Association between the Fatty Liver Index and Risk of Type 2 Diabetes in the EPIC-Potsdam Study

Abstract

The fatty liver index (FLI) predicts fatty liver by using BMI, waist circumference, γ-glutamyltransferase and triglycerides. We investigated the association between the FLI and the risk of type 2 diabetes and evaluated to what extent single FLI components contribute to the diabetes risk. We analysed a case-cohort study (random sub-cohort: 1922; incident cases: 563) nested within the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam study. The proportion of exposure effect (PEE) explained by single FLI components was evaluated and effect decomposition using inverse probability weighting (IPW) was applied. Women and men with a FLI ≥60 compared to those with a FLI <30 had a multivariable-adjusted Hazard Ratio (HR) of 17.6; 95% confidence interval (CI) 11.1-28.0 and HR: 10.9; 95% CI 6.22-19.2, respectively. Adjustment for BMI or waist circumference attenuated this association in men [PEEBMI (95% CI) = 53.8% (43.9%-65.8%); PEEwaist (95% CI) = 54.8% (44.2%-68.8%)]. In women, adjustment for waist circumference attenuated the association to a lesser degree than in men [PEEwaist (95% CI) = 31.1%; (21.9%-43.1%)] while BMI had no appreciable effect [PEEBMI (95% CI) = 11.0% (2.68%-21.0%)]. γ-glutamyltransferase and triglycerides showed only a small attenuation in women [PEEGGT(95% CI) = 3.11% (-0.72%-4.48%); PEETG (95% CI) = 6.36% (3.81%-9.92%)] and in men [PEEGGT = 0%; PEETG (95% CI) = 6.23% (2.03%-11.8%)]. In women, the total effect was decomposed into a direct effect and 4 indirect effects (HRBMI = 1.10; HRwaist = 1.28; HRGGT = 0.97 and HRTG = 1.03). In men, the 4 indirect effects were HRBMI = 1.25; HRwaist = 1.29; HRGGT = 0.97 and HRTG = 0.99. These data suggest that the FLI, as a proxy for fatty liver, is associated with risk of type 2 diabetes. This association is only partly explained by standard estimates of overall and abdominal body fatness, particularly among women.

Gamma glutamyltransferase and risk of future dementia in middle‐aged to older Finnish men: A new prospective cohort study

“…The FLI -a simple algorithm for predicting fatty liver -is based on BMI, waist circumference, triglycerides, and GGT, and has a diagnostic accuracy ranging from 0.72 to 0.84%. [22,23] All statistical analyses were conducted using Stata version 14 (Stata Corp, College Station, Texas). Serum GGT values were weakly to moderately and positively correlated with physical measures (BMI, blood pressure, and physical activity) and with several lipid, metabolic, and inflammation markers (CRP).…”

Combined effects of lifestyle risk factors on fatty liver index

“…Therefore, new non-invasive tools for detecting hepatic steatosis in an early phase are needed to prevent progression of liver disease and associated metabolic comorbidities. Although the FLI algorithm has recently been shown to improve the identification of fatty liver when compared with other non-invasive methods [11,20,[29][30][31], as yet, only few studies have been available on the clinical applications of FLI or the effects of lifestyle factors on FLI. The values are expressed as mean ± SD (FLI, BMI, waist circumference) or as geometric means and 95% confidence intervals (GGT, triglycerides, ALT, CRP, cholesterol, HDL, LDL) p < 0.0005 for linear trend for both mean values and proportions, except p = 0.001 for ALT data on proportions among women Alcohol drinking, cigarette smoking, and physical inactivity are currently the main modifiable high-risk determinants of lifestyle [1].…”

Fatty Liver Index (FLI) in Predicting Non-alcoholic Fatty Liver Disease (NAFLD)

“…Accordingly, non-invasive imaging methods, particularly ultrasound are used to diagnose fatty liver disease in most cases (8). Transient elastography is a novel non-invasive method, which is developed to measure tissue stiffness, using the shear wave velocity named rameter (CAP) has been implemented using FibroScan to assess liver steatosis based on ultrasound attenuation (9).…”