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Article(id=1322210927710163441, tenantId=1146029695717560320, journalId=1319373162502258839, issueId=1322210896617783335, articleNumber=null, orderNo=null, doi=10.16016/j.2097-0927.202410082, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1729440000000, receivedDateStr=2024-10-21, revisedDate=1732809600000, revisedDateStr=2024-11-29, acceptedDate=null, acceptedDateStr=null, onlineDate=1793169250657, onlineDateStr=2026-10-28, pubDate=1745942400000, pubDateStr=2025-04-30, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793169250657, onlineIssueDateStr=2026-10-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793169250657, creator=13701087609, updateTime=1793169250657, updator=13701087609, issue=Issue{id=1322210896617783335, tenantId=1146029695717560320, journalId=1319373162502258839, year='2025', volume='47', issue='8', pageStart='749', pageEnd='892', issueExtLink='null', onlineDate='null', pubDate='1745942400000', pubDateStr='2025-04-30', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793169243243, creator='13701087609', updateTime=1793325231503, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1322865158855184775, tenantId=1146029695717560320, journalId=1319373162502258839, issueId=1322210896617783335, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1322865158855184776, tenantId=1146029695717560320, journalId=1319373162502258839, issueId=1322210896617783335, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=876, endPage=884, ext={EN=ArticleExt(id=1322210929543074291, articleId=1322210927710163441, tenantId=1146029695717560320, journalId=1319373162502258839, language=EN, title=Construction of a new biological age model based on NHANES database and its predictive role for mortality outcomes, columnId=1322210927869546994, journalTitle=Journal of Army Medical University, columnName=Public Health and Preventive Medicine, runingTitle=null, highlight=null, articleAbstract=
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. Pearson 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, n=3 884) and decelerated biological aging group (biological age acceleration <0, n=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, P<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 (P<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 (P<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, cardiovascular disease, chronic obstructive pulmonary disease, and cancer](HR=1.62, 95% CI: 1.28-2.06, P<0.001). ROC curve analysis revealed that new-agefixed Bioage significantly increased the AUC values (AUC=0.781, P<0.001; AUC=0.731, P<0.001) for middle-aged and elderly populations, while biological age acceleration significantly improved the AUC values (AUC=0.756, P<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.

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‍

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，n=3 884）和生物衰老延缓组（生物年龄加速<0，n=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，P<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（P<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（P<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（HR=1.62，95%CI：1.28-2.06，P<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，P<0.001；AUC=0.731，P<0.001），而生物年龄加速显著提高中年人群AUC值（AUC=0.756，P<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。

, authors=叶婷婷, 郜攀, authorsList=叶婷婷, 郜攀, authorCompany=null, correspAuthors=郜攀, authorNote=null, correspAuthorsNote=
郜 攀，E-mail：
GAO Pan，E-mail：
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A biological age model based on physical examination data to predict mortality in a Chinese population［J］. iScience，2024，27（3）：108891., articleTitle=A biological age model based on physical examination data to predict mortality in a Chinese population, refAbstract=null), Reference(id=1322210936056828463, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322210927710163441, doi=10.3389/fpubh.2024.1451737, pmid=null, pmcid=null, year=2024, volume=12, issue=null, pageStart=1451737, pageEnd=null, url=null, language=null, rfNumber=17, rfOrder=16, authorNames=HE Q, LUO H, MEI J, journalName=Front Public Health, refType=null, unstructuredReference=HE Q，LUO H，MEI J，et al. The association between accelerated biological aging and the risk of osteoarthritis：a cross-sectional study［J］. Front Public Health，2024，12：1451737., articleTitle=The association between accelerated biological aging and the risk of osteoarthritis：a cross-sectional study, refAbstract=null), Reference(id=1322210936119743024, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322210927710163441, doi=10.1039/D4FO01241K, pmid=null, pmcid=null, year=2024, volume=15, issue=19, pageStart=10121, pageEnd=10135, url=null, language=null, rfNumber=18, rfOrder=17, authorNames=ZHANG J, WANG X Y, YANG S, journalName=Food Funct, refType=null, unstructuredReference=ZHANG J，WANG X Y，YANG S，et al. Relationship of dietary natural folate and synthetic folic acid co-exposure patterns with biological aging：findings from NHANES 2003-2018［J］. Food Funct，2024，15（19）：10121-10135., articleTitle=Relationship of dietary natural folate and synthetic folic acid co-exposure patterns with biological aging：findings from NHANES 2003-2018, refAbstract=null)], funds=null, companyList=[AuthorCompany(id=1322210930721673724, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322210927710163441, xref=1, ext=[AuthorCompanyExt(id=1322210930730062333, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322210927710163441, companyId=1322210930721673724, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1Department of Geriatric and Special Medical Services，First Affiliated Hospital，Army Medical University （Third Military Medical University），Chongqing，China), AuthorCompanyExt(id=1322210930734256638, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322210927710163441, companyId=1322210930721673724, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1陆军军医大学（第三军医大学）第一附属医院老年医学与特勤医学科，重庆)])], figs=[ArticleFig(id=1322210931896078865, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322210927710163441, language=EN, label=null, caption=null, figureFileSmall=cVZuGPm4pRuXJZh19iKQsw==, figureFileBig=GRGq21ASt1ztzoTYumKyaA==, tableContent=null), ArticleFig(id=1322210931992547858, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322210927710163441, language=CN, label=图1, caption=不同数量的变量组合计算得到的new Bioage（BE）和new-agefixed Bioage（BEC）对于死亡结局的ROC曲线AUC值（A）以及与实际年龄相关性（B）

BE1~14分别代表8~21个变量计算的new Bioage； BEC1~14分别代表8~21个变量计算的new-agefixed Bioage

, figureFileSmall=cVZuGPm4pRuXJZh19iKQsw==, figureFileBig=GRGq21ASt1ztzoTYumKyaA==, tableContent=null), ArticleFig(id=1322210932214845971, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322210927710163441, language=EN, label=null, caption=null, figureFileSmall=M6VeaFSVYF5qUtKGHLlo8g==, figureFileBig=OXUPsh1a85VRH3bF7uAXkA==, tableContent=null), ArticleFig(id=1322210932286149140, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322210927710163441, language=CN, label=图2, caption=各种生物年龄与参与者实际年龄相关性分析, figureFileSmall=M6VeaFSVYF5qUtKGHLlo8g==, figureFileBig=OXUPsh1a85VRH3bF7uAXkA==, tableContent=null), ArticleFig(id=1322210932349063701, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322210927710163441, language=EN, label=null, caption=null, figureFileSmall=GI4WJ2JpfBWL8AnRWo1YKg==, figureFileBig=1eN3daNC0mhr7oaf0lbCMw==, tableContent=null), ArticleFig(id=1322210934030979606, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322210927710163441, language=CN, label=图3, caption=各种生物年龄之间的相关性分析

热图中的数值代表了不同生物年龄之间的Pearson相关系数，散点图对应两种生物年龄之间实际值的比较，X轴和Y轴表示不同模型计算的生物年龄；红色虚线表示线性拟合，图中的分布趋势显示了两种模型的拟合度；如果点分布越接近这条线，说明两者之间的关系越线性

, figureFileSmall=GI4WJ2JpfBWL8AnRWo1YKg==, figureFileBig=1eN3daNC0mhr7oaf0lbCMw==, tableContent=null), ArticleFig(id=1322210934093894167, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322210927710163441, language=EN, label=null, caption=null, figureFileSmall=null, figureFileBig=null, tableContent=
临床参数r值P值所属功能
糖化血红蛋白0.3100.000代谢功能
血浆胰岛素水平0.2171.06E-152代谢功能
血糖水平0.2492.98E-211代谢功能
总胆固醇0.3850.000代谢功能
甘油三酯0.1981.65E-133代谢功能
收缩压0.5340.000心脏功能
舒张压0.1661.21E-94心脏功能
第1秒用力呼气量-0.5780.000肺功能
用力肺活量-0.4230.000肺功能
峰值呼气流速-0.4100.000肺功能
血尿素氮0.3640.000肾功能
肌酐0.1651.68E-91肾功能
尿酸0.1499.51E-75肾功能
白蛋白-0.2456.22E-204肝功能
碱性磷酸酶0.1566.14E-82肝功能
C-反应蛋白0.1152.26E-45炎症指标
红细胞计数-0.1219.97E-51血液参数
平均细胞体积0.1531.63E-80血液参数
红细胞分布宽度0.1724.91E-101血液参数
体质量指数0.1293.28E-60身体测量
腰围0.3410.000身体测量
), ArticleFig(id=1322210934169391640, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322210927710163441, language=CN, label=表1, caption=

NHANESⅢ数据库参与者实验室和体格检查指标与实际年龄相关性分析

, figureFileSmall=null, figureFileBig=null, tableContent=
临床参数r值P值所属功能
糖化血红蛋白0.3100.000代谢功能
血浆胰岛素水平0.2171.06E-152代谢功能
血糖水平0.2492.98E-211代谢功能
总胆固醇0.3850.000代谢功能
甘油三酯0.1981.65E-133代谢功能
收缩压0.5340.000心脏功能
舒张压0.1661.21E-94心脏功能
第1秒用力呼气量-0.5780.000肺功能
用力肺活量-0.4230.000肺功能
峰值呼气流速-0.4100.000肺功能
血尿素氮0.3640.000肾功能
肌酐0.1651.68E-91肾功能
尿酸0.1499.51E-75肾功能
白蛋白-0.2456.22E-204肝功能
碱性磷酸酶0.1566.14E-82肝功能
C-反应蛋白0.1152.26E-45炎症指标
红细胞计数-0.1219.97E-51血液参数
平均细胞体积0.1531.63E-80血液参数
红细胞分布宽度0.1724.91E-101血液参数
体质量指数0.1293.28E-60身体测量
腰围0.3410.000身体测量
), ArticleFig(id=1322210934240694809, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322210927710163441, language=EN, label=null, caption=null, figureFileSmall=null, figureFileBig=null, tableContent=
临床指标总人群（n=8 234）生物衰老加速组（n=3 884）生物衰老延缓组（n=4 350）P值加权统计量
实际年龄/年45.00（32.00~56.00）45.00（30.00~57.00）44.00（33.00~56.00）0.259-1.15
年龄分组0.8430.14
青年（20~44岁）3 777（50.00）1 732（19.90）2 045（30.10）
中年（45~64岁）3 023（38.00）1 480（15.50）1 543（22.60）
老年（65~79岁）1 434（12.00）672（4.80）762（7.18）
BMI27.55（24.06~31.93）30.65（26.37~35.81）26.07（23.2~29.37）<0.00123.50
表型年龄/年37.49（24.66~50.16）40.51（25.60~53.60）35.93 （24.04~47.66）0.0016.99
K-D生物年龄/年43.48（31.53~55.60）46.78（32.28~59.43）41.86（30.96~53.11）<0.0018.61
R-生物年龄/年145.81（118.56~183.55）138.81（118.45~185.66）152.66（118.60~182.69）<0.001-1.18
R-表型年龄/年58.41（46.26~70.82）61.38（47.31~73.94）56.84（45.74~68.62）<0.0016.48
新生物年龄/年39.05（25.60~56.29）56.19（41.58~71.68）29.55（19.55~42.19）<0.00150.90
新年龄校正生物年龄/年43.22（31.38~55.65）47.78（33.10~60.37）41.04（30.15~52.14）<0.00111.40
生物年龄加速/年-0.95（-3.35~1.60）2.36（1.00~4.31）-2.84（-4.66~-1.42）<0.001132.00
男性比例/%4 117（49.90）1 523（15.20）2 594（34.60）<0.001260.00
白种人/%3 915（70.00）1 465（23.80）2 450（46.20）<0.001110.00
教育水平<0.00174.00
低于高中教育2 261（17.90）1 302（9.61）959（8.26）
高中教育1 939（23.40）1 005（11.00）934（12.40）
大学或更高4 026（58.60）1 571（19.50）2 455（39.10）
家庭收入<0.00161.80
低水平1 538（12.30）889（6.83）649（5.45）
中水平3 934（43.90）1 972（19.80）1 962（24.10）
高水平2 076（37.20）687（10.70）1 389（26.50）
已婚（或有伴侣）5 133（65.80）2 226（24.50）2 907（41.30）<0.00121.10
吸烟状态<0.00122.30
现吸烟者1 910（22.10）1 061（11.20）849（10.80）
既往吸烟者1 942（23.90）813（8.77）1 129（15.10）
未吸烟者4 378（54.00）2 007（20.20）2 371（33.80）
重度饮酒者643（8.40）290（3.25）353（5.12）0.1382.19
保险状况<0.00131.50
政府保险1 518（12.70）882（6.64）636（6.07）
无保险2 204（20.50）1 152（9.88）1 052（10.60）
私人保险4 507（66.70）1 846（23.60）2 661（43.10）
体育运动<0.00159.50
低强度5 534（61.90）2 907（28.70）2 627（33.10）
中强度942（13.20）366（4.48）576（8.75）
高强度1 758（24.90）611（6.96）1 147（17.90）
合并症
高血压2 537（26.90）1 585（15.10）952（11.90）<0.001177.00
糖尿病821（7.00）627（5.18）194（1.83）<0.00127.90
心血管疾病555（5.10）331（2.70）223（2.35）<0.001170.00
慢性阻塞性肺疾病467（5.50）301（3.23）166（2.24）<0.00142.10
癌症642（8.10）283（3.04）359（5.03）0.0111.12
), ArticleFig(id=1322210934341358106, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322210927710163441, language=CN, label=表2, caption=

NHANES 2007-2010年参与者的基本资料比较 ［M（P25%~P75%），n（%）］

, figureFileSmall=null, figureFileBig=null, tableContent=
临床指标总人群（n=8 234）生物衰老加速组（n=3 884）生物衰老延缓组（n=4 350）P值加权统计量
实际年龄/年45.00（32.00~56.00）45.00（30.00~57.00）44.00（33.00~56.00）0.259-1.15
年龄分组0.8430.14
青年（20~44岁）3 777（50.00）1 732（19.90）2 045（30.10）
中年（45~64岁）3 023（38.00）1 480（15.50）1 543（22.60）
老年（65~79岁）1 434（12.00）672（4.80）762（7.18）
BMI27.55（24.06~31.93）30.65（26.37~35.81）26.07（23.2~29.37）<0.00123.50
表型年龄/年37.49（24.66~50.16）40.51（25.60~53.60）35.93 （24.04~47.66）0.0016.99
K-D生物年龄/年43.48（31.53~55.60）46.78（32.28~59.43）41.86（30.96~53.11）<0.0018.61
R-生物年龄/年145.81（118.56~183.55）138.81（118.45~185.66）152.66（118.60~182.69）<0.001-1.18
R-表型年龄/年58.41（46.26~70.82）61.38（47.31~73.94）56.84（45.74~68.62）<0.0016.48
新生物年龄/年39.05（25.60~56.29）56.19（41.58~71.68）29.55（19.55~42.19）<0.00150.90
新年龄校正生物年龄/年43.22（31.38~55.65）47.78（33.10~60.37）41.04（30.15~52.14）<0.00111.40
生物年龄加速/年-0.95（-3.35~1.60）2.36（1.00~4.31）-2.84（-4.66~-1.42）<0.001132.00
男性比例/%4 117（49.90）1 523（15.20）2 594（34.60）<0.001260.00
白种人/%3 915（70.00）1 465（23.80）2 450（46.20）<0.001110.00
教育水平<0.00174.00
低于高中教育2 261（17.90）1 302（9.61）959（8.26）
高中教育1 939（23.40）1 005（11.00）934（12.40）
大学或更高4 026（58.60）1 571（19.50）2 455（39.10）
家庭收入<0.00161.80
低水平1 538（12.30）889（6.83）649（5.45）
中水平3 934（43.90）1 972（19.80）1 962（24.10）
高水平2 076（37.20）687（10.70）1 389（26.50）
已婚（或有伴侣）5 133（65.80）2 226（24.50）2 907（41.30）<0.00121.10
吸烟状态<0.00122.30
现吸烟者1 910（22.10）1 061（11.20）849（10.80）
既往吸烟者1 942（23.90）813（8.77）1 129（15.10）
未吸烟者4 378（54.00）2 007（20.20）2 371（33.80）
重度饮酒者643（8.40）290（3.25）353（5.12）0.1382.19
保险状况<0.00131.50
政府保险1 518（12.70）882（6.64）636（6.07）
无保险2 204（20.50）1 152（9.88）1 052（10.60）
私人保险4 507（66.70）1 846（23.60）2 661（43.10）
体育运动<0.00159.50
低强度5 534（61.90）2 907（28.70）2 627（33.10）
中强度942（13.20）366（4.48）576（8.75）
高强度1 758（24.90）611（6.96）1 147（17.90）
合并症
高血压2 537（26.90）1 585（15.10）952（11.90）<0.001177.00
糖尿病821（7.00）627（5.18）194（1.83）<0.00127.90
心血管疾病555（5.10）331（2.70）223（2.35）<0.001170.00
慢性阻塞性肺疾病467（5.50）301（3.23）166（2.24）<0.00142.10
癌症642（8.10）283（3.04）359（5.03）0.0111.12
), ArticleFig(id=1322210934412661275, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322210927710163441, language=EN, label=null, caption=null, figureFileSmall=null, figureFileBig=null, tableContent=
调整模型HR95%CIP值
Model1
生物衰老延缓Ref.
生物衰老加速2.191.74~2.75<0.001
Model2
生物衰老延缓Ref.
生物衰老加速1.781.40~2.25<0.001
Model3
生物衰老延缓Ref.
生物衰老加速1.621.28~2.06<0.001
), ArticleFig(id=1322210934479770140, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322210927710163441, language=CN, label=表3, caption=

生物衰老加速对NHANES 2007-2010年参与者死亡结局的影响

, figureFileSmall=null, figureFileBig=null, tableContent=
调整模型HR95%CIP值
Model1
生物衰老延缓Ref.
生物衰老加速2.191.74~2.75<0.001
Model2
生物衰老延缓Ref.
生物衰老加速1.781.40~2.25<0.001
Model3
生物衰老延缓Ref.
生物衰老加速1.621.28~2.06<0.001
), ArticleFig(id=1322210934559461917, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322210927710163441, language=EN, label=null, caption=null, figureFileSmall=null, figureFileBig=null, tableContent=
不同年龄人群累计死亡AUC（95%CI）SEP值Cut-off敏感度特异度
总体人群731
基础模型0.839（0.824~0.854）0.008
基础模型+新年龄校正生物年龄0.862（0.848~0.876）0.007<0.00111.020.8360.747
基础模型+生物年龄加速0.848（0.833~0.863）0.008<0.00110.530.8220.750
青年人群65
基础模型0.716（0.650~0.782）0.034
基础模型+新年龄校正生物年龄0.745（0.682~0.809）0.0320.1751.3150.5480.841
基础模型+生物年龄加速0.712（0.637~0.786）0.0380.9640.8990.5800.805
中年人群270
基础模型0.722（0.688~0.757）0.018
基础模型+新年龄校正生物年龄0.781（0.749~0.812）0.016<0.0011.5720.6200.822
基础模型+生物年龄加速0.756（0.723~0.790）0.017<0.0011.0640.6610.768
老年人群396
基础模型0.690（0.658~0.722）0.016
基础模型+新年龄校正生物年龄0.731（0.699~0.762）0.016<0.0010.9080.5910.785
基础模型+生物年龄加速0.700（0.668~0.732）0.0160.0800.9130.5600.755
), ArticleFig(id=1322210934647542302, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322210927710163441, language=CN, label=表4, caption=

调整新年龄校正生物年龄和生物年龄加速对于AUC的影响

, figureFileSmall=null, figureFileBig=null, tableContent=
不同年龄人群累计死亡AUC（95%CI）SEP值Cut-off敏感度特异度
总体人群731
基础模型0.839（0.824~0.854）0.008
基础模型+新年龄校正生物年龄0.862（0.848~0.876）0.007<0.00111.020.8360.747
基础模型+生物年龄加速0.848（0.833~0.863）0.008<0.00110.530.8220.750
青年人群65
基础模型0.716（0.650~0.782）0.034
基础模型+新年龄校正生物年龄0.745（0.682~0.809）0.0320.1751.3150.5480.841
基础模型+生物年龄加速0.712（0.637~0.786）0.0380.9640.8990.5800.805
中年人群270
基础模型0.722（0.688~0.757）0.018
基础模型+新年龄校正生物年龄0.781（0.749~0.812）0.016<0.0011.5720.6200.822
基础模型+生物年龄加速0.756（0.723~0.790）0.017<0.0011.0640.6610.768
老年人群396
基础模型0.690（0.658~0.722）0.016
基础模型+新年龄校正生物年龄0.731（0.699~0.762）0.016<0.0010.9080.5910.785
基础模型+生物年龄加速0.700（0.668~0.732）0.0160.0800.9130.5600.755
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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
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叶婷婷 , 郜攀
陆军军医大学学报 | 公共卫生与预防医学 2025,47(8): 876-884
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陆军军医大学学报 |公共卫生与预防医学 2025 , 47 (8) : 876 -884
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
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叶婷婷, 郜攀
作者信息
  • 1陆军军医大学（第三军医大学）第一附属医院老年医学与特勤医学科，重庆
通讯作者:
郜 攀，E-mail：
GAO Pan，E-mail：
Construction of a new biological age model based on NHANES database and its predictive role for mortality outcomes
Tingting YE, Pan GAO
Affiliations
  • 1Department of Geriatric and Special Medical Services，First Affiliated Hospital，Army Medical University （Third Military Medical University），Chongqing，China
出版时间: 2025-04-30 doi: 10.16016/j.2097-0927.202410082
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摘要
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‍

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，n=3 884）和生物衰老延缓组（生物年龄加速<0，n=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，P<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（P<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（P<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（HR=1.62，95%CI：1.28-2.06，P<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，P<0.001；AUC=0.731，P<0.001），而生物年龄加速显著提高中年人群AUC值（AUC=0.756，P<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。

‍美国国家健康与营养调查数据库  /  生物年龄  /  生物年龄加速  /  表型年龄
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. Pearson 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, n=3 884) and decelerated biological aging group (biological age acceleration <0, n=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, P<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 (P<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 (P<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, cardiovascular disease, chronic obstructive pulmonary disease, and cancer](HR=1.62, 95% CI: 1.28-2.06, P<0.001). ROC curve analysis revealed that new-agefixed Bioage significantly increased the AUC values (AUC=0.781, P<0.001; AUC=0.731, P<0.001) for middle-aged and elderly populations, while biological age acceleration significantly improved the AUC values (AUC=0.756, P<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.

National Health and Nutrition Examination Survey database  /  bioage  /  bioage acceleration  /  phenoage
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. 陆军军医大学学报, 2025 , 47 (8) : 876 -884 . DOI: 10.16016/j.2097-0927.202410082
Tingting YE, Pan GAO. Construction of a new biological age model based on NHANES database and its predictive role for mortality outcomes[J]. Journal of Army Medical University, 2025 , 47 (8) : 876 -884 . DOI: 10.16016/j.2097-0927.202410082
正文
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随着全球老龄化加剧，个体健康管理和衰老相关疾病的防控已成为医学研究重点［1］。生物年龄作为评估个体健康状态和衰老程度的指标，越来越被重视［2］。相较于实际年龄，生物年龄更能准确反映个体的生理衰老过程，并与多种健康风险和临床结局密切相关［3-4］。因此，构建和优化生物年龄模型具有重要的临床应用前景。
目前，生物年龄的计算方法包括Klemera P-Doubal S计算的生物年龄（K-D BioAge）［5］、Gompertz风险比例模型表型年龄（PhenoAge）及DNA甲基化时钟等［6-9］。这些方法利用不同生物标志物估算个体的生物年龄，从而预测健康结局。然而，这些模型在指标选择和人群适用性上存在局限性，例如，K-D生物年龄虽然简单易用，但使用的生物标志物较少，验证也不够广泛；表型年龄整合了多种生理功能指标，主要应用于老年人群；DNA甲基化时钟精度较高，但其高成本限制了临床广泛应用［10］。
本研究基于美国国家健康与营养调查（National Health and Nutrition Examination Survey，NHANES）数据库，筛选与实际年龄高度相关的生物标志物，构建新生物年龄模型，并评估其对死亡结局的预测价值［11］。NHANES数据库具有广泛的人群代表性和丰富的健康数据，为生物年龄模型的开发和验证提供了可靠基础［12-13］。本研究拟开发适用于大范围人群的生物年龄模型，并探讨其在人群预后和健康管理中的应用潜力。
1　资料与方法
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1.1　研究对象
本研究以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。以2007-2008年和2009-2010年NHANES参与者作为测试集对象，纳入标准：（1）年龄20~79岁；（2）女性参与者在调查期间未怀孕；（3）具备完整的随访数据；（4）具备完整的临床数据以计算生物年龄。排除标准：数据存在极端的生化指标值等质量问题。最终共纳入8 234名测试集参与者。
1.2　变量筛选与分类
为了筛选与实际年龄相关的生物标志物，首先对NHANESⅢ的数据与实际年龄进行Pearson相关性分析，筛选标准为相关系数r的绝对值>0.1且P值<0.05。根据临床特征，将这些生物标志物分为8类：代谢功能、心脏功能、肺功能、肾功能、肝功能、炎症指标、血液参数、身体测量（表1）。通过多重线性回归模型，计算每个个体的生物年龄值，并采用受试者特征曲线（ROC）及受试者特征曲线下面积（AUC）评估生物年龄与死亡风险的相关性。
1.3　协变量的定义
定义以下协变量［14］：婚姻状况（已婚或同居）、经济状况（基于家庭收入贫困比fPIR划分为低、中、高水平：fPIR≤1，1<fPIR<4，fPIR≥4分别定义为“低水平”、“中水平”和“高水平”）、吸烟状态（从未吸烟、当前吸烟者和戒烟者）、重度饮酒者（男性每天饮酒超过2杯，女性每天超过1杯）、体育运动（基于每周代谢当量METs划分为低、中、高强度：METs<600，600≤METs<1 200，METs≥1200分别定义为“低强度”、“中强度”和“高强度”）。此外，合并症包括高血压、糖尿病、心血管疾病（如冠心病、心肌梗死、卒中等），以及慢性阻塞性肺疾病（COPD）等。根据参与者的年龄将其分为青年组（20~44岁）、中年组（45~64岁）和老年组（65~79岁）。
1.4　生物年龄以及生物年龄加速的计算
参考已有研究中的Gompertz风险比例模型表型年龄（PhenoAge）［7］、K-D生物年龄（K-D BioAge）［5，15］，并结合R软件BioAge包计算R-生物年龄（R-Bioage）和R-表型年龄（R-Phenoage）［8］。根据本研究的变量，通过多重线性回归方法计算新生物年龄（new Bioage），并校正实际年龄后得到新年龄校正生物年龄（new-agefixed Bioage）。通过构建新年龄校正生物年龄与实际年龄的线性回归计算预测生物年龄，新年龄校正生物年龄与预测生物年龄的差值即为生物年龄加速，差值为正值表示生物衰老加速，负值表示生物衰老延缓。
1.5　统计学分析
由于NHANES使用复杂的多阶段概率抽样设计，故本研究采用R软件（V4.4.1）中的“survey”包中svydesign进行加权统计分析，而连续变量描述性分析采用svyby完成，统计量采用svyranktest计算；分类变量的描述性分析采用svytable命令完成，统计量采用svychisq计算。Cox回归分析采用svycoxph命令完成。生物年龄相关性根据BioAge包中的agevar、plot_ba和plot_ba命令完成，ROC曲线采用R语言的“pROC”包进行统计分析；绘制ROC曲线采用ggplot命令完成。连续变量以中位数（25%~75%）表示，分类变量以频数（%）表示，所有P值双侧检验，P<0.05为差异具有统计学意义。
2　结果
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2.1　基于NHANESⅢ数据库构建新生物年龄模型
参考KLEMERA和LEVINE的方法［5，15］，NHANESⅢ数据库中筛选18~79岁、未报告怀孕的参与者，共计17 522人。根据与实际年龄相关性，筛选出21项指标（表1），并根据器官功能和体检结果分为8类。每类至少选择1种或以上指标进入生物年龄计算模型，最终共构建287 091种模型，并使用ROC分析生物年龄对死亡影响的AUC，检测不同个数变量组合的最大AUC值。结果显示，由“HbA1c”“Glucose”“SBP”“PEF”“UA”“ALP”“CRP”“MCV”“RDW”共计9个变量构建的新年龄校正生物年龄模型具有最大AUC值（AUC=0.889 2，P<0.001，图1），同时与实际年龄相关性较好。
2.2　生物年龄与NHANESⅢ参与者实际年龄的相关性分析
多种生物年龄与NHANESⅢ参与者的实际年龄均表现出显著相关性（P <0.001）。其中，new Bioage的相关性较低（r=0.713），而校正年龄后的new-agefixed Bioage与实际年龄的相关性显著提高（r=0.962，P<0.001）。见图2。
进一步分析各种生物年龄之间的相关性，结果如图3所示，所有生物年龄模型间均有较好的相关性，最低相关性为new Bioage与PhenoAge之间（r=0.78，P<0.001），而new-agefixed Bioage与其他生物年龄模型具有较高的相关性，其中最低为new-agefixed Bioage与R Bioage的相关性（r=0.86，P<0.001）。
2.3　NHANES 2007-2010年参与者分组比较
为了验证新年龄校正生物年龄的死亡结局预测效果，本研究根据NHANES 2007-2010年参与者的数据进行分析，并依据生物年龄加速结果将参与者分为生物衰老加速组（加速≥0）和生物衰老延缓组（加速<0）。表2显示，与生物衰老加速组相比，生物衰老延缓组的R-生物年龄水平以及男性、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（P<0.05）。在BMI、表型年龄、K-D生物年龄、R-表型年龄、新生物年龄、新年龄校正生物年龄、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中，生物衰老延缓组显著低于生物衰老加速组（P<0.05）。2组实际年龄、年龄分组比例、重度饮酒者比例无统计学差异。
2.4　生物年龄加速对NHANES 2007-2010年参与者死亡结局的影响
进一步分析新年龄校正生物年龄模型对NHANES 2007-2010年参与者死亡结局的影响。根据NHANES数据库资料，本研究纳入8 234例参与者（统计“MORTSTAT”变量），累计731例在随访过程中死亡。以死亡作为结局，观察生物年龄加速对死亡结局的预测效果。加权Cox回归分析显示，与生物衰老延缓组相比，生物衰老加速组在最终模型（Model3）中死亡风险显著增加（HR=1.62，95% CI：1.28~2.06，P<0.001）。
通过ROC分析，结果说明（表4），与基础模型相比，新年龄校正生物年龄和生物年龄加速均显著提高了总体人群AUC值（P<0.001）。在青年人群中，与基础模型相比，调整新年龄校正生物年龄和生物年龄加速对AUC值无统计学差异。与基础模型比较，在中年人群中，加入新年龄校正生物年龄和生物年龄加速显著增加了AUC值（P<0.001）。在老年人群中，加入新年龄校正生物年龄显著提高AUC值（AUC=0.731，P<0.001），加入生物年龄加速对AUC值无显著影响。
3　讨论
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本研究通过NHANESⅢ数据库构建了新年龄校正生物年龄模型，并在NHANES 2007-2010年中验证了新年龄校正生物年龄模型与实际年龄及其他生物年龄模型（如K-D Bioage、PhenoAge）的相关性。此外，ROC结果提示新年龄校正生物年龄模型能够预测中老年人群死亡风险，生物年龄加速则在中年人群中的预测效果显著。
既往K-D生物年龄使用少量生物标志物估算生物年龄，易忽略某些与衰老密切相关的关键生理功能。表型年龄虽然整合了多种生理指标，但主要适用于老年人群的健康和死亡风险预测。而DNA甲基化时钟成本高昂且技术复杂，目前难以广泛应用于大规模人群研究。近年来，JIA等［16］采集浙江大学体检人群资料，通过Lasso回归算法开发生物年龄模型。模型对于死亡结局具有较好预测作用，未来需要更多的人群验证模型稳定性。本研究利用NHANES数据库实验室和体格检查指标，采用被证实有效的多重线性回归模型，构建了具有较强预测力的新年龄校正生物年龄模型。该模型在中老年人群中对死亡风险的预测力显著，新年龄校正生物年龄是实际年龄校正后的生物年龄，本研究结果表明新年龄校正生物年龄模型能显著提升预测死亡结局的准确性。因此，本研究模型具有一定的临床应用优势。
ROC结果显示，新年龄校正生物年龄模型对全体人群及中老年人群的死亡风险具有显著预测作用。中年和老年组的AUC值分别为0.781和0.731，表明模型能有效区分不同风险水平的个体。因此，新年龄校正生物年龄模型更适合中老年人群的健康管理和预后评估。生物年龄加速在中年人群中显著提高了AUC值，因此生物年龄加速可能成为研究中年人群预后的重要工具。它反映了个体的生理衰老速度，能够识别出那些虽然年龄较轻但健康风险较高的个体，为个性化健康管理提供依据。
本研究仍存在一些局限性。首先，NHANES数据库为横断面调查，无法反映指标随时间的变化，未来需要纵向研究来验证模型的长期预测作用［17-18］。其次，研究主要基于常规实验室和体检指标，未充分考虑其他生理系统（如神经系统）的影响。此外，NHANES特定人群构成可能限制了结果的推广性，需进一步研究以验证模型在其他人群中的适用性。本研究基于NHANESⅢ和NHANES 2007-2010年的数据，这一时间段虽然较为久远，但在研究设计和数据采集方面具有较高的规范性和可靠性，为分析生物年龄模型提供了重要的数据支持。此外，由于生物年龄的理论基础和计算方法主要依赖于生物标志物与年龄的稳态关系，而这些标志物的生物学特性在短期内不会发生根本性变化，本研究的结论在科学性和稳定性上仍具有较高参考价值。未来的研究可以结合更新周期的数据，进一步验证和拓展生物年龄模型的预测价值，从而更好地服务于当前人群的健康管理和疾病预测需求。
综上所述，本研究基于NHANES数据库构建的新年龄校正生物年龄模型和生物年龄加速模型在中老年人群的死亡结局预测中展现了重要的临床应用潜力。未来可通过纵向研究和更多生物标志物，进一步优化模型并扩大其临床应用范围。

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2025年第47卷第8期
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doi: 10.16016/j.2097-0927.202410082
  • 接收时间：2024-10-21
  • 首发时间：2026-10-28
  • 出版时间：2025-04-30
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  • 收稿日期：2024-10-21
  • 修回日期：2024-11-29
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    1陆军军医大学（第三军医大学）第一附属医院老年医学与特勤医学科，重庆

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郜 攀，E-mail：
GAO Pan，E-mail：
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2种不同金属材料的力学参数
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Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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