Salimi Sh, Vehtari A, Salive M, Kaeberlein M, Raftery D, Ferrucci L
Department of Anesthesiology and Pain Medicine, University of Washington, Seattle, WA, USA.
Department of Computer Science, Aalto University, Aalto, Finland.
Nat Commun. 2025 May 5;16(1):4007. doi: 10.1038/s41467-025-58819-x.
Medical practice mainly addresses single diseases, neglecting multimorbidity as a heterogeneous health decline across organ systems. Aging is a multidimensional process and cannot be captured by a single metric. Therefore, we assessed global health in longitudinal studies, BLSA (n = 907), InCHIANTI (n = 986), and NHANES (n = 40,790), by examining disease severities in 13 bodily systems, generating the Body Organ Disease Number (BODN), reflecting progressive system morbidities. We used Bayesian ordinal models, regressing BODN over organ specific and all organs disease severities to obtain Body System-Specific Clocks and the Body Clock, respectively. The Body Clock is BODN weighted by the posterior coefficient of diseases for each individual. It supersedes the frailty index, predicting disability, geriatric syndrome, SPPB, and mortality with ≥90% accuracy. The Health Octo Tool, derived from Bodily System-Specific Clocks, the Body Clock and Clocks that incorporate walking speed and disability and their aging rates, captures multidimensional aging heterogeneity across organs and individuals.
医学实践主要关注单一疾病，而忽视了多病共存这一涉及多个器官系统的异质性健康衰退情况。衰老乃是一个多维度的过程，无法通过单一指标来衡量。因此，我们在纵向研究中，即巴尔的摩纵向衰老研究（BLSA，n = 907）、基安蒂地区老年人健康与营养状况调查（InCHIANTI，n = 986）以及美国国家健康与营养检查调查（NHANES，n = 40,790）中，通过检查13个身体系统的疾病严重程度，生成身体器官疾病数量（BODN），以反映各系统逐渐出现的疾病情况，从而评估整体健康状况。我们使用贝叶斯序数模型，分别将BODN对特定器官及所有器官的疾病严重程度进行回归分析，以获得特定身体系统时钟和身体时钟。身体时钟是由每个个体疾病的后验系数加权的BODN。它取代了衰弱指数，对残疾、老年综合征、短身体性能测试（SPPB）和死亡率的预测准确率≥90%。健康八维度工具源自特定身体系统时钟、身体时钟以及纳入步行速度和残疾情况及其衰老率的时钟，它能够捕捉不同器官和个体之间多维度的衰老异质性。
