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Review
. 2024 Aug 23;23(1):310.
doi: 10.1186/s12933-024-02392-y.

Diagnostic and prognostic value of triglyceride glucose index: a comprehensive evaluation of meta-analysis

Sandeep Samethadka Nayak  1 , Dona Kuriyakose  2 , Lakshmi D Polisetty  1   3 , Anjali Avinash Patil  4 , Daniyal Ameen  1 , Rakshita Bonu  5 , Samatha P Shetty  6 , Pubali Biswas  5 , Micheal T Ulrich  7 , Negin Letafatkar  8 , Arman Habibi  8 , Mohammad-Hossein Keivanlou  8 , Sara Nobakht  8 , Abdulhadi Alotaibi  9 , Soheil Hassanipour  10 , Ehsan Amini-Salehi  11
Affiliations
Review

Diagnostic and prognostic value of triglyceride glucose index: a comprehensive evaluation of meta-analysis

Sandeep Samethadka Nayak et al. Cardiovasc Diabetol. .

Abstract

Objective: The present umbrella review aims to collate and summarize the findings from previous meta-analyses on the Triglyceride and Glucose (TyG) Index, providing insights to clinicians, researchers, and policymakers regarding the usefulness of this biomarker in various clinical settings.

Methods: A comprehensive search was conducted in PubMed, Scopus, and Web of Science up to April 14, 2024, without language restrictions. The AMSTAR2 checklist assessed the methodological quality of the included meta-analyses. Statistical analyses were performed using Comprehensive Meta-Analysis (CMA) software.

Results: A total of 32 studies were finally included. The results revealed significant associations between the TyG index and various health outcomes. For kidney outcomes, a high TyG index was significantly associated with an increased risk of contrast-induced nephropathy (CIN) (OR = 2.24, 95% CI: 1.82-2.77) and chronic kidney disease (CKD) (RR = 1.46, 95% CI: 1.32-1.63). High TyG index was significantly associated with an increased risk of type 2 diabetes mellitus (T2DM) (RR = 3.53, 95% CI: 2.74-4.54), gestational diabetes mellitus (GDM) (OR = 2.41, 95% CI: 1.48-3.91), and diabetic retinopathy (DR) (OR = 2.34, 95% CI: 1.31-4.19). Regarding metabolic diseases, the TyG index was significantly higher in patients with obstructive sleep apnea (OSA) (SMD = 0.86, 95% CI: 0.57-1.15), metabolic syndrome (MD = 0.83, 95% CI: 0.74-0.93), and non-alcoholic fatty liver disease (NAFLD) (OR = 2.36, 95% CI: 1.88-2.97) compared to those without these conditions. In cerebrovascular diseases, a higher TyG index was significantly associated with an increased risk of dementia (OR = 1.14, 95% CI: 1.12-1.16), cognitive impairment (OR = 2.31, 95% CI: 1.38-3.86), and ischemic stroke (OR = 1.37, 95% CI: 1.22-1.54). For cardiovascular outcomes, the TyG index showed significant associations with an increased risk of heart failure (HF) (HR = 1.21, 95% CI: 1.12-1.30), atrial fibrillation (AF) (SMD = 1.22, 95% CI: 0.57-1.87), and hypertension (HTN) (RR = 1.52, 95% CI: 1.25-1.85).

Conclusion: The TyG index is a promising biomarker for screening and predicting various medical conditions, particularly those related to insulin resistance and metabolic disorders. However, the heterogeneity and methodological quality of the included studies suggest the need for further high-quality research to confirm these findings and refine the clinical utility of the TyG index.

Keywords: Cardiovascular outcomes; Clinical significance; Insulin resistance; Meta-analysis; Triglyceride and glucose (TyG) index; TyG; Umbrella review.

PubMed Disclaimer

Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Research trend of triglyceride glucose index. A Overlay visualization B Timeline visualization
Fig. 2
Fig. 2
Study selection process
Fig. 3
Fig. 3
Summary of study characteristics. A Distribution of studies by country: Annual publication trends of articles. C Usage of statistical software and analysis tools across the studies
Fig. 4
Fig. 4
Quality assessment of included studies based on AMSTAR 2 appraisal tool
Fig. 5
Fig. 5
The association between TyG index and CIN.A Forest plot. B Publication bias. C Sensitivity analysis. D Prediction interval E Power analysis
Fig. 6
Fig. 6
The association between TyG index and CKD. A Forest plot. B Publication bias. C Sensitivity analysis. D Prediction interval
Fig. 7
Fig. 7
The association between TyG index and T2DM reported as risk ratio. A Forest plot. B Publication bias. C Sensitivity analysis. D Prediction interval. E Power analysis
Fig. 8
Fig. 8
The association between TyG index and T2DM reported as hazard ratio. A Forest plot. B Publication bias. C Sensitivity analysis. D Prediction interval. E Power analysis
Fig. 9
Fig. 9
The association between TyG index and GDM. A Forest plot. B Publication bias. C Sensitivity analysis. D Prediction interval. E Power analysis
Fig. 10
Fig. 10
The association between TyG index and DR as a categorical variable. A Forest plot. B Publication bias. C Sensitivity analysis. D Prediction interval. E Power analysis
Fig. 11
Fig. 11
The association between TyG index and DR as a continuous variable. A Forest plot. B Publication bias. C Sensitivity analysis. D Prediction interval. E Power analysis
Fig. 12
Fig. 12
The association between TyG index and OSA. A Forest plot. B Publication bias. C Sensitivity analysis. D Prediction interval. E Power analysis
Fig. 13
Fig. 13
The association between TyG index and metabolic syndrome. A Forest plot. B Publication bias. C Sensitivity analysis. D Prediction interval. E Power analysis
Fig. 14
Fig. 14
The association between TyG index and NAFLD as a continuous variable. A Forest plot. B Publication bias. C Sensitivity analysis. D Prediction interval. E Power analysis
Fig. 15
Fig. 15
The association between TyG index and NAFLD as a categorical variable. A Forest plot. B Publication bias. C Sensitivity analysis. D Prediction interval. E Power analysis
Fig. 16
Fig. 16
The association between TyG index and dementia. A Forest plot of TyG as a categorical index B Power analysis of TyG as a categorical index. C Forest plot of TyG as a continuous index. D Power analysis of TyG as a continuous index
Fig. 17
Fig. 17
The association between TyG index as a categorical variable and cognitive impairment. A Forest plot. B Publication bias. C Sensitivity analysis. D Prediction interval. E Power analysis
Fig. 18
Fig. 18
The association between TyG index as a continuous variable and cognitive impairment. A Forest plot. B Publication bias. C Sensitivity analysis. D Prediction interval. E Power analysis
Fig. 19
Fig. 19
The association between TyG index and ischemic stroke. A Forest plot. B Publication bias. C Sensitivity analysis. D Prediction interval. E Power analysis
Fig. 20
Fig. 20
The association between TyG index and ischemic stroke recurrence. A Forest plot. B Publication bias. C Sensitivity analysis. D Prediction interval. E Power analysis
Fig. 21
Fig. 21
The association between TyG index and ischemic stroke poor functional outcomes. A Forest plot. B Publication bias. C Sensitivity analysis. D Prediction interval. E Power analysis
Fig. 22
Fig. 22
The association between TyG index and ischemic stroke mortality. A Forest plot. B Publication bias. C Sensitivity analysis. D Prediction interval. E Power analysis
Fig. 23
Fig. 23
The association between TyG index and heart failure as a categorical variable. A Forest plot. B Publication bias. C Sensitivity analysis. D Prediction interval
Fig. 24
Fig. 24
The association between TyG index and heart failure as a continues variable. A Forest plot. B Publication bias. C Sensitivity analysis. D Prediction interval
Fig. 25
Fig. 25
The association between TyG index and atrial fibrillation. A Forest plot. B Publication bias. C Sensitivity analysis. D Prediction interval. E Power analysis
Fig. 26
Fig. 26
The association between TyG index and hypertension. A Forest plot. B Publication bias. C Sensitivity analysis. D Prediction interval
Fig. 27
Fig. 27
The association between TyG index as a categorical variable and atrial stiffness. A Forest plot. B Publication bias. C Sensitivity analysis. D Prediction interval E Power analysis
Fig. 28
Fig. 28
The association between TyG index as a continuous variable and atrial stiffness. A Forest plot. B Publication bias. C Sensitivity analysis. D Prediction interval E Power analysis
Fig. 29
Fig. 29
The association between TyG index as a continuous variable and coronary artery calcification. A Forest plot. B Publication bias. C Sensitivity analysis. D Prediction interval. E Power analysis
Fig. 30
Fig. 30
The association between TyG index as a categorical variable and coronary artery calcification. A Forest plot. B Publication bias. C Sensitivity analysis. D Prediction interval. E Power analysis
Fig. 31
Fig. 31
The association between TyG index as a categorical variable and major cardiovascular outcomes following PCI. A Forest plot. B Publication bias. C Sensitivity analysis. D Prediction interval
Fig. 32
Fig. 32
The association between TyG index as a continuous variable and major cardiovascular outcomes following PCI. A Forest plot. B Publication bias. C Sensitivity analysis. D Prediction interval
Fig. 33
Fig. 33
The association between TyG index as a categorical variable and all-cause mortality following PCI. A Forest plot. B Publication bias. C Sensitivity analysis. D Prediction interval
Fig. 34
Fig. 34
The association between TyG index as a continuous variable and all-cause mortality outcomes following PCI. A Forest plot. B Publication bias. C Sensitivity analysis. D Prediction interval
Fig. 35
Fig. 35
The association between TyG index as a categorical variable and post-PCI revascularization. A Forest plot. B Publication bias. C Sensitivity analysis. D Prediction interval
Fig. 36
Fig. 36
The association between TyG index as a continuous variable and post-PCI revascularization. A Forest plot. B Publication bias. C Sensitivity analysis. D Prediction interval
Fig. 37
Fig. 37
The association between TyG index as a categorical variable and post-PCI myocardial infarction. A Forest plot. B Publication bias. C Sensitivity analysis. D Prediction interval
Fig. 38
Fig. 38
The association between TyG index as a continuous variable and post-PCI myocardial infarction. A Forest plot. B Publication bias. C Sensitivity analysis. D Prediction interval
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