Sebastiani Paola, Thyagarajan Bharat, Sun Fangui, Schupf Nicole, Newman Anne B, Montano Monty, Perls Thomas T
Department of Biostatistics, Boston University School of Public Health, 801 Massachusetts Avenue, Boston, MA, 02118, USA.
Department of Laboratory Medicine and Pathology, University of Minnesota Medical School, MMC 609 Mayo, 420 Delaware, Minneapolis, MN, 55455, USA.
Aging Cell. 2017 Apr;16(2):329-338. doi: 10.1111/acel.12557. Epub 2017 Jan 6.
Because people age differently, age is not a sufficient marker of susceptibility to disabilities, morbidities, and mortality. We measured nineteen blood biomarkers that include constituents of standard hematological measures, lipid biomarkers, and markers of inflammation and frailty in 4704 participants of the Long Life Family Study (LLFS), age range 30-110 years, and used an agglomerative algorithm to group LLFS participants into clusters thus yielding 26 different biomarker signatures. To test whether these signatures were associated with differences in biological aging, we correlated them with longitudinal changes in physiological functions and incident risk of cancer, cardiovascular disease, type 2 diabetes, and mortality using longitudinal data collected in the LLFS. Signature 2 was associated with significantly lower mortality, morbidity, and better physical function relative to the most common biomarker signature in LLFS, while nine other signatures were associated with less successful aging, characterized by higher risks for frailty, morbidity, and mortality. The predictive values of seven signatures were replicated in an independent data set from the Framingham Heart Study with comparable significant effects, and an additional three signatures showed consistent effects. This analysis shows that various biomarker signatures exist, and their significant associations with physical function, morbidity, and mortality suggest that these patterns represent differences in biological aging. The signatures show that dysregulation of a single biomarker can change with patterns of other biomarkers, and age-related changes of individual biomarkers alone do not necessarily indicate disease or functional decline.
由于人们衰老的方式不同,年龄并不是易患残疾、发病和死亡的充分标志。我们在长寿家庭研究(LLFS)的4704名参与者中测量了19种血液生物标志物,包括标准血液学指标的成分、脂质生物标志物以及炎症和衰弱标志物,这些参与者年龄在30至110岁之间,并使用凝聚算法将LLFS参与者分组为不同的集群,从而产生26种不同的生物标志物特征。为了测试这些特征是否与生物衰老的差异相关,我们使用LLFS中收集的纵向数据,将它们与生理功能的纵向变化以及癌症、心血管疾病、2型糖尿病和死亡率的发病风险进行关联分析。相对于LLFS中最常见的生物标志物特征,特征2与显著更低的死亡率、发病率以及更好的身体功能相关,而其他九个特征与衰老不太成功相关,其特征是衰弱、发病和死亡风险更高。七个特征的预测价值在来自弗雷明汉心脏研究的独立数据集中得到了重复验证,具有相当显著的效果,另外三个特征也显示出一致的效果。该分析表明存在各种生物标志物特征,并且它们与身体功能、发病率和死亡率的显著关联表明这些模式代表了生物衰老的差异。这些特征表明单个生物标志物的失调可以随着其他生物标志物的模式而变化,仅单个生物标志物的年龄相关变化不一定表明疾病或功能衰退。