基于NHANES数据库构建新生物年龄模型及其对参与者死亡结局的预测作用

YE Tingting; GAO Pan

doi:10.16016/j.2097-0927.202410082

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基于NHANES数据库构建新生物年龄模型及其对参与者死亡结局的预测作用

Public Health and Preventive Medicine | 更新时间：2025-04-25

- 基于NHANES数据库构建新生物年龄模型及其对参与者死亡结局的预测作用
- Journal of Army Medical University Vol. 47, Issue 8, Pages: 876-884(2025)
- 作者机构：
  
  陆军军医大学（第三军医大学）第一附属医院老年医学与特勤医学科，重庆
- 作者简介：
  
  GAO Pan，E-mail：pgao210@tmmu.edu.cn
- 基金信息：
  
  the National Key Research and Development Program(2020YFC2008903);the General Project of Natural Science Foundation of Chongqing(CSTB2022NSCQ-MSX0209)
- DOI：10.16016/j.2097-0927.202410082
  CLC： R195.1;R195.3;R339.39
- Received：21 October 2024，
  
  Revised：29 November 2024，
  
  Published：30 April 2025
- 稿件说明：
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‍叶婷婷，郜攀.基于NHANES数据库构建新生物年龄模型及其对参与者死亡结局的预测作用［J］.陆军军医大学学报，2025，47（8）：876-884.DOI：10.16016/j.2097-0927.202410082.

‍YE T T，GAO P.Construction of a new biological age model based on NHANES database and its predictive role for mortality outcomes［J］.J Army Med Univ，2025，47（8）：876-884.DOI：10.16016/j.2097-0927.202410082.
‍叶婷婷，郜攀.基于NHANES数据库构建新生物年龄模型及其对参与者死亡结局的预测作用［J］.陆军军医大学学报，2025，47（8）：876-884.DOI：10.16016/j.2097-0927.202410082. DOI：

‍YE T T，GAO P.Construction of a new biological age model based on NHANES database and its predictive role for mortality outcomes［J］.J Army Med Univ，2025，47（8）：876-884.DOI：10.16016/j.2097-0927.202410082. DOI：

摘要

‍

目的

基于美国国家健康与营养调查（National Health and Nutrition Examination Survey，NHANES）数据库构建新生物年龄模型，并评估模型对死亡结局的预测作用。

方法

纳入NHANES 2007-2010年，年龄在20~79岁的参与者作为测试集，排除怀孕女性及临床资料不完整者，共计8 234名。另采用NHANESⅢ中18~79岁非怀孕的17 522名参与者作为训练集，用于构建新生物年龄模型。通过多重线性回归构建新生物年龄和新年龄校正生物年龄模型，并计算其他种类生物年龄。Pearson相关分析评估生物年龄与实际年龄相关性。基于新年龄校正生物年龄模型计算生物年龄加速，根据生物年龄加速将参与者分为生物衰老加速组（生物年龄加速≥0，

=3 884）和生物衰老延缓组（生物年龄加速

0，

=4 350）。使用R软件survey包进行加权数据比较，Cox回归分析生物年龄加速对参与者死亡的影响。受试者特征曲线（ROC）计算新年龄校正生物年龄和生物年龄加速对参与者死亡受试者特征曲线下面积（AUC）的影响。

结果

由9个变量构成的新年龄校正生物年龄模型对死亡结局具有最大AUC（AUC=0.889 2，

0.001），且与实际年龄及其他生物年龄具有较高相关性。与生物衰老加速组相比，生物衰老延缓组男性、白种人、高中及以上教育程度、中高水平家庭收入、已婚（或有伴侣）、体育运动强度、私人保险及癌症人群比例显著升高（

0.05）；而BMI、生物年龄加速水平以及吸烟者、高血压、糖尿病、心血管疾病和慢性阻塞性肺疾病比例显著降低（

0.05）。Cox回归提示，与生物衰老延缓组比较，生物衰老加速组在Model3［调整年龄分组、性别、种族、BMI、教育程度、家庭收入、已婚（或有伴侣）、吸烟状态、重度饮酒、保险状况、体育运动、高血压、糖尿病、心血管疾病、慢性阻塞性肺疾病与癌症］中死亡风险显著增加（

=1.62，95%

：1.28-2.06，

0.001）。ROC结果显示，新年龄校正生物年龄模型显著增加中、老年人群AUC值（AUC=0.781，

0.001；AUC=0.731，

0.001），而生物年龄加速显著提高中年人群AUC值（AUC=0.756，

0.001）。

结论

新年龄校正生物年龄模型与实际年龄及其他生物年龄具有较高相关性，且对死亡结局具有较好的预测作用，尤其适用于中老年人群。

Abstract

Objective‍　‍To develop a new biological age model based on the National Health and Nutrition Examination Survey (NHANES) database and evaluate its predictive performance for mortality outcomes. Methods‍　After excluding pregnant women and those with incomplete clinical data

a total of 8 234 participants aged 20~79 years from NHANES 2007-2010 were included and assigned into a testing set

and another 17 522 non-pregnant participants （17~79 years old） subjected from NHANESⅢ were enrolled and served as a training set to construct the new biological age (new Bioage) model. Multiple linear regression was used to construct models for new Bioage and age-adjusted biological age (new-agefixed Bioage)

and other types of biological ages were calculated. P

earson correlation analysis was performed to assess the correlation between biological age and chronological age. Based on new-agefixed Bioage

biological age acceleration was calculated

and then according to the results

the participants were divided into accelerated biological aging group (biological age acceleration ≥0

=3 884) and decelerated biological aging group (biological age acceleration

=4 350). Weighted data comparisons were conducted using the survey package in R

and Cox regression analysis was used to assess the impact of biological age acceleration on the mortality of the participants. Receiver operating characteristic (ROC) curve was plotted to determine the effect of new-agefixed Bioage and biological age acceleration on the area under the curve (AUC) for the mortality. Results‍　The new-agefixed Bioage model

composed of 9 variables

had the highest AUC value (AUC=0.889 2

0.001) in predicting mortality outcomes and showed high correlation with chronological age and other biological ages. The decelerated biological aging group had significantly larger proportions of males

whites

education level of high school or higher

middle to high household income

married (or with a partner)

engaging in physical activity

having private insurance

and cancer (

0.05)

lower BMI

slower biological age acceleration level

and less proportions of smokers

hypertension

diabetes

cardiovascular disease

and chronic obstructive pulmonary disease when compared with the accelerated biological aging group (

0.05). Cox regression analysis indicated that

compared to the decelerated biological aging group

the accelerated biological aging group had a significantly increased risk of mortality in Model3 [adjust for age group

gender

race

BMI

education level

household income

married (or with a partner)

smoking status

heavy alcohol consumption

insurance status

physical activity

hypertension

diabetes

ardiovascular disease

chronic obstructive pulmonary disease

and cancer

]

(

=1.62

95%

: 1.28-2.06

0.001). ROC curve analysis revealed that new-agefixed Bioage significantly increased the AUC values (AUC=0.781

0.001; AUC=0.731

0.001) for middle-aged and elderly populations

while biological age acceleration significantly improved the AUC values (AUC=0.756

0.001) for the middle-aged population. Conclusion‍　‍The new-agefixed Bioage model demonstrates high correlation with chronological age and other biological ages

and has good predictive performance for mortality outcomes

particularly in middle-aged and elderly populations.

关键词

Keywords

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