甘油三酯-葡萄糖指数对老年糖尿病前期患者肌少症的预测价值

doi:10.3877/cma.j.issn.2095-8757.2025.02.002

目的

探讨甘油三酯-葡萄糖（TyG）指数在老年糖尿病前期患者肌少症诊断中的预测价值。

方法

采用单中心、横断面研究方式，收集2023年1月至2024年6月就诊于中南大学湘雅二医院的65岁及以上老年糖尿病前期患者的一般特征和实验室检查结果，根据有无肌少症分为肌少症组和非肌少症组。采用χ²检验和t检验比较组间差异，筛选可能影响肌少症发生的危险因素，Pearson相关性分析评估各指标与四肢骨骼肌质量指数和握力之间的关系，使用Logistic回归分析探究糖尿病前期患者发生肌少症的危险因素，采用受试者工作特征（ROC）曲线分析TyG指数对老年糖尿病前期患者肌少症的预测价值。

结果

入组211例老年糖尿病前期患者，确诊肌少症107例（肌少症组），非肌少症104例（非肌少症组）。肌少症组TyG指数为（8.81±0.54），非肌少症组为（8.43±0.40），差异有统计学意义（t=5.820，P<0.05）。TyG指数与患者ASMI及握力均呈负相关关系（r=-0.224、-0.215，P<0.01），是糖尿病前期患者发生肌少症的危险因素（OR=1.646，95%CI：0.426-6.358，P<0.001），其预测老年糖尿病前期患者发生肌少症的ROC曲线下面积为0.706（95%CI：0.636-0.775），最佳截断值为8.631，灵敏度为70.2%，特异度为62.6%。

结论

TyG指数或是预测老年糖尿病前期患者发生肌少症的有效指标。

关键词: 肌少症, 糖尿病前期, 甘油三酯-葡萄糖指数, 四肢骨骼肌质量指数

Objective

To explore the predictive value of the triglyceride-glucose (TyG) index for diagnosing sarcopenia in elderly patients with prediabetes.

Methods

A single-center, cross-sectional study was conducted. General characteristics and laboratory test results were collected from prediabetic patients aged≥65 years attending the Second Xiangya Hospital of Central South University between January 2023 and June 2024. Participants were divided into sarcopenia and non-sarcopenia groups based on the presence of sarcopenia. Differences between groups were compared to identify potential risk factors for sarcopenia using χ² test and t test. Pearson correlation analysis was used to assess the relationship between various indicators and appendicular skeletal muscle mass index (ASMI) and grip strength. Logistic regression analysis was employed to investigate risk factors for sarcopenia in prediabetic patients. Receiver operating characteristic (ROC) curve analysis was used to evaluate the predictive value of the TyG index for sarcopenia in elderly prediabetic patients.

Results

A total of 211 elderly prediabetic patients were enrolled, including 102 males (accounting for 48.3%), with a mean age of (78.72±6.68) years. Sarcopenia was diagnosed in 107 patients. The TyG index was significantly higher in the sarcopenia group (8.81±0.54) than in the non-sarcopenia group (8.43±0.40) (t=5.820, P < 0.05). The TyG index showed a negative correlation with ASMI and grip strength (r=-0.224, -0.215; P < 0.01). It was identified as a risk factor for sarcopenia in prediabetic patients (OR=1.646, 95%CI: 0.426-6.358, P < 0.001). The area under the ROC curve (AUC) for the TyG index in predicting sarcopenia in elderly prediabetic patients was 0.706 (95%CI: 0.636-0.775). The optimal cut-off value was 8.631, with a sensitivity of 70.2% and specificity of 62.6%.

Conclusion

The TyG index may be an effective predictor of sarcopenia in elderly patients with prediabetes.

Key words: Sarcopenia, Prediabetes, Triglyceride-glucose index, Appendicular skeletal muscle mass index

表1 肌少症组和非肌少症组临床资料的比较[例（%）或

±s]

组别	例数	男性	年龄（岁）	BMI（kg/m²）	ASMI（kg/m²）	SBP（mmHg）	DBP（mmHg）
肌少症组	107	56（52.3%）	78.99±7.32	21.32±2.54	5.81±0.79	137.30±20.07	76.80±11.13
非肌少症组	104	46（44.2%）	78.44±5.98	23.99±3.30	7.06±0.86	138.76±14.69	74.76±9.48
检验值		χ²=1.388	t=0.598	t=6.573	t=10.987	t=0.604	t=1.435
P值		>0.05	>0.05	<0.01	<0.01	>0.05	>0.05
组别	例数	握力（kg）	TC（mmol/L）	TG（mmol/L）	HDL-C（mmol/L）	LDL-C（mmol/L）	TyG指数
肌少症组	107	18.47±5.95	4.27±1.11	1.70±0.79	1.15±0.33	2.47±0.96	8.81±0.54
非肌少症组	104	22.01±7.23	3.90±0.93	1.16±0.41	1.19±0.37	2.13±0.73	8.43±0.40
检验值		t=3.878	t=2.627	t=6.257	t=0.829	t=2.901	t=5.820
P值		<0.05	<0.01	<0.01	>0.05	<0.01	<0.05

表1 肌少症组和非肌少症组临床资料的比较[例（%）或

±s]

组别	例数	男性	年龄（岁）	BMI（kg/m²）	ASMI（kg/m²）	SBP（mmHg）	DBP（mmHg）
肌少症组	107	56（52.3%）	78.99±7.32	21.32±2.54	5.81±0.79	137.30±20.07	76.80±11.13
非肌少症组	104	46（44.2%）	78.44±5.98	23.99±3.30	7.06±0.86	138.76±14.69	74.76±9.48
检验值		χ²=1.388	t=0.598	t=6.573	t=10.987	t=0.604	t=1.435
P值		>0.05	>0.05	<0.01	<0.01	>0.05	>0.05
组别	例数	握力（kg）	TC（mmol/L）	TG（mmol/L）	HDL-C（mmol/L）	LDL-C（mmol/L）	TyG指数
肌少症组	107	18.47±5.95	4.27±1.11	1.70±0.79	1.15±0.33	2.47±0.96	8.81±0.54
非肌少症组	104	22.01±7.23	3.90±0.93	1.16±0.41	1.19±0.37	2.13±0.73	8.43±0.40
检验值		t=3.878	t=2.627	t=6.257	t=0.829	t=2.901	t=5.820
P值		<0.05	<0.01	<0.01	>0.05	<0.01	<0.05

表2 各因素与ASMI及握力之间的关系（Pearson相关分析）

因素	ASMI		握力
因素	r值	P值	r值	P值
BMI	0.483	<0.001	0.144	0.037
年龄	-0.003	0.967	0.029	0.674
SBP	0.054	0.434	0.041	0.555
DBP	0.003	0.963	0.046	0.509
TC	-0.162	0.018	-0.006	0.936
TG	-0.144	0.037	-0.145	0.035
HDL-C	-0.206	0.003	-0.004	0.959
LDL-C	-0.122	0.077	-0.002	0.978
TyG指数	-0.224	0.001	-0.215	0.002

表2 各因素与ASMI及握力之间的关系（Pearson相关分析）

因素	ASMI		握力
因素	r值	P值	r值	P值
BMI	0.483	<0.001	0.144	0.037
年龄	-0.003	0.967	0.029	0.674
SBP	0.054	0.434	0.041	0.555
DBP	0.003	0.963	0.046	0.509
TC	-0.162	0.018	-0.006	0.936
TG	-0.144	0.037	-0.145	0.035
HDL-C	-0.206	0.003	-0.004	0.959
LDL-C	-0.122	0.077	-0.002	0.978
TyG指数	-0.224	0.001	-0.215	0.002

图1 甘油三酯-葡萄糖指数预测老年糖尿病前期患者发生肌少症的受试者工作特征曲线

表3 老年糖尿病前期患者发生肌少症的多因素分析

因素	β	SE	Wald χ²	P值	OR值	95%CI
BMI	-0.395	0.076	27.103	<0.001	0.673	0.580-0.782
TC	-0.728	0.642	1.287	0.257	0.483	0.137-1.699
TG	0.151	0.515	0.086	0.770	1.163	0.424-3.189
LDL-C	1.033	0.699	2.187	0.139	2.810	0.715-11.055
TyG指数	2.801	0.689	16.503	<0.001	1.646	0.426-6.358

表3 老年糖尿病前期患者发生肌少症的多因素分析

因素	β	SE	Wald χ²	P值	OR值	95%CI
BMI	-0.395	0.076	27.103	<0.001	0.673	0.580-0.782
TC	-0.728	0.642	1.287	0.257	0.483	0.137-1.699
TG	0.151	0.515	0.086	0.770	1.163	0.424-3.189
LDL-C	1.033	0.699	2.187	0.139	2.810	0.715-11.055
TyG指数	2.801	0.689	16.503	<0.001	1.646	0.426-6.358

[1]	Batsis JA, Villareal DT. Sarcopenic obesity in older adults: aetiology, epidemiology and treatment strategies[J]. Nat Rev Endocrinol, 2018, 14(9):513-537.
[2]	von Haehling S, Morley JE, Anker SD. An overview of sarcopenia: Facts and numbers on prevalence and clinical impact[J]. J Cachexia Sarcopenia Muscle, 2010, 1(2):129-133.
[3]	Cruz-Jentoft AJ, Sayer AA. Sarcopenia[J]. Lancet, 2019, 393(10191):2636-2646.
[4]	Nishikawa H, Asai A, Fukunishi S, et al. Metabolic syndrome and sarcopenia[J]. Nutrients, 2021, 13(10):3519.
[5]	Eckel RH, Grundy SM, Zimmet PZ. The metabolic syndrome[J]. Lancet, 2005, 365(9468):1415-1428.
[6]	McAuley KA, Williams SM, Mann JI, et al. Diagnosing insulin resistance in the general population[J]. Diabetes Care, 2001, 24(3):460-464.
[7]	Wallace TM, Levy JC, Matthews DR. Use and abuse of HOMA modeling[J]. Diabetes Care, 2004, 27(6):1487-1495.
[8]	Simental-Mendía LE, Rodríguez-Morán M, Guerrero-Romero F. The product of fasting glucose and triglycerides as surrogate for identifying insulin resistance in apparently healthy subjects[J]. Metab Syndr Relat Disord, 2008, 6(4):299-304.
[9]	Lee S, Choi S, Kim HJ, et al. Cutoff values of surrogate measures of insulin resistance for metabolic syndrome in Korean non-diabetic adults[J]. J Korean Med Sci, 2006, 21(4):695-700.
[10]	Vasques ACJ, Novaes FS, Maria da Saúde de Oliveira, et al. TyG index performs better than HOMA in a Brazilian population: A hyperglycemic clamp validated study[J]. Diabetes Res Clin Pract, 2011, 93(3):e98-e100.
[11]	Seung-Hwan L, Hyuk-Sang K, Yong-Moon P, et al. Predicting the development of diabetes using the product of triglycerides and glucose: the Chungju Metabolic Disease Cohort (CMC) study[J]. PLoS One, 2014, 9(2):e90430.
[12]	Adams-Huet B, Zubirán R, Remaley AT, et al. The triglyceride-glucose index is superior to homeostasis model assessment of insulin resistance in predicting metabolic syndrome in an adult population in the United States[J]. J Clin Lipidol, 2024, 18(4):e518-e524.
[13]	Wang SJ, Shi J, Peng Y, et al. Stronger association of triglyceride glucose index than the HOMA-IR with arterial stiffness in patients with type 2 diabetes: A real-world single-centre study[J]. Cardiovasc Diabetol, 2021, 20(1):82.
[14]	Echouffo-Tcheugui JB, Perreault L, Ji LN, et al. Diagnosis and management of prediabetes: A review[J]. JAMA, 2023, 329(14):1206-1216.
[15]	Simental-Mendía LE, Rodríguez-Morán M, Guerrero-Romero F. The product of fasting glucose and triglycerides as surrogate for identifying insulin resistance in apparently healthy subjects[J]. Metab Syndr Relat Disord, 2008, 6(4):299-304.
[16]	Chen LK, Woo J, Assantachai P, et al. Asian working group for sarcopenia: 2019 consensus update on sarcopenia diagnosis and treatment[J]. J Am Med Dir Assoc, 2020, 21(3):300-307.e2.
[17]	Gong H, Liu Y, Lyu X, et al. Lipoprotein subfractions in patients with sarcopenia and their relevance to skeletal muscle mass and function[J]. Exp Gerontol, 2021, 159:111668.
[18]	Cleasby ME, Jamieson PM, Atherton PJ. Insulin resistance and sarcopenia: mechanistic links between common co-morbidities[J]. J Endocrinol, 2016, 229(2):R67-81.
[19]	Li CW, Yu K, Shyh-Chang N, et al. Pathogenesis of sarcopenia and the relationship with fat mass: descriptive review[J]. J Cachexia Sarcopenia Muscle, 2022, 13(2):781-794.
[20]	Chevalier-Larsen ES, Merry DE. Cellular and molecular mechanisms of muscle atrophy[J]. Dis Model Mech, 2012, 5(1):141-145.
[21]	Baczek J, Silkiewicz M, Wojszel ZB. Myostatin as a biomarker of muscle wasting and other pathologies-state of the art and knowledge gaps[J]. Nutrients, 2020, 12(8):2401.
[22]	Kim B, Kim G, Lee YK, et al. Triglyceride-glucose index as a potential indicator of sarcopenic obesity in older people[J]. Nutrients, 2023, 15(3):555.
[23]	Li MH, Ji R, Liu Xi, et al. Associations of metabolic syndrome and its components with sarcopenia, and the mediating role of insulin resistance: Findings from NHANES database[J]. BMC Endocr Disord, 2024, 24(1):203.
[24]	Lu BW, Li JC, Liang XZ, et al. Association between atherogenic index of plasma, body mass index, and sarcopenia: A cross-sectional and longitudinal analysis study based on older adults in China[J]. Aging Clin Exp Res, 2025, 37(1):122.
[25]	Liu ZJ, Zhu CF. Causal relationship between insulin resistance and sarcopenia[J]. Diabetol Metab Syndr, 2023, 15(1):46.
[26]	Sung-Ho A, Jun-Hyuk L, Ji-Won L. Inverse association between triglyceride glucose index and muscle mass in Korean adults: 2008-2011 KNHANES[J]. Lipids Health Dis, 2020, 19(1):243.
[27]	Moon S, Park JS, Ahn Y. The cut-off values of triglycerides and glucose index for metabolic syndrome in american and korean adolescents[J]. J Korean Med Sci, 2017, 32(3):427-433.
[28]	Distefano G, Goodpaster BH. Effects of exercise and aging on skeletal muscle[J]. Cold Spring Harb Perspect Med, 2018, 8(3):a029785.
[29]	Le Couteur DG, Solon-Biet SM, Cogger VC, et al. Branched chain amino acids, aging and age-related health[J]. Ageing Res Rev, 2020, 64:101198.

[1]	李田香, 赵瑞娜, 康琳, 毕江涵, 陈聪, 杨萌. 基于超声的影像组学模型对老年人肌少症的诊断价值[J/OL]. 中华医学超声杂志（电子版）, 2025, 22(01): 70-78.
[2]	姚放鸣, 谷邦宁, 杨旭辉, 曾子俊, 吴佳威, 何敏聪, 何晓铭, 魏秋实, 何伟, 刘文刚. 下肢肌肉分布与内翻型膝骨关节炎进展及肌少症的相关性[J/OL]. 中华关节外科杂志(电子版), 2025, 19(03): 292-301.
[3]	高伟聪, 李丽, 张中华, 朱向辉, 刘素巧. 宫颈癌患者糖调节受损对改良根治术后2年内复发的影响作用[J/OL]. 中华妇幼临床医学杂志(电子版), 2024, 20(02): 231-237.
[4]	奚玲, 仝瀚文, 缪骥, 毛永欢, 沈晓菲, 杜峻峰, 刘晔. 基于肌少症构建的造口旁疝危险因素预测模型[J/OL]. 中华普外科手术学杂志(电子版), 2025, 19(01): 48-51.
[5]	刘国路, 李乾, 王以金, 王苏贵, 胡好, 张璐. 甘油三酯-葡萄糖指数与肾细胞癌患者术后预后的关系[J/OL]. 中华腔镜泌尿外科杂志(电子版), 2025, 19(04): 492-497.
[6]	徐艳, 江秀娟, 王超, 江圆满. 股直肌剪切波弹性成像对COPD并发肌少症的诊断意义[J/OL]. 中华肺部疾病杂志(电子版), 2024, 17(03): 454-457.
[7]	马强. 衰弱和肌少症对肾功能评估的影响及干预策略[J/OL]. 中华肾病研究电子杂志, 2025, 14(01): 60-60.
[8]	孙顗淼, 张颖. 糖尿病患者急性脑梗死取栓术后发生对比剂肾病的影响因素及预测模型建立[J/OL]. 中华肾病研究电子杂志, 2024, 13(04): 188-194.
[9]	曹仔怡, 周加军. CKD-MBD与肌少症：深入探究其影响的研究新进展[J/OL]. 中华临床医师杂志(电子版), 2025, 19(01): 75-79.
[10]	奚培培, 周加军. 慢性肾脏病患者肌少症机制和诊治的研究进展[J/OL]. 中华临床医师杂志(电子版), 2024, 18(05): 491-495.
[11]	王广宁, 李玉杰, 高坤范, 刘慧松. 短链脂肪酸在肌少症发病机制中的作用[J/OL]. 中华老年病研究电子杂志, 2024, 11(04): 31-37.
[12]	曹婧然, 董福强, 张立剑, 刘长乐, 张煜坤, 陈康寅. 老年冠心病患者的营养风险、肌少症和衰弱筛查分析[J/OL]. 中华老年病研究电子杂志, 2024, 11(02): 5-9.
[13]	曹娟, 朱亚, 吴玉泉, 胡旭钢, 董芳, 洪逸莲, 桂莹. 改良Morse跌倒评估量表对老年住院患者跌倒风险的预测价值[J/OL]. 中华老年病研究电子杂志, 2024, 11(02): 1-4.
[14]	管惠兰, 张靖梅, 解艳红. 社区老年人对肌少症知识知晓率的调查分析[J/OL]. 中华老年病研究电子杂志, 2024, 11(02): 36-43.
[15]	王超珺, 董志勇, 赵宛鄂, 胡嵩浩, 刘昭晖. 肌少症对肥胖患者袖状胃切除术后效果的影响研究[J/OL]. 中华肥胖与代谢病电子杂志, 2024, 10(04): 283-287.

阅读次数

全文

摘要

选择文件类型/文献管理软件名称

选择包含的内容

Value of the triglyceride-glucose index in predicting sarcopenia in elderly prediabetic patients

模态框（Modal）标题

选择文件类型/文献管理软件名称

选择包含的内容

Value of the triglyceride-glucose index in predicting sarcopenia in elderly prediabetic patients