Changes in Insulin Resistance Index and the Risk of Liver Fibrosis in Patients with Nonalcoholic Fatty Liver Disease without Diabetes: Kangbuk Samsung Health Study

Association between nonalcoholic fatty liver disease and left ventricular diastolic dysfunction: A 7-year retrospective cohort study of 3,380 adults using serial echocardiography

AIM

Left ventricular diastolic dysfunction (LVDD) has been observed in people with nonalcoholic fatty liver disease (NAFLD) in cross-sectional studies but the causal relationship is unclear. This study aimed to investigate the impact of NAFLD and the fibrotic progression of the disease on the development of LVDD, assessed by serial echocardiography, in a large population over a 7-year longitudinal setting.

METHODS

This retrospective cohort study included the data of 3,380 subjects from a medical health check-up program. We defined subjects having NAFLD by abdominal ultrasonography and assessed significant liver fibrosis by the aspartate transaminase (AST) to platelet ratio index (APRI), the NAFLD fibrosis score (NFS), and the fibrosis-4 (FIB-4) index. LVDD was defined using serial echocardiography. A parametric Cox proportional hazards model was used.

RESULTS

During 11,327 person-years of follow-up, there were 560 (16.0 %) incident cases of LVDD. After adjustment for multiple risk factors, subjects with NAFLD showed an increased adjusted hazard ratio (aHR) of 1.21 (95 % confidence interval [CI]=1.02-1.43) for incident LVDD compared to those without. The risk of LV diastolic dysfunction increased progressively with increasing degree of hepatic steatosis (P< 0.001). Compared to subjects without NAFLD, the multivariable-aHR (95 % CI) for LVDD in subjects with APRI < 0.5 and APRI ≥ 0.5 were 1.20 (1.01-1.42) and 1.36 (0.90-2.06), respectively (P= 0.036), while other fibrosis prediction models (NFS and FIB-4 index) showed insignificant results.

CONCLUSIONS

This study demonstrated that NAFLD was associated with an increased risk of LVDD in a large cohort. More severe forms of hepatic steatosis and/or significant liver fibrosis may increase the risk of developing LVDD.

Insulin Resistance, Non-Alcoholic Fatty Liver Disease and Type 2 Diabetes Mellitus: Clinical and Experimental Perspective

It has been generally accepted that insulin resistance (IR) and reduced insulin secretory capacity are the basic pathogenesis of type 2 diabetes mellitus (T2DM). In addition to genetic factors, the persistence of systemic inflammation caused by obesity and the associated threat of lipotoxicity increase the risk of T2DM. In particular, the main cause of IR is obesity and subjects with T2DM have a higher body mass index (BMI) than normal subjects according to recent studies. The prevalence of T2DM with IR has increased with increasing BMI during the past three decades. According to recent studies, homeostatic model assessment of IR was increased compared to that of the 1990s. Rising prevalence of obesity in Korea have contributed to the development of IR, non-alcoholic fatty liver disease and T2DM and cutting this vicious cycle is important. My colleagues and I have investigated this pathogenic mechanism on this theme through clinical and experimental studies over 20 years and herein, I would like to summarize some of our studies with deep gratitude for receiving the prestigious 2023 Sulwon Award.

Insulin Resistance/Sensitivity Measures as Screening Indicators of Metabolic-Associated Fatty Liver Disease and Liver Fibrosis

BACKGROUND

Measures of insulin resistance (IR)/sensitivity (IS) are emerging tools to identify metabolic-associated fatty liver disease (MAFLD). However, the comprehensive assessment of the performance of various indicators is limited. Moreover, the utility of measures of IR/IS in detecting liver fibrosis remains unclear.

AIMS

To evaluate the predictive ability of seventeen IR/IS and two beta cell function indices to identify MAFLD and liver fibrosis.

METHODS

A cross-sectional study was conducted on individuals aged 25-75 years. Transient elastography was used to estimate liver stiffness and controlled attenuation parameter. The following measures were computed: homeostatic model assessment (HOMA/HOMA2) for IR, IS, and beta cell function; QUICKI; Bennett index; glucose/insulin; FIRI; McAuley index; Reynaud index; SPISE index; TyG; TyG-BMI; TyG-WC; TyG-WHtR; TG/HDL; and METS-IR. Subgroup analyses were performed according to age, gender, diabetes status, and body weight.

RESULTS

A total of 644 individuals were included in our analysis. MAFLD and significant liver fibrosis were detected in 320 (49.7%) and 80 (12.4%) of the participants, respectively. All measures of IR/IS identified MAFLD and liver fibrosis. However, TyG-WC, TyG-BMI, and TyG-WHtR were the top three indicators that identified MAFLD. Measures that include insulin level in their mathematical calculation, namely, Raynaud index, HOMA-IR, HOMA 2-IR, FIRI, and QUICKI had the best performance in identifying liver fibrosis in the entire population, as well as among the study subgroups.

CONCLUSIONS

TyG-WC, TyG-BMI, and TyG-WHtR were the best predictors of MAFLD. Insulin-based measures had better performances in the detection of advanced fibrosis. This was independent of age, gender, obesity, or diabetes status.

Factors Associated with Liver Fibrosis in Chinese Patients with Type 2 Diabetes Mellitus and Non-Alcoholic Fatty Liver Disease

Purpose

Non-alcoholic fatty liver disease (NAFLD) and type 2 diabetes mellitus (T2DM) are frequently co-occurring diseases. Liver fibrosis (LF), with increasing incidence, has a prognostic value for NAFLD mortality. Our study aimed to investigate the relevant factors for FL in T2DM individuals with NAFLD.

Patients and Methods

A total of 565 T2DM patients with NAFLD from Hebei General Hospital participated in the study. Patients underwent an abdominal ultrasound, a questionnaire and laboratory tests. The fibrosis-4 index (FIB-4) was used to evaluate LF, with FIB ≥1.3 indicating LF and FIB ≥2.67 indicating F3-4 fibrosis.

Results

Compared with NLF group, LF group had higher levels of systolic blood pressure (SBP), alanine aminotransferase (ALT), aspartate aminotransferase (AST), and γ-glutamyl transpeptidase (GGT). The glomerular filtration rate (GFR), low-density lipoprotein cholesterol (LDL), glycated hemoglobin (HbA1c), and platelets (PLT) in LF patients were lower than those without LF. Patients with LF were older than those without LF. ALT, AST, and GGT in patients with severe LF were higher than those with mild LF, while platelet was lower. Age, SBP, duration of diabetes, ALT, AST, and GGT were positively correlated with FIB-4, while eGFR, TC, LDL, and HbA1c were negatively correlated with FIB-4. Logistic regression showed that age, SBP, ALT, GGT, LDL, and PLT were independently associated with LF.

Conclusion

For T2DM patients combined with NAFLD, older age, higher SBP, higher ALT, higher GGT, lower LDL, and lower PLT were relevant factors for LF.

Machine learning models including insulin resistance indexes for predicting liver stiffness in United States population: Data from NHANES

Background

Prevention and treatment of liver fibrosis at an early stage is of great prognostic importance, whereas changes in liver stiffness are often overlooked in patients before the onset of obvious clinical symptoms. Recognition of liver fibrosis at an early stage is therefore essential.

Objective

An XGBoost machine learning model was constructed to predict participants' liver stiffness measures (LSM) from general characteristic information, blood test metrics and insulin resistance-related indexes, and to compare the fit efficacy of different datasets for LSM.

Methods

All data were obtained from the National Health and Nutrition Examination Survey (NHANES) for the time interval January 2017 to March 2020. Participants' general characteristics, Liver Ultrasound Transient Elastography (LUTE) information, indicators of blood tests and insulin resistance-related indexes were collected, including homeostasis model assessment of insulin resistance (HOMA-IR) and metabolic score for insulin resistance (METS-IR). Three datasets were generated based on the above information, respectively named dataset A (without the insulin resistance-related indexes as predictor variables), dataset B (with METS-IR as a predictor variable) and dataset C (with HOMA-IR as a predictor variable). XGBoost regression was used in the three datasets to construct machine learning models to predict LSM in participants. A random split was used to divide all participants included in the study into training and validation cohorts in a 3:1 ratio, and models were developed in the training cohort and validated with the validation cohort.

Results

A total of 3,564 participants were included in this study, 2,376 in the training cohort and 1,188 in the validation cohort, and all information was not statistically significantly different between the two cohorts (p > 0.05). In the training cohort, datasets A and B both had better predictive efficacy than dataset C for participants' LSM, with dataset B having the best fitting efficacy [±1.96 standard error (SD), (-1.49,1.48) kPa], which was similarly validated in the validation cohort [±1.96 SD, (-1.56,1.56) kPa].

Conclusions

XGBoost machine learning models built from general characteristic information and clinically accessible blood test indicators are practicable for predicting LSM in participants, and a dataset that included METS-IR as a predictor variable would improve the accuracy and stability of the models.

Metabolic Score for Insulin Resistance Is Inversely Related to Incident Advanced Liver Fibrosis in Patients with Non-Alcoholic Fatty Liver Disease

We determined the relationships between metabolic score for IR (METS-IR), triglyceride-glucose (TyG) index, and homeostatic model assessment for IR (HOMA-IR) and incident advanced liver fibrosis (ALF) and assessed the abilities of the three IR indicators to predict ALF in patients with non-alcoholic fatty liver disease (NAFLD) in adults with NAFLD who were aged 40–69 years old. Among 2218 participants with NAFLD at baseline, the areas under the receiver operating characteristic curve for predicting ALF of the METS-IR was 0.744 (0.679–0.810), significantly higher than that of TyG index (0.644 (0.569–0.720)) or that of HOMA-IR (0.633 (0.556–0.710)). Among 1368 patients with NAFLD and without ALF at baseline, 260 (19.0%) patients with NAFLD progressed to ALF during the 16-year follow-up period. Multivariable Cox proportional hazard regression analysis revealed that the adjusted hazard ratio (95% confidence interval) for incident ALF in the highest tertiles of METS-IR, TyG index, and HOMA-IR compared with the lowest tertile of each IR indicator were 0.5 (0.36–0.91), 0.7 (0.49–1.00), and 1.01 (0.71–1.42), respectively. METS-IR was inversely associated with ALF in patients with NAFLD, which cautiously suggests that the risk of ALF may need to be evaluated when metabolic parameters improve in individuals with NAFLD.

Impact of COVID-19 Lockdown on Non-Alcoholic Fatty Liver Disease and Insulin Resistance in Adults: A before and after Pandemic Lockdown Longitudinal Study

Background: Non-alcoholic fatty liver disease is a chronic disease caused by the accumulation of fat in the liver related to overweight and obesity, insulin resistance, hyperglycemia, and high levels of triglycerides and leads to an increased cardiovascular risk. It is considered a global pandemic, coinciding with the pandemic in 2020 caused by the “coronavirus disease 2019” (COVID-19). Due to COVID-19, the population was placed under lockdown. The aim of our study was to evaluate how these unhealthy lifestyle modifications influenced the appearance of metabolic alterations and the increase in non-alcoholic fatty liver disease. Methods: A prospective study was carried out on 6236 workers in a Spanish population between March 2019 and March 2021. Results: Differences in the mean values of anthropometric and clinical parameters before and after lockdown were revealed. There was a statistically significant worsening in non-alcoholic fatty liver disease (NAFLD) and in the insulin resistance scales, with increased body weight, BMI, cholesterol levels with higher LDL levels, and glucose and a reduction in HDL levels. Conclusions: Lockdown caused a worsening of cardiovascular risk factors due to an increase in liver fat estimation scales and an increased risk of presenting with NAFLD and changes in insulin resistance.