Triglyceride glucose-body mass index in identifying high-risk groups of pre-diabetes

Jiang, Chunyuan; Yang, Ruijuan; Kuang, Maobin; Meng, Yu; Zhong, Mingchun; Zou, Yang

doi:10.1186/s12944-021-01594-7

Cited by 47 publications

(43 citation statements)

References 52 publications

(59 reference statements)

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“…The calculation result with TG and glucose is defined as the TyG index ( 52 ). At the same time, TyG-BMI, TyG-WC, and TyG-WHtR are obtained by multiplying TyG with BMI, WC, and WHtR ( 53 – 55 ). The 12 indices except WC are calculated as follows:…”

Section: Methodsmentioning

confidence: 99%

Obesity- and lipid-related indices as a predictor of obesity metabolic syndrome in a national cohort study

Gui

Liu

et al. 2023

Front. Public Health

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ObjectiveMetabolic syndrome is a common condition among middle-aged and elderly people. Recent studies have reported the association between obesity- and lipid-related indices and metabolic syndrome, but whether those conditions could predict metabolic syndrome is still inconsistent in a few longitudinal studies. In our study, we aimed to predict metabolic syndrome by obesity- and lipid-related indices in middle-aged and elderly Chinese adults.MethodA national cohort study that consisted of 3,640 adults (≥45 years) was conducted. A total of 13 obesity- and lipid-related indices, including body mass index (BMI), waist circumference (WC), waist-to-height ratio (WHtR), conicity index (CI), visceral adiposity index (VAI), Chinese visceral adiposity index (CVAI), lipid accumulation product (LAP), a body shape index (ABSI), body roundness index (BRI), and triglyceride glucose index (TyG-index) and its correlation index (TyG-BMI, TyG-WC, and TyG-WHtR), were recorded. Metabolic syndrome (MetS) was defined based on the criteria of the National Cholesterol Education Program Adult Treatment Panel III (2005). Participants were categorized into two groups according to the different sex. Binary logistic regression analyses were used to evaluate the associations between the 13 obesity- and lipid-related indices and MetS. Receiver operating characteristic (ROC) curve studies were used to identify the best predictor of MetS.ResultsA total of 13 obesity- and lipid-related indices were independently associated with MetS risk, even after adjustment for age, sex, educational status, marital status, current residence, history of drinking, history of smoking, taking activities, having regular exercises, and chronic diseases. The ROC analysis revealed that the 12 obesity- and lipid-related indices included in the study were able to discriminate MetS [area under the ROC curves (AUC > 0.6, P < 0.05)] and ABSI was not able to discriminate MetS [area under the ROC curves (AUC < 0.6, P > 0.05)]. The AUC of TyG-BMI was the highest in men, and that of CVAI was the highest in women. The cutoff values for men and women were 187.919 and 86.785, respectively. The AUCs of TyG-BMI, CVAI, TyG-WC, LAP, TyG-WHtR, BMI, WC, WHtR, BRI, VAI, TyG index, CI, and ABSI were 0.755, 0.752, 0.749, 0.745, 0.735, 0.732, 0.730, 0.710, 0.710, 0.674, 0.646, 0.622, and 0.537 for men, respectively. The AUCs of CVAI, LAP, TyG-WC, TyG-WHtR, TyG-BMI, WC, WHtR, BRI, BMI, VAI, TyG-index, CI, and ABSI were 0.687, 0.674, 0.674, 0.663, 0.656, 0.654, 0.645, 0.645, 0.638, 0.632, 0.607, 0.596, and 0.543 for women, respectively. The AUC value for WHtR was equal to that for BRI in predicting MetS. The AUC value for LAP was equal to that for TyG-WC in predicting MetS for women.ConclusionAmong middle-aged and older adults, all obesity- and lipid-related indices, except ABSI, were able to predict MetS. In addition, in men, TyG-BMI is the best indicator to indicate MetS, and in women, CVAI is considered the best hand to indicate MetS. At the same time, TyG-BMI, TyG-WC, and TyG-WHtR performed better than BMI, WC, and WHtR in predicting MetS in both men and women. Therefore, the lipid-related index outperforms the obesity-related index in predicting MetS. In addition to CVAI, LAP showed a good predictive correlation, even more closely than lipid-related factors in predicting MetS in women. It is worth noting that ABSI performed poorly, was not statistically significant in either men or women, and was not predictive of MetS.

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Section: Methodsmentioning

confidence: 99%

Obesity- and lipid-related indices as a predictor of obesity metabolic syndrome in a national cohort study

Gui

Liu

et al. 2023

Front. Public Health

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“…In August 2021, Khamseh et al proved that TyG-BMI could reliably predict liver fibrosis in overweight or obese people without Diabetes (16). In November of the same year, Jiang et al found in a cohort study that a higher TyG-BMI would significantly increase the risk of prediabetes in the population, especially in women, nonobese people and people under 50 years old (17). Apart from that, Early studies have found that TyG-BMI, which consists of fasting blood glucose, triglyceride and obesity status, can more efficiently predict IR than TyG index, BMI and other obesity indexes (21).…”

Section: Comparisons With Other Studies and What Does The Current Wor...mentioning

confidence: 99%

“…A more convenient and cheaper IR measurement is critical for prospective risk assessment, extensive population screening, and treatment monitoring. Triglyceride glucose-body mass index (TyG-BMI) is a new obesity-related parameter that has been developed in recent years, and it's obtained by multiplying TyG (composed of fasting plasma glucose and triglycerides) with BMI (derived from weight and height) (13)(14)(15)(16)(17)(18)(19)(20)(21). These are routine physical examination indicators that are easy to obtain.…”

Section: Introductionmentioning

confidence: 99%

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Triglyceride Glucose-Body Mass Index and Risk of Incident Type 2 Diabetes Mellitus in Japanese People With Normal Glycemic Level: A Population-Based Longitudinal Cohort Study

et al. 2022

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BackgroundIt has been proved that triglyceride glucose-body mass index (TyG-BMI) is a readily available and clinically significant indicator of insulin resistance (IR). Nevertheless, the association between TyG-BMI and incident Type 2 diabetes mellitus (T2DM) remains uncertain. This study aimed to study the relationship between TyG-BMI and T2DM and explore the predictive characteristics of TyG-BMI.MethodsOur study was conducted as a longitudinal cohort study. 8,430 men and 7,034 women were enrolled and analyzed. They were both non-diabetic subjects with normal glycemic levels. Follow-up lasted for 13 years, from 1994 to 2016. To make the number of TyG-BMI in each group similar, the subjects were divided into four groups with 3866 subjects in each group.ResultsDuring the 13-year follow-up period, 373 subjects were diagnosed with incident T2DM. Our multivariate Cox regression analysis revealed that TyG-BMI was an independent predictor of incident T2DM. In addition, our research identified four specific groups, young people (18-44 years old), women, the non-hypertensive population and non-drinkers were at significantly higher risk of developing TyG-BMI-related diabetes (P-interaction< 0.05). The best threshold TyG-BMI for predicting incident T2DM was 197.2987 (area under the curve 0.7738).ConclusionsOur longitudinal cohort study demonstrated the positive correlation between baseline TyG-BMI and risk of incident T2DM in Japanese with normal glycemic levels, and this risk was significantly higher in the young people, women, the non-hypertensive population and non-drinkers.

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“…The mechanism of action of TyG in T2DM pathology is not evident, but it has been suggested that high blood glucose levels raise the level of reactive oxygen species (ROS) that mediate beta cell injury [ 121 ]. Higher TyG index values have shown clear associations between BMI and the development of T2DM [ 121 , 122 ].…”

Section: Clinical Complications Of T2dmmentioning

confidence: 99%

Recent Progress in the Diagnosis and Management of Type 2 Diabetes Mellitus in the Era of COVID-19 and Single Cell Multi-Omics Technologies

Kupai

Várkonyi

Török

et al. 2022

Life

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Type 2 diabetes mellitus (T2DM) is one of the world’s leading causes of death and life-threatening conditions. Therefore, we review the complex vicious circle of causes responsible for T2DM and risk factors such as the western diet, obesity, genetic predisposition, environmental factors, and SARS-CoV-2 infection. The prevalence and economic burden of T2DM on societal and healthcare systems are dissected. Recent progress on the diagnosis and clinical management of T2DM, including both non-pharmacological and latest pharmacological treatment regimens, are summarized. The treatment of T2DM is becoming more complex as new medications are approved. This review is focused on the non-insulin treatments of T2DM to reach optimal therapy beyond glycemic management. We review experimental and clinical findings of SARS-CoV-2 risks that are attributable to T2DM patients. Finally, we shed light on the recent single-cell-based technologies and multi-omics approaches that have reached breakthroughs in the understanding of the pathomechanism of T2DM.

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Triglyceride glucose-body mass index in identifying high-risk groups of pre-diabetes

Cited by 47 publications

References 52 publications

Obesity- and lipid-related indices as a predictor of obesity metabolic syndrome in a national cohort study

Obesity- and lipid-related indices as a predictor of obesity metabolic syndrome in a national cohort study

Triglyceride Glucose-Body Mass Index and Risk of Incident Type 2 Diabetes Mellitus in Japanese People With Normal Glycemic Level: A Population-Based Longitudinal Cohort Study

Recent Progress in the Diagnosis and Management of Type 2 Diabetes Mellitus in the Era of COVID-19 and Single Cell Multi-Omics Technologies

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