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Article(id=1311349960193434069, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1311349919793905738, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202512414, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1766505600000, receivedDateStr=2025-12-24, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1790579794214, onlineDateStr=2026-09-28, pubDate=1782316800000, pubDateStr=2026-06-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790579794214, onlineIssueDateStr=2026-09-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790579794214, creator=13701087609, updateTime=1790579794214, updator=13701087609, issue=Issue{id=1311349919793905738, tenantId=1146029695717560320, journalId=1227665162245664772, year='2026', volume='53', issue='12', pageStart='2113', pageEnd='2304', issueExtLink='null', onlineDate='null', pubDate='1782316800000', pubDateStr='2026-06-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1790579784582, creator='13701087609', updateTime=1790588408144, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1311386089701597565, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1311349919793905738, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1311386089701597566, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1311349919793905738, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=2117, endPage=2125, ext={EN=ArticleExt(id=1311349960440898006, articleId=1311349960193434069, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Associations of triglyceride-glucose index combined with classical and novel obesity indices and multimorbidity in middle-aged and elderly Chinese, columnId=1228016567443718970, journalTitle=Modern Preventive Medicine, columnName=Epidemiology and Statistical Methods Advances, runingTitle=null, highlight=null, articleAbstract=
Background

With the accelerated aging of China’s population, multimorbidity has become a major health challenge for middle-aged and older adults, placing higher demands on clinical care and health management systems. Obesity and insulin resistance are common pathophysiological foundations for multiple chronic diseases. However, there is a lack of large-scale prospective studies systematically evaluating the combined effects of the triglyceride-glucose (TyG) index, traditional obesity indices, and novel obesity indices on the risk of multimorbidity.

Objective

To investigate the impact and predictive value of the TyG index combined with traditional and novel obesity indices on the risk of multimorbidity, providing a scientific basis for early identification, risk stratification, and precision nursing interventions for chronic disease comorbidity.

Methods

Based on prospective cohort data from the China Health and Retirement Longitudinal Study (CHARLS) from 2011 to 2020, this study included 2 574 middle-aged and older participants aged 45 years or older without chronic diseases at baseline. Traditional and novel obesity indices were calculated, including body mass index (BMI), waist circumference (WC), waist-to-height ratio (WHtR), weight-adjusted waist index (WWI), a body shape index (ABSI), body roundness index (BRI), and Chinese visceral adiposity index (CVAI). Additionally, the TyG index and its derivatives combined with obesity indices (e.g., TyG-WC, TyG-BRI) were calculated. Cox proportional hazards regression models were used to analyze the associations between these indices and the risk of multimorbidity, while restricted cubic spline analysis was employed to examine dose-response relationships. Receiver operating characteristic (ROC) curves were used to evaluate the predictive performance of these indices.

Results

The overall incidence of multimorbidity was 57.8%. After multivariate adjustment, all obesity indices and TyG-related indices were significantly positively associated with the risk of multimorbidity (P<0.05). Among these, WC showed the strongest association (hazard ratio ［HR］=1.39, 95% confidence interval ［CI］: 1.22-1.57), followed by BMI (HR=1.26, 95%CI: 1.10-1.43). The HRs for WHtR and BRI were both 1.35 (95%CI: 1.18-1.54), while the HRfor CVAI was 1.28 (95%CI: 1.12-1.45). The TyG index alone was significantly associated with the risk of multimorbidity (HR=1.21, 95%CI: 1.06-1.37), and its combination with obesity indices further enhanced the association: TyG-WC had an HRof 1.36 (95%CI: 1.20-1.54), TyG-BRI had an HRof 1.33 (95%CI: 1.17-1.52), and TyG-CVAI had an HRof 1.29 (95%CI: 1.13-1.46). Dose-response analysis revealed that most indices exhibited a nonlinear positive association with multimorbidity risk (all P for nonlinearity<0.05). ROC curve analysis showed that among traditional and novel obesity indices, WHtR and BRI had the best predictive performance (both AUC=0.560). Among TyG derivatives, TyG-BRI had the highest predictive performance (AUC=0.562), followed by TyG-WHtR (AUC=0.561) and TyG-WC (AUC=0.558).The DeLong test revealed that only TyG-WHtR exhibited a significantly higher AUC compared with TyG alone (P=0.038).

Conclusion

The combined use of the TyG index and obesity indices provides a more comprehensive assessment of multimorbidity risk in middle-aged and older adults. Compared with other obesity indexes, TyG-WHtR and TyG-BRI demonstrated superior performance in risk identification, supporting their utility as practical screening indicators in clinical nursing assessments and community health management.

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背景

随着我国人口老龄化进程加快，慢性病共病已成为中老年人群健康的重大挑战，也对临床护理和健康管理体系提出了更高要求。肥胖与胰岛素抵抗是多种慢性病的共同病理生理基础，然而，目前尚缺乏大型前瞻性研究系统评估甘油三酯-葡萄糖（Triglyceride-glucose index，TyG）指数及传统与新型肥胖指标对慢性病共病发病风险的影响。

目的

本研究旨在探讨TyG指数联合传统及新型肥胖指标对慢性病共病发病风险的影响和预测价值，为慢性病共病的早期识别、风险分层和精准护理干预提供科学依据。

方法

本研究基于中国健康与养老追踪调查（CHARLS）2011—2020年的前瞻性队列数据，纳入2 574名基线无慢性病的45岁及以上中老年参与者。计算体重指数（Body Mass Index，BMI）、腰围（Waist Circumference，WC）、腰高比（Waist-to-Height Ratio，WHtR）、体重调整腹围指数（Weight-adjusted Waist Index，WWI）、身体形态指数（A Body Shape Index，ABSI）、身体圆度指数（Body Roundness Index，BRI）、内脏脂肪指数（Chinese Visceral Adiposity Index，CVAI）传统与新型肥胖指标，以及TyG指数及其与各肥胖指标组合的衍生指标（如TyG-WC、TyG-BRI等）。采用Cox比例风险回归模型分析各指标与慢性病共病发病风险的关联，并采用限制性立方样条分析其剂量-反应关系。使用受试者工作特征（Receiver Operating Characteristic Curve，ROC）曲线评估各指标的预测能力。

结果

慢性病共病总体发病率为57.8%。多因素调整后，所有肥胖指标和TyG相关指标均与慢性病共病风险呈显著正相关（P<0.05）。其中，WC与慢性病共病的关联强度最大（HR=1.39，95%CI：1.22~1.57），BMI次之（HR=1.26，95%CI：1.10~1.43），WHtR和BRI风险比为1.35（95%CI：1.18~1.54），CVAI为1.28（95%CI：1.12~1.45）。TyG指数单独与慢性病共病风险显著相关（HR=1.21，95%CI: 1.06-1.37），但其与肥胖指标组合后关联性增强，TyG-WC风险比达1.36（95%CI：1.20~1.54），TyG-BRI为1.33（95%CI：1.17~1.52），TyG-CVAI为1.29（95%CI：1.13~1.46）。剂量-反应分析显示，多数指标与风险呈非线性正相关（all P for nonlinearity<0.05）。ROC曲线分析表明，在传统与新型肥胖指标中，WHtR和BRI的预测效能最佳（AUC均为0.560）；在TyG衍生指标中，TyG-BRI预测效能最优（AUC=0.562），其次为TyG-WHtR（AUC=0.561）和TyG-WC（AUC=0.558）。DeLong检验结果显示，仅TyG-WHtR的AUC显著高于TyG单一指标（P=0.038）。

结论

TyG指数与肥胖指标联合应用可更全面评估中老年人群慢性病共病风险，其中TyG-WHtR和TyG-BRI在风险识别中比其他TyG联合肥胖指标表现更佳，可为临床护理评估和社区健康管理中实用的风险筛查指标提供支持。

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杨丽雅（1975—），女，本科，副主任护师，研究方向：糖尿病、肥胖症的护理干预及管理

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杨丽雅，E-mail:
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Cardiovascular Diabetology, 2025, 24(1): 377., articleTitle=Associations of six insulin resistance-related indices with the risk and progression of cardio-renal-metabolic multimorbidity: evidence from the UK biobank, refAbstract=null), Reference(id=1311349975628472927, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311349960193434069, doi=null, pmid=null, pmcid=null, year=2023, volume=null, issue=null, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=[19], rfOrder=25, authorNames=赵玉, journalName=null, refType=null, unstructuredReference=赵玉．老年慢性共病患者社区护理需求现状及影响因素研究［D］．广州：暨南大学，2023．, articleTitle=老年慢性共病患者社区护理需求现状及影响因素研究, refAbstract=null), Reference(id=1311349975708164704, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311349960193434069, doi=null, pmid=null, pmcid=null, year=2023, volume=null, issue=null, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=[19], rfOrder=26, authorNames=Zhao Y, journalName=null, refType=null, unstructuredReference=Zhao Y. Study on the status and influencing factors of community nursing needs among elderly patients with chronic multimorbidity［D］. Guangzhou: Jinan University, 2023. (In Chinese), articleTitle=Study on the status and influencing factors of community nursing needs among elderly patients with chronic multimorbidity, refAbstract=null)], funds=[Fund(id=1311349973292245571, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311349960193434069, awardId=2024KY499, language=CN, fundingSource=浙江省医药卫生科技计划项目(2024KY499), fundOrder=null, country=null), Fund(id=1311349973388714564, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311349960193434069, awardId=2023-4-085, language=CN, fundingSource=金华市科技计划项目(2023-4-085), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1311349966648467979, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311349960193434069, xref=1., ext=[AuthorCompanyExt(id=1311349966652662284, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311349960193434069, companyId=1311349966648467979, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=Department of Nursing, Jinhua Central Hospital, Jinhua, Zhejiang 321000, China), AuthorCompanyExt(id=1311349966661050893, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311349960193434069, companyId=1311349966648467979, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1.金华市中心医院护理部，浙江 金华 321000)]), AuthorCompany(id=1311349966728159758, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311349960193434069, xref=2., ext=[AuthorCompanyExt(id=1311349966736548367, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311349960193434069, companyId=1311349966728159758, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2.金华市中心医院内分泌代谢科)])], figs=[ArticleFig(id=1311349970578530869, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311349960193434069, language=EN, label=Fig.1, caption=Dose-response relationships between classical and novel obesity indices and multimorbidity risk, figureFileSmall=/LjX9M/Rsa8aCU5iVnPcxw==, figureFileBig=aus7rW1Kq0WzweDhy8JxIg==, tableContent=null), ArticleFig(id=1311349970654028342, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311349960193434069, language=CN, label=图1, caption=传统和新型肥胖指标与慢性病共病发病风险的剂量-反应关系, figureFileSmall=/LjX9M/Rsa8aCU5iVnPcxw==, figureFileBig=aus7rW1Kq0WzweDhy8JxIg==, tableContent=null), ArticleFig(id=1311349970834383415, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311349960193434069, language=EN, label=Fig.2, caption=Dose-response relationships between TyG and its obesity derivatives and multimorbidity risk, figureFileSmall=YuQxyuQHMdTjJaFw8mYpeg==, figureFileBig=WnZts7v/OXLnVb7s4aV17Q==, tableContent=null), ArticleFig(id=1311349970897297976, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311349960193434069, language=CN, label=图2, caption=TyG及其肥胖衍生指标与慢性病共病发病风险的剂量-反应关系, figureFileSmall=YuQxyuQHMdTjJaFw8mYpeg==, figureFileBig=WnZts7v/OXLnVb7s4aV17Q==, tableContent=null), ArticleFig(id=1311349970964406841, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311349960193434069, language=EN, label=Fig.3, caption=ROC curves of classical and novel obesity indices, and TyG and its obesity derivatives for predicting multimorbidity risk, figureFileSmall=zAl3DAbDLoGgt+Q1N3xuFA==, figureFileBig=gZbX6nAIBbyXNXU7ux5HxA==, tableContent=null), ArticleFig(id=1311349971031515706, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311349960193434069, language=CN, label=图3, caption=传统和新型肥胖指标、TyG及其肥胖衍生指标对慢性病共病发病风险的预测ROC曲线

注：A：传统和新型肥胖指标对慢性病共病发病风险的预测ROC曲线；B：TyG及其肥胖衍生指标对慢性病共病发病风险的预测ROC曲线。

, figureFileSmall=zAl3DAbDLoGgt+Q1N3xuFA==, figureFileBig=gZbX6nAIBbyXNXU7ux5HxA==, tableContent=null), ArticleFig(id=1311349971098624571, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311349960193434069, language=EN, label=Table 1, caption=

Baseline characteristics of the study participants

, figureFileSmall=null, figureFileBig=null, tableContent=
变量总计（n=2 574）TyG分组
Tertile 1（n=858）Tertile 2（n=858）Tertile 3（n=858）
慢性病共病发病（n［%］）
否1 085（42.2）401（46.7）356（41.5）328（38.2）
是1 489（57.8）457（53.3）502（58.5）530（61.8）
年龄（岁，n［%］）
≤601 748（67.9）571（66.6）587（68.4）590（68.8）
>60826（32.1）287（33.4）271（31.6）268（31.2）
性别（n［%］）
男1 254（48.7）481（56.1）396（46.2）377（43.9）
女1 320（51.3）377（43.9）462（53.8）481（56.1）
居住地（n［%］）
农村1 682（65.3）608（70.9）547（63.8）527（61.4）
城市892（34.7）250（29.1）311（36.2）331（38.6）
婚姻状况（n［%］）
已婚2 310（89.7）775（90.3）768（89.5）767（89.4）
其他264（10.3）83（9.7）90（10.5）91（10.6）
教育程度（n［%］）
文盲1 141（44.4）372（43.4）385（44.9）384（44.8）
小学549（21.3）177（20.6）183（21.4）189（22.1）
初中594（23.1）209（24.4）189（22.1）196（22.9）
高中及以上288（11.2）100（11.7）100（11.7）88（10.3）
吸烟（n［%］）
从不吸烟1 553（60.3）482（56.2）532（62）539（62.8）
现在吸烟872（33.9）324（37.8）282（32.9）266（31）
从前吸烟149（5.8）52（6.1）44（5.1）53（6.2）
饮酒（n［%］）
否947（36.8）348（40.6）308（35.9）291（33.9）
是1 627（63.2）510（59.4）550（64.1）567（66.1）
BMI（，kg/m2）22.85±2.8822.11±2.6722.85±2.8523.6±2.91
WC（，cm）82.68±10.880.41±9.5182.74±11.0584.89±11.29
WHtR（）0.52±0.070.51±0.060.52±0.070.54±0.07
WWI（）10.94±1.2310.75±1.0810.96±1.311.09±1.29
ABSI（）0.08±0.010.08±0.010.08±0.010.08±0.01
BRI（）3.89±1.323.54±1.143.92±1.324.21±1.38
CVAI（）87.46±42.7760.33±32.1585.18±37.79116.88±37.72
TyG（）8.57±0.627.94±0.268.51±0.159.27±0.44
TyG-BMI（）196.28±31.31175.68±22.42194.34±24.82218.81±29.79
TyG-WC（）709.96±115.22639.02±80.08703.79±95.74787.09±115.1
TyG-WHtR（）4.49±0.774.02±0.544.45±0.654.99±0.76
TyG-WWI（）93.84±13.4285.46±9.293.25±11.29102.82±13.31
TyG-ABSI（）0.7±0.090.64±0.070.7±0.080.76±0.09
TyG-BRI（）33.53±12.2328.12±9.2233.36±11.3339.12±13.24
TyG-CVAI（）765.68±410.09481.79±259.83725.75±325.541 089.51±379.67
), ArticleFig(id=1311349971174122044, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311349960193434069, language=CN, label=表1, caption=

研究人群基线特征

, figureFileSmall=null, figureFileBig=null, tableContent=
变量总计（n=2 574）TyG分组
Tertile 1（n=858）Tertile 2（n=858）Tertile 3（n=858）
慢性病共病发病（n［%］）
否1 085（42.2）401（46.7）356（41.5）328（38.2）
是1 489（57.8）457（53.3）502（58.5）530（61.8）
年龄（岁，n［%］）
≤601 748（67.9）571（66.6）587（68.4）590（68.8）
>60826（32.1）287（33.4）271（31.6）268（31.2）
性别（n［%］）
男1 254（48.7）481（56.1）396（46.2）377（43.9）
女1 320（51.3）377（43.9）462（53.8）481（56.1）
居住地（n［%］）
农村1 682（65.3）608（70.9）547（63.8）527（61.4）
城市892（34.7）250（29.1）311（36.2）331（38.6）
婚姻状况（n［%］）
已婚2 310（89.7）775（90.3）768（89.5）767（89.4）
其他264（10.3）83（9.7）90（10.5）91（10.6）
教育程度（n［%］）
文盲1 141（44.4）372（43.4）385（44.9）384（44.8）
小学549（21.3）177（20.6）183（21.4）189（22.1）
初中594（23.1）209（24.4）189（22.1）196（22.9）
高中及以上288（11.2）100（11.7）100（11.7）88（10.3）
吸烟（n［%］）
从不吸烟1 553（60.3）482（56.2）532（62）539（62.8）
现在吸烟872（33.9）324（37.8）282（32.9）266（31）
从前吸烟149（5.8）52（6.1）44（5.1）53（6.2）
饮酒（n［%］）
否947（36.8）348（40.6）308（35.9）291（33.9）
是1 627（63.2）510（59.4）550（64.1）567（66.1）
BMI（，kg/m2）22.85±2.8822.11±2.6722.85±2.8523.6±2.91
WC（，cm）82.68±10.880.41±9.5182.74±11.0584.89±11.29
WHtR（）0.52±0.070.51±0.060.52±0.070.54±0.07
WWI（）10.94±1.2310.75±1.0810.96±1.311.09±1.29
ABSI（）0.08±0.010.08±0.010.08±0.010.08±0.01
BRI（）3.89±1.323.54±1.143.92±1.324.21±1.38
CVAI（）87.46±42.7760.33±32.1585.18±37.79116.88±37.72
TyG（）8.57±0.627.94±0.268.51±0.159.27±0.44
TyG-BMI（）196.28±31.31175.68±22.42194.34±24.82218.81±29.79
TyG-WC（）709.96±115.22639.02±80.08703.79±95.74787.09±115.1
TyG-WHtR（）4.49±0.774.02±0.544.45±0.654.99±0.76
TyG-WWI（）93.84±13.4285.46±9.293.25±11.29102.82±13.31
TyG-ABSI（）0.7±0.090.64±0.070.7±0.080.76±0.09
TyG-BRI（）33.53±12.2328.12±9.2233.36±11.3339.12±13.24
TyG-CVAI（）765.68±410.09481.79±259.83725.75±325.541 089.51±379.67
), ArticleFig(id=1311349971274785341, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311349960193434069, language=EN, label=Table 2, caption=

Associations between classical and novel obesity indices and the risk of multimorbidity

, figureFileSmall=null, figureFileBig=null, tableContent=
Model 1
HR（95%CI）		Model 2
HR（95%CI）		Model 3
HR（95%CI）
BMI
Tertile 11.00（Reference）1.00（Reference）1.00（Reference）
Tertile 21.05（0.93~1.19）1.08（0.95~1.23）1.09（0.96~1.24）
Tertile 31.21（1.06~1.37）1.23（1.08~1.40）1.26（1.10~1.43）
WC
Tertile 11.00（Reference）1.00（Reference）1.00（Reference）
Tertile 21.17（1.03~1.33）1.18（1.04~1.35）1.19（1.05~1.35）
Tertile 31.37（1.21~1.55）1.37（1.21~1.56）1.39（1.22~1.57）
WHtR
Tertile 11.00（Reference）1.00（Reference）1.00（Reference）
Tertile 21.10（0.96~1.25）1.11（0.97~1.26）1.11（0.97~1.26）
Tertile 31.38（1.22~1.56）1.35（1.19~1.54）1.35（1.18~1.54）
WWI
Tertile 11.00（Reference）1.00（Reference）1.00（Reference）
Tertile 21.06（0.94~1.21）1.05（0.93~1.20）1.04（0.92~1.19）
Tertile 31.35（1.19~1.53）1.29（1.13~1.47）1.27（1.11~1.45）
ABSI
Tertile 11.00（Reference）1.00（Reference）1.00（Reference）
Tertile 21.20（1.06~1.36）1.19（1.05~1.35）1.18（1.04~1.34）
Tertile 31.31（1.16~1.49）1.26（1.11~1.43）1.24（1.09~1.42）
BRI
Tertile 11.00（Reference）1.00（Reference）1.00（Reference）
Tertile 21.10（0.96~1.25）1.11（0.97~1.26）1.11（0.97~1.26）
Tertile 31.38（1.22~1.56）1.35（1.19~1.54）1.35（1.18~1.54）
CVAI
Tertile 11.00（Reference）1.00（Reference）1.00（Reference）
Tertile 21.11（0.97~1.26）1.09（0.96~1.24）1.09（0.96~1.24）
Tertile 31.32（1.17~1.49）1.28（1.12~1.45）1.28（1.12~1.45）
), ArticleFig(id=1311349971341894206, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311349960193434069, language=CN, label=表2, caption=

传统和新型肥胖指标与慢性病共病发病风险的关联关系

, figureFileSmall=null, figureFileBig=null, tableContent=
Model 1
HR（95%CI）		Model 2
HR（95%CI）		Model 3
HR（95%CI）
BMI
Tertile 11.00（Reference）1.00（Reference）1.00（Reference）
Tertile 21.05（0.93~1.19）1.08（0.95~1.23）1.09（0.96~1.24）
Tertile 31.21（1.06~1.37）1.23（1.08~1.40）1.26（1.10~1.43）
WC
Tertile 11.00（Reference）1.00（Reference）1.00（Reference）
Tertile 21.17（1.03~1.33）1.18（1.04~1.35）1.19（1.05~1.35）
Tertile 31.37（1.21~1.55）1.37（1.21~1.56）1.39（1.22~1.57）
WHtR
Tertile 11.00（Reference）1.00（Reference）1.00（Reference）
Tertile 21.10（0.96~1.25）1.11（0.97~1.26）1.11（0.97~1.26）
Tertile 31.38（1.22~1.56）1.35（1.19~1.54）1.35（1.18~1.54）
WWI
Tertile 11.00（Reference）1.00（Reference）1.00（Reference）
Tertile 21.06（0.94~1.21）1.05（0.93~1.20）1.04（0.92~1.19）
Tertile 31.35（1.19~1.53）1.29（1.13~1.47）1.27（1.11~1.45）
ABSI
Tertile 11.00（Reference）1.00（Reference）1.00（Reference）
Tertile 21.20（1.06~1.36）1.19（1.05~1.35）1.18（1.04~1.34）
Tertile 31.31（1.16~1.49）1.26（1.11~1.43）1.24（1.09~1.42）
BRI
Tertile 11.00（Reference）1.00（Reference）1.00（Reference）
Tertile 21.10（0.96~1.25）1.11（0.97~1.26）1.11（0.97~1.26）
Tertile 31.38（1.22~1.56）1.35（1.19~1.54）1.35（1.18~1.54）
CVAI
Tertile 11.00（Reference）1.00（Reference）1.00（Reference）
Tertile 21.11（0.97~1.26）1.09（0.96~1.24）1.09（0.96~1.24）
Tertile 31.32（1.17~1.49）1.28（1.12~1.45）1.28（1.12~1.45）
), ArticleFig(id=1311349971417391679, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311349960193434069, language=EN, label=Table 3, caption=

Association between TyG and its obesity derivatives with the risk of multimorbidity

, figureFileSmall=null, figureFileBig=null, tableContent=
Model 1
HR（95%CI）		Model 2
HR（95%CI）		Model 3
HR（95%CI）
TyG
Tertile 11.00（Reference）1.00（Reference）1.00（Reference）
Tertile 21.10（0.97~1.25）1.09（0.96~1.24）1.10（0.97-1.25）
Tertile 31.21（1.07~1.37）1.20（1.06~1.36）1.21（1.06~1.37）
TyG-BMI
Tertile 11.00（Reference）1.00（Reference）1.00（Reference）
Tertile 21.05（0.92~1.19）1.06（0.93~1.21）1.07（0.94~1.22）
Tertile 31.21（1.07~1.37）1.22（1.08~1.39）1.23（1.08~1.40）
TyG-WC
Tertile 11.00（Reference）1.00（Reference）1.00（Reference）
Tertile 21.11（0.98~1.26）1.11（0.98~1.27）1.11（0.98~1.26）
Tertile 31.36（1.20~1.54）1.36（1.20~1.54）1.36（1.20~1.54）
TyG-WHtR
Tertile 11.00（Reference）1.00（Reference）1.00（Reference）
Tertile 21.11（0.98~1.26）1.12（0.98~1.27）1.11（0.97~1.26）
Tertile 31.35（1.19~1.53）1.33（1.17~1.52）1.32（1.16~1.50）
TyGWWI
Tertile 11.00（Reference）1.00（Reference）1.00（Reference）
Tertile 21.06（0.94~1.21）1.05（0.93~1.20）1.04（0.91~1.18）
Tertile 31.34（1.19~1.52）1.30（1.14~1.48）1.28（1.12~1.46）
TyGABSI
Tertile 11.00（Reference）1.00（Reference）1.00（Reference）
Tertile 21.09（0.96~1.24）1.08（0.95~1.23）1.06（0.93~1.21）
Tertile 31.36（1.20~1.54）1.31（1.16~1.49）1.30（1.14~1.47）
TyGBRI
Tertile 11.00（Reference）1.00（Reference）1.00（Reference）
Tertile 21.11（0.97~1.26）1.12（0.98~1.27）1.11（0.97~1.26）
Tertile 31.37（1.21~1.55）1.35（1.18~1.53）1.33（1.17~1.52）
TyGCVAI
Tertile 11.00（Reference）1.00（Reference）1.00（Reference）
Tertile 21.11（0.97~1.26）1.09（0.96~1.24）1.08（0.95~1.23）
Tertile 31.35（1.19~1.53）1.30（1.15~1.48）1.29（1.13~1.46）
), ArticleFig(id=1311349972990255680, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311349960193434069, language=CN, label=表3, caption=

TyG及其肥胖衍生指标与慢性病共病发病风险的关联

, figureFileSmall=null, figureFileBig=null, tableContent=
Model 1
HR（95%CI）		Model 2
HR（95%CI）		Model 3
HR（95%CI）
TyG
Tertile 11.00（Reference）1.00（Reference）1.00（Reference）
Tertile 21.10（0.97~1.25）1.09（0.96~1.24）1.10（0.97-1.25）
Tertile 31.21（1.07~1.37）1.20（1.06~1.36）1.21（1.06~1.37）
TyG-BMI
Tertile 11.00（Reference）1.00（Reference）1.00（Reference）
Tertile 21.05（0.92~1.19）1.06（0.93~1.21）1.07（0.94~1.22）
Tertile 31.21（1.07~1.37）1.22（1.08~1.39）1.23（1.08~1.40）
TyG-WC
Tertile 11.00（Reference）1.00（Reference）1.00（Reference）
Tertile 21.11（0.98~1.26）1.11（0.98~1.27）1.11（0.98~1.26）
Tertile 31.36（1.20~1.54）1.36（1.20~1.54）1.36（1.20~1.54）
TyG-WHtR
Tertile 11.00（Reference）1.00（Reference）1.00（Reference）
Tertile 21.11（0.98~1.26）1.12（0.98~1.27）1.11（0.97~1.26）
Tertile 31.35（1.19~1.53）1.33（1.17~1.52）1.32（1.16~1.50）
TyGWWI
Tertile 11.00（Reference）1.00（Reference）1.00（Reference）
Tertile 21.06（0.94~1.21）1.05（0.93~1.20）1.04（0.91~1.18）
Tertile 31.34（1.19~1.52）1.30（1.14~1.48）1.28（1.12~1.46）
TyGABSI
Tertile 11.00（Reference）1.00（Reference）1.00（Reference）
Tertile 21.09（0.96~1.24）1.08（0.95~1.23）1.06（0.93~1.21）
Tertile 31.36（1.20~1.54）1.31（1.16~1.49）1.30（1.14~1.47）
TyGBRI
Tertile 11.00（Reference）1.00（Reference）1.00（Reference）
Tertile 21.11（0.97~1.26）1.12（0.98~1.27）1.11（0.97~1.26）
Tertile 31.37（1.21~1.55）1.35（1.18~1.53）1.33（1.17~1.52）
TyGCVAI
Tertile 11.00（Reference）1.00（Reference）1.00（Reference）
Tertile 21.11（0.97~1.26）1.09（0.96~1.24）1.08（0.95~1.23）
Tertile 31.35（1.19~1.53）1.30（1.15~1.48）1.29（1.13~1.46）
), ArticleFig(id=1311349973065753153, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311349960193434069, language=EN, label=Table 4, caption=

Comparison of predictive performance for the risk of multimorbidity among classical and novel obesity indices, and TyG and its obesity derivatives

, figureFileSmall=null, figureFileBig=null, tableContent=
指标最佳截断值约登指数灵敏度（%）特异度（%）P
传统和新型肥胖指标
BMI24.0780.07934.773.3-
WC84.4500.09947.562.40.336
WHtR0.5360.10644.566.10.135
WWI11.4710.08931.078.00.986
ABSI0.0800.06771.335.40.340
BRI4.0550.10644.566.10.135
CVAI87.2580.10053.656.40.403
TyG及其肥胖衍生指标
TyG8.4840.08054.953.1-
TyG-BMI205.5240.08539.269.30.244
TyG-WC717.6870.09449.060.50.073
TyG-WHtR4.5050.10251.558.70.038
TyG-WWI95.3840.09547.861.70.126
TyG-ABSI0.7330.09136.672.50.514
TyG-BRI35.8180.11042.668.40.083
TyG-CVAI860.9470.09841.967.90.083
), ArticleFig(id=1311349973162222146, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1311349960193434069, language=CN, label=表4, caption=

传统和新型肥胖指标、TyG及其肥胖衍生指标对慢性病共病发病风险的预测效能比较

, figureFileSmall=null, figureFileBig=null, tableContent=
指标最佳截断值约登指数灵敏度（%）特异度（%）P
传统和新型肥胖指标
BMI24.0780.07934.773.3-
WC84.4500.09947.562.40.336
WHtR0.5360.10644.566.10.135
WWI11.4710.08931.078.00.986
ABSI0.0800.06771.335.40.340
BRI4.0550.10644.566.10.135
CVAI87.2580.10053.656.40.403
TyG及其肥胖衍生指标
TyG8.4840.08054.953.1-
TyG-BMI205.5240.08539.269.30.244
TyG-WC717.6870.09449.060.50.073
TyG-WHtR4.5050.10251.558.70.038
TyG-WWI95.3840.09547.861.70.126
TyG-ABSI0.7330.09136.672.50.514
TyG-BRI35.8180.11042.668.40.083
TyG-CVAI860.9470.09841.967.90.083
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TyG指数联合传统和新型肥胖指标与慢性病共病发病风险关联研究
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杨丽雅 1 , 盛晓雯 1 , 杜厉英 2 , 姜灵芝 2 , 刘艳芬 2
现代预防医学 | 流行病与统计方法 2026,53(12): 2117-2125
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现代预防医学 |流行病与统计方法 2026 , 53 (12) : 2117 -2125
TyG指数联合传统和新型肥胖指标与慢性病共病发病风险关联研究
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杨丽雅（1975—），女，本科，副主任护师，研究方向：糖尿病、肥胖症的护理干预及管理

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杨丽雅（1975—），女，本科，副主任护师，研究方向：糖尿病、肥胖症的护理干预及管理

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杨丽雅1 , 盛晓雯1, 杜厉英2, 姜灵芝2, 刘艳芬2
作者信息
  • 1.金华市中心医院护理部，浙江 金华 321000
  • 2.金华市中心医院内分泌代谢科
通讯作者:
杨丽雅，E-mail:
作者简介:

杨丽雅（1975—），女，本科，副主任护师，研究方向：糖尿病、肥胖症的护理干预及管理

Associations of triglyceride-glucose index combined with classical and novel obesity indices and multimorbidity in middle-aged and elderly Chinese
Li-ya YANG1 , Xiao-wen SHENG1, Li-ying DU2, Ling-zhi JIANG2, Yan-fen LIU2
Affiliations
  • Department of Nursing, Jinhua Central Hospital, Jinhua, Zhejiang 321000, China
出版时间: 2026-06-25 doi: 10.20043/j.cnki.MPM.202512414
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摘要
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背景

随着我国人口老龄化进程加快，慢性病共病已成为中老年人群健康的重大挑战，也对临床护理和健康管理体系提出了更高要求。肥胖与胰岛素抵抗是多种慢性病的共同病理生理基础，然而，目前尚缺乏大型前瞻性研究系统评估甘油三酯-葡萄糖（Triglyceride-glucose index，TyG）指数及传统与新型肥胖指标对慢性病共病发病风险的影响。

目的

本研究旨在探讨TyG指数联合传统及新型肥胖指标对慢性病共病发病风险的影响和预测价值，为慢性病共病的早期识别、风险分层和精准护理干预提供科学依据。

方法

本研究基于中国健康与养老追踪调查（CHARLS）2011—2020年的前瞻性队列数据，纳入2 574名基线无慢性病的45岁及以上中老年参与者。计算体重指数（Body Mass Index，BMI）、腰围（Waist Circumference，WC）、腰高比（Waist-to-Height Ratio，WHtR）、体重调整腹围指数（Weight-adjusted Waist Index，WWI）、身体形态指数（A Body Shape Index，ABSI）、身体圆度指数（Body Roundness Index，BRI）、内脏脂肪指数（Chinese Visceral Adiposity Index，CVAI）传统与新型肥胖指标，以及TyG指数及其与各肥胖指标组合的衍生指标（如TyG-WC、TyG-BRI等）。采用Cox比例风险回归模型分析各指标与慢性病共病发病风险的关联，并采用限制性立方样条分析其剂量-反应关系。使用受试者工作特征（Receiver Operating Characteristic Curve，ROC）曲线评估各指标的预测能力。

结果

慢性病共病总体发病率为57.8%。多因素调整后，所有肥胖指标和TyG相关指标均与慢性病共病风险呈显著正相关（P<0.05）。其中，WC与慢性病共病的关联强度最大（HR=1.39，95%CI：1.22~1.57），BMI次之（HR=1.26，95%CI：1.10~1.43），WHtR和BRI风险比为1.35（95%CI：1.18~1.54），CVAI为1.28（95%CI：1.12~1.45）。TyG指数单独与慢性病共病风险显著相关（HR=1.21，95%CI: 1.06-1.37），但其与肥胖指标组合后关联性增强，TyG-WC风险比达1.36（95%CI：1.20~1.54），TyG-BRI为1.33（95%CI：1.17~1.52），TyG-CVAI为1.29（95%CI：1.13~1.46）。剂量-反应分析显示，多数指标与风险呈非线性正相关（all P for nonlinearity<0.05）。ROC曲线分析表明，在传统与新型肥胖指标中，WHtR和BRI的预测效能最佳（AUC均为0.560）；在TyG衍生指标中，TyG-BRI预测效能最优（AUC=0.562），其次为TyG-WHtR（AUC=0.561）和TyG-WC（AUC=0.558）。DeLong检验结果显示，仅TyG-WHtR的AUC显著高于TyG单一指标（P=0.038）。

结论

TyG指数与肥胖指标联合应用可更全面评估中老年人群慢性病共病风险，其中TyG-WHtR和TyG-BRI在风险识别中比其他TyG联合肥胖指标表现更佳，可为临床护理评估和社区健康管理中实用的风险筛查指标提供支持。

慢性病共病  /  甘油三酯-葡萄糖  /  肥胖指数  /  体重指数  /  腰围  /  队列研究
Background

With the accelerated aging of China’s population, multimorbidity has become a major health challenge for middle-aged and older adults, placing higher demands on clinical care and health management systems. Obesity and insulin resistance are common pathophysiological foundations for multiple chronic diseases. However, there is a lack of large-scale prospective studies systematically evaluating the combined effects of the triglyceride-glucose (TyG) index, traditional obesity indices, and novel obesity indices on the risk of multimorbidity.

Objective

To investigate the impact and predictive value of the TyG index combined with traditional and novel obesity indices on the risk of multimorbidity, providing a scientific basis for early identification, risk stratification, and precision nursing interventions for chronic disease comorbidity.

Methods

Based on prospective cohort data from the China Health and Retirement Longitudinal Study (CHARLS) from 2011 to 2020, this study included 2 574 middle-aged and older participants aged 45 years or older without chronic diseases at baseline. Traditional and novel obesity indices were calculated, including body mass index (BMI), waist circumference (WC), waist-to-height ratio (WHtR), weight-adjusted waist index (WWI), a body shape index (ABSI), body roundness index (BRI), and Chinese visceral adiposity index (CVAI). Additionally, the TyG index and its derivatives combined with obesity indices (e.g., TyG-WC, TyG-BRI) were calculated. Cox proportional hazards regression models were used to analyze the associations between these indices and the risk of multimorbidity, while restricted cubic spline analysis was employed to examine dose-response relationships. Receiver operating characteristic (ROC) curves were used to evaluate the predictive performance of these indices.

Results

The overall incidence of multimorbidity was 57.8%. After multivariate adjustment, all obesity indices and TyG-related indices were significantly positively associated with the risk of multimorbidity (P<0.05). Among these, WC showed the strongest association (hazard ratio ［HR］=1.39, 95% confidence interval ［CI］: 1.22-1.57), followed by BMI (HR=1.26, 95%CI: 1.10-1.43). The HRs for WHtR and BRI were both 1.35 (95%CI: 1.18-1.54), while the HRfor CVAI was 1.28 (95%CI: 1.12-1.45). The TyG index alone was significantly associated with the risk of multimorbidity (HR=1.21, 95%CI: 1.06-1.37), and its combination with obesity indices further enhanced the association: TyG-WC had an HRof 1.36 (95%CI: 1.20-1.54), TyG-BRI had an HRof 1.33 (95%CI: 1.17-1.52), and TyG-CVAI had an HRof 1.29 (95%CI: 1.13-1.46). Dose-response analysis revealed that most indices exhibited a nonlinear positive association with multimorbidity risk (all P for nonlinearity<0.05). ROC curve analysis showed that among traditional and novel obesity indices, WHtR and BRI had the best predictive performance (both AUC=0.560). Among TyG derivatives, TyG-BRI had the highest predictive performance (AUC=0.562), followed by TyG-WHtR (AUC=0.561) and TyG-WC (AUC=0.558).The DeLong test revealed that only TyG-WHtR exhibited a significantly higher AUC compared with TyG alone (P=0.038).

Conclusion

The combined use of the TyG index and obesity indices provides a more comprehensive assessment of multimorbidity risk in middle-aged and older adults. Compared with other obesity indexes, TyG-WHtR and TyG-BRI demonstrated superior performance in risk identification, supporting their utility as practical screening indicators in clinical nursing assessments and community health management.

Multimorbidity  /  Triglyceride-glucose index  /  Obesity indices  /  Body mass index  /  Waist circumference  /  Cohort study
杨丽雅, 盛晓雯, 杜厉英, 姜灵芝, 刘艳芬. TyG指数联合传统和新型肥胖指标与慢性病共病发病风险关联研究. 现代预防医学, 2026 , 53 (12) : 2117 -2125 . DOI: 10.20043/j.cnki.MPM.202512414
Li-ya YANG, Xiao-wen SHENG, Li-ying DU, Ling-zhi JIANG, Yan-fen LIU. Associations of triglyceride-glucose index combined with classical and novel obesity indices and multimorbidity in middle-aged and elderly Chinese[J]. Modern Preventive Medicine, 2026 , 53 (12) : 2117 -2125 . DOI: 10.20043/j.cnki.MPM.202512414
正文
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慢性病共病的患病率在我国中老年人群中呈快速上升趋势，已从2011年的37.21%显著增长至2018年的55.25%，提示我国中老年人健康正面临日益严峻的慢性病共病挑战［1］。在人口老龄化进程加速的中国，识别慢性病共病的早期风险因素对制定有效的预防策略至关重要。
肥胖与胰岛素抵抗是多种慢性病的共同病理生理基础，两者之间的相互作用可能对慢性病共病发展产生协同影响［2］。传统肥胖指标如体重指数（Body Mass Index，BMI）与腰围（Waist Circumference，WC）虽广泛用于评估肥胖状态，但其对内脏脂肪分布及代谢异常的反映能力有限［3-4］。与此同时，研究者开发出甘油三酯-葡萄糖指数（Triglyceride-glucose index，TyG）作为评估胰岛素抵抗的简易替代指标，并建立了包括体重调整腹围指数（Weight-adjusted Waist Index，WWI）、身体形态指数（A Body Shape Index，ABSI）、身体圆度指数（Body Roundness Index，BRI）以及中国内脏脂肪指数（Chinese Visceral Adiposity Index，CVAI）等新型指标在内的肥胖评价体系［5-7］。大量研究已证实，这些简易指标在心血管代谢疾病风险评估中具有良好的效度，且TyG指数与肥胖指标联合使用可更全面地反映代谢异常与疾病风险［8］。
然而，目前尚缺乏在大型中老年人群中系统评估TyG指数与传统及新型肥胖指标联合作用对慢性病共病发病风险影响的前瞻性研究。因此，本研究旨在探讨TyG指数联合多种肥胖指标与中老年人群慢性病共病发病风险之间的关联，明确这些指标单独及联合使用时的预测效能，为早期识别高风险个体、优化共病防控策略及制定精准化护理干预方案提供科学依据。
1　材料与方法
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1.1　研究对象
本研究基于中国健康与养老纵向研究（China Health and Retirement Longitudinal Study，CHARLS）2011年至2020年的纵向队列数据进行了一项全国性观察研究，对45岁及以上中老年人群的慢性病共病发病风险进行前瞻性分析。该队列初始共纳入17 705名参与者。根据研究目的，按照纳入排除标准筛选，最终共有2 574名基线无慢性病的参与者被纳入前瞻性分析队列。本研究已获得北京大学生物医学伦理审查委员会批准的伦理批准（批准号：IRB 00001052-11015），所有参与者均签署了知情同意书。
纳入标准：（1）年龄≥45岁的受访者；（2）基线时无慢性病。排除标准：（1）基线时已患任何慢性病；（2）缺失关键暴露指标数据：包括身高、体重、BMI、WC、甘油三酯（Triglyceride, TG）、空腹血糖（Fasting Plasma Glucose, FPG）、高密度脂蛋白胆固醇（High-Density Lipoprotein Cholesterol, HDL-C）；（3）缺失重要协变量数据：包括年龄和性别。
1.2　研究数据
本研究问卷所收集的多维度信息，包括基本人口学特征（年龄、性别、居住地、教育程度、婚姻状况、吸烟及饮酒状况）、标准化体格测量指标（身高、体重及腰围）、共患慢性疾病情况（高血压、糖尿病及肾脏疾病等）以及血液学指标。在人体测量方面，CHARLS团队遵循标准化的操作流程，测量要求参与者身着轻便衣物；腰围在正常呼气末于脐水平进行测量。静脉血样本由专业团队在参与者空腹过夜后采集，并储存于-80°C环境中。随后按照标准化流程检测了FPG、TG、HDL-C等生化指标。
1.3　结局定义
本研究中的慢性病共病定义为同时患有两种或两种以上特定慢性疾病，所涉及的慢性病包括高血压、血脂异常、糖尿病、心脏病、中风、慢性肺病、哮喘、肝病、癌症、消化系统疾病、肾脏疾病、关节炎、精神疾病和记忆相关疾病，这些疾病状态均基于参与者的自我报告进行判定。
1.4　暴露定义
在暴露定义方面，首先基于人体测量数据计算了传统及新型肥胖指标，包括BMI、WC、腰高比（Waist-to-Height Ratio，WHtR）、WWI、ABSI、BRI以及CVAI。所有传统及新型肥胖指标均按其数值的三分位数进行分组，分别为Tertile 1（T1）、Tertile 2（T2）和Tertile 3（T3）。
其次，计算TyG及其与各肥胖指标相乘所得的衍生指标，包括TyG-BMI、TyG-WC、TyG-WHtR、TyG-WWI、TyG-ABSI、TyG-BRI和TyG-CVAI。所有TyG及其肥胖衍生指标均按其数值的三分位数进行分组，分别为T1、T2和T3。
鉴于CHARLS数据库中TG和FPG的原始检测结果均以mmol/L为单位，而上述指标计算公式要求使用mg/dL单位，本研究在计算前统一进行单位换算。换算关系为：TG（1 mmol/L=88.57 mg/dL）、FPG（1 mmol/L=18.02 mg/dL）［9］。
1.5　协变量
本研究考虑的协变量包括年龄（分为≤60岁和>60岁）、性别（男、女）、居住地（农村、城市）、婚姻状况（已婚、其他）、教育程度（文盲、小学、初中、高中及以上）、吸烟状况（从不吸烟、现在吸烟、从前吸烟）以及饮酒状况（否、是）。
1.6　统计分析
本研究采用R语言（版本4.4.2）进行所有统计分析。连续变量描述的形式为均值和标准差。用例数和百分比来描述分类变量。以基线调查（2011年）作为随访起点，以首次发生慢性病共病事件、失访或至研究终点（2020年随访结束）作为随访终点，采用Cox比例风险回归模型评估各暴露指标与慢性病共病发病风险之间的关联。首先，分别将传统肥胖指标（包括体重指数、腰围）、新型肥胖指标（包括腰高比、体重调整腹围指数、身体形态指数、身体圆度指数、内脏脂肪指数）、TyG及其所有肥胖衍生指标，按其在研究人群中的三分位数进行分组（T1-3），以最低组（T1）作为参照。构建三个逐步调整的Cox回归模型：模型1为未调整任何变量的粗模型；模型2调整年龄和性别；模型3在模型2基础上进一步调整居住地、婚姻状况、教育程度、吸烟状况、饮酒状况。结果以风险比（Hazard Ratio，HR）及其95%置信区间（Confidence Interval，CI）表示。其次，为探究各暴露指标与慢性病共病风险之间可能存在的非线性剂量-反应关系，采用限制性立方样条方法，将各指标以连续变量形式纳入Cox模型，在调整协变量的基础上，设置3个节点进行可视化展示。最后，为评估各指标对慢性病共病发病风险的预测能力，绘制受试者工作特征曲线（Receiver Operating Characteristic Curve，ROC），并计算曲线下面积（Area Under the Curve，AUC）。所有假设检验均为双侧检验，检验水准α=0.05。
2　结果
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2.1　研究人群基线特征
本研究最终纳入2 574名基线无慢性病共病的中老年参与者。根据TyG指数三分位数分组，参与者被分为T1（低水平组，n=858）、T2（中水平组，n=858）和T3（高水平组，n=858）三组。慢性病共病的总体发病率为57.8%，且发病率随TyG指数升高而递增（T1：53.3%；T2：58.5%；T3：61.8%）。T1组中男性比例较高（56.1%），而T3组中女性比例较高（56.1%）。所有人体测量学指标及实验室衍生指标均随TyG指数升高呈现一致的递增趋势。如，BMI、WC及所有新型肥胖指标在T3组的均值均显著高于T1组。TyG指数与各肥胖指标组合的衍生值在T3组也达到最高水平。见表1。
2.2　传统和新型肥胖指标与慢性病共病发病风险的关联
模型3结果显示，在调整所有纳入的协变量后，与BMI最低分位组相比，最高分位组的慢性病共病发病风险增加了26%（HR：1.26，95%CI：1.10~1.43），WC的最高三分位组与慢性病共病发病风险呈显著正相关（HR：1.39，95%CI：1.22~1.57），WHtR的最高组同样与风险显著正相关（HR：1.35，95%CI：1.18~1.54）。WWI的最高组同样表现出显著正相关（HR：1.27，95%CI：1.11~1.45），ABSI的最高组与慢性病共病风险呈正相关（HR：1.24，95%CI：1.09~1.42），BRI的最高组与风险显著正相关（HR：1.35，95%CI：1.18~1.54），CVAI的最高组亦显示出显著正相关（HR：1.28，95%CI：1.12~1.45）。见表2。
2.3　TyG及其肥胖衍生指标与慢性病共病发病风险的关联
在TyG及其肥胖衍生指标的分析中，模型3结果显示TyG指数最高三分位组与慢性病共病发病风险呈显著正相关（HR：1.21，95%CI：1.06~1.37）。TyG-BMI与慢性病共病风险呈显著正相关（HR：1.23，95%CI：1.08~1.40)，TyG-WC的最高组与慢性病共病风险呈显著正相关（HR：1.36，95%CI：1.20~1.54），在各衍生指标中关联强度最高。TyG-WHtR的最高组与慢性病共病风险显著正相关（HR：1.32，95%CI：1.16~1.50），TyG-WWI的最高组亦表现出显著正相关（HR：1.28，95%CI：1.12~1.46），TyG-ABSI的最高组呈正相关（HR：1.30，95%CI：1.14~1.47）。TyG-BRI的最高组同样显示出显著正相关（HR：1.33，95%CI：1.17~1.52），TyG-CVAI的最高组与风险显著正相关（HR：1.29，95%CI：1.13~1.46）。见表3。
2.4　传统和新型肥胖指标、TyG及其肥胖衍生指标与慢性病共病发病风险的剂量-反应关系
限制性立方样条分析显示，传统肥胖指标（体重指数、腰围）、新型肥胖指标（腰高比、体重调整腹围指数、身体形态指数、身体圆度指数、内脏脂肪指数）与慢性病共病发病风险之间均存在显著的非线性正向剂量-反应关系（all P for nonlinearity<0.05，P for overall<0.05）。见图1~2。
TyG指数与慢性病共病发病风险之间存在显著的剂量-反应关系。风险随TyG指数的升高而持续增加，且该关联呈现线性趋势，未观察到显著的非线性趋势（P for nonlinearity<0.001）。TyG与各肥胖指标组合的衍生指标与慢性病共病发病风险之间均存在显著的非线性正向剂量-反应关系（all P for nonlinearity<0.05，P for overall<0.05）。各风险曲线随指标值的升高呈现单调递增趋势，在指标值达到各自临界点（cut-off值）后，风险上升幅度更为显著。
2.5　传统和新型肥胖指标、TyG及其肥胖衍生指标对慢性病共病发病风险的预测能力
在传统与新型肥胖指标中，WHtR与BRI显示出最高的预测效能，其AUC均为0.560。WC与CVAI的预测能力次之，AUC值均为0.556。BMI和WWI的预测效能接近，AUC值分别为0.547和0.548。ABSI的预测能力相对最低，AUC值为0.533。DeLong检验结果显示，各肥胖指标与BMI相比，AUC差异均无统计学意义。见图3。
在TyG及其衍生指标中，TyG-BRI的预测效能最佳，AUC值为0.562，约登指数为0.110，对应的灵敏度与特异度分别为42.6%和68.4%；。TyG-WHtR紧随其后，AUC值为0.561，约登指数为0.102，灵敏度与特异度分别为51.5%和58.7%；TyG-WC的AUC值为0.558，TyG-CVAI为0.557。TyG-WWI与TyG-BMI的AUC值分别为0.554和0.553。TyG-ABSI和TyG指数本身的预测能力相对较弱，AUC值分别为0.547和0.541。DeLong检验结果显示，仅TyG-WHtR的AUC显著高于TyG单一指标（P=0.038），其余衍生指标与TyG相比差异均无统计学意义。见表4。
3　讨论
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本研究基于全国代表性的中老年队列，系统评估了TyG指数、传统及新型肥胖指标与慢性病共病发病风险之间的前瞻性关联。研究结果表明，TyG指数及所有肥胖指标均与慢性病共病风险呈显著正相关，其中以WC及其与TyG的联合指标（TyG-WC）的关联强度最为突出。这一发现支持了内脏脂肪累积与胰岛素抵抗在慢性病共病发生中的协同作用机制。
在肥胖指标的单独分析中，WC的慢性病共病风险预测效能优于BMI，这与既往研究结果一致，提示腹型肥胖较全身性肥胖具有更强的代谢危害性。大规模的国际荟萃分析显示，腰围每增加一个标准差，2型糖尿病的相对风险显著增加，甚至超过BMI的影响［10］。研究表明，腰围与多种慢性病的死亡风险显著相关，包括癌症和心血管疾病，而BMI在这些方面的预测效能较弱［11］。此外，腰围与代谢综合征的风险因素之间的关联性也更为显著，研究建议在临床实践中应常规测量腰围，以更好地评估和管理患者的心脏代谢风险［12］。根据国际动脉粥样硬化学会和国际心血管风险联盟的共识声明，腰围不仅能够独立于BMI提供信息，还能更好地评估心血管和代谢风险，因此建议在临床实践中常规测量腰围［12］。同样，中国既往研究也发现WC在预测慢性病共病发病风险时预测效能高于BMI［13］。在新型指标中，WHtR与BRI表现出与WC相似的慢性病共病预测能力，可能由于这些指标同时考虑了身高对腰围的校正，更适用于不同体型个体的风险评估。
TyG指数作为胰岛素抵抗的替代标志物，其单独使用时的风险预测能力有限，但当与肥胖指标结合后，多项衍生指标（如TyG-BRI、TyG-WHtR）的预测效能得到提升，其中TyG-WC的组合在风险关联强度上表现最佳。在整合TyG指数与经典肥胖指标的研究脉络中，大量证据表明将TyG与BMI、WC、WHtR等传统指标相结合，能显著提升其对心血管疾病风险的评估能力［14-15］。其中，TyG-WC与TyG-WHtR在多类研究中均显示出与心血管结局更密切的关联［5］。本研究的发现与此趋势一致，在针对慢性病共病的风险预测中，TyG与肥胖指标联合表现出最强的风险关联强度［16］。这进一步支持了TyG-WC作为一种整合胰岛素抵抗与中心性肥胖的简易复合指标，在评估中老年人群慢性病共病风险方面具有重要应用价值，可成为临床与公共卫生实践中识别高风险个体的实用工具。剂量-反应分析进一步揭示，绝大多数指标与慢性病共病风险之间存在非线性正向关联，且在指标达到较高水平时风险上升更为显著。这提示针对中老年人群的慢性病共病预防和护理可能需要关注这些指标的临界阈值，实施分层管理策略。值得关注的是，随着精准护理管理理念的发展，将TyG-WC、TyG-BRI等简易复合指标纳入老年慢性病共病风险评估体系，能够为多学科协作的精准护理模式提供有效的风险分层工具，有助于实现老年慢性病共病人群的早期筛查与个性化干预［17］。与本研究结果一致，Liu等人开展的大型前瞻性队列研究也表明，TyG-WHtR与心-肾-代谢性慢性病共病的发生及进展显著相关，在多阶段疾病转换中表现出良好的预测效能［18］。同时，社区护理需求研究表明，老年慢性病共病患者对健康促进、疾病管理及护理技术支持的需求尤为突出［19］，提示在应用TyG-WC、TyG-BRI等指标进行风险识别后，应重点关注中高龄、肥胖、慢性病共病种类多且日常生活能力依赖的老年人群的社区护理需求，建立以需求为导向的护理支持体系。
本研究基于全国中老年人前瞻性队列数据全面且系统地探讨TyG与传统及新型肥胖指数联合与慢性病共病的关联，研究较为全面地比较了各类指标的预测效能，为TyG相关联合指标在慢性病共病筛查中的应用提供了来自中国中老年人群理论证据。然而，研究存在一定的局限性。本研究采用了CHARLS登记问卷数据，慢性疾病诊断依赖于参与者自报告的医生诊断信息，这种数据收集方法可能会引入回忆偏差。尽管CHARLS研究具有前瞻性设计优势，但其观察性质排除了对研究人群进行主动干预设计，从而阻碍了本研究探讨干预措施对TyG与其肥胖衍生物对慢性病共病风险的影响，无法推断因果关系。尽管当前统计分析已调整了一些混杂因素，但结果仍可能受到残余混杂因素的影响，如遗传因素。
本研究通过前瞻性队列分析，系统评估了传统及新型肥胖指标、TyG指数及其衍生指标与中国中老年人群慢性病共病发病风险之间的关系。结果证实，所有肥胖指标及TyG相关指标均与慢性病共病风险呈显著正相关，其中腹型肥胖指标（如WC）及其与TyG的联合指标（特别是TyG-WHtR）表现出最强的风险预测能力，为临床实践和社区护理中开展慢性病共病风险筛查提供了更为实用、高效的综合评估指标。未来研究可进一步探索这些指标在不同人群亚组中的适用性，推动建立个体化、精准化的慢性病共病风险预警与管理体系。
基金
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  • 浙江省医药卫生科技计划项目(2024KY499)
  • 金华市科技计划项目(2023-4-085)
文献
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2026年第53卷第12期
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doi: 10.20043/j.cnki.MPM.202512414
  • 接收时间：2025-12-24
  • 首发时间：2026-09-28
  • 出版时间：2026-06-25
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  • 收稿日期：2025-12-24
基金
浙江省医药卫生科技计划项目(2024KY499)
金华市科技计划项目(2023-4-085)
作者信息
    1.金华市中心医院护理部，浙江 金华 321000
    2.金华市中心医院内分泌代谢科

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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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