Sun Yangyang, Wu Wei, Sun Xiaoqin, Ren Ling, Ren Zhengyun, Gong Gu
Department of Anesthesiology, The Affiliated Hospital, Southwest Medical University, Luzhou, China.
Department of Anesthesiology, The General Hospital of Western Theater Command, Chengdu, China.
Medicine (Baltimore). 2025 Jul 18;104(29):e43431. doi: 10.1097/MD.0000000000043431.
Cognitive decline in older adults is a growing public health concern, and traditional measures such as chronological age are insufficient for accurately assessing cognitive function. Phenotypic age (PhenoAge) and phenotypic age acceleration (PhenoAgeAccel), which reflect biological age and aging acceleration, may be better predictors of cognitive decline. Additionally, physical activity (PA) has been recognized for its protective effects on aging and cognitive health. This study explored the role of PhenoAge and PhenoAgeAccel in cognitive performance and investigated whether PA moderates this relationship. We used data from the National Health and Nutrition Examination Survey, which analyzed 1298 participants aged 60 years and older. PhenoAge was calculated using 10 biomarkers, and PhenoAgeAccel was derived as the difference between chronological age and PhenoAge. Cognitive performance was assessed using the Digit Symbol Substitution Test. The relationship between PhenoAge, PhenoAgeAccel, and low cognitive performance was analyzed using weighted logistic regression models. Subgroup and sensitivity analyses were conducted, and the interactions between PhenoAgeAccel and PA were evaluated. Both PhenoAge and PhenoAgeAccel scores were significantly associated with low cognitive performance. The highest quartiles of PhenoAge (odds ratio = 3.22, P = .025) and PhenoAgeAccel (odds ratio = 2.31, P = .022) were associated with higher odds of low cognitive performance. By contrast, chronological age did not show a significant relationship with cognitive performance. PA was found to moderate the association between PhenoAgeAccel and cognitive performance (P for interaction = .045). Higher levels of PA attenuated the impact of PhenoAgeAccel on cognitive decline. Receiver operating characteristic curve analysis showed that PhenoAge (area under the curve [AUC] = 0.562), PhenoAgeAccel (AUC = 0.589), and chronological age (AUC = 0.513) were significantly different. In conclusion, PhenoAgeAccel and PA are significant predictors of cognitive decline, with PA offering a protective effect against the impact of accelerated aging on cognition.
老年人认知能力下降是一个日益严重的公共卫生问题，而诸如实际年龄等传统指标不足以准确评估认知功能。反映生物年龄和衰老加速的表型年龄（PhenoAge）和表型年龄加速（PhenoAgeAccel）可能是认知能力下降的更好预测指标。此外，身体活动（PA）对衰老和认知健康的保护作用已得到认可。本研究探讨了PhenoAge和PhenoAgeAccel在认知表现中的作用，并调查了PA是否调节这种关系。我们使用了来自美国国家健康与营养检查调查的数据，该调查分析了1298名60岁及以上的参与者。PhenoAge使用10种生物标志物计算得出，PhenoAgeAccel通过实际年龄与PhenoAge的差值得出。认知表现通过数字符号替换测验进行评估。使用加权逻辑回归模型分析PhenoAge、PhenoAgeAccel与低认知表现之间的关系。进行了亚组分析和敏感性分析，并评估了PhenoAgeAccel与PA之间的相互作用。PhenoAge和PhenoAgeAccel得分均与低认知表现显著相关。PhenoAge最高四分位数（优势比=3.22，P=0.025）和PhenoAgeAccel最高四分位数（优势比=2.31，P=0.022）与低认知表现的较高几率相关。相比之下，实际年龄与认知表现未显示出显著关系。发现PA调节了PhenoAgeAccel与认知表现之间的关联（交互作用P=0.045）。较高水平的PA减弱了PhenoAgeAccel对认知能力下降的影响。受试者工作特征曲线分析表明，PhenoAge（曲线下面积[AUC]=0.562）、PhenoAgeAccel（AUC=0.589）和实际年龄（AUC=0.513）存在显著差异。总之，PhenoAgeAccel和PA是认知能力下降的重要预测指标，PA对加速衰老对认知的影响具有保护作用。
