Fasting Triglycerides and Glucose Index is More Suitable for the Identification of Metabolically Unhealthy Individuals in the Chinese Adult Population: A Nationwide Study

Overview

Journal J Diabetes Investig

Specialty Endocrinology

Date 2018 Nov 13

PMID 30417578

Citations 64

Authors

Xinwen Yu

Li Wang

Wencheng Zhang

Jie Ming

Aihua Jia

Shaoyong Xu

Qiaoyue Li

Qiuhe Ji

Affiliations

Soon will be listed here.

Abstract

Aims/introduction: Metabolic unhealth can be defined by the components of metabolic syndrome, which is closely connected to insulin resistance. We aimed to determine a simple index to identify metabolic unhealth in the Chinese adult population.

Materials And Methods: A total of 30,291 individuals were screened from the China National Diabetes and Metabolic Disorders Study carried out from June 2007 to May 2008. Metabolic unhealth was defined using components of metabolic syndrome, except waist circumference. We compared the three surrogate indices of insulin resistance: the product of fasting triglycerides and glucose (TyG), triglycerides divided by high-density lipoprotein cholesterol and the metabolic score for insulin resistance for the evaluation of metabolic status.

Results: All indices had high sensitivity and specificity for the identification of metabolic unhealth, especially the TyG index with an area under the curve of 0.863 for men and 0.867 for women. Participants were divided into subgroups for further analysis. The TyG index also showed high diagnostic values, especially for younger individuals and men with normal waist circumference. Sex-specific cut-offs for three indices were also used to define metabolic unhealth. The TyG index showed the highest agreement with κ values of 0.603 and 0.605 for men and women between the components of metabolic syndrome and three indices.

Conclusions: We propose that the TyG index, just read in one blood laboratory test report, is simpler and more suitable for the identification of metabolically unhealthy individuals as well as who have high risk of cardiometabolic diseases of the Chinese adult population.

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References

Abbasi F, Reaven G . Comparison of two methods using plasma triglyceride concentration as a surrogate estimate of insulin action in nondiabetic subjects: triglycerides × glucose versus triglyceride/high-density lipoprotein cholesterol. Metabolism. 2011; 60(12):1673-6. DOI: 10.1016/j.metabol.2011.04.006. View

Xu S, Ming J, Xing Y, Gao B, Yang C, Ji Q . Regional differences in diabetes prevalence and awareness between coastal and interior provinces in China: a population-based cross-sectional study. BMC Public Health. 2013; 13:299. PMC: 3703289. DOI: 10.1186/1471-2458-13-299. View

Hashimoto Y, Hamaguchi M, Fukuda T, Ohbora A, Kojima T, Fukui M . Fatty liver as a risk factor for progression from metabolically healthy to metabolically abnormal in non-overweight individuals. Endocrine. 2017; 57(1):89-97. DOI: 10.1007/s12020-017-1313-6. View

Grundy S, Brewer Jr H, Cleeman J, Smith Jr S, Lenfant C . Definition of metabolic syndrome: report of the National Heart, Lung, and Blood Institute/American Heart Association conference on scientific issues related to definition. Arterioscler Thromb Vasc Biol. 2004; 24(2):e13-8. DOI: 10.1161/01.ATV.0000111245.75752.C6. View

Alberti K, Eckel R, Grundy S, Zimmet P, Cleeman J, Donato K . Harmonizing the metabolic syndrome: a joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation;.... Circulation. 2009; 120(16):1640-5. DOI: 10.1161/CIRCULATIONAHA.109.192644. View

Mazidi M, Kengne A, Katsiki N, Mikhailidis D, Banach M . Lipid accumulation product and triglycerides/glucose index are useful predictors of insulin resistance. J Diabetes Complications. 2018; 32(3):266-270. DOI: 10.1016/j.jdiacomp.2017.10.007. View

Wang B, Zhang M, Liu Y, Sun X, Zhang L, Wang C . Utility of three novel insulin resistance-related lipid indices for predicting type 2 diabetes mellitus among people with normal fasting glucose in rural China. J Diabetes. 2018; 10(8):641-652. DOI: 10.1111/1753-0407.12642. View

McLaughlin T, Reaven G, Abbasi F, Lamendola C, Saad M, Waters D . Is there a simple way to identify insulin-resistant individuals at increased risk of cardiovascular disease?. Am J Cardiol. 2005; 96(3):399-404. DOI: 10.1016/j.amjcard.2005.03.085. View

Mendis S, Davis S, Norrving B . Organizational update: the world health organization global status report on noncommunicable diseases 2014; one more landmark step in the combat against stroke and vascular disease. Stroke. 2015; 46(5):e121-2. DOI: 10.1161/STROKEAHA.115.008097. View

10.

Yang W, Lu J, Weng J, Jia W, Ji L, Xiao J . Prevalence of diabetes among men and women in China. N Engl J Med. 2010; 362(12):1090-101. DOI: 10.1056/NEJMoa0908292. View

11.

Bello-Chavolla O, Almeda-Valdes P, Gomez-Velasco D, Viveros-Ruiz T, Cruz-Bautista I, Romo-Romo A . METS-IR, a novel score to evaluate insulin sensitivity, is predictive of visceral adiposity and incident type 2 diabetes. Eur J Endocrinol. 2018; 178(5):533-544. DOI: 10.1530/EJE-17-0883. View

12.

Matthews D, Hosker J, Rudenski A, Naylor B, Treacher D, Turner R . Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia. 1985; 28(7):412-9. DOI: 10.1007/BF00280883. View

13.

Ruderman N, Schneider S, Berchtold P . The "metabolically-obese," normal-weight individual. Am J Clin Nutr. 1981; 34(8):1617-21. DOI: 10.1093/ajcn/34.8.1617. View

14.

Velho S, Paccaud F, Waeber G, Vollenweider P, Marques-Vidal P . Metabolically healthy obesity: different prevalences using different criteria. Eur J Clin Nutr. 2010; 64(10):1043-51. DOI: 10.1038/ejcn.2010.114. View

15.

Zhou B . [Predictive values of body mass index and waist circumference to risk factors of related diseases in Chinese adult population]. Zhonghua Liu Xing Bing Xue Za Zhi. 2002; 23(1):5-10. View

16.

Hinnouho G, Czernichow S, Dugravot A, Batty G, Kivimaki M, Singh-Manoux A . Metabolically healthy obesity and risk of mortality: does the definition of metabolic health matter?. Diabetes Care. 2013; 36(8):2294-300. PMC: 3714476. DOI: 10.2337/dc12-1654. View

17.

Xing Y, Xu S, Jia A, Cai J, Zhao M, Guo J . Recommendations for revision of Chinese diagnostic criteria for metabolic syndrome: A nationwide study. J Diabetes. 2017; 10(3):232-239. DOI: 10.1111/1753-0407.12578. View

18.

Wildman R, Muntner P, Reynolds K, McGinn A, Rajpathak S, Wylie-Rosett J . The obese without cardiometabolic risk factor clustering and the normal weight with cardiometabolic risk factor clustering: prevalence and correlates of 2 phenotypes among the US population (NHANES 1999-2004). Arch Intern Med. 2008; 168(15):1617-24. DOI: 10.1001/archinte.168.15.1617. View

19.

Oliveros E, Somers V, Sochor O, Goel K, Lopez-Jimenez F . The concept of normal weight obesity. Prog Cardiovasc Dis. 2014; 56(4):426-33. DOI: 10.1016/j.pcad.2013.10.003. View

20.

Simental-Mendia L, Rodriguez-Moran M, Guerrero-Romero F . The product of fasting glucose and triglycerides as surrogate for identifying insulin resistance in apparently healthy subjects. Metab Syndr Relat Disord. 2008; 6(4):299-304. DOI: 10.1089/met.2008.0034. View