69E-07）。肠道微生物组分析显示，Erysipelotrichaceae UCG-003 和 Bacteroides vulgatus 与 PhenoAgeAccel 呈负相关。衰老相关因素的综合网络分析强调了临床、营养和脂质组学变量之间的复杂联系，如犬尿氨酸途径代谢物、氨基酸、体脂率和胰岛素抵抗之间的正相关。此外，在饮食、体脂率、遗传变异和肠道微生物物种与 PhenoAgeAccel 的关联中，观察到与炎症、免疫反应和营养及能量代谢相关的血液生物标志物的潜在中介效应。