Relationship Between Six Insulin Resistance Surrogates and Nonalcoholic Fatty Liver Disease Among Older Adults: A Cross-Sectional Study

Abstract

Background Non-alcoholic fatty liver disease (NAFLD) represents a large and growing public health problem. Insulin resistance (IR) plays a crucial role in the pathogenesis of NAFLD. The aim of this study was to determine the association of triglyceride-glucose (TyG) index, TyG index with body mass index (TyG-BMI), lipid accumulation product (LAP), visceral adiposity index (VAI), triglycerides/high-density lipoprotein cholesterol ratio (TG/HDL-c) and metabolic score for IR (METS-IR) with NAFLD in older adults and to compare the discriminatory abilities of these six IR surrogates for NAFLD. Methods This cross-sectional study included 72,225 subjects aged ≥60 years living in Xinzheng, Henan Province, from January 2021 to December 2021. The data were collected from the annual health examination dataset. Logistic regression models were used to examine the relationships between the six indicators and NAFLD risk. The area under the receiver operating characteristic curve (AUC) was used to compare the discriminatory ability of different IR surrogates for NAFLD under the influence of potential risk factors. Results After adjusting for multiple covariates, compared with the first quintile, the odds ratios (ORs) and 95% confidence intervals (CIs) of the highest quintiles of TyG-BMI were the most obvious (OR:43.02, 95% CI:38.89–47.72), followed by the METS-IR (OR:34.49, 95% CI:31.41–37.95). Restricted cubic spline analysis reported that there were non-linear positive association and dose-response relationship between 6 IR surrogates and NAFLD risk. Compared with other IR-related indicators (LAP, TyG, TG/HDL-c and VAI), TyG-BMI showed the highest AUC (AUC:0.8059;95% CI:0.8025–0.8094). Additionally, METS-IR also had a high predictive performance for NAFLD, and the AUC was greater than 0.75 (AUC:0.7959;95% CI:0.7923–0.7994). Conclusion TyG-BMI and METS-IR had pronounced discrimination ability to NAFLD, which are recommended as complementary markers for the assessment of NAFLD risk both in clinic and in future epidemiological studies.

Prognostic significance of glucose‐lipid metabolic index in pancreatic cancer patients with diabetes mellitus

“…The predictive efficacy of TG/HDL‐c (AUC = 0.674) was found to be superior to that of TyG (AUC = 0.616) and TyG‐BMI (AUC = 0.633), as depicted in Figure 2. Contrary to our findings, in studies of benign diseases, TyG‐BMI and TyG showed superior efficacy in evaluating IR than TG/HDL‐c 27,28 . We speculated that cholesterol metabolism may affect the progression of PC, as IR results in elevated triglyceride levels and decreased HDL‐c levels 46 .…”

“…Insulin resistance plays a crucial role in the pathophysiology of diabetes mellitus and causes a variety of metabolic disorders such as hyperglycemia, hyperinsulinemia, dyslipidemia and visceral obesity 16,26,28,39,40 . Currently, the homeostatic model assessment of IR and the hyperinsulinemic euglycemic clamp are accurate methods for assessing IR.…”

“…Currently, the homeostatic model assessment of IR and the hyperinsulinemic euglycemic clamp are accurate methods for assessing IR. However, due to their complexity, high cost of operation, and lack of a standardized method for insulin determination, these two methods are not favorable for clinical promotion 27,28,41 . Three metabolic indices, namely TyG, TyG‐BMI, and TG/HDL‐c, have been identified as reliable indicators for assessing the degree of IR due to their simplicity and practicality 26–28,41 .…”

“…TNM staging was performed according to the 8th edition of the AJCC PC staging system. Based on the above indicators, TyG, TyG‐BMI, TG/HDL‐c and GLMI were calculated, respectively, and the calculation methods were as follows 27,28 :$$$$ \mathrm{TyG}=\ln\ \left[\mathrm{TG}\ \left(\mathrm{mg}/\mathrm{dL}\right)\times \mathrm{FPG}\ \left(\mathrm{mg}/\mathrm{dL}\right)/2\right]; $$$$$$$$ \mathrm{TG}/\mathrm{HDL}\hbox{-} \mathrm{c}=\mathrm{TG}\ \left(\mathrm{mg}/\mathrm{dL}\right)\times \mathrm{HDL}\ \left(\mathrm{mg}/\mathrm{dL}\right); $$$$$$$$ \mathrm{BMI}=\mathrm{Weight}\ \left(\mathrm{kg}\right)/{\mathrm{Height}}^2\ \left({\mathrm{m}}^2\right)\ \left(\mathrm{kg}/{\mathrm{m}}^2\right); $$$$$$$$ \mathrm{TyG}\hbox{-} \mathrm{BMI}=\mathrm{TyG}\times \mathrm{BMI}; $$$$$$$$ \mathrm{GLMI}=\mathrm{TyG}\hbox{-} \mathrm{BMI}\times \mathrm{TC}/\mathrm{HDL}\hbox{-} \mathrm{c}. $$$$…”

The impact of triglyceride glucose-body mass index on all-cause and cardiovascular mortality in elderly patients with diabetes mellitus: evidence from NHANES 2007–2016

“…Many studies supported the close association between TyG-BMI index and visceral adiposity [ 26 ], type 2 DM [ 27 ], and metabolic syndrome [ 28 ]. The clinical value of TyG-BMI index on predicting occurrence of hypertension [ 29 ], nonalcoholic fatty liver disease [ 30 ], DM [ 31 ], and severe coronary artery disease [ 32 ] have been demonstrated in previous studies. Apart from the substantial correlation with various illness, TyG-BMI index was also useful for predicting clinical outcomes.…”