Wei Chengcheng, Yang Qian, He Jingke, Luo Yu, Han Kun, Li Junkun, Su Shuai, Zhang Jindong, Wang Hongxing, Wang Delin
Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Institute for Brain Science and Disease, Key Laboratory of Major Brain Disease and Aging Research (Ministry of Education), Chongqing Medical University, Chongqing, China.
Front Nutr. 2025 Mar 25;12:1538289. doi: 10.3389/fnut.2025.1538289. eCollection 2025.
This study aims to investigate the comprehensive association between healthy dietary patterns, biological aging, and kidney stones in a large population-based cohort.
We analyzed data from 6 cycles of NHANES surveys from 2007 to 2018 and included a total of 26,755 participants. The association was examined using logistic regression, restricted cubic splines, and a mediation model. Machine learning with Shapley Additive Explanations (SHAP) was used to determine the relative importance of dietary factors. Sensitivity analysis was conducted to validate the stability of the results.
A higher healthy dietary score was significantly associated with a reduced risk of kidney stone prevalence, as indicated by the Alternative Healthy Eating Index (AHEI) (OR = 0.76, 95% CI 0.69-0.84), the Dietary Approaches to Stop Hypertension Index (DASHI) (OR = 0.67, 95% CI 0.58-0.77), the Healthy Eating Index 2020 (HEI-2020) (OR = 0.80, 95% CI 0.72-0.89), and the Mediterranean Diet Index (MEDI) (OR = 0.81, 95% CI 0.73-0.89). Conversely, higher aging indicators were associated with an increased risk of kidney stones, including the following: Klemera-Doubal Method Age (KDMAge) (OR = 2.40, 95% CI 1.70-3.37) and Phenotypic Age (PhenoAge) (OR = 2.36, 95% CI 1.75-3.19). Mediation analyses suggested that aging indicators significantly mediated the relationship between healthy dietary patterns and kidney stones. Machine learning with SHAP revealed the relative importance of dietary patterns and specific dietary components in this association. The sensitivity analysis was largely consistent with the primary analyses.
These findings provide valuable insights into the complex interplay between dietary patterns, biological aging, and the risk of kidney stone. Promoting healthy dietary patterns may be an effective strategy for kidney stone prevention, potentially through the modulation of biological aging processes.
本研究旨在调查在一个基于人群的大型队列中，健康饮食模式、生物衰老与肾结石之间的综合关联。
我们分析了2007年至2018年6轮美国国家健康与营养检查调查（NHANES）的数据，共纳入26,755名参与者。使用逻辑回归、受限立方样条和中介模型来检验这种关联。采用带有夏普利值加法解释（SHAP）的机器学习方法来确定饮食因素的相对重要性。进行敏感性分析以验证结果的稳定性。
较高的健康饮食得分与肾结石患病率风险降低显著相关，具体表现为替代健康饮食指数（AHEI）（比值比[OR]=0.76，95%置信区间[CI]0.69 - 0.84）、终止高血压饮食方法指数（DASHI）（OR = 0.67，95% CI 0.58 - 0.77）、2020年健康饮食指数（HEI - 2020）（OR = 0.80，95% CI 0.72 - 0.89）以及地中海饮食指数（MEDI）（OR = 0.81，95% CI 0.73 - 0.89）。相反，较高的衰老指标与肾结石风险增加相关，包括以下指标：克莱梅拉 - 杜巴尔法年龄（KDMAge）（OR = 2.40，95% CI 1.70 - 3.37）和表型年龄（PhenoAge）（OR = 2.36，95% CI 1.75 - 3.19）。中介分析表明，衰老指标显著介导了健康饮食模式与肾结石之间的关系。带有SHAP的机器学习揭示了饮食模式和特定饮食成分在这种关联中的相对重要性。敏感性分析在很大程度上与主要分析结果一致。
这些发现为饮食模式、生物衰老与肾结石风险之间的复杂相互作用提供了有价值的见解。促进健康饮食模式可能是预防肾结石的有效策略，可能是通过调节生物衰老过程来实现。
