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Article(id=1311350301668508018, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1311350193946194882, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202601208, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1768233600000, receivedDateStr=2026-01-13, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1790579875627, onlineDateStr=2026-09-28, pubDate=1786291200000, pubDateStr=2026-08-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790579875627, onlineIssueDateStr=2026-09-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790579875627, creator=13701087609, updateTime=1790579875627, updator=13701087609, issue=Issue{id=1311350193946194882, tenantId=1146029695717560320, journalId=1227665162245664772, year='2026', volume='53', issue='15', pageStart='2689', pageEnd='2880', issueExtLink='null', onlineDate='null', pubDate='1786291200000', pubDateStr='2026-08-10', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1790579849945, creator='13701087609', updateTime=1790588319569, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1311385718195315029, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1311350193946194882, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1311385718195315030, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1311350193946194882, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=2766, endPage=2771, ext={EN=ArticleExt(id=1311350301869834611, articleId=1311350301668508018, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Longitudinal association of dynamic trajectories and cumulative changes of geriatric nutritional risk index with all-cause mortality risk in the elderly, columnId=1228016572783063333, journalTitle=Modern Preventive Medicine, columnName=Nutrition and Food Hygiene, runingTitle=null, highlight=null, articleAbstract=
Objective

To investigate the longitudinal association of dynamic trajectories and cumulative changes of the geriatric nutritional risk index (GNRI) with all-cause mortality risk in the elderly.

Methods

Based on the cohort data of the Chinese Longitudinal Healthy Longevity Survey (CLHLS) in 2012 and 2014, GNRI change indicators were constructed. With 2014 as the baseline, a 4-year prospective follow-up was conducted until 2018. K-means clustering was used to classify GNRI trajectory categories and calculate individual cumulative GNRI. Multivariable Cox proportional hazards models were used to examine the association with all-cause mortality. Restricted cubic spline (RCS) was used to explore the dose-response relationship, and time-dependent receiver operating characteristic curves were used to evaluate predictive performance.

Results

A total of 1 102 participants were included, and 339 deaths occurred during follow-up (30.76%).Trajectory analysis showed that compared with the low-low group, the all-cause mortality risk was reduced by 47% (HR=0.53,95% CI: 0.37-0.77) in the high-high group and 39% (HR=0.61, 95% CI: 0.44-0.84) in the low-high group. In the cumulative GNRI quartile groups, the mortality risk of the Q3 and Q4 groups was reduced by 36% (HR=0.64, 95% CI: 0.45-0.91) and 54%(HR=0.46, 95% CI: 0.31-0.68) compared with the Q1 group. Cumulative GNRI was non-linearly and negatively associated with all-cause mortality risk, with AUC values of 0.692, 0.694, 0.720 and 0.712 at 12, 24, 36 and 48 months, respectively.

Conclusion

GNRI trajectories and cumulative GNRI were significantly associated with all-cause mortality risk among the elderly population. Cumulative GNRI has a good predictive value for all-cause mortality risk.

, authors=Zheng-xie WU, Ming-yue LIU, Cai-jun HAN, Hai-xia CUI, Zheng QU, Mei-hua PIAO, authorsList=Zheng-xie WU, Ming-yue LIU, Cai-jun HAN, Hai-xia CUI, Zheng QU, Mei-hua PIAO, authorCompany=null, correspAuthors=Mei-hua PIAO, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1311350303467864450, articleId=1311350301668508018, tenantId=1146029695717560320, journalId=1227665162245664772, language=CN, title=老年营养风险指数动态轨迹和累积变化与老年人群全因死亡风险的纵向关联研究, columnId=1228016573034721597, journalTitle=现代预防医学, columnName=营养与食品卫生, runingTitle=null, highlight=null, articleAbstract=
目的

探讨老年营养风险指数（geriatric nutritional risk index，GNRI）动态轨迹和累积变化与老年人群全因死亡风险的纵向关联。

方法

基于中国老年健康影响因素跟踪调查2012与2014年的队列数据构建GNRI变化表征，以2014年为基线时间，进行为期4年的前瞻性观察，随访至2018年结束。采用K-means聚类划分GNRI轨迹类别并计算个体累积GNRI；通过多因素Cox比例风险模型分析其与全因死亡的关联，限制性立方样条探索剂量-反应关系，时间依赖性受试者工作特征曲线评估预测效能。

结果

共纳入1 102名研究对象，随访期间死亡339例（30.76%）。轨迹分析显示，高-高组、低-高组相较低-低组全因死亡风险分别降低47%（HR=0.53，95% CI：0.37～0.77）、39%（HR=0.61，95% CI：0.44～0.84）；累积GNRI四分位分组中，Q3、Q4组较Q1组死亡风险分别降低36%（HR=0.64，95% CI：0.45～0.91）、54%（HR=0.46，95% CI：0.31～0.68）。累积GNRI与全因死亡风险呈非线性负相关，其在12、24、36和48个月的AUC值分别为0.692、0.694、0.720和0.712。

结论

GNRI变化轨迹和累积GNRI的变化与老年人群的全因死亡风险之间存在显著关联，累积GNRI对全因死亡风险有较好的预测价值。

, authors=吴政燮, 刘铭玥, 韩才均, 崔海霞, 曲政, 朴美花, authorsList=吴政燮, 刘铭玥, 韩才均, 崔海霞, 曲政, 朴美花, authorCompany=null, correspAuthors=朴美花, authorNote=

吴政燮（1983—），男，硕士，主治医师，研究方向：心血管代谢性疾病早期防治

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朴美花，E-mail：
, copyrightStatement=本刊刊出的所有文章不代表中华预防医学会和本刊编委会的观点，除非特别声明。, copyrightOwner=中华预防医学会和四川大学华西公共卫生学院, extLink=null, articleAbsUrl=null, sourceXml=25xS22kkRHxvvHendGks0g==, magXml=WUrmwXkBrXQE4HGjSkvIBw==, pdfUrl=null, pdf=qbWRienSW1Pnbo6zyhRMnw==, pdfFileSize=806769, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=vCJc0mun0KusYLUwFiiHwQ==, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=sjnaAF/OpB3TzhewEhBSvQ==, mapNumber=null, fund=null)}, authors=[Author(id=1311350303765660039, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350301668508018, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=null, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1311350303836963209, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350301668508018, authorId=1311350303765660039, language=EN, stringName=Zheng-xie WU, firstName=Zheng-xie, middleName=null, lastName=WU, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=null, address=Department of Laboratory Medicine, Yanbian University Hospital, Yanji, Jilin 133000, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1311350303925043594, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350301668508018, authorId=1311350303765660039, language=CN, stringName=吴政燮, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=null, address=延边大学附属医院（延边医院）医学检验科，吉林 延吉 133000, bio={"content":"

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Nutrition & Metabolism, 2024, 21(1): 48., articleTitle=The associations between the Geriatric Nutritional Risk Index and all-cause, cancer-specific, and cardiovascular mortality in the U.S. population: a large-scale pooled survey, refAbstract=null)], funds=[Fund(id=1311350310254248375, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350301668508018, awardId=ydkj202230, language=CN, fundingSource=延边大学应用基础项目(ydkj202230), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1311350303677579651, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350301668508018, xref=null, ext=[AuthorCompanyExt(id=1311350303685968260, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350301668508018, companyId=1311350303677579651, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=Department of Laboratory Medicine, Yanbian University Hospital, Yanji, Jilin 133000, China), AuthorCompanyExt(id=1311350303694356869, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350301668508018, companyId=1311350303677579651, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=延边大学附属医院（延边医院）医学检验科，吉林 延吉 133000)])], figs=[ArticleFig(id=1311350307527950762, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350301668508018, language=EN, label=Figure 1, caption=Cluster analysis and visualization of GNRI, figureFileSmall=ypudjQHPVoL+hxrbg3S5Jw==, figureFileBig=vCJc0mun0KusYLUwFiiHwQ==, tableContent=null), ArticleFig(id=1311350307590865324, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350301668508018, language=CN, label=图1, caption=GNRI聚类分析与可视化, figureFileSmall=ypudjQHPVoL+hxrbg3S5Jw==, figureFileBig=vCJc0mun0KusYLUwFiiHwQ==, tableContent=null), ArticleFig(id=1311350307846717869, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350301668508018, language=EN, label=Figure 2, caption=Kaplan-Meier survival curves of elderly individuals across different GNRI groups during a 4-year follow-up, figureFileSmall=8mtmioUPxsSGOIVnAmSPYw==, figureFileBig=JMbNxoRYDoS3W6Y7u3YvYg==, tableContent=null), ArticleFig(id=1311350307938992558, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350301668508018, language=CN, label=图2, caption=不同GNRI分组老年人群随访4年时的Kaplan-Meier生存曲线, figureFileSmall=8mtmioUPxsSGOIVnAmSPYw==, figureFileBig=JMbNxoRYDoS3W6Y7u3YvYg==, tableContent=null), ArticleFig(id=1311350308014490031, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350301668508018, language=EN, label=Figure 3, caption=Dose-response relationship between cumulative GNRI and all-cause mortality risk, figureFileSmall=QAf5VwVX2kUgBmYfeImSew==, figureFileBig=JsXGBpyreKzsH0ijZ52JKw==, tableContent=null), ArticleFig(id=1311350308102570416, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350301668508018, language=CN, label=图3, caption=累积GNRI与全因死亡风险的剂量-反应关系, figureFileSmall=QAf5VwVX2kUgBmYfeImSew==, figureFileBig=JsXGBpyreKzsH0ijZ52JKw==, tableContent=null), ArticleFig(id=1311350308215816625, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350301668508018, language=EN, label=Figure 4, caption=Time-dependent ROC curve analysis of cumulative GNRI for predicting all-cause mortality at different time points, figureFileSmall=c/s3VgHy7lzZB8uKGbcQjQ==, figureFileBig=hQfIsCbYXY5wMHu7wUDW6Q==, tableContent=null), ArticleFig(id=1311350308299702706, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350301668508018, language=CN, label=图4, caption=不同时间点的累积GNRI对全因死亡预测效能的Time-ROC曲线分析, figureFileSmall=c/s3VgHy7lzZB8uKGbcQjQ==, figureFileBig=hQfIsCbYXY5wMHu7wUDW6Q==, tableContent=null), ArticleFig(id=1311350308375200179, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350301668508018, language=EN, label=Table 1, caption=

Comparison of baseline characteristics of study participants ［M （P25， P75）， n（%）， （）］

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项目总人数 (n=1 102)死亡χ2/t/Z值P值
否 (n=763)是 (n=339)
年龄(岁)80 (71, 90)76 (69, 84)91 (84, 100)-16.39<0.001
男性574 (52.09)429 (56.23)145 (42.77)17.02<0.001
现在吸烟228 (20.69)178 (23.33)50 (14.75)12.440.002
现在饮酒202 (18.33)154 (20.18)48 (14.16)5.720.057
居住农村913 (82.85)634 (83.09)279 (82.30)1.450.486
每天参加锻炼186 (16.88)139 (18.22)47 (13.86)3.220.200
文盲619 (56.17)375 (49.15)244 (71.98)55.06<0.001
经济满足日常开支948 (86.03)665 (87.16)283 (83.48)2.640.104
获得足够医疗服务1 034 (93.83)720 (94.36)314 (92.63)1.230.268
高血压史334 (30.31)237 (31.06)97 (28.61)0.670.414
糖尿病史36 (3.27)22 (2.88)14 (4.13)1.150.283
心脏病史112 (10.16)72 (9.44)40 (11.80)1.440.231
中风或心血管疾病史69 (6.26)51 (6.68)18 (5.31)0.760.385
BMI(kg/m2)21.43 (19.14, 24.22)22.03 (19.61, 24.53)20.20 (18.10, 22.69)1.170.241
空腹血糖(mmol/L)5.03 (4.45, 5.77)5.02 (4.45, 5.73)5.09 (4.42, 5.93)-1.010.312
血尿素氮(mmol/L)6.38 (5.24, 7.56)6.21 (5.14, 7.33)6.81 (5.54, 8.04)-5.12<0.001
肌酐(μmol/L)77.00 (67.00, 91.38)75.50 (66.90, 88.90)79.20 (68.00, 98.35)-3.46<0.001
甘油三酯(mmol/L)1.07 (0.80, 1.48)1.09 (0.81, 1.52)1.00 (0.79, 1.35)-2.150.031
胆固醇(mmol/L)4.77 ± 0.984.82 ± 0.954.64 ± 1.032.820.005
hs-CRP(mg/L)1.11 (0.54, 2.44)0.97 (0.49, 1.96)1.68 (0.69, 3.95)-6.32<0.001
RBC(1012/L)4.25 ± 0.604.36 ± 0.554.03 ± 0.658.14<0.001
维生素D3(ng/mL)40.30 (29.50, 56.38)44.00 (33.10, 59.65)32.20 (22.95, 47.30)-8.78<0.001
GNRI聚类分组114.99<0.001
低-低182 (16.52)79 (43.41)103 (56.59)
高-高361 (32.76)300 (83.10)61 (16.90)
高-低236 (21.42)135 (57.20)101 (42.80)
低-高323 (29.31)249 (77.09)74 (22.91)
累积GNRI四分位分组127.99<0.001
Q1276 (25.05)125 (45.29)151 (54.71)
Q2275 (24.95)180 (65.45)95 (34.55)
Q3275 (24.95)221 (80.36)54 (19.64)
Q4276 (25.05)237 (85.87)39 (14.13)
), ArticleFig(id=1311350308446503348, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350301668508018, language=CN, label=表1, caption=

研究对象基线特征比较［M（P25，P75），n（%），（）］

, figureFileSmall=null, figureFileBig=null, tableContent=
项目总人数 (n=1 102)死亡χ2/t/Z值P值
否 (n=763)是 (n=339)
年龄(岁)80 (71, 90)76 (69, 84)91 (84, 100)-16.39<0.001
男性574 (52.09)429 (56.23)145 (42.77)17.02<0.001
现在吸烟228 (20.69)178 (23.33)50 (14.75)12.440.002
现在饮酒202 (18.33)154 (20.18)48 (14.16)5.720.057
居住农村913 (82.85)634 (83.09)279 (82.30)1.450.486
每天参加锻炼186 (16.88)139 (18.22)47 (13.86)3.220.200
文盲619 (56.17)375 (49.15)244 (71.98)55.06<0.001
经济满足日常开支948 (86.03)665 (87.16)283 (83.48)2.640.104
获得足够医疗服务1 034 (93.83)720 (94.36)314 (92.63)1.230.268
高血压史334 (30.31)237 (31.06)97 (28.61)0.670.414
糖尿病史36 (3.27)22 (2.88)14 (4.13)1.150.283
心脏病史112 (10.16)72 (9.44)40 (11.80)1.440.231
中风或心血管疾病史69 (6.26)51 (6.68)18 (5.31)0.760.385
BMI(kg/m2)21.43 (19.14, 24.22)22.03 (19.61, 24.53)20.20 (18.10, 22.69)1.170.241
空腹血糖(mmol/L)5.03 (4.45, 5.77)5.02 (4.45, 5.73)5.09 (4.42, 5.93)-1.010.312
血尿素氮(mmol/L)6.38 (5.24, 7.56)6.21 (5.14, 7.33)6.81 (5.54, 8.04)-5.12<0.001
肌酐(μmol/L)77.00 (67.00, 91.38)75.50 (66.90, 88.90)79.20 (68.00, 98.35)-3.46<0.001
甘油三酯(mmol/L)1.07 (0.80, 1.48)1.09 (0.81, 1.52)1.00 (0.79, 1.35)-2.150.031
胆固醇(mmol/L)4.77 ± 0.984.82 ± 0.954.64 ± 1.032.820.005
hs-CRP(mg/L)1.11 (0.54, 2.44)0.97 (0.49, 1.96)1.68 (0.69, 3.95)-6.32<0.001
RBC(1012/L)4.25 ± 0.604.36 ± 0.554.03 ± 0.658.14<0.001
维生素D3(ng/mL)40.30 (29.50, 56.38)44.00 (33.10, 59.65)32.20 (22.95, 47.30)-8.78<0.001
GNRI聚类分组114.99<0.001
低-低182 (16.52)79 (43.41)103 (56.59)
高-高361 (32.76)300 (83.10)61 (16.90)
高-低236 (21.42)135 (57.20)101 (42.80)
低-高323 (29.31)249 (77.09)74 (22.91)
累积GNRI四分位分组127.99<0.001
Q1276 (25.05)125 (45.29)151 (54.71)
Q2275 (24.95)180 (65.45)95 (34.55)
Q3275 (24.95)221 (80.36)54 (19.64)
Q4276 (25.05)237 (85.87)39 (14.13)
), ArticleFig(id=1311350308517806517, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350301668508018, language=EN, label=Table 2, caption=

Association between GNRI grouping categories and all-cause mortality risk in elderly participants

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分组类别模型1模型2模型3
HR (95%CI)P值HR (95%CI)P值HR (95%CI)P值趋势性P值
GNRI聚类
低-低1.001.001.00
高-高0.22 (0.16～0.31)<0.0010.56 (0.40～0.80)0.0010.53 (0.37～0.77)<0.001
高-低0.67 (0.51～0.89)0.0050.99 (0.75～1.32)0.9510.95 (0.72～1.27)0.743
低-高0.31 (0.23～0.42)<0.0010.58 (0.42～0.79)<0.0010.61 (0.44～0.84)0.003
累积GNRI四分位<0.001
Q11.001.001.00
Q20.53 (0.41～0.68)<0.0010.72 (0.56～0.94)0.0160.79 (0.60～1.03)0.081
Q30.28 (0.20～0.38)<0.0010.59 (0.42～0.83)0.0020.64 (0.45～0.91)0.012
Q40.19 (0.14～0.27)<0.0010.48 (0.33～0.70)<0.0010.46 (0.31～0.68)<0.001
), ArticleFig(id=1311350310157779382, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311350301668508018, language=CN, label=表2, caption=

不同GNRI分组类别与老年人群全因死亡风险的相关性

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分组类别模型1模型2模型3
HR (95%CI)P值HR (95%CI)P值HR (95%CI)P值趋势性P值
GNRI聚类
低-低1.001.001.00
高-高0.22 (0.16～0.31)<0.0010.56 (0.40～0.80)0.0010.53 (0.37～0.77)<0.001
高-低0.67 (0.51～0.89)0.0050.99 (0.75～1.32)0.9510.95 (0.72～1.27)0.743
低-高0.31 (0.23～0.42)<0.0010.58 (0.42～0.79)<0.0010.61 (0.44～0.84)0.003
累积GNRI四分位<0.001
Q11.001.001.00
Q20.53 (0.41～0.68)<0.0010.72 (0.56～0.94)0.0160.79 (0.60～1.03)0.081
Q30.28 (0.20～0.38)<0.0010.59 (0.42～0.83)0.0020.64 (0.45～0.91)0.012
Q40.19 (0.14～0.27)<0.0010.48 (0.33～0.70)<0.0010.46 (0.31～0.68)<0.001
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老年营养风险指数动态轨迹和累积变化与老年人群全因死亡风险的纵向关联研究
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吴政燮 , 刘铭玥 , 韩才均 , 崔海霞 , 曲政 , 朴美花
现代预防医学 | 营养与食品卫生 2026,53(15): 2766-2771
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现代预防医学 |营养与食品卫生 2026 , 53 (15) : 2766 -2771
老年营养风险指数动态轨迹和累积变化与老年人群全因死亡风险的纵向关联研究
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吴政燮, 刘铭玥, 韩才均, 崔海霞, 曲政, 朴美花
作者信息
  • 延边大学附属医院（延边医院）医学检验科，吉林 延吉 133000
通讯作者:
朴美花，E-mail：
作者简介:

吴政燮（1983—），男，硕士，主治医师，研究方向：心血管代谢性疾病早期防治

Longitudinal association of dynamic trajectories and cumulative changes of geriatric nutritional risk index with all-cause mortality risk in the elderly
Zheng-xie WU, Ming-yue LIU, Cai-jun HAN, Hai-xia CUI, Zheng QU, Mei-hua PIAO
Affiliations
  • Department of Laboratory Medicine, Yanbian University Hospital, Yanji, Jilin 133000, China
出版时间: 2026-08-10 doi: 10.20043/j.cnki.MPM.202601208
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摘要
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目的

探讨老年营养风险指数（geriatric nutritional risk index，GNRI）动态轨迹和累积变化与老年人群全因死亡风险的纵向关联。

方法

基于中国老年健康影响因素跟踪调查2012与2014年的队列数据构建GNRI变化表征，以2014年为基线时间，进行为期4年的前瞻性观察，随访至2018年结束。采用K-means聚类划分GNRI轨迹类别并计算个体累积GNRI；通过多因素Cox比例风险模型分析其与全因死亡的关联，限制性立方样条探索剂量-反应关系，时间依赖性受试者工作特征曲线评估预测效能。

结果

共纳入1 102名研究对象，随访期间死亡339例（30.76%）。轨迹分析显示，高-高组、低-高组相较低-低组全因死亡风险分别降低47%（HR=0.53，95% CI：0.37～0.77）、39%（HR=0.61，95% CI：0.44～0.84）；累积GNRI四分位分组中，Q3、Q4组较Q1组死亡风险分别降低36%（HR=0.64，95% CI：0.45～0.91）、54%（HR=0.46，95% CI：0.31～0.68）。累积GNRI与全因死亡风险呈非线性负相关，其在12、24、36和48个月的AUC值分别为0.692、0.694、0.720和0.712。

结论

GNRI变化轨迹和累积GNRI的变化与老年人群的全因死亡风险之间存在显著关联，累积GNRI对全因死亡风险有较好的预测价值。

老年营养风险指数  /  全因死亡  /  队列研究
Objective

To investigate the longitudinal association of dynamic trajectories and cumulative changes of the geriatric nutritional risk index (GNRI) with all-cause mortality risk in the elderly.

Methods

Based on the cohort data of the Chinese Longitudinal Healthy Longevity Survey (CLHLS) in 2012 and 2014, GNRI change indicators were constructed. With 2014 as the baseline, a 4-year prospective follow-up was conducted until 2018. K-means clustering was used to classify GNRI trajectory categories and calculate individual cumulative GNRI. Multivariable Cox proportional hazards models were used to examine the association with all-cause mortality. Restricted cubic spline (RCS) was used to explore the dose-response relationship, and time-dependent receiver operating characteristic curves were used to evaluate predictive performance.

Results

A total of 1 102 participants were included, and 339 deaths occurred during follow-up (30.76%).Trajectory analysis showed that compared with the low-low group, the all-cause mortality risk was reduced by 47% (HR=0.53,95% CI: 0.37-0.77) in the high-high group and 39% (HR=0.61, 95% CI: 0.44-0.84) in the low-high group. In the cumulative GNRI quartile groups, the mortality risk of the Q3 and Q4 groups was reduced by 36% (HR=0.64, 95% CI: 0.45-0.91) and 54%(HR=0.46, 95% CI: 0.31-0.68) compared with the Q1 group. Cumulative GNRI was non-linearly and negatively associated with all-cause mortality risk, with AUC values of 0.692, 0.694, 0.720 and 0.712 at 12, 24, 36 and 48 months, respectively.

Conclusion

GNRI trajectories and cumulative GNRI were significantly associated with all-cause mortality risk among the elderly population. Cumulative GNRI has a good predictive value for all-cause mortality risk.

Geriatric nutritional risk index  /  All-cause mortality  /  Cohort study
吴政燮, 刘铭玥, 韩才均, 崔海霞, 曲政, 朴美花. 老年营养风险指数动态轨迹和累积变化与老年人群全因死亡风险的纵向关联研究. 现代预防医学, 2026 , 53 (15) : 2766 -2771 . DOI: 10.20043/j.cnki.MPM.202601208
Zheng-xie WU, Ming-yue LIU, Cai-jun HAN, Hai-xia CUI, Zheng QU, Mei-hua PIAO. Longitudinal association of dynamic trajectories and cumulative changes of geriatric nutritional risk index with all-cause mortality risk in the elderly[J]. Modern Preventive Medicine, 2026 , 53 (15) : 2766 -2771 . DOI: 10.20043/j.cnki.MPM.202601208
正文
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中国老年人群中，营养不良以及存在营养风险的总体患病率均处于相对较高的水平，其中社区老年人营养不良风险检出率为36.6%[1-2]。营养状况作为影响老年人健康的关键因素之一，其动态演变通过调节机体免疫功能及炎症反应等机制，可以影响疾病预后转归[3]。鉴于此，对于社区及居家老年人，建议定期进行营养筛查，以便及时发现潜在的营养问题并采取相应干预措施[1]。
老年营养风险指数（geriatric nutritional risk index，GNRI）作为一种综合考量血清白蛋白水平、体重等的多维度因素的评估工具，在临床实践中展现出良好的预后预测价值[4-5]。然而，现有类似的研究多聚焦于单一时点GNRI测量与预后的关联分析[6]，缺乏基于大规模人群队列的纵向研究。因此，本研究基于前瞻性队列研究设计，通过GNRI动态变化与累积变化揭示营养状态变化与全因死亡风险的时序关联，旨在为制定老年人群个体化健康管理策略提供循证依据。
1　对象与方法
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1.1　研究对象
数据来源于中国老年健康影响因素跟踪调查（Chinese Longitudinal Healthy Longevity Survey, CLHLS）[7]2012与2014年的队列随访数据，以2014年调查为基线建立前瞻性队列研究，初始纳入60岁及以上老年参与者1 460名，该人群均完成了2012和2014年的生物医学指标检测。进一步排除了缺失问卷调查（n=14）、健康体检指标（n=131）、血液检测指标（n=48）以及2018年调查时失访（n=165）的人群，最终纳入1 102名研究对象进行分析。
CLHLS已通过北京大学生物医学伦理委员会审批（审批编号：IRB00001052-13074），所有参与调查的被访者及其家属均已签署知情同意书，自愿参与本次调查及样本采集工作。
1.2　资料收集
研究纳入的变量涵盖三个维度：社会人口学及行为学特征（年龄、性别、吸烟状况、饮酒情况、锻炼习惯、教育水平、经济条件、医疗保障及疾病病史）、人体测量学指标（身高、体重）、2012和2014年采集的血液检测数据[血清白蛋白（albumin，ALB）、空腹血糖、甘油三酯、胆固醇、血尿素氮、肌酐、超敏C-反应蛋白（high-sensitivity C-reactive protein，hs-CRP）、红细胞计数（red blood cell count，RBC）及维生素D3]。研究以2018年随访作为观察终点，统一划分研究结局：死亡事件经家属标准化问卷核实死亡日期与死因予以确认，2018年随访存活者归为生存结局。随访时间按月统计，自2014年基线调查当月开始，至死亡发生当月或2018年随访截止当月结束。
1.3　变量计算
（1）GNRI = [1.489×ALB（g/L）] +[41.7×（实际体重（kg）/理想体重（kg）]，理想体重（男性）=身高(cm)-100-[(身高-150)/4]；理想体重（女性）=身高(cm)-100-[(身高-150)/2.5]。如果实际体重大于理想体重，则实际体重/理想体重设为1[4]。（2）累积GNRI=(2012年GNRI + 2014年GNRI)/2×时间(2014-2012)(年)[8]。（3）身体质量指数（body mass index,BMI）计算公式：BMI=体重（kg）/身高的二次方（m2）。
1.4　统计学分析
使用R 4.3.2软件进行统计分析和制图。K-means聚类分析是一种无监督机器学习方法[9]。本研究对2012和2014年的GNRI测量进行K-means聚类分析，通过肘部法则和轮廓系数确定拐点，结合临床意义将研究对象划分为四组：（1）低-低组（86.96～93.88），呈现长期稳定低值；（2）高-高组（107.81～107.62），呈现长期稳定高值；（3）高-低组（100.08～95.80），呈现由高至低的下降趋势；（4）低-高组（96.25～105.73），呈现由低至高的升高趋势。直方图和概率密度图表明，累积GNRI在聚类的每个分组中均呈现出大致正态分布的特征。见图1。
本研究对正态分布的计量资料以（）表示，组间的比较采用两样本独立t检验，非正态分布的计量资料用［M（P25，P75）］表示，组间比较采用Mann Whitney U检验，分类变量以频数和构成比（%）表示，采用χ2检验比较组间差异。使用Kaplan-Meier绘制GNRI聚类分组和累积GNRI四分位分组（Q1～Q4）生存曲线，采用Log-rank检验对不同组别间的生存概率差异进行评估；运用Cox比例风险模型校正混杂因素，分析GNRI聚类分组及累积GNRI四分位分组（Q1～Q4）与生存风险之间的关联；通过限制性立方样条图（restricted cubic spline，RCS）分析累积GNRI与全因死亡风险的剂量-反应关系，并利用时间依赖性受试者工作特征（time-receiver operating characteristic,Time-ROC）曲线，探究累积GNRI对全因死亡风险的预测效应及价值。双侧检验，检验水准α=0.05。
2　结果
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2.1　研究对象基线特征
在纳入研究的1 102名研究对象中，死亡339例（30.76%），年龄80（71, 90）岁，男性574名（52.09%）。存活组与死亡组相比，在年龄、性别、吸烟、教育、血尿素氮、肌酐、甘油三酯、胆固醇、hs-CRP、RBC、维生素D3、GNRI聚类分组、累积GNRI四分位分组等指标的组间差异具有统计学意义（P<0.05）。见表1。
2.2　不同GNRI分组的老年人群全因死亡的Kaplan-Meier生存分析
Log-rank检验结果表明，GNRI聚类分组和累积GNRI四分位分组的老年人群全因死亡生存概率存在差异，差异具有统计学意义(均P<0.001)。见图2。
2.3　不同GNRI分组类别与老年人群全因死亡风险的相关性
在校正了混杂因素后，模型3的GNRI聚类分组的高-高组和低-高组的死亡风险，比低-低组降低了47%（HR=0.53，95%CI：0.37～0.77）和39%（HR=0.61，95%CI：0.44～0.84）；模型3的累积GNRI四分位分组的Q3组和Q4组的死亡风险，比Q1组降低了36%（HR=0.64，95%CI：0.45～0.91）和54%（HR=0.46，95%CI：0.31～0.68），整体趋势性检验P<0.001。见表2。
2.4　累积GNRI与全因死亡风险的剂量-反应关系
结果显示，在校正了年龄、性别、吸烟、饮酒、教育、血尿素氮、肌酐、甘油三酯、胆固醇、hs-CRP、维生素D3和RBC等混杂因素后，累积GNRI与全因死亡风险呈非线性负相关（非线性P值=0.009）。见图3。
2.5　不同时间点累积GNRI预测老年人群全因死亡风险的效能分析
通过Time-ROC曲线，评估累积GNRI对全因死亡风险的动态预测能力。结果显示：在12、24、36和48个月时间节点的AUC值分别为0.692（特异度：0.582、敏感度：0.725）、0.694（特异度：0.654、敏感度：0.657）、0.720（特异度：0.694、敏感度：0.656）、0.712（特异度：0.698、敏感度：0.642）。在随访期间累积GNRI的C指数无明显衰减，累积GNRI对老年人群全因死亡风险的预测效能稳定。见图4。
3　讨论
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本研究采用GNRI作为评估工具，通过纵向分析探讨GNRI动态变化和累积变化对老年人群全因死亡风险的预后价值。本研究发现，老年人群GNRI变化轨迹和累积GNRI的变化与全因死亡风险之间存在显著关联。累积GNRI对全因死亡风险的预测能力在随访期内表现较好，且预测效能稳定。
老年人群营养不良的核心发病机制是营养摄入减少、高代谢消耗状态及营养素生物利用度下降，而动态监测有助于分析病因，针对不同个体制定精准防控干预措施[1]。本研究发现，在GNRI聚类分组中，高-高组和低-高组的死亡风险较低-低组分别降低47%（HR=0.53，95%CI：0.37～0.77）和39%（HR= 0.61，95%CI：0.44～0.84）。一项纳入中国广东省4家医院的多中心回顾性队列研究[10]与巴西的单中心研究[11]均得出类似的结论，即维持良好营养状况或改善营养不良状态可显著降低死亡风险。值得关注的是，本研究中高-低组与低-低组的死亡风险无显著差异（HR=0.95，95% CI：0.72～1.27），这一结果揭示了“营养状态由好变差”对老年生存预后的潜在负面影响。这种动态变化的差异，提示持续良好的营养储备或营养状态的正向转变，能通过维持免疫功能、抑制慢性炎症[3]、保护器官功能等机制，为老年人群提供抵御疾病的“基础保障”[12-13]。而对于营养状态较差的人群，给予适当的营养干预，使其营养状态向更健康的方向转变，能够显著提高其生存率和生活质量[14]。
累积GNRI通过整合长期营养状态变化，反映了机体长期营养储备、代谢功能及抗应激能力的持续水平，而这些因素直接影响老年人群的疾病抵抗力及生存预后[15-17]。本研究发现，累积GNRI的Q3、Q4组相较Q1组，全因死亡风险分别降低了36%（HR=0.64，95% CI：0.45～0.91）和54%（HR=0.46，95% CI：0.31～0.68），整体趋势性检验P<0.001。累积GNRI与全因死亡风险存在显著非线性负剂量-反应关系（非线性P值= 0.009）。这一结果与Shen X等[18]的研究结果类似，提示临床若能识别出这些长期营养储备低的高危人群，并将更多的临床资源向高危人群倾斜[19]，就有可能改善预后[20-21]。进一步的Time-ROC曲线分析结果显示，累积GNRI在12、24、36和48个月时间节点的AUC值分别为0.692、0.694、0.720和0.712；在随访期间累积GNRI的C指数无明显衰减。这一结果与一项关于美国人群全因死亡率的研究结果类似[22]，提示累积GNRI对中国老年人群随访至48个月内的全因死亡风险具有较为稳定的预测效能。
综上所述，GNRI动态变化模式和随访期内累积变化水平对老年人群死亡风险的预测及分层管理具有一定临床参考价值。这一发现提示，在老年人群中开展定期营养筛查、动态监测并实施个性化干预，有助于优化老年营养不良的管理策略。未来研究可延长随访时限，进一步验证其更长周期的预测价值，并探索轨迹转折点的最佳干预时机。
本研究存在一定的局限性：首先，本研究提到了早期干预的重要性，但缺乏对干预措施的验证，可能限制其临床直接应用。其次，尽管已经控制了多种混杂因素，但不能排除一些未测量的混杂因素影响（如种族、个体的遗传倾向、日常活动模式等）。最后，本研究只测量了两个时间点（2012和2014年），这可能无法捕捉短期波动或随时间变化的全部轨迹，导致对研究对象的状态判断不准确，稀释真实的关联效应。鉴于上述局限性，有必要建立多中心、大样本的纵向研究来进一步验证这些发现。
基金
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  • 延边大学应用基础项目(ydkj202230)
文献
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2026年第53卷第15期
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doi: 10.20043/j.cnki.MPM.202601208
  • 接收时间：2026-01-13
  • 首发时间：2026-09-28
  • 出版时间：2026-08-10
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  • 收稿日期：2026-01-13
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延边大学应用基础项目(ydkj202230)
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    延边大学附属医院（延边医院）医学检验科，吉林 延吉 133000

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朴美花，E-mail：
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2种不同金属材料的力学参数
科
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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