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Measurement of Lifespan in Drosophila melanogaster
Published on: January 7, 2013
生物年龄结构用于预测中国人口的死亡率
Kaiyue Wang1, Jingli Gao2, Ying Liu1
1Department of Nutrition and Food Hygiene, School of Public Health, Institute of Nutrition, Fudan University, Shanghai, 200032, China.
GeroScience
|March 28, 2025
概括
这项研究开发了生物年龄 (BA) 模型,Pheno-Age和KDM-Age,使用中国人群中的常规临床标志物. 这些模型准确地预测死亡风险,有助于健康跨度研究.
科学领域:
- 老年学与公共卫生
- 生物标志物开发 生物标志物开发
- 流行病学 流行病学
背景情况:
- 增加健康寿命需要通过生物年龄 (BA) 准确测量衰老.
- 现有的BA模型需要对不同人群进行验证和适应,特别是在中国.
- 常规的临床标记为开发可访问的BA评估提供了实际基础.
研究的目的:
- 构建和验证生物年龄模型 (Pheno-Age和KDM-Age),使用中国人口的常规临床标志物.
- 评估这些BA模型的预测性能及其对死亡率的加速.
- 调查人口和生活方式因素对BA加速和死亡率之间的关联的影响.
主要方法:
- 使用莱文的方法和KDM方法分别开发的Pheno-Age和KDM-Age,基于Kailuan研究I (n=83,571) 的基线临床标志物.
- 用Kailuan研究II (n=21,229) 的数据验证BA模型并计算BA加速.
- 使用曲线下的面积 (AUC) 和校准图表对死亡率的预测性能的评估;危险比率 (HR) 的COX回归.
主要成果:
- 芬诺年龄和KDM年龄在两个队列中都表现出强大的死亡率预测性能 (AUC范围从0.806到0.867).
- 较高的生物年龄加速 (Pheno-Age和KDM-Age) 与增加的死亡风险 (HRs>1.16) 有显著的关联.
- 在年轻人 (≤60岁),吸烟者和饮酒者中,预测性关联更为明显,这表明了显著的相互作用.
结论:
- 年龄和KDM年龄是验证的工具,用于预测中国人口的死亡率,独立于时间年龄.
- 生物衰老加速作为死亡风险的强有力的指标,提供了超越时间衰老的见解.
- 这些发现支持常规临床标记的实用性,用于开发实用的生物年龄评估,以指导健康跨度干预.
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