Waist Circumference and Insulin Resistance Are the Most Predictive Metabolic Factors for Steatosis and Fibrosis

Prevalence of Steatotic Liver Disease Subtypes and Association With Metabolic Risk Factors in the Framingham Heart Study

Recent updates in nomenclature and diagnostic criteria encompass the diverse phenotypes associated with steatotic liver disease (SLD).1 These updates aim to reflect the current understanding of SLD, promote disease awareness and research, and reduce stigma. Notably, the term metabolic dysfunction-associated steatotic liver disease (MASLD) is defined as hepatic steatosis with at least 1 of 5 cardiometabolic criteria without any other cause of steatosis. A new category, MetALD, includes those with MASLD and high alcohol intake.1 We aimed to characterize SLD using this nomenclature in the Framingham Heart Study (FHS) and to quantify its association with cardiometabolic risk factors.

Association between triglyceride-glucose related indices with the all-cause and cause-specific mortality among the population with metabolic syndrome

BACKGROUND

Triglyceride-glucose (TyG) index has been determined to play a role in the onset of metabolic syndrome (MetS). Whether the TyG index and TyG with the combination of obesity indicators are associated with the clinical outcomes of the MetS population remains unknown.

METHOD

Participants were extracted from multiple cycles of the National Health and Nutrition Examination Survey (NHANES) between 1999 and 2018 years. Three indicators were constructed including TyG index, TyG combining with waist circumference (TyG-WC), and TyG combining with waist-to-height ratio (TyG-WHtR). The MetS was defined according to the National Cholesterol Education Program (NCPE) Adult Treatment Panel III. Kaplan-Meier (KM) curves, restricted cubic splines (RCS), and the Cox proportional hazard model were used to evaluate the associations between TyG-related indices and mortality of the MetS population. The sensitive analyses were performed to check the robustness of the main findings.

RESULTS

There were 10,734 participants with MetS included in this study, with 5,570 females and 5,164 males. The median age of the study population was 59 years old. The multivariate Cox regression analyses showed high levels of TyG-related indices were significantly associated with the all-cause mortality of MetS population [TyG index: adjustedhazard ratio (aHR): 1.36, 95%confidence interval (CI): 1.18-1.56, p < 0.001; TyG-WHtR index: aHR = 1.29, 95%CI: 1.13-1.47, p < 0.001]. Meanwhile, the TyG-WC and TyG-WHtR index were associated with cardiovascular mortality of the MetS population (TyG-WC: aHR = 1.45, 95%CI: 1.13-1.85, p = 0.004; TyG-WHtR: aHR = 1.50 95%CI: 1.17-1.92, p = 0.002). Three TyG-related indices showed consistent significant correlations with diabetes mortality (TyG: aHR = 4.06, 95%CI: 2.81-5.87, p < 0.001; TyG-WC: aHR = 2.55, 95%CI: 1.82-3.58, p < 0.001; TyG-WHtR: aHR = 2.53 95%CI: 1.81-3.54, p < 0.001). The RCS curves showed a non-linear trend between TyG and TyG-WC indices with all-cause mortality (p for nonlinearity = 0.004 and 0.001, respectively). The sensitive analyses supported the positive correlations between TyG-related indices with mortality of the MetS population.

CONCLUSION

Our study highlights the clinical value of TyG-related indices in predicting the survival of the MetS population. TyG-related indices would be the surrogate biomarkers for the follow-up of the MetS population.

Machine learning prediction of hepatic steatosis using body composition parameters: A UK Biobank Study

Non-alcoholic fatty liver disease (NAFLD) has emerged as the most prevalent chronic liver disease worldwide, yet detection has remained largely based on surrogate serum biomarkers, elastography or biopsy. In this study, we used a total of 2959 participants from the UK biobank cohort and established the association of dual-energy X-ray absorptiometry (DXA)-derived body composition parameters and leveraged machine learning models to predict NAFLD. Hepatic steatosis reference was based on MRI-PDFF which has been extensively validated previously. We found several significant associations with traditional measurements such as abdominal obesity, as defined by waist-to-hip ratio (OR = 2.50 (male), 3.35 (female)), android-gynoid ratio (OR = 3.35 (male), 6.39 (female)) and waist circumference (OR = 1.79 (male), 3.80 (female)) with hepatic steatosis. Similarly, A Body Shape Index (Quantile 4 OR = 1.89 (male), 5.81 (female)), and for fat mass index, both overweight (OR = 6.93 (male), 2.83 (female)) and obese (OR = 14.12 (male), 5.32 (female)) categories were likewise significantly associated with hepatic steatosis. DXA parameters were shown to be highly associated such as visceral adipose tissue mass (OR = 8.37 (male), 19.03 (female)), trunk fat mass (OR = 8.64 (male), 25.69 (female)) and android fat mass (OR = 7.93 (male), 21.77 (female)) with NAFLD. We trained machine learning classifiers with logistic regression and two histogram-based gradient boosting ensembles for the prediction of hepatic steatosis using traditional body composition indices and DXA parameters which achieved reasonable performance (AUC = 0.83-0.87). Based on SHapley Additive exPlanations (SHAP) analysis, DXA parameters that had the largest contribution to the classifiers were the features predicted with significant association with NAFLD. Overall, this study underscores the potential utility of DXA as a practical and potentially opportunistic method for the screening of hepatic steatosis.

Appropriate sleep duration modifying the association of insulin resistance and hepatic steatosis is varied in different status of metabolic disturbances among adults from the United States, NHANES 2017-March 2020

Steatosis is the hepatic manifestation of metabolic syndrome (MetS) and its developing is closely associated with insulin resistance. Shortened sleep has adverse effects on hepatic steatosis and the underlying mechanism remains unknown. We conceived to evaluate whether sleep duration was a lifestyle factor modifying the association between insulin resistance and hepatic steatosis and whether it was varied in different status of metabolic disturbances. We performed a cross-sectional analysis on 2264 adults of United States representing a population of 138,319,512 with MetS or pre-MetS from National Health and Nutrition Examination Survey (NHANES) 2017-March 2020. Participants underwent hepatic transient elastography and laboratory tests. The sleep duration was obtained from interviews. Results showed that insulin resistance was significantly associated with hepatic steatosis among participants with metabolic disturbances (OR = 1.85, 95% CI: 1.30-2.65). Significant moderation of sleep duration on the association between insulin resistance and hepatic steatosis was observed when sleep duration was dichotomized by 6.5- (P = 0.042) or 9.5-hour (P = 0.031). The risk of hepatic steatosis associated with insulin resistance was increased when sleep duration was ≤ 6.5 h and > 9.5 h. Furthermore, the moderation effect of 6.5-hour sleeping was only significant among participants with pre-MetS while that of 9.5-hour sleeping was only significant among participants with MetS. In conclusion, insufficient or excessive sleep increased the risk of hepatic steatosis associated with insulin resistance. Appropriate sleep duration was advocated and varied in different status of metabolic disturbances. Ensuring adequate sleep should be highlighted before MetS occurs and excessive sleep should be prevented for participants with MetS.

Insulin resistance and central obesity determine hepatic steatosis and explain cardiovascular risk in steatotic liver disease

Background

Metabolic dysfunction-associated steatotic liver disease (MASLD) has recently been proposed to replace non-alcoholic fatty liver disease and focus on patients with progressive disease due to the presence of metabolic dysfunction. However, it is unclear whether the new definition actually identifies patients with hepatic steatosis at increased cardiovascular risk.

Methods

A total of 4,286 asymptomatic subjects from the SAKKOPI study aged 45-80 years undergoing screening colonoscopy were analyzed. Steatosis was diagnosed by abdominal ultrasound. MASLD was diagnosed according to the recent expert consensus. Insulin resistance was assessed by homeostasis model assessment-insulin resistance score (HOMA-IR) (cutoff: ≥2.5), subclinical inflammation was estimated by ferritin/CRP/uric acid, and cardiovascular risk was assessed using SCORE2/ASCVD.

Results

Mean age was 59.4 ± 8.5 years, 51.6% were male; mean BMI was 27.0 ± 4.5 kg/m², 9.2% had type 2 diabetes mellitus. In total, 1,903 (44.4%) were diagnosed with hepatic steatosis and were characterized by more severe metabolic dysfunction including insulin resistance (47.1% vs. 12.2%, p < 0.001) and central obesity (waist circumference ≥102/88 cm, 71.8% vs. 37.1%, p < 0.001). This translated into higher (subclinical) inflammation (ferritin 153 vs. 95 mg/dL, p < 0.001, uric acid 6.3 mg/dL vs. 5.2 mg/dL, p < 0.001) and 10-year cardiovascular risk (SCORE2 7.8 points vs. 5.1 points, p < 0.001, ASCVD 17.9 points vs. 10.8 points, p < 0.001). 99.0% of subjects with steatosis met the MASLD definition, 95.4% met the MAFLD definition, and 53.6% met the definition of metabolic syndrome, while 95.4% of subjects without steatosis also met the MASLD criteria for metabolic dysfunction compared to 69.0% and 17.4% who met the MAFLD and metabolic syndrome criteria, respectively. Forward stepwise regression indicated that waist circumference, HOMA-IR, and triglycerides were most relevant in explaining the presence of hepatic steatosis across all subgroups of increasing metabolic dysfunction. At the same time, hepatic steatosis was not associated with cardiovascular risk in the overall cohort (SCORE2: B = 0.060, 95% CI: -0.193-0.314, and p = 0.642) and in patients with metabolic dysfunction after adjusting for age, sex, and these three metabolic dysfunction components.

Conclusion

Although hepatic steatosis is associated with increased central obesity and insulin resistance, metabolic dysfunction per se rather than hepatic steatosis explains cardiovascular risk in these patients.