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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 Aug; 10(8):641-652.JD

Abstract

BACKGROUND

Inexpensive and easily measured indices are needed for the early prediction of type 2 diabetes mellitus (T2DM) in rural areas of China. The aim of this study was to compare triglyceride glucose (TyG), visceral adiposity (VAI), and lipid accumulation product (LAP) with traditional individual measures and their ratios for predicting T2DM.

METHODS

Data for 11 113 people with baseline normal fasting glucose in a rural Chinese cohort were followed for a median of 6.0 years. Cox proportional hazards regression was used to calculate covariate-adjusted hazard ratios (aHRs) and 95% confidence intervals (95% CIs) and receiver operating characteristic analysis was used to compare the ability of traditional measures and TyG, VAI, and LAP at baseline to predict T2DM at follow-up.

RESULTS

Among individual measures, fasting plasma glucose (FPG) and waist circumference (WC) were strongly associated with T2DM. Of all lipid ratios, an elevated triglycerides (TG) to high-density lipoprotein cholesterol (HDL-C) ratio was associated the most with T2DM. Compared with the first quartiles of TyG, VAI, and LAP, their fourth quartiles were associated with T2DM for men (aHR 3.54 [95% CI 2.08-6.03], 2.89 [1.72-4.87], and 5.02 [2.85-8.85], respectively) and women (6.15 [3.48-10.85], 4.40 [2.61-7.42], and 6.49 [3.48-12.12], respectively). For predicting T2DM risk, TyG, VAI, and LAP were mostly superior to the TG: HDL-C ratio, but did not differ from FPG and WC.

CONCLUSIONS

Prediction of T2DM was not improved by TyG, VAI, and LAP versus FPG or WC alone. Therefore, TyG, VAI, and LAP may not be inexpensive tools for predicting T2DM in rural Chinese people.

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Authors+Show Affiliations

Wang B
Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, China.
Zhang M
Department of Preventive Medicine, Shenzhen University Health Science Center, Shenzhen, China.
Liu Y
The Affiliated Luohu Hospital of Shenzhen University Health Science Center, Shenzhen, China.
Sun X
The Affiliated Luohu Hospital of Shenzhen University Health Science Center, Shenzhen, China.
Zhang L
Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, China.
Wang C
Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, China.
Li L
Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, China.
Ren Y
Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, China. Department of Preventive Medicine, Shenzhen University Health Science Center, Shenzhen, China. The Affiliated Luohu Hospital of Shenzhen University Health Science Center, Shenzhen, China.
Han C
Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, China. Department of Preventive Medicine, Shenzhen University Health Science Center, Shenzhen, China. The Affiliated Luohu Hospital of Shenzhen University Health Science Center, Shenzhen, China.
Zhao Y
Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, China. Department of Preventive Medicine, Shenzhen University Health Science Center, Shenzhen, China. The Affiliated Luohu Hospital of Shenzhen University Health Science Center, Shenzhen, China.
Zhou J
Department of Preventive Medicine, Shenzhen University Health Science Center, Shenzhen, China. The Affiliated Luohu Hospital of Shenzhen University Health Science Center, Shenzhen, China.
Pang C
Department of Prevention and Health Care, Military Hospital of Henan Province, Zhengzhou, China.
Yin L
Department of Prevention and Health Care, Military Hospital of Henan Province, Zhengzhou, China.
Feng T
Department of Prevention and Health Care, Military Hospital of Henan Province, Zhengzhou, China.
Zhao J
Department of Prevention and Health Care, Military Hospital of Henan Province, Zhengzhou, China.
Hu D
Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, China.

MeSH

AdiposityAdultAsian Continental Ancestry GroupBlood GlucoseChinaCholesterol, HDLDiabetes Mellitus, Type 2FastingFemaleFollow-Up StudiesHumansInsulin ResistanceIntra-Abdominal FatLipid Accumulation ProductMaleMiddle AgedRural PopulationTriglycerides

Pub Type(s)

Journal Article

Language

eng

PubMed ID

29322661

Citation

Wang, Bingyuan, et al. "Utility of Three Novel Insulin Resistance-related Lipid Indices for Predicting Type 2 Diabetes Mellitus Among People With Normal Fasting Glucose in Rural China." Journal of Diabetes, vol. 10, no. 8, 2018, pp. 641-652.
Wang B, Zhang M, Liu Y, et al. 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.
Wang, B., Zhang, M., Liu, Y., Sun, X., Zhang, L., Wang, C., Li, L., Ren, Y., Han, C., Zhao, Y., Zhou, J., Pang, C., Yin, L., Feng, T., Zhao, J., & Hu, D. (2018). Utility of three novel insulin resistance-related lipid indices for predicting type 2 diabetes mellitus among people with normal fasting glucose in rural China. Journal of Diabetes, 10(8), 641-652. https://doi.org/10.1111/1753-0407.12642
Wang B, et al. 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. PubMed PMID: 29322661.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Utility of three novel insulin resistance-related lipid indices for predicting type 2 diabetes mellitus among people with normal fasting glucose in rural China. AU - Wang,Bingyuan, AU - Zhang,Ming, AU - Liu,Yu, AU - Sun,Xizhuo, AU - Zhang,Lu, AU - Wang,Chongjian, AU - Li,Linlin, AU - Ren,Yongcheng, AU - Han,Chengyi, AU - Zhao,Yang, AU - Zhou,Junmei, AU - Pang,Chao, AU - Yin,Lei, AU - Feng,Tianping, AU - Zhao,Jingzhi, AU - Hu,Dongsheng, Y1 - 2018/02/09/ PY - 2017/07/10/received PY - 2017/12/17/revised PY - 2018/01/07/accepted PY - 2018/1/13/pubmed PY - 2018/12/12/medline PY - 2018/1/12/entrez KW - cohort study KW - diabetes KW - fat accumulation KW - insulin resistance KW - lipid index KW - 糖尿病 KW - 胰岛素抵抗 KW - 脂肪堆积 KW - 血脂指数 KW - 队列研究 SP - 641 EP - 652 JF - Journal of diabetes JO - J Diabetes VL - 10 IS - 8 N2 - BACKGROUND: Inexpensive and easily measured indices are needed for the early prediction of type 2 diabetes mellitus (T2DM) in rural areas of China. The aim of this study was to compare triglyceride glucose (TyG), visceral adiposity (VAI), and lipid accumulation product (LAP) with traditional individual measures and their ratios for predicting T2DM. METHODS: Data for 11 113 people with baseline normal fasting glucose in a rural Chinese cohort were followed for a median of 6.0 years. Cox proportional hazards regression was used to calculate covariate-adjusted hazard ratios (aHRs) and 95% confidence intervals (95% CIs) and receiver operating characteristic analysis was used to compare the ability of traditional measures and TyG, VAI, and LAP at baseline to predict T2DM at follow-up. RESULTS: Among individual measures, fasting plasma glucose (FPG) and waist circumference (WC) were strongly associated with T2DM. Of all lipid ratios, an elevated triglycerides (TG) to high-density lipoprotein cholesterol (HDL-C) ratio was associated the most with T2DM. Compared with the first quartiles of TyG, VAI, and LAP, their fourth quartiles were associated with T2DM for men (aHR 3.54 [95% CI 2.08-6.03], 2.89 [1.72-4.87], and 5.02 [2.85-8.85], respectively) and women (6.15 [3.48-10.85], 4.40 [2.61-7.42], and 6.49 [3.48-12.12], respectively). For predicting T2DM risk, TyG, VAI, and LAP were mostly superior to the TG: HDL-C ratio, but did not differ from FPG and WC. CONCLUSIONS: Prediction of T2DM was not improved by TyG, VAI, and LAP versus FPG or WC alone. Therefore, TyG, VAI, and LAP may not be inexpensive tools for predicting T2DM in rural Chinese people. SN - 1753-0407 UR - https://www.unboundmedicine.com/medline/citation/29322661/Utility_of_three_novel_insulin_resistance_related_lipid_indices_for_predicting_type_2_diabetes_mellitus_among_people_with_normal_fasting_glucose_in_rural_China_ L2 - https://doi.org/10.1111/1753-0407.12642 DB - PRIME DP - Unbound Medicine ER -