Doan Dieu Ni Thi, Ku Boncho, Kim Kahye, Lee Kunho, Kim Jaeuk U
Digital Health Research Division, Korea Institute of Oriental Medicine, Daejeon, South Korea.
School of Korean Convergence Medical Science, University of Science and Technology, Daejeon, South Korea.
Biogerontology. 2025 Mar 30;26(2):80. doi: 10.1007/s10522-025-10222-1.
Aging is a complex process that affects human health and lifespan. While chronological age (CA) is a significant risk factor for many diseases, it does not fully capture biological changes that influence health span. This study explores cognitive measures using the Seoul Neuropsychological Screening Battery and body composition profiles as potential biological age (BA) markers in the older population. Multiple linear regression, principal component analysis (PCA), and the Klemera-Doubal (KDM) methods were used to construct sex-specific BA formulas from 296 healthy individuals (160 women, 136 men, mean age: 70.3 years). The BA formulas were applied to a new cohort of 708 diseased people (376 women, 332 men, mean age: 73.5 years) to generate BAs for each sex. Subsequently, we compared the classification power of CA, BAs, and selected variables when differentiating the healthy group from the comorbidity cohort, with sex stratification. As a result, we found that BAs from PCA and KDM were significantly higher than CA in the diseased group but comparable in the healthy group. BAs from PCA and KDM methods yielded higher classification accuracies than CA alone. Notably, frontal executive domain score and body reactance emerged as two promising markers for aging. These findings suggest that body composition measures and cognitive assessments offer a more accurate reflection of biological health than CA alone. A cohort with a wider age range is needed to generalize these findings.
衰老过程复杂，影响人类健康和寿命。虽然实足年龄（CA）是许多疾病的重要风险因素，但它并不能完全反映影响健康寿命的生物学变化。本研究采用首尔神经心理筛查量表探索认知指标，并将身体成分特征作为老年人群潜在的生物学年龄（BA）标志物。运用多元线性回归、主成分分析（PCA）和克莱梅拉 - 杜巴尔（KDM）方法，从296名健康个体（160名女性，136名男性，平均年龄：70.3岁）构建特定性别的BA公式。将这些BA公式应用于708名患病个体的新队列（376名女性，332名男性，平均年龄：73.5岁），以生成各性别的BA值。随后，我们在区分健康组和共病队列时，按性别分层比较了CA、BA和选定变量的分类能力。结果发现，PCA和KDM得出的BA值在患病组中显著高于CA，但在健康组中相当。PCA和KDM方法得出的分类准确率高于单独使用CA。值得注意的是，额叶执行领域得分和身体电抗成为衰老的两个有前景的标志物。这些发现表明，身体成分测量和认知评估比单独的CA更能准确反映生物健康状况。需要一个年龄范围更广的队列来推广这些发现。
