Faquih Tariq O, van Hylckama Vlieg Astrid, Surendran Praveen, Butterworth Adam S, Li-Gao Ruifang, de Mutsert Renée, Rosendaal Frits R, Noordam Raymond, van Heemst Diana, Willems van Dijk Ko, Mook-Kanamori Dennis O
Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands.
Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, Massachusetts, USA.
J Gerontol A Biol Sci Med Sci. 2025 Feb 10;80(3). doi: 10.1093/gerona/glae280.
Chronological age is a major risk factor for numerous diseases. However, chronological age does not capture the complex biological aging process. The difference between chronological age and biologically driven aging could be more informative in reflecting health status. Here, we set out to develop a metabolomic age prediction model by applying ridge regression and bootstrapping with 826 metabolites (678 endogenous and 148 xenobiotics) measured by an untargeted platform in relatively healthy blood donors aged 18-75 years from the INTERVAL study (N = 11 977; 50.2% men). After bootstrapping internal validation, the metabolomic age prediction models demonstrated high performance with an adjusted R2 of 0.83 using all metabolites and 0.82 using only endogenous metabolites. The former was significantly associated with obesity and cardiovascular disease in the Netherlands Epidemiology of Obesity study (N = 599; 47.0% men; age range = 45-65) due to the contribution of medication-derived metabolites-namely salicylate and ibuprofen-and environmental exposures such as cotinine. Additional metabolomic age prediction models using all metabolites were developed for men and women separately. The models had high performance (R² = 0.85 and 0.86) but shared a moderate correlation of 0.72. Furthermore, we observed 163 sex-dimorphic metabolites, including threonine, glycine, cholesterol, and androgenic and progesterone-related metabolites. Our strongest predictors across all models were novel and included hydroxyasparagine (Model Endo + Xeno β = 4.74), vanillylmandelate (β = 4.07), and 5,6-dihydrouridine (β = -4.2). Our study presents a robust metabolomic age model that reveals distinct sex-based age-related metabolic patterns and illustrates the value of including xenobiotic to enhance metabolomic prediction accuracy.
实足年龄是众多疾病的主要风险因素。然而，实足年龄并不能反映复杂的生物衰老过程。实足年龄与生物驱动的衰老之间的差异在反映健康状况方面可能更具参考价值。在此，我们着手通过岭回归和自抽样法，利用非靶向平台在年龄为18 - 75岁的相对健康献血者（来自INTERVAL研究，N = 11977；男性占50.2%）中测量的826种代谢物（678种内源性代谢物和148种外源性物质）来开发代谢组年龄预测模型。经过自抽样内部验证，代谢组年龄预测模型表现出高性能，使用所有代谢物时调整后的R²为0.83，仅使用内源性代谢物时为0.82。在荷兰肥胖流行病学研究（N = 599；男性占47.0%；年龄范围 = 45 - 65岁）中，由于药物衍生代谢物（即水杨酸盐和布洛芬）以及诸如可替宁等环境暴露因素的作用，前者与肥胖和心血管疾病显著相关。另外，还分别为男性和女性开发了使用所有代谢物的代谢组年龄预测模型。这些模型具有高性能（R² = 0.85和0.86），但相关性中等，为0.72。此外，我们观察到163种性别二态性代谢物，包括苏氨酸、甘氨酸、胆固醇以及与雄激素和孕酮相关的代谢物。我们在所有模型中最强的预测因子都是新发现的，包括羟基天冬酰胺（模型内源性 + 外源性物质β = 4.74）、香草扁桃酸（β = 4.07）和5,6 - 二氢尿苷（β = -4.2）。我们的研究提出了一个强大的代谢组年龄模型，该模型揭示了基于性别的独特年龄相关代谢模式，并说明了纳入外源性物质以提高代谢组预测准确性的价值。
