美国、英国、墨西哥、哥斯达黎加和中国将人体测量年龄(AnthropoAge)作为生物年龄测量方法的多国评估:基于人口的纵向研究

Carlos Alberto Fermín-Martínez, Daniel Ramírez-García, Neftali Eduardo Antonio-Villa, Jerónimo Prezalonso-Espinosa, Diego Aguilar-Ramírez, Carmen García-Peña, Luis Miguel Gutierrez-Robledo, Jacqueline A. Seiglie, Omar Yaxmehen Bello-Chavolla
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摘要

方法:我们分析了全球老龄化网关(Gateway to Global Aging)中统一的多国数据,包括来自美国、英国、墨西哥、哥斯达黎加和中国的研究。我们使用体重指数和腰围身高比来估算 50-90 岁参与者的 AnthropoAge 和 AnthropoAgeAccel,分别作为 BA 和年龄加速度的替代指标。我们使用 Cox 模型比较了 AnthropoAge 和实际年龄(CA)对全因死亡率的预测能力,描述了所有国家的老龄化趋势,并使用广义估计方程(GEE)探讨了 AnthropoAgeAccel 的纵向评估对预测新发功能衰退和老年相关疾病的实用性。研究结果:利用 55628 名参与者的数据,我们发现 AnthropoAge(c 统计量为 0.772)在预测死亡率方面优于 CA(0.76),不受合并症、性别、种族/民族、教育程度和生活方式的影响;这一结果在除墨西哥以外的大多数国家都得到了验证。加速衰老的个体的死亡风险要高出约 39%,AnthropoAge 还发现了每年生物衰老速度加快的趋势。在纵向分析中,较高的 AnthropoAgeAccel 值可独立预测自我报告的健康恶化和新出现的基本/工具性日常生活活动(ADL/IADL)障碍、糖尿病、高血压、癌症、慢性肺病、心肌梗塞和中风。结论AnthropoAge是一种与年龄相关结果相关的稳健且可重复的生物统计学指标。该指标的应用有助于模拟不同人群的生物衰老加速趋势,但重新校准可能会提高其在拉丁美洲等代表性不足人群中的实用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multinational evaluation of anthropometric age (AnthropoAge) as a measure of biological age in the USA, England, Mexico, Costa Rica, and China: a population-based longitudinal study
OBJECTIVE: To validate AnthropoAge, a new metric of biological age (BA), for prediction of all-cause mortality and age-related outcomes and characterize population-specific aging patterns using multinational longitudinal cohorts. METHODS: We analyzed harmonized multinational data from the Gateway to Global Aging, including studies from the US, England, Mexico, Costa Rica, and China. We used body mass index and waist-to-height ratio to estimate AnthropoAge and AnthropoAgeAccel in participants aged 50-90 years old as proxies of BA and age acceleration, respectively. We compared the predictive capacity for all-cause mortality of AnthropoAge and chronological age (CA) using Cox models, described aging trends in all countries and explored the utility of longitudinal assessments of AnthropoAgeAccel to predict new-onset functional decline and age-related diseases using generalized estimating equations (GEE). FINDINGS: Using data from 55,628 participants, we found AnthropoAge (c-statistic 0.772) outperformed CA (0.76) for prediction of mortality independently of comorbidities, sex, race/ethnicity, education, and lifestyle; this result was replicated in most countries individually except for Mexico. Individuals with accelerated aging had a ~39% higher risk of death, and AnthropoAge also identified trends of faster biological aging per year. In longitudinal analyses, higher AnthropoAgeAccel values were independently predictive of self-reported health deterioration and new-onset deficits in basic/instrumental activities of daily living (ADL/IADL), diabetes, hypertension, cancer, chronic lung disease, myocardial infarction, and stroke. CONCLUSIONS: AnthropoAge is a robust and reproducible BA metric associated with age-related outcomes. Its implementation could facilitate modeling trends of biological aging acceleration in different populations, although recalibration may enhance its utility in underrepresented populations such as individuals from Latin America.
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