Utilizing Nutrition-Related Biomarkers to Develop a Nutrition-Related Aging Clock for the Chinese Demographic

Yaqing Ma¹Yamin Dang²Lv-Tao Zeng¹Xin Gao²Si-Jia Li²Li-Qun Zhang²Jin Li²Xiao-Yang Zhou²Shan-Shan Ren³Hong-Lei Liu⁴Ruo-Mei Qi²Jing Pang²Ju Cui²Tie-Mei Zhang²Jian-Ping Cai^1,2*

• ¹Peking University Fifth School of Clinical Medicine, Beijing, Beijing, China
• ²National Center of Gerontology of National Health Commission , P.R. China, The Key Laboratory of Geriatrics, Beijing Institute of Geriatrics, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital, beijing, China
• ³Department of Clinical Nutrition, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Med-icine, Chinese Academy of Medical Sciences, Beijing 100730, P.R China, Beijing, China
• ⁴Beijing Advanced Innovation Center for Big Data-based Precision Medicine,, Capital Medical University, Bei-jing, P.R. China, China

This study aims to explore the relationship between nutrition-related biomarkers, body composition, and oxidative stress indicators with the human aging process. Human aging is a complex physiological process, and understanding its mechanisms is crucial for developing effective prevention and intervention strategies. This research examined 100 healthy individuals aged 26-85 years using quantitative analysis methods to measure the concentrations of 9 amino acids and 13 vitamins in plasma, alongside oxidative stress indicators 8-oxoguanosine (8-oxoGuo) and 8-oxodeoxyguanosine (8-oxo-dGuo) in the urine. Additionally, we conducted a systematic assessment of physiological differences across various age groups by integrating body composition measurements obtained through bioelectrical impedance analysis (BIA). The results indicate that the younger group had considerably lower levels of oxidative stress markers than the older group, and the plasma concentrations of specific amino acids and vitamins significantly change with age. We found a correlation between blood nutrient content and age and successfully developed a nutrition-related aging clock model to predict an individual's biological age based on the presented findings. The Light Gradient Boosting Machine algorithm achieved competitive performance, with a mean absolute error of 2.5877 and a coefficient of determination (R2) of 0.8807. This model reveals a significant correlation between nutrition-related biomarkers, body composition, oxidative stress markers, and age-related changes. Furthermore, through correlation analysis with biochemical indicators and physical examination results, the model further reflects the physiological changes under different aging rates. These findings provide a new perspective on understanding individual nutrition status differences in aging and offer important scientific evidence for in-depth research into aging mechanisms and the development of aging intervention strategies.