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Article(id=1322192331730699061, tenantId=1146029695717560320, journalId=1319373162502258839, issueId=1322192284670603597, articleNumber=null, orderNo=null, doi=10.16016/j.2097-0927.202505021, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1746720000000, receivedDateStr=2025-05-09, revisedDate=1751558400000, revisedDateStr=2025-07-04, acceptedDate=null, acceptedDateStr=null, onlineDate=1793164817030, onlineDateStr=2026-10-28, pubDate=1755187200000, pubDateStr=2025-08-15, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793164817030, onlineIssueDateStr=2026-10-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793164817030, creator=13701087609, updateTime=1793164817030, updator=13701087609, issue=Issue{id=1322192284670603597, tenantId=1146029695717560320, journalId=1319373162502258839, year='2025', volume='47', issue='15', pageStart='1685', pageEnd='1824', issueExtLink='null', onlineDate='null', pubDate='1755187200000', pubDateStr='2025-08-15', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=0, createTime=1793164805810, creator='13701087609', updateTime=1793164826583, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1322192371861799855, tenantId=1146029695717560320, journalId=1319373162502258839, issueId=1322192284670603597, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1322192371861799856, tenantId=1146029695717560320, journalId=1319373162502258839, issueId=1322192284670603597, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=1792, endPage=1799, ext={EN=ArticleExt(id=1322192331936219958, articleId=1322192331730699061, tenantId=1146029695717560320, journalId=1319373162502258839, language=EN, title=Predictive value of TyG-BMI, AIP, and postprandial glucose excursion for sarcopenia in patients with type 2 diabetes mellitus, columnId=1320435174995772212, journalTitle=Journal of Army Medical University, columnName=Clinical Medicine, runingTitle=null, highlight=null, articleAbstract=
Objective‍

To evaluate the predictive value of triglyceride-glucose body mass index (TyG-BMI), atherogenic index of plasma (AIP), and postprandial glucose excursion (PPGE) for sarcopenia in patients with type 2 diabetes mellitus (T2DM).

Methods‍

An observational cohort trial was conducted on 590 hospitalized T2DM patients (322 males and 268 females) admitted to Department of Endocrinology of the First Affiliated Hospital of Chongqing Medical University between January 2023 and December 2024. General demographic data and key metabolic indicators, including fasting plasma glucose (FPG), postprandial 2-hour plasma glucose (PPG), triglyceride (TG) were collected, and then TyG-BMI, AIP, and PPGE were calculated. Based on the Asian Working Group for Sarcopenia (AWGS) 2019 criteria, they were divided into a sarcopenia group (n=140) and a non-sarcopenia group (n=450). After 1∶1 propensity score matching (PSM), 102 patients with sarcopenia were matched with 102 without. Differences in metabolic indicators FPG, PPG, TyG-BMI, AIP and PPGE were compared between the 2 groups. Spearman correlation analysis was conducted to assess correlations of these indicators with sarcopenia. Conditional logistic regression analysis was performed to identify independent risk factors, and receiver operating characteristic (ROC) curves were plotted to assess predictive performance. An external validation cohort consisting of 192 T2DM patients from Department of Endocrinology of Jiangbei Branch of First Affiliated Hospital of Army Medical University between January 2023 and December 2024 was included to validate the prediction model.

Results‍

After PSM, the baseline data of the 2 groups was balanced (P>0.05). The sarcopenia group showed higher levels of PPG, AIP, and PPGE, but lower TyG-BMI value than the non-sarcopenia group (all P=0.001). Spearman correlation analysis revealed that the TyG-BMI was negatively correlated (r=-0.404, P=0.001), whereas AIP and PPGE were positively correlated with concomitant sarcopenia (r=0.280,P=0.001;r=0.372, P=0.001) in the T2DM patients. Conditional logistic regression analysis identified AIP (OR=14.367) and PPGE (OR=1.245) as independent risk factors, while TyG-BMI (OR=0.966) as independent protective factors. ROC curve analysis revealed that the area under the curve (AUC) of the combined model of TyG-BMI, AIP and PPGE in predicting sarcopenia was 0.918, and the AUC value was 0.954 in external validation, with good sensitivity and specificity.

Conclusion‍

TyG-BMI, AIP, and PPGE are important metabolic predictors in T2DM patients with sarcopenia. Their combination has good screening value for sarcopenia, and can be applied in external prediction for T2DM patients.

, authors=Yue CHEN1, Weiwei YU2, Jin GAO1, Xingyu CHEN3, Qingfeng CHENG4, Jian CUI5, authorsList=Yue CHEN, Weiwei YU, Jin GAO, Xingyu CHEN, Qingfeng CHENG, Jian CUI, authorCompany=null, correspAuthors=Qingfeng CHENG, Jian CUI, authorNote=null, correspAuthorsNote=null, copyrightStatement=© 2025 Journal of Army Medical University, copyrightOwner=Editorial Office of Journal of Army Medical University, 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=1322192332657640251, articleId=1322192331730699061, tenantId=1146029695717560320, journalId=1319373162502258839, language=CN, title=TyG-BMI指数、AIP联合餐后血糖波动对2型糖尿病患者肌少症的预测作用, columnId=1322185541475488183, journalTitle=陆军军医大学学报, columnName=临床医学, runingTitle=null, highlight=null, articleAbstract=
目的

探究甘油三酯-葡萄糖体质指数（triglyceride-glucose body mass index，TyG-BMI）、动脉粥样硬化指数（atherogenic index of plasma，AIP）联合餐后血糖波动（postprandial glucose excursions，PPGE）对2型糖尿病（type 2 diabetes mellitus，T2DM）患者肌少症的预测价值。

方法

本研究为观察性横断面研究，选取2023年1月至2024年12月在重庆医科大学附属第一医院内分泌科住院治疗的T2DM患者590例，其中男性322例，女性268例。收集其一般人口学资料及关键代谢指标［空腹血糖（fasting plasma glucose，FPG）、餐后2 h血糖（postprandial 2-hour plasma glucose，PPG）、甘油三酯（triglyceride，TG）等］，计算TyG-BMI、AIP和PPGE。按照亚洲肌少症工作组（Asian Working Group for Sarcopenia，AWGS）2019年标准诊断肌少症，并据此将患者分为肌少症组（n=140）与非肌少症组（n=450）。对2组患者进行1∶1倾向性评分匹配（propensity score matching，PSM），匹配后纳入肌少症组与非肌少症组各102例。比较2组FPG、PPG、TyG-BMI、AIP和PPGE等代谢指标的差异，进行Spearman相关分析，并采用条件Logistic回归模型分析T2DM患者肌少症的影响因素，并绘制受试者工作特征（receptor operating characteristic，ROC）曲线评估预测效能。收集2023年1月至2024年12月在陆军军医大学第一附属医院江北院区内分泌科的192例外部数据进行模型验证。

结果

PSM后，2组的基线资料均衡。肌少症组的PPG、AIP和PPGE水平均值较非肌少症组更高，TyG-BMI指数则更低（P=0.001）。Spearman相关分析显示，TyG-BMI指数与T2DM患者肌少症呈显著负相关（r=-0.404，P=0.001），AIP和PPGE与T2DM患者肌少症均呈显著正相关（r=0.280，P=0.001；r=0.372，P=0.001）。条件Logistic回归分析显示，AIP（OR=14.367）和PPGE（OR=1.245）为T2DM患者肌少症的独立危险因素，TyG-BMI指数（OR=0.966）为T2DM患者肌少症的独立保护因素。ROC曲线显示，联合应用TyG-BMI指数、AIP和PPGE的预测模型曲线下面积（area under the curve，AUC）为0.918，外部验证中AUC为0.954，敏感度和特异性均优。

结论

TyG-BMI指数、AIP及PPGE是T2DM患者肌少症的重要代谢预测指标，三者联合应用具有良好的2型糖尿病患者肌少症筛查价值与外部适用性。

, authors=陈月1, 喻维薇2, 高瑾1, 陈星宇3, 程庆丰4, 崔剑5, authorsList=陈月, 喻维薇, 高瑾, 陈星宇, 程庆丰, 崔剑, authorCompany=null, correspAuthors=程庆丰, 崔剑, authorNote=

同等贡献者

CHEN Yue and YU Weiwei contributed equally to the article.

, correspAuthorsNote=
崔剑，E-mail：
程庆丰，E-mail：
CUI Jian，E-mail：
CHENG Qingfeng，E-mail：
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Adv Nutr，2017，8（4）：624-634., articleTitle=Energy，protein，carbohydrate，and lipid intakes and their effects on morbidity and mortality in critically ill adult patients：a systematic review, refAbstract=null)], funds=null, companyList=[AuthorCompany(id=1322192332913492796, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322192331730699061, xref=1, ext=[AuthorCompanyExt(id=1322192332926075709, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322192331730699061, companyId=1322192332913492796, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=11Department of Quality Control), AuthorCompanyExt(id=1322192332934464318, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322192331730699061, companyId=1322192332913492796, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, 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协变量PSM前χ2/t/ZPPSM后χ2/t/ZP
非肌少症组（n=450）肌少症组（n=140）非肌少症组（n=102）肌少症组（n=102）
性别（女）206（45.8）62（44.3）0.0960.75739（38.2）45（44.1）0.7290.393
年龄/岁60.50±10.3963.15±10.37-7.6470.01061.01±8.9661.65±10.11-0.4760.634
糖尿病病程/月96 （36，156）96 （27，144）-1.0980.27284 （33，156）96 （36，144）-0.0170.987
BMI/（kg/m2）25.17±2.7222.20±2.8311.1720.00123.28±2.4223.22±2.550.1800.858
吸烟史187（41.6）70（50.0）3.0970.07849（48.0）44（43.1）0.4940.482
HbA1c/%8.41±3.438.77±3.21-1.0970.2739.21±3.279.01±2.990.4760.635
), ArticleFig(id=1322192337481089912, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322192331730699061, language=CN, label=表1, caption=

PSM前后2组的一般资料对比［x¯±s， M（P25，P75），n（%）］

, figureFileSmall=null, figureFileBig=null, tableContent=
协变量PSM前χ2/t/ZPPSM后χ2/t/ZP
非肌少症组（n=450）肌少症组（n=140）非肌少症组（n=102）肌少症组（n=102）
性别（女）206（45.8）62（44.3）0.0960.75739（38.2）45（44.1）0.7290.393
年龄/岁60.50±10.3963.15±10.37-7.6470.01061.01±8.9661.65±10.11-0.4760.634
糖尿病病程/月96 （36，156）96 （27，144）-1.0980.27284 （33，156）96 （36，144）-0.0170.987
BMI/（kg/m2）25.17±2.7222.20±2.8311.1720.00123.28±2.4223.22±2.550.1800.858
吸烟史187（41.6）70（50.0）3.0970.07849（48.0）44（43.1）0.4940.482
HbA1c/%8.41±3.438.77±3.21-1.0970.2739.21±3.279.01±2.990.4760.635
), ArticleFig(id=1322192337552393081, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322192331730699061, language=EN, label=null, caption=null, figureFileSmall=null, figureFileBig=null, tableContent=
指标非肌少症组（n=102）肌少症组（n=102）χ2/t/ZP
FPG/（mmol/L）8.37±1.748.28±2.220.3360.737
PPG/（mmol/L）19.78±4.5022.50±3.954.5900.001
TG/（mmol/L）2.30±2.462.43±1.500.4530.651
HDL-C/（mmol/L）1.21±0.411.14±0.351.2880.199
TyG-BMI235.06±28.56211.25±25.886.2400.001
AIP0.15±0.360.31±0.293.4440.001
PPGE11.41±3.8114.22±3.225.6940.001
), ArticleFig(id=1322192337623696250, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322192331730699061, language=CN, label=表2, caption=

PSM后2组的关键代谢指标对比

, figureFileSmall=null, figureFileBig=null, tableContent=
指标非肌少症组（n=102）肌少症组（n=102）χ2/t/ZP
FPG/（mmol/L）8.37±1.748.28±2.220.3360.737
PPG/（mmol/L）19.78±4.5022.50±3.954.5900.001
TG/（mmol/L）2.30±2.462.43±1.500.4530.651
HDL-C/（mmol/L）1.21±0.411.14±0.351.2880.199
TyG-BMI235.06±28.56211.25±25.886.2400.001
AIP0.15±0.360.31±0.293.4440.001
PPGE11.41±3.8114.22±3.225.6940.001
), ArticleFig(id=1322192337694999419, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322192331730699061, language=EN, label=null, caption=null, figureFileSmall=null, figureFileBig=null, tableContent=
指标βSEWald χ2POR95%CI
PPG0.0340.0890.1450.7041.0340.869~1.230
TyG-BMI-0.0350.00820.2830.0010.9660.952~0.981
AIP2.6650.63717.4880.00114.3674.120~50.099
PPGE0.2190.1024.6270.0311.2451.020~1.520
), ArticleFig(id=1322192337812439932, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322192331730699061, language=CN, label=表3, caption=

匹配分层后T2DM患者肌少症的条件Logistic回归分析

, figureFileSmall=null, figureFileBig=null, tableContent=
指标βSEWald χ2POR95%CI
PPG0.0340.0890.1450.7041.0340.869~1.230
TyG-BMI-0.0350.00820.2830.0010.9660.952~0.981
AIP2.6650.63717.4880.00114.3674.120~50.099
PPGE0.2190.1024.6270.0311.2451.020~1.520
), ArticleFig(id=1322192337883743101, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322192331730699061, language=EN, label=null, caption=null, figureFileSmall=null, figureFileBig=null, tableContent=
项目AUC95%CI敏感度特异性
模型10.9330.896~0.9700.8240.881
模型20.9540.926~0.9820.8820.874
), ArticleFig(id=1322192337955046270, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322192331730699061, language=CN, label=表4, caption=

联合模型在外部验证集中的诊断效能

, figureFileSmall=null, figureFileBig=null, tableContent=
项目AUC95%CI敏感度特异性
模型10.9330.896~0.9700.8240.881
模型20.9540.926~0.9820.8820.874
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TyG-BMI指数、AIP联合餐后血糖波动对2型糖尿病患者肌少症的预测作用
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陈月 1 , 喻维薇 2 , 高瑾 1 , 陈星宇 3 , 程庆丰 4 , 崔剑 5
陆军军医大学学报 | 临床医学 2025,47(15): 1792-1799
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陆军军医大学学报 |临床医学 2025 , 47 (15) : 1792 -1799
TyG-BMI指数、AIP联合餐后血糖波动对2型糖尿病患者肌少症的预测作用
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陈月1, 喻维薇2, 高瑾1, 陈星宇3, 程庆丰4 , 崔剑5
作者信息
  • 1陆军军医大学（第三军医大学）第一附属医院江北院区（陆军第九五八医院）：质量控制科，重庆
  • 2陆军军医大学（第三军医大学）第一附属医院江北院区（陆军第九五八医院）：皮肤性病科，重庆
  • 3陆军军医大学（第三军医大学）第一附属医院江北院区（陆军第九五八医院）：内分泌科，重庆
  • 4重庆医科大学附属第一医院内分泌内科，重庆
  • 5陆军军医大学（第三军医大学）第一附属医院疼痛科，重庆
通讯作者:
崔剑，E-mail：
程庆丰，E-mail：
CUI Jian，E-mail：
CHENG Qingfeng，E-mail：
作者简介:

同等贡献者

CHEN Yue and YU Weiwei contributed equally to the article.

Predictive value of TyG-BMI, AIP, and postprandial glucose excursion for sarcopenia in patients with type 2 diabetes mellitus
Yue CHEN1, Weiwei YU2, Jin GAO1, Xingyu CHEN3, Qingfeng CHENG4 , Jian CUI5
Affiliations
  • 11Department of Quality Control
  • 2Department of Dermatology and Venereology
  • 3Department of Endocrinology，Jiangbei Branch/No.9, Hospital of PLA Army, First Affiliated Hospital，Army Medical University （Third Military Medical University），Chongqing
  • 4Department of Endocrinology，the First Affiliated Hospital of Chongqing Medical University，Chongqing
  • 5Department of Pain Medicine，First Affiliated Hospital，Army Medical University （Third Military Medical University），Chongqing，China
出版时间: 2025-08-15 doi: 10.16016/j.2097-0927.202505021
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摘要
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目的

探究甘油三酯-葡萄糖体质指数（triglyceride-glucose body mass index，TyG-BMI）、动脉粥样硬化指数（atherogenic index of plasma，AIP）联合餐后血糖波动（postprandial glucose excursions，PPGE）对2型糖尿病（type 2 diabetes mellitus，T2DM）患者肌少症的预测价值。

方法

本研究为观察性横断面研究，选取2023年1月至2024年12月在重庆医科大学附属第一医院内分泌科住院治疗的T2DM患者590例，其中男性322例，女性268例。收集其一般人口学资料及关键代谢指标［空腹血糖（fasting plasma glucose，FPG）、餐后2 h血糖（postprandial 2-hour plasma glucose，PPG）、甘油三酯（triglyceride，TG）等］，计算TyG-BMI、AIP和PPGE。按照亚洲肌少症工作组（Asian Working Group for Sarcopenia，AWGS）2019年标准诊断肌少症，并据此将患者分为肌少症组（n=140）与非肌少症组（n=450）。对2组患者进行1∶1倾向性评分匹配（propensity score matching，PSM），匹配后纳入肌少症组与非肌少症组各102例。比较2组FPG、PPG、TyG-BMI、AIP和PPGE等代谢指标的差异，进行Spearman相关分析，并采用条件Logistic回归模型分析T2DM患者肌少症的影响因素，并绘制受试者工作特征（receptor operating characteristic，ROC）曲线评估预测效能。收集2023年1月至2024年12月在陆军军医大学第一附属医院江北院区内分泌科的192例外部数据进行模型验证。

结果

PSM后，2组的基线资料均衡。肌少症组的PPG、AIP和PPGE水平均值较非肌少症组更高，TyG-BMI指数则更低（P=0.001）。Spearman相关分析显示，TyG-BMI指数与T2DM患者肌少症呈显著负相关（r=-0.404，P=0.001），AIP和PPGE与T2DM患者肌少症均呈显著正相关（r=0.280，P=0.001；r=0.372，P=0.001）。条件Logistic回归分析显示，AIP（OR=14.367）和PPGE（OR=1.245）为T2DM患者肌少症的独立危险因素，TyG-BMI指数（OR=0.966）为T2DM患者肌少症的独立保护因素。ROC曲线显示，联合应用TyG-BMI指数、AIP和PPGE的预测模型曲线下面积（area under the curve，AUC）为0.918，外部验证中AUC为0.954，敏感度和特异性均优。

结论

TyG-BMI指数、AIP及PPGE是T2DM患者肌少症的重要代谢预测指标，三者联合应用具有良好的2型糖尿病患者肌少症筛查价值与外部适用性。

2型糖尿病  /  肌少症  /  甘油三酯-葡萄糖体质指数  /  动脉粥样硬化指数  /  餐后血糖波动
Objective‍

To evaluate the predictive value of triglyceride-glucose body mass index (TyG-BMI), atherogenic index of plasma (AIP), and postprandial glucose excursion (PPGE) for sarcopenia in patients with type 2 diabetes mellitus (T2DM).

Methods‍

An observational cohort trial was conducted on 590 hospitalized T2DM patients (322 males and 268 females) admitted to Department of Endocrinology of the First Affiliated Hospital of Chongqing Medical University between January 2023 and December 2024. General demographic data and key metabolic indicators, including fasting plasma glucose (FPG), postprandial 2-hour plasma glucose (PPG), triglyceride (TG) were collected, and then TyG-BMI, AIP, and PPGE were calculated. Based on the Asian Working Group for Sarcopenia (AWGS) 2019 criteria, they were divided into a sarcopenia group (n=140) and a non-sarcopenia group (n=450). After 1∶1 propensity score matching (PSM), 102 patients with sarcopenia were matched with 102 without. Differences in metabolic indicators FPG, PPG, TyG-BMI, AIP and PPGE were compared between the 2 groups. Spearman correlation analysis was conducted to assess correlations of these indicators with sarcopenia. Conditional logistic regression analysis was performed to identify independent risk factors, and receiver operating characteristic (ROC) curves were plotted to assess predictive performance. An external validation cohort consisting of 192 T2DM patients from Department of Endocrinology of Jiangbei Branch of First Affiliated Hospital of Army Medical University between January 2023 and December 2024 was included to validate the prediction model.

Results‍

After PSM, the baseline data of the 2 groups was balanced (P>0.05). The sarcopenia group showed higher levels of PPG, AIP, and PPGE, but lower TyG-BMI value than the non-sarcopenia group (all P=0.001). Spearman correlation analysis revealed that the TyG-BMI was negatively correlated (r=-0.404, P=0.001), whereas AIP and PPGE were positively correlated with concomitant sarcopenia (r=0.280,P=0.001;r=0.372, P=0.001) in the T2DM patients. Conditional logistic regression analysis identified AIP (OR=14.367) and PPGE (OR=1.245) as independent risk factors, while TyG-BMI (OR=0.966) as independent protective factors. ROC curve analysis revealed that the area under the curve (AUC) of the combined model of TyG-BMI, AIP and PPGE in predicting sarcopenia was 0.918, and the AUC value was 0.954 in external validation, with good sensitivity and specificity.

Conclusion‍

TyG-BMI, AIP, and PPGE are important metabolic predictors in T2DM patients with sarcopenia. Their combination has good screening value for sarcopenia, and can be applied in external prediction for T2DM patients.

type 2 diabetes mellitus  /  sarcopenia  /  triglyceride-glucose index  /  atherogenic index of plasma  /  postprandial glucose excursion
陈月, 喻维薇, 高瑾, 陈星宇, 程庆丰, 崔剑. TyG-BMI指数、AIP联合餐后血糖波动对2型糖尿病患者肌少症的预测作用. 陆军军医大学学报, 2025 , 47 (15) : 1792 -1799 . DOI: 10.16016/j.2097-0927.202505021
Yue CHEN, Weiwei YU, Jin GAO, Xingyu CHEN, Qingfeng CHENG, Jian CUI. Predictive value of TyG-BMI, AIP, and postprandial glucose excursion for sarcopenia in patients with type 2 diabetes mellitus[J]. Journal of Army Medical University, 2025 , 47 (15) : 1792 -1799 . DOI: 10.16016/j.2097-0927.202505021
正文
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2型糖尿病（type 2 diabetes mellitus，T2DM）是我国常见的慢性代谢性疾病，其患病率逐年上升，中国有超过1.18亿糖尿病患者，占全球糖尿病患者总数的22%，为全球糖尿病负担较重的国家之一［1］。近年来，随着人群老龄化和糖尿病病程延长，T2DM患者肌少症的患病率也逐年增加［2］。肌少症主要表现为骨骼肌质量和功能进行性丧失，不仅可导致跌倒、骨折等不良结局，还可加重胰岛素抵抗、增加心血管事件风险，从而显著影响T2DM患者的生活质量与预后［3-4］。因此，探索简便、经济、有效的预测指标，以实现T2DM患者肌少症的早期识别和干预，对于糖尿病全病程管理具有重要意义。近年研究发现，胰岛素抵抗和脂质代谢紊乱是T2DM患者肌少症的核心病理基础［5-6］。甘油三酯-葡萄糖体质指数（triglyceride-glucose body mass index，TyG-BMI）作为一种基于空腹血糖（fasting plasma glucose，FPG）、甘油三酯（triglycerides，TG）和BMI的复合指标，能够敏感反映全身胰岛素抵抗程度，相较于甘油三酯-葡萄糖（triglyceride-glucose，TyG）指数，TyG-BMI预测胰岛素抵抗发生风险的能力更强［7］。动脉粥样硬化指数（atherogenic index of plasma，AIP）能够反映血脂代谢紊乱和动脉硬化风险，也与T2DM并发症密切关联［8］。此外，T2DM患者餐后血糖波动（postprandial glucose excursions，PPGE）可引起氧化应激、慢性炎症等代谢失衡状态，可能参与肌肉数量、质量和功能的损耗［9］。目前，已有研究提出用于预测2型糖尿病患者肌少症的生物标志物，如中性粒细胞与淋巴细胞计数比值等炎症标志物、剪切波弹性成像联合肾功能指标等［10-11］，但这些指标检测成本较高，获取渠道有限，尚难以在基层医疗机构或资源有限地区广泛开展。鉴于TyG-BMI、AIP和PPGE能分别反映胰岛素抵抗、血脂异常和肌肉损耗，三者联合能够更全面地捕捉T2DM患者代谢紊乱的多个维度，从而显著提高对T2DM合并肌少症风险的预测能力。基于此，本研究旨在探讨TyG-BMI指数、AIP及PPGE对T2DM患者肌少症的独立及协同预测作用，并评估其在临床早期筛查中的应用价值，以期提高T2DM患者肌少症的早期识别准确性，为优化个体化干预提供依据，尤其适用于基层或资源有限场所。
1　资料与方法
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1.1　一般资料
本研究为观察性横断面研究，选取2023年1月至2024年12月在重庆医科大学附属第一医院内分泌科住院治疗的T2DM患者作为研究对象。纳入标准：①符合《中国2型糖尿病防治指南（2020年版）》诊断标准，确诊为T2DM［12］；②年龄≥18岁；③临床资料完整可查。排除标准：①继发性糖尿病，如库欣综合征、胰腺切除术后；②合并恶性肿瘤、严重肝肾功能不全或自身免疫性疾病；③近期使用糖皮质激素或免疫抑制剂；④妊娠或哺乳期妇女。最终纳入患者590例，其中男性322例，女性268例，根据是否患肌少症将患者分为肌少症组（n=140）和非肌少症组（n=450）。本研究通过重庆医科大学附属第一医院伦理委员会审批（ZZ2025-392-01）。
1.2　研究方法
1.2.1　肌少症的诊断
参照亚洲肌少症工作组（Asian Working Group for Sarcopenia，AWGS）2019年发布的肌少症诊断标准［13］，采用四肢骨骼肌质量指数（appendicular skeletal muscle mass index，ASMI）进行评价，其公式为：ASMI=四肢骨骼肌质量（kg）/身高的平方（m²）。采用双能X线吸收测量法（dual energy x-ray absorptiometry，DEXA）测定患者的骨骼肌质量，以男性ASMI<7.0 kg/m2、女性ASMI<5.4 kg/m2诊断为肌少症。
1.2.2　数据收集
通过电子病历系统及问卷调查收集所有入组患者的一般资料，包括：①人口学特征（年龄、性别、糖尿病病程、吸烟史）；②体格测量指标，身高、体质量、BMI、糖化血红蛋白（hemoglobin A1c，HbA1c）等。体格测量指标由受过训练的专业医护人员使用标准设备在患者空腹状态下进行测量。
1.2.3　关键代谢指标
所有患者入院禁食10 h后于次日早晨，空腹状态采集静脉血5 mL，检测FPG、餐后2 h血糖（postprandial plasma glucose，PPG）、TG、高密度脂蛋白胆固醇（high density lipoprotein cholesterol，HDL-C）等指标，检测仪器及试剂由医院临床检验科统一提供并严格质控。AIP值来源于每例患者的原始计算结果，最终报告的是全体样本AIP的平均值，而非均数代入法计算所得，以确保更真实反映个体间代谢差异。TyG-BMI指数、AIP、PPGE的计算公式如下：
TyG-BMI=ln（TG×FPG2） ×BMI
AIP=log10（TGHDL-C）
PPGE=PPG-FPG
其中TyG-BMI公式中TG与FPG采用mg/dL的单位，AIP公式中TG与HDL-C采用mmol/L的单位，PPGE公式中PPG 与FPG采用mmol/L的单位。
1.3　样本量估算与倾向性评分匹配（propensity score matching，PSM）方法
本研究构建多指标联合预测模型，依据预测模型研究的样本量计算原则［14］，通常每个预测变量应包含10~20个结局事件，由于本研究的预测目标为T2DM患者是否患肌少症，因此“诊断为肌少症”即为结局事件。本研究计划纳入的核心变量（TyG-BMI指数、AIP、PPGE）及协变量（性别、年龄、糖尿病病程、BMI、吸烟史和HbA1c）共 9个，因此至少需要90~180例肌少症患者样本。根据前期预调查及实际病例分布情况，最终共纳入T2DM患者590例，其中肌少症组140例，满足建模样本量要求。
为控制非随机分组所导致的基线差异与混杂偏倚，本研究基于PSM进行组间匹配分析。以性别、年龄、糖尿病病程、BMI、吸烟史及HbA1c为匹配协变量，使用条件Logistic回归模型计算倾向性得分，采用1∶1最近邻匹配法（caliper=0.02），对肌少症组与非肌少症组患者进行匹配。匹配前后2组患者的一般资料及关键代谢指标进行比较，以验证匹配质量并分析相关结局变量。
1.4　预测模型的构建与评估
为评估不同代谢指标组合对T2DM患者肌少症的预测价值，本研究构建并比较了以下预测模型：①单一指标模型，分别纳入TyG-BMI指数、AIP、PPGE构建单一指标预测模型；②模型1，AIP联合TyG-BMI指数；③模型2，TyG-BMI指数、AIP和PPGE三者联合。所有模型基于条件Logistic回归建立。
1.5　外部验证集的建立与处理
为验证模型的泛化能力与稳定性，本研究收集了于2023年1月至2024年12月在陆军军医大学第一附属医院江北院区内分泌科住院的T2DM患者192例作为外部验证集，其中肌少症患者36例，非肌少症患者156例。本研究通过陆军军医大学第一附属医院江北院区（陆军第九五八医院）伦理委员会审批（ER2025KY074）。纳入与排除标准与建模数据一致，提取其基本临床资料、关键代谢指标（TyG-BMI指数、AIP、PPGE）及肌少症诊断情况，采用本研究构建的联合预测模型进行预测评估，绘制受试者工作特征（receptor operating characteristic，ROC）曲线，计算曲线下面积（area under the curve，AUC）、敏感度、特异性指标。
1.6　统计学分析
采用SPSS 27.0软件进行数据处理与统计分析。符合正态分布的计量资料以x¯±s表示，组间比较采用独立样本t检验；非正态分布的数据以M（P25，P75）表示，组间比较采用Mann-Whitney U检验。计数资料以n （%）表示，组间比较采用χ²检验。采用Spearman相关分析评估TyG-BMI指数、AIP与肌少症的相关性。对于PSM匹配后的样本，采用条件Logistic回归模型分析T2DM肌少症的独立危险因素。绘制ROC曲线，比较AUC、敏感度和特异性。以P<0.05为差异具有统计学意义。
2　结果
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2.1　PSM前后T2DM患者肌少症组与非肌少症组一般资料对比
为控制混杂偏倚，本研究对2组进行PSM（1∶1匹配）后，共获得匹配样本204例，肌少症组102例，非肌少症组102例。匹配前，肌少症组与非肌少症组在性别、糖尿病病程、吸烟史和HbA1c方面差异无统计学意义，但在年龄和BMI方面差异显著（P<0.05）。其中，肌少症组年龄较大，BMI较低。匹配后，2组在性别、年龄、糖尿病病程、BMI、吸烟史及HbA1c方面差异均无统计学意义（表1），表明匹配效果良好，实现基线均衡。
2.2　PSM后T2DM患者肌少症组与非肌少症组的关键代谢指标对比
肌少症组的PPG、AIP和PPGE水平均值较非肌少症组更高，而TyG-BMI指数则更低（P=0.001）。2组的FPG、TG和HDL-C对比差异无统计学意义（表2）。
2.3　TyG-BMI指数、AIP和PPGE与T2DM患者肌少症的相关性分析
Spearman相关分析显示，TyG-BMI指数与T2DM患者肌少症呈显著负相关（r=-0.404，P=0.001）。而AIP和PPGE与T2DM患者肌少症均呈显著正相关（r=0.280，P=0.001；r=0.372，P=0.001）。
2.4　T2DM患者肌少症的条件Logistic回归分析
条件Logistic回归分析显示，AIP［OR=14.367（95%CI：4.120~50.099）］和PPGE［OR=1.245（95%CI：1.020~1.520）］为T2DM患者肌少症的独立危险因素。而TyG-BMI指数［OR=0.966（95%CI：0.952~0.981）］为T2DM患者肌少症的独立保护因素（表3）。
2.5　TyG-BMI指数、AIP和PPGE对T2DM患者肌少症的临床预测价值
分别使用TyG-BMI指数、AIP、PPGE和联合模型进行预测分析（图1）。ROC曲线分析结果显示，TyG-BMI指数的AUC为0.733，敏感度为0.804，特异性为0.569。AIP的AUC为0.706，敏感度为0.765，特异性为0.647。PPGE的AUC为0.715，敏感度为0.833，特异性为0.451。模型1的AUC为0.861，敏感度为0.775，特异性为0.784。模型2的AUC为0.918，敏感度为0.863，特异性为0.824。联合预测模型的AUC均高于单一指标，其中模型2的AUC最高。
2.6　联合预测模型在外部验证集中的预测效能
将构建模型应用于外部验证集（肌少症组n=36，非肌少症组n=156），结果显示该模型1预测T2DM患者肌少症的AUC为0.933，模型2预测T2DM患者肌少症的AUC为0.954，与建模集的预测效能相近，提示模型具有良好的外部可推广性（图2、表4）。
3　讨论
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肌少症作为一种与年龄、代谢、营养状态密切相关的新型并发症，在T2DM患者中发病风险很高［15］。由于肌少症是一种进行性且广泛的疾病，与肌肉数量和质量及其功能下降相关［16］。因此，肌肉减少不仅会增加老年T2DM患者的跌倒风险，还会导致患者骨折和身体残疾风险增加，以及行动能力减退、生活质量下降和死亡率增加［17-18］。T2DM患者糖脂代谢异常、血糖波动与肌肉丢失之间的潜在联系日益受到关注［2］。
3.1　血糖和血脂紊乱显著增加肌少症风险
本研究通过PSM控制了年龄、性别、BMI、糖尿病病程等混杂变量，匹配后2组在主要协变量间差异不再具有统计学意义，表明数据集具有良好的基线均衡性。该策略有效降低了潜在的混杂偏倚，增强了分析结果的内部效度，也为后续因果推断提供了可靠基础。在基线均衡的前提下，与非肌少症组相比，肌少症组患者的PPG、AIP及PPGE水平均显著升高，而TyG-BMI指数明显降低。高PPG和PPGE反映出患者存在更明显的血糖波动，可能通过氧化应激、胰岛素抵抗等机制加速肌肉蛋白的分解［19］。进一步的条件Logistic回归分析表明，PPGE作为动态血糖参数，与T2DM肌少症的风险显著正相关（OR=1.245），这一结果提示PPGE作为动态血糖指标，具有独立于传统血糖参数的临床预测价值。
此外，AIP水平升高提示脂质代谢紊乱可能参与肌肉减少过程，本研究发现AIP是T2DM患者肌少症的独立危险因素（OR=14.367）。AIP通过量化TG/HDL-C比值，反映促动脉粥样硬化的脂质代谢特征，其升高可能是由于促炎途径激活肌肉分解代谢［20-21］。高血脂水平可直接诱导骨骼肌细胞脂毒性，抑制线粒体氧化磷酸化并加速蛋白质降解［22］。需注意的是，本研究回归模型基于PSM后的匹配数据，采用条件Logistic回归分析匹配后样本，其OR值反映的是匹配层内的暴露效应（即控制年龄、性别等混杂因素后的关联强度），而非全人群的绝对风险。因此，研究结论更适用于与本研究病例具有相似基线特征的人群。
3.2　TyG-BMI指数升高可能具有保护作用
本研究发现，TyG-BMI指数作为整合血糖、血脂和体质量的信息综合指标，其水平与T2DM患者肌少症发生风险呈负相关（OR=0.966），提示TyG-BMI指数越低，患肌少症的风险越高。一项中国老年人队列研究显示TyG指数与肌少症发生率之间存在负相关，但该关联在调整BMI后消失［23］。另一项来自韩国的横断面研究报告了TyG指数与肌少症和肌肉减少性肥胖之间呈正相关，但该研究并未考虑BMI的影响［24］。本研究认为BMI是造成这些差异的原因。同时，也有多项研究结果与本研究结果一致［20，25-26］。
TyG-BMI指数整合了胰岛素抵抗与肥胖因素，能够更全面反映代谢状态对肌肉的影响。关于TyG-BMI指数与肌少症的负相关，其保护性机制可能存在双重路径。较高TyG-BMI指数可能代表“代谢性肥胖但肌肉保留”表型，其机制或与脂肪组织分泌的肌动蛋白（如脂联素）可能通过AMPK/PGC-1α通路促进肌肉再生［27］。也可能是因为存在“能力储备效应”，BMI升高会造成脂肪和肌肉质量同时增加，肥胖患者充足的脂质储备可减少蛋白质分解供能，延缓肌肉消耗［28-29］。TyG-BMI指数在T2DM患者肌少症中所起的“保护性”作用，提示临床上应关注体质量偏低或营养状态不良的T2DM患者，并结合代谢指标进行风险评估。
3.3　联合预测模型在识别高风险人群中表现优越
本研究基于常规代谢指标，构建了多指标联合预测模型用于识别T2DM患者肌少症的风险。ROC曲线分析显示，TyG-BMI指数、AIP、PPGE三者联合预测模型（模型2）的AUC高达0.918，明显优于任一单项指标，具有更高的敏感度与特异性。这提示该模型可在初筛阶段快速识别高风险人群，尤其在基层医院或体检中心具备较强推广意义。此外，模型1（TyG-BMI指数联合AIP）的预测效能亦表现良好，可根据医院的检测条件灵活选择。为进一步验证模型的稳定性与泛化能力，本研究引入了来自陆军军医大学第一附属医院江北院区内分泌科的T2DM患者数据进行外部验证。结果表明，模型1在独立样本中的AUC为0.933，模型2为0.954，表现出优良的泛化能力与稳定性。进一步证实了基于常规指标构建的联合预测模型具备良好的外部可重复性与临床转化潜力，便于在不同地区与人群中推广应用。
本研究仍存在一定局限性：①本研究为单中心研究，样本量相对有限，尚无法明确因果关系。②部分代谢指标如PPGE仅基于一次检测，基于单次葡萄糖耐量试验的PPGE可能受饮食、应激等因素干扰，未来建议采用连续血糖监测提升精度。后续研究将考虑开展多中心、大样本的前瞻性队列研究，进一步验证联合模型的动态预测效能，并结合肌力、肌功能指标建立更为完善的评估体系。
综上所述，本研究基于易于获得的常规临床代谢指标，构建了可推广的T2DM患者肌少症预测模型，证实了TyG-BMI指数、AIP及PPGE对T2DM患者肌少症的联合预测价值。在临床工作中应针对老年、营养不良或体质量明显下降的患者群体，定期监测上述代谢指标，结合体成分分析仪进行早期识别。同时，针对高风险人群应及早实施营养干预、力量训练和个体化代谢管理，以延缓或逆转肌少症的发生发展。本研究为T2DM患者肌少症的早期识别和精准干预提供了新思路，具有重要临床转化价值。

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2025年第47卷第15期
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doi: 10.16016/j.2097-0927.202505021
  • 接收时间：2025-05-09
  • 首发时间：2026-10-28
  • 出版时间：2025-08-15
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  • 收稿日期：2025-05-09
  • 修回日期：2025-07-04
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    1陆军军医大学（第三军医大学）第一附属医院江北院区（陆军第九五八医院）：质量控制科，重庆
    2陆军军医大学（第三军医大学）第一附属医院江北院区（陆军第九五八医院）：皮肤性病科，重庆
    3陆军军医大学（第三军医大学）第一附属医院江北院区（陆军第九五八医院）：内分泌科，重庆
    4重庆医科大学附属第一医院内分泌内科，重庆
    5陆军军医大学（第三军医大学）第一附属医院疼痛科，重庆

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崔剑，E-mail：
程庆丰，E-mail：
CUI Jian，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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