Markers of insulin resistance associated with non-alcoholic fatty liver disease in non-diabetic population

Association between serum uric acid-to-high-density lipoprotein cholesterol ratio and insulin resistance in an American population: A population-based analysis

INTRODUCTION

Previous studies have demonstrated a correlation between the serum uric acid-to-high-density lipoprotein cholesterol ratio (UHR) and insulin resistance (IR) in individuals with type 2 diabetes mellitus. However, no existing studies have investigated the relationship between IR and UHR in the general population. Therefore, the primary objective of this study was to investigate the correlation between UHR and IR in the general American population.

METHODS

A sample of 8,817 participants was selected from the 2013 to 2020 National Health and Nutrition Examination Survey (NHANES). Homeostatic model assessment of insulin resistance (HOMA-IR) was used to assess insulin resistance. Multiple logistic regression, generalized smooth curve fitting, and subgroup analysis were used to assess the association between IR and UHR.

RESULTS

Multiple logistic regression analysis indicated a significant correlation between insulin resistance and UHR, with odds ratios (OR) of 1.07 (95% CI = 1.03-1.11) in males and 1.18 (95% CI = 1.13-1.25) in females. A non-linear relationship and saturation effect between IR risk and UHR were observed, characterized by an inverted L-shaped curve and a critical inflection point at 8.82. It was found that the area under the ROC curve (AUC) of UHR was significantly larger (AUC = 0.703 for males and 0.747 for females, all P < 0.01) compared with the use of UA or HDL-C alone. Subgroup analysis showed that this independent association remain consistent regardless of race, age, BMI, diabetes, moderate activities, education level, alcohol drinking, and gender.

CONCLUSION

Elevated UHR demonstrates a significant correlation with insulin resistance, so it can be used as a potential indicator of insulin resistance within the American population.

Predicting Non-Alcoholic Steatohepatitis: A Lipidomics-Driven Machine Learning Approach

Nonalcoholic fatty liver disease (NAFLD), nowadays the most prevalent chronic liver disease in Western countries, is characterized by a variable phenotype ranging from steatosis to nonalcoholic steatohepatitis (NASH). Intracellular lipid accumulation is considered the hallmark of NAFLD and is associated with lipotoxicity and inflammation, as well as increased oxidative stress levels. In this study, a lipidomic approach was used to investigate the plasma lipidome of 12 NASH patients, 10 Nonalcoholic Fatty Liver (NAFL) patients, and 15 healthy controls, revealing significant alterations in lipid classes, such as glycerolipids and glycerophospholipids, as well as fatty acid compositions in the context of steatosis and steatohepatitis. A machine learning XGBoost algorithm identified a panel of 15 plasma biomarkers, including HOMA-IR, BMI, platelets count, LDL-c, ferritin, AST, FA 12:0, FA 18:3 ω3, FA 20:4 ω6/FA 20:5 ω3, CAR 4:0, LPC 20:4, LPC O-16:1, LPE 18:0, DG 18:1_18:2, and CE 20:4 for predicting steatohepatitis. This research offers insights into the connection between imbalanced lipid metabolism and the formation and progression of NAFL D, while also supporting previous research findings. Future studies on lipid metabolism could lead to new therapeutic approaches and enhanced risk assessment methods, as the shift from isolated steatosis to NASH is currently poorly understood.

Association between surrogate marker of insulin resistance and bone mineral density in US adults without diabetes

This study examines the relationship between TyG-BMI, an indicator of insulin resistance, and bone mineral density in US adults without diabetes, revealing a positive association. The findings suggest that higher TyG-BMI levels may be linked to a lower risk of osteoporosis, providing a basis for future research in this area.

OBJECTIVE

Patients with osteoporosis are often diagnosed with type 2 diabetes or prediabetes. Insulin resistance is a prediabetic state, and triglyceride glucose-body mass index (TyG-BMI) has been recognized as a potential predictor of it, valuable in assessing prediabetes, atherosclerosis, and other diseases. However, the validity of TyG-BMI in osteoporosis studies remains inadequate.

PURPOSE

The purpose of this study was to evaluate the relationship between TyG-BMI and BMD as well as the effect of TyG-BMI on the odds of developing osteoporosis in US adults without diabetes.

METHODS

National Health and Nutrition Examination Survey data were obtained. The relationship between TyG-BMI and BMD was evaluated via multivariate linear regression models. Smoothed curve fitting and threshold effect analysis explored potential non-linear relationships, and age, gender, and race subgroup analyses were performed. In addition, multivariate logistic regression models were employed to analyze its potential role in the development of osteoporosis.

RESULTS

In a study of 6501 participants, we observed a significant positive correlation between the TyG-BMI index and BMD, even after adjusting for covariates and categorizing TyG-BMI. The study identified specific TyG-BMI folding points-112.476 for the total femur BMD, 100.66 for the femoral neck BMD, 107.291 for the intertrochanter BMD, and 116.58 for the trochanter BMD-indicating shifts in the relationship's strength at these thresholds. While the association's strength slightly decreased after the folding points, it remained significant. Subgroup analyses further confirmed the positive TyG-BMI and BMD correlation. Multivariate linear regression analyses indicated a lower osteoporosis risk in participants with higher TyG-BMI levels, particularly in menopausal women over 40 and men over 60.

CONCLUSION

This study suggests a positive correlation between BMD and TyG-BMI in US adults without diabetes. Individuals with higher levels of TyG-BMI may have a lower risk of osteoporosis.

Lipid-based insulin-resistance markers predict cardiovascular events in metabolic dysfunction associated steatotic liver disease

BACKGROUND

Insulin resistance (IR) is the cornerstone of Metabolic Dysfunction Associated Steatotic Liver Disease (MASLD), pathophysiologically being the key link between MASLD, metabolic disorders, and cardiovascular (CV) diseases. There are no prospective studies comparing the predictive values of different markers of insulin resistance (IR) in identifying the presence of MASLD and the associated risk of cardiovascular events (CVEs).

METHODS

Post hoc analysis of the prospective Plinio Study, involving dysmetabolic patients evaluated for the presence of MASLD. The IR markers considered were Homeostatic Model Assessment for IR (HOMA-IR), Triglycerides-Glycemia (TyG) index, Triglycerides to High-Density Lipoprotein Cholesterol ratio (TG/HDL-C), Lipid Accumulation Product (LAP) and Visceral Adiposity Index (VAI). Receiver operative characteristic (ROC) analyses were performed to find the optimal cut-offs of each IR marker for detecting MASLD and predicting CVEs in MASLD patients. Logistic and Cox multivariable regression analyses were performed, after dichotomizing the IR markers based on the optimal cut-offs, to assess the factors independently associated with MASLD and the risk of CVEs.

RESULTS

The study included 772 patients (age 55.6 ± 12.1 years, 39.4% women), of whom 82.8% had MASLD. VAI (Area Under the Curve [AUC] 0.731), TyG Index (AUC 0.723), and TG/HDL-C ratio (AUC: 0.721) predicted MASLD but was greater with HOMA-IR (AUC: 0.792) and LAP (AUC: 0.787). After a median follow-up of 48.7 (25.4-75.8) months, 53 MASLD patients experienced CVEs (1.8%/year). TyG index (AUC: 0.630), LAP (AUC: 0.626), TG/HDL-C (AUC: 0.614), and VAI (AUC: 0.590) demonstrated comparable, modest predictive values in assessing the CVEs risk in MASLD patients.

CONCLUSION

In dysmetabolic patients HOMA-IR and LAP showed the best accuracy in detecting MASLD. The possible use of lipid-based IR markers in stratifying the CV risk in patients with MASLD needs further validation in larger cohorts.

High-Density Lipoprotein Is Located Alongside Insulin in the Islets of Langerhans of Normal and Rodent Models of Diabetes

Recent studies have implicated pre-beta and beta lipoproteins (VLDL and LDL) in the etiopathogenesis of complications of diabetes mellitus (DM). In contrast, alpha lipoprotein (HDL) is protective of the beta cells of the pancreas. This study examined the distribution of HDL in the islets of Langerhans of murine models of type 1 diabetic rats (streptozotocin (STZ)-induced DM in Wistar rats) and type 2 models of DM rats (Goto-Kakizaki (GK), non-diabetic Zucker lean (ZL), and Zucker diabetic and fatty (ZDF)). The extent by which HDL co-localizes with insulin or glucagon in the islets of the pancreas was also investigated. Pancreatic tissues of Wistar non-diabetic, diabetic Wistar, GK, ZL, and ZDF rats were processed for immunohistochemistry. Pancreatic samples of GK rats fed with either a low-fat or a high-fat diet were prepared for transmission immune-electron microscopy (TIEM) to establish the cytoplasmic localization of HDL in islet cells. HDL was detected in the core and periphery of pancreatic islets of Wistar non-diabetic and diabetic, GK, ZL, and ZDF rats. The average total of islet cells immune positive for HDL was markedly (<0.05) reduced in GK and ZDF rats in comparison to Wistar controls. The number of islet cells containing HDL was also remarkably (p < 0.05) reduced in Wistar diabetic rats and GK models fed on high-fat food. The co-localization study using immunofluorescence and TIEM techniques showed that HDL is detected alongside insulin within the secretory granules of β-cells. HDL did not co-localize with glucagon. This observation implies that HDL may contribute to the metabolism of insulin.

Fibroblast Growth Factor 21: A More Effective Biomarker Than Free Fatty Acids and Other Insulin Sensitivity Measures for Predicting Non-alcoholic Fatty Liver Disease in Saudi Arabian Type 2 Diabetes Patients

Background Non-alcoholic fatty liver disease (NAFLD) is more prevalent among individuals with type 2 diabetes (T2DM), elevating their risk of cardiovascular diseases (CVDs) and premature mortality. There is a need to modify treatment strategies to prevent or delay these adverse outcomes. Currently, there are no sensitive or specific biomarkers for predicting NAFLD in Saudi T2DM patients. Therefore, we aimed to explore the possibility of using fibroblast growth factor 21 (FGF-21), free fatty acids (FFAs), homeostatic model assessment for insulin resistance (HOMA-IR), and quantitative insulin sensitivity check index (QUICKI) as possible markers. Methodology In this study, a total of 67 T2DM patients were recruited. NAFLD was detected by ultrasonography in 28 patients. Plasma glucose, FFAs, FGF-21, and serum insulin were measured in fasting blood samples. HOMA-IR and QUICKI were calculated. The means of the two groups with and without NAFLD were statistically compared. The receiver operating characteristics (ROC) curve and the area under the curve (AUC) were used to assess the ability to identify NAFLD. Results The mean levels of FGF-21 and HOMA-IR were significantly higher and that of QUICKI was significantly lower in patients with NAFLD than in those without (p < 0.001, p = 0.023, and p = 0.018, respectively). FGF-21 had the highest AUC to identify NAFLD (AUC = 0.981, 95% confidence interval = 0.954-1, P < 0.001). The AUCs for HOMA-IR, QUICKI, and FFA were <0.7. The highest sensitivity, specificity, positive likelihood ratio, and the lowest negative likelihood ratio were found when FGF-21 was used to predict NAFLD. Conclusions FGF-21 may be used as a biomarker to predict NAFLD in people with T2DM due to its high sensitivity and specificity compared to the other markers.