A new aging measure captures morbidity and mortality risk across diverse subpopulations from NHANES IV: A cohort study.
A new aging measure captures morbidity and mortality risk across diverse subpopulations from NHANES IV: A cohort study.
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DOI：
10.1371/journal.pmed.1002718
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发表时间：
2018-12-01
期刊：
影响因子：
15.8
通讯作者：
Levine, Morgan
中科院分区：
文献类型：
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作者：
Liu, Zuyun;Kuo, Pei-Lun;Levine, Morgan
背景技术背景：一个人的衰老速度对他/她的死亡和疾病风险有重要影响;因此，使用可观察的特征量化衰老对临床，基础和观察性研究具有重要应用。基于常规的临床化学生物标志物，我们以前开发了一种新的衰老指标，表型年龄，代表人口中的预期年龄，对应于一个人的估计死亡风险。本研究的目的是评估其在不同亚群中区分各种健康结果风险的适用性，这些亚群包括健康和不健康群体，不同年龄组以及具有各种种族/民族，社会经济和健康行为特征的人。表型年龄的计算是基于实际年龄和9个多因素的线性组合。系统临床化学生物标志物，根据我们以前建立的方法。我们还估计了表型年龄加速（Phenotypic Age Accel），它代表了考虑实足年龄后的表型年龄（即，一个人在生理上是否比预期的更老[正值]或更年轻[负值]）。所有分析均使用NHANES IV（1999-2010年，最初用于开发测量的独立样本）进行。我们的分析样本包括11，432名年龄在20-84岁之间的成年人和185名年龄最大的85岁老年人。在12.6年的随访中，我们共观察到1,012例死亡（基于截至2011年12月31日的国家死亡指数数据）。比例风险模型和受试者工作特征曲线被用来评估全因和特定原因死亡率预测。总体而言，患有更多疾病的参与者具有更大的表型年龄。例如，在年轻人中，患有1种疾病的人在表型上比无疾病的人年长0.2岁，患有2种或3种疾病的人在表型上年长约0.6岁。在校正实际年龄和性别后，表型年龄与全因死亡率和特定原因死亡率（脑血管疾病死亡率除外）显著相关。全因死亡率的结果对按年龄、种族/民族、教育、疾病计数和健康行为进行的分层是稳健的。此外，表型年龄与看似健康的参与者（定义为无疾病且BMI正常的参与者）以及最年长的老年人的死亡率相关，即使在调整疾病患病率后也是如此。这项研究的主要局限性是缺乏纵向数据表型年龄和疾病incidence.CONCLUSIONS：在一个全国代表性的美国成年人口，表型年龄与死亡率，即使调整后的实际年龄。总的来说，这种关联在不同的分层中是强大的，特别是年龄，疾病计数，健康行为和死亡原因。我们还观察到表型年龄和个体的疾病数量之间存在很强的关联。这些研究结果表明，这种新的老化措施可以作为一个有用的工具，以促进识别的风险个人和评估干预措施的有效性，也可能有助于调查潜在的生物学机制老化。然而，需要在其他队列中进行进一步评价。
BACKGROUND: A person's rate of aging has important implications for his/her risk of death and disease; thus, quantifying aging using observable characteristics has important applications for clinical, basic, and observational research. Based on routine clinical chemistry biomarkers, we previously developed a novel aging measure, Phenotypic Age, representing the expected age within the population that corresponds to a person's estimated mortality risk. The aim of this study was to assess its applicability for differentiating risk for a variety of health outcomes within diverse subpopulations that include healthy and unhealthy groups, distinct age groups, and persons with various race/ethnic, socioeconomic, and health behavior characteristics.METHODS AND FINDINGS: Phenotypic Age was calculated based on a linear combination of chronological age and 9 multi-system clinical chemistry biomarkers in accordance with our previously established method. We also estimated Phenotypic Age Acceleration (PhenoAgeAccel), which represents Phenotypic Age after accounting for chronological age (i.e., whether a person appears older [positive value] or younger [negative value] than expected, physiologically). All analyses were conducted using NHANES IV (1999-2010, an independent sample from that originally used to develop the measure). Our analytic sample consisted of 11,432 adults aged 20-84 years and 185 oldest-old adults top-coded at age 85 years. We observed a total of 1,012 deaths, ascertained over 12.6 years of follow-up (based on National Death Index data through December 31, 2011). Proportional hazard models and receiver operating characteristic curves were used to evaluate all-cause and cause-specific mortality predictions. Overall, participants with more diseases had older Phenotypic Age. For instance, among young adults, those with 1 disease were 0.2 years older phenotypically than disease-free persons, and those with 2 or 3 diseases were about 0.6 years older phenotypically. After adjusting for chronological age and sex, Phenotypic Age was significantly associated with all-cause mortality and cause-specific mortality (with the exception of cerebrovascular disease mortality). Results for all-cause mortality were robust to stratifications by age, race/ethnicity, education, disease count, and health behaviors. Further, Phenotypic Age was associated with mortality among seemingly healthy participants-defined as those who reported being disease-free and who had normal BMI-as well as among oldest-old adults, even after adjustment for disease prevalence. The main limitation of this study was the lack of longitudinal data on Phenotypic Age and disease incidence.CONCLUSIONS: In a nationally representative US adult population, Phenotypic Age was associated with mortality even after adjusting for chronological age. Overall, this association was robust across different stratifications, particularly by age, disease count, health behaviors, and cause of death. We also observed a strong association between Phenotypic Age and the disease count an individual had. These findings suggest that this new aging measure may serve as a useful tool to facilitate identification of at-risk individuals and evaluation of the efficacy of interventions, and may also facilitate investigation into potential biological mechanisms of aging. Nevertheless, further evaluation in other cohorts is needed.