ALT/AST as an Independent Risk Factor of Gestational Diabetes Mellitus Compared with TG/HDL-C

The association of ALT to HDL-C ratio with type 2 diabetes in 50-74 years old adults: a population-based study

There is limited information about the relationship between diabetes mellitus (DM) and ALT to HDL-C ratio. This study aims to investigate this relationship for the first time in Iran. The data of this study were taken from the third phase of the Shahroud Eye Cohort Study, which was conducted in 2019 with the participation of 4394 people aged 50-74. ALT and HDL-C levels were measured using a BT-1500 autoanalyzer. The mean ALT/HDL-C ratio was reported along with 95% confidence intervals (CI). The multiple logistic regression was used to examine the association between this ratio and DM, while controlling for the effects of other independent variables. The mean and standard deviation of the ALT/HDL-C ratio in all participants were 16.62 ± 11.22 (95% CI 16.28-16.96). The prevalence of DM was 34.7% and individuals with DM had a mean ALT/HDL-C ratio that was 1.80 units higher than those without diabetes (P < 0.001). Also, in individuals with DM, the HDL-C was found to be 0.035 (mmol/L) lower (P < 0.001), while ALT was 1.13 (IU/L) higher (P < 0.001) compared to those without diabetes. Additionally, after controlling for confounding factors, the odds of developing DM increased in a non-linear manner with an increase in the ALT/HDL-C ratio. Abdominal obesity, advanced age, female gender, and hypertension were also found to be associated with increased odds of DM. In conclusion, an increase in the ALT/ HDL-C ratiowas associated with higher odds of DM. This ratio can serve as an important predictor for diabetes mellitus.

Genetic Causal Relationship Between Alanine Aminotransferase Levels and Risk of Gestational Diabetes Mellitus: Mendelian Randomization Analysis Based on Two Samples

Gestational diabetes mellitus (GDM) is a frequent complication of pregnancy. The specific mechanisms underlying GDM have not yet been fully elucidated. Contemporary research indicates a potential association between liver enzyme irregularities and an increased risk of metabolic disorders, including diabetes. The alanine aminotransferase (ALT) level is recognized as a sensitive marker of liver injury. An increase in ALT levels is hypothesized to be linked to the pathogenesis of insulin resistance and diabetes. Nonetheless, the definitive causal link between ALT levels and GDM still needs to be determined. This investigation utilized two-sample Mendelian randomization (MR) to examine the genetic causation between alanine aminotransferase (ALT) and GDM. We acquired alanine aminotransferase (ALT)-related GWAS summary data from the UK Biobank, Million Veteran Program, Rotterdam Study, and Lifeline Study. Gestational diabetes data were obtained from the FinnGen Consortium. We employed various MR analysis techniques, including inverse-variance weighted (IVW), MR Egger, weighted median, simple, and weighted weighting. In addition to MR-Egger intercepts, Cochrane's Q test was also used to assess heterogeneity in the MR data, and the MR-PRESSO test was used to assess horizontal pleiotropy. To assess the association's sensitivity, a leave-one-out approach was employed. The IVW results confirmed the independent risk factor for GDM development, as indicated by the ALT level (p = .011). As shown by leave-one-out analysis, horizontal pleiotrophy did not significantly skew the causative link (p > .05). Our dual-sample MR analysis provides substantiated evidence of a genetic causal relationship between alanine aminotransferase (ALT) levels and gestational diabetes.

A Simplified Screening Model to Predict the Risk of Gestational Diabetes Mellitus in Pregnant Chinese Women

INTRODUCTION

This study aimed to develop a simplified screening model to identify pregnant Chinese women at risk of gestational diabetes mellitus (GDM) in the first trimester.

METHODS

This prospective study included 1289 pregnant women in their first trimester (6-12 weeks of gestation) with clinical parameters and laboratory data. Logistic regression was performed to extract coefficients and select predictors. The performance of the prediction model was assessed in terms of discrimination and calibration. Internal validation was performed through bootstrapping (1000 random samples).

RESULTS

The prevalence of GDM in our study cohort was 21.1%. Maternal age, prepregnancy body mass index (BMI), a family history of diabetes, fasting blood glucose levels, the alanine transaminase to aspartate aminotransferase ratio (ALT/AST), and the triglyceride to high-density lipoprotein cholesterol ratio (TG/HDL-C) were selected for inclusion in the prediction model. The Hosmer-Lemeshow goodness-of-fit test showed good consistency between prediction and actual observation, and bootstrapping indicated good internal performance. The area under the receiver operating characteristic curve (ROC-AUC) of the multivariate logistic regression model and the simplified clinical screening model was 0.825 (95% confidence interval [CI] 0.797-0.853, P < 0.001) and 0.784 (95% CI 0.750-0.818, P < 0.001), respectively. The performance of our prediction model was superior to that of three other published models.

CONCLUSION

We developed a simplified clinical screening model for predicting the risk of GDM in pregnant Chinese women. The model provides a feasible and convenient protocol to identify women at high risk of GDM in early pregnancy. Further validations are needed to evaluate the performance of the model in other populations.

TRIAL REGISTRATION

ClinicalTrials.gov identifier: NCT03246295.

Serum alanine transaminase is predictive of fasting and postprandial insulin and glucagon concentrations in type 2 diabetes

The liver plays a key role in glucose homeostasis. Serum liver enzyme levels, including alanine transaminase (ALT), aspartate transaminase (AST) and gamma-glutamyl transferase (GGT), are reportedly predictive of the risk of type 2 diabetes (T2D). However, the link between the liver enzyme profile and metabolic derangements in T2D, particularly the secretion of both insulin and glucagon, is not clear. This study evaluated its relationships with glycemia, insulin and glucagon both during fasting and after an oral glucose load or a mixed meal in T2D. 15 healthy and 43 T2D subjects ingested a 75 g glucose drink. 86 T2D subjects consumed a mixed meal. Venous blood was sampled for measurements of blood glucose and plasma insulin, C-peptide and glucagon. Blood glucose, plasma insulin, C-peptide and glucagon concentrations, both fasting and after oral glucose, correlated directly with ALT, while fewer and weaker correlations were observed with GGT or AST. Subgroup analysis in T2D subjects ascertained that plasma insulin, C-peptide and glucagon concentrations after oral glucose were higher with increasing ALT. Similar findings were observed in the T2D subjects who received a mixed meal. In conclusion, serum liver enzyme profile, particularly ALT, reflects dysregulated fasting and nutrient-stimulated plasma insulin and glucagon concentrations in T2D.

Associations of alanine aminotransferase/aspartate aminotransferase with insulin resistance and β-cell function in women

We tested whether alanine aminotransferase/aspartate aminotransferase (ALT/AST), a marker of hepatosteatosis, associates with insulin resistance, β-cell function and postglucose glycemia. We studied 311 young and 148 middle-aged Japanese women, whose BMI averaged < 23.0 kg/m2. Insulinogenic index and Matsuda index were evaluated in 110 young and 65 middle-aged women. In two groups of women, ALT/AST was associated positively with homeostasis model assessment insulin resistance (HOMA-IR) and inversely with Matsuda index. In middle-aged women only, the ratio was also associated positively with fasting and postload glycemia and HbA1c. The ratio showed negative association with disposition index (a product of insulinogenic index and Matsuda index). On multivariate linear regression analysis, HOMA-IR emerged as a single determinant of ALT/AST in young and middle-aged women (standardized β: 0.209, p = 0.003 and 0.372, p = 0.002, respectively). ALT/AST was associated with insulin resistance and β-cell function even in non-obese Japanese women, suggesting a pathophysiologic basis in its prediction of diabetic risk.

The reversed De Ritis ratio for predicting in-hospital mortality among intensive care patients with organophosphate poisoning

INTRODUCTION

The uncontrolled use of pesticides signifies a substantial health hazard. This study was designed to explore the prognostic role of on-admission hepatic aminotransferases [alanine aminotransferase (ALT) and aspartate aminotransferase (AST), and the reversed De Ritis ratio (ALT/AST)] in the prediction of in-hospital mortality among patients with acute organophosphate (OP) poisoning.

PATIENTS AND METHODS

We conducted a retrospective study based on extracting the required information from the specific medical records for acutely OP-intoxicated patients admitted to the intensive care unit.

RESULTS

A total of 49 acutely malathion-intoxicated patients were enrolled in the study. The in-hospital mortality rate was 32.7%. Patients were stratified into survivors and non-survivors. Compared to the survivors, the non-survivors had significantly lower Glasgow coma scale scores, mean arterial blood pressure, significantly higher reversed De Ritis ratio (ALT/AST), and ALT and AST activities. The reversed De Ritis ratio (ALT/AST) and ALT demonstrated good discrimination between the survivors and the non-survivors with an area under the curve (AUC) of 0.708 vs 0.781, respectively, however, AST showed satisfactory discrimination, AUC of 0.694.

CONCLUSION

Hepatic aminotransferases are useful in predicting in-hospital mortality in acute OP poisoning. ALT is the most specific biomarker. However, the reversed De Ritis ratio (ALT/AST) is the most sensitive one.

Double Filtration Plasmapheresis with Polyvinyl Alcohol-Based Membrane Lowers Serum Inflammation and Toxins in Patients with Hyperlipidemia

Hyperlipidemia is increasing in prevalence and is highly correlated with cardiovascular disease (CVD). Lipid-lowering medications prevent CVD but may not be suitable when the side effects are intolerable or hypercholesterolemia is too severe. Double-filtration plasmapheresis (DF) has shown its therapeutic effect on hyperlipidemia, but its side effects are not yet known. We enrolled 45 adults with hyperlipidemia in our study. The sera before and two weeks after DF were evaluated, and we also analyzed perfluorochemicals to see if DF could remove these lipophilic toxins. After DF, all lipid profile components (total cholesterol, triglycerides, high-density lipoprotein [HDL], and low-density lipoprotein [LDL]) had significantly decreased. Leukocyte counts increased while platelet levels decreased, which may have been caused by the puncture wound from DF and consumption of platelets during the process. As for uremic toxins and inflammation, levels of C-reactive protein, uric acid, and alanine transaminase (ALT) all decreased, which may be related to the removal of serum perfluorooctane sulfonate (PFOS) and improvement of renal function. The total cholesterol/HDL ratio and triglycerides were significantly higher in the diabetes mellitus (DM) group at baseline but did not significantly differ after DF. In conclusion, DF showed potential for improving inflammation and removing serum lipids and PFOS in adults with hyperlipidemia.

Role of liver parameters in diabetes mellitus - a narrative review

Diabetes mellitus is characterized by hyperglycemia and abnormalities in insulin secretion and function. This review article focuses on various liver parameters, including albumin, alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP), alpha fetoprotein (AFP), alpha 1 antitrypsin (AAT), ammonia, bilirubin, bile acid, gamma-glutamyl transferase (GGT), immunoglobulin, lactate dehydrogenase (LDH), and total protein. These parameters play significant roles in the development of different types of diabetes such as type 1 diabetes (T1DM), type 2 diabetes (T2DM) and gestational diabetes (GDM). The article highlights that low albumin levels may indicate inflammation, while increased ALT and AST levels are associated with liver inflammation or injury, particularly in non-alcoholic fatty liver disease (NAFLD). Elevated ALP levels can be influenced by liver inflammation, biliary dysfunction, or bone metabolism changes. High bilirubin levels are independently linked to albuminuria in T1DM and an increased risk of T2DM. Elevated GGT levels are proposed as markers of oxidative stress and liver dysfunction in T2DM. In GDM, decreased serum AFP levels may indicate impaired embryo growth. Decreased AFP levels in T2DM can hinder the detection of hepatocellular carcinoma. Hyperammonemia can cause encephalopathy in diabetic ketoacidosis, and children with T1DM and attention deficit hyperactivity disorder often exhibit higher ammonia levels. T2DM disrupts the regulation of nitrogen-related metabolites, leading to increased blood ammonia levels. Bile acids affect glucose regulation by activating receptors on cell surfaces and nuclei, and changes in bile acid metabolism are observed in T2DM. Increased LDH activity reflects metabolic disturbances in glucose utilization and lactate production, contributing to diabetic complications. Poor glycemic management may be associated with elevated levels of IgA and IgG serum antibodies, and increased immunoglobulin levels are also associated with T2DM.

Association between ALT/AST and Muscle Mass in Patients with Type 2 Diabetes Mellitus

Objective

The alanine aminotransferase/aspartate aminotransferase (ALT/AST) ratio is thought to be related to metabolic disorders and insulin resistance. Type 2 diabetes mellitus (T2DM) is a high-risk population for low muscle mass. This study was performed to evaluate the association between ALT/AST and muscle mass in subjects with T2DM.

Method

This cross-sectional study enrolled 1068 subjects (566 males and 502 females) with T2DM. General information, medical history, and blood samples were collected. Skeletal muscle index (SMI) was detected using dual-energy X-ray absorptiometry. Logistic regression analysis was utilized to determine the correlation of ALT/AST and low muscle mass in subjects with T2DM. Multiple linear regression analysis was utilized to evaluate the association between ALT/AST, SMI and other metabolic characteristics.

Result

Of all subjects, 115 men (20.3%) and 71 women (14.1%) presented low muscle mass. ALT/AST was related to an increased risk for low muscle mass in both genders. Multiple linear regression analysis displayed that SMI was negatively associated with ALT/AST, age, glycosylated hemoglobin (HbA1c), and high-density lipoprotein cholesterol (HDL) in male group. While in female group, SMI was positively associated with systolic blood pressure (SBP) and negatively associated with ALT/AST and age. Furthermore, ALT/AST was associated with age and BMI in both genders.

Conclusion

ALT/AST was negatively associated with muscle mass in subjects with T2DM.

Machine learning model for the prediction of prostate cancer in patients with low prostate-specific antigen levels: A multicenter retrospective analysis

Objective

The aim of this study was to develop a predictive model to improve the accuracy of prostate cancer (PCa) detection in patients with prostate specific antigen (PSA) levels ≤20 ng/mL at the initial puncture biopsy.

Methods

A total of 146 patients (46 with Pca, 31.5%) with PSA ≤20 ng/mL who had undergone transrectal ultrasound-guided 12+X prostate puncture biopsy with clear pathological results at the First Affiliated Hospital of Guangxi Medical University (November 2015 to December 2021) were retrospectively evaluated. The validation group was 116 patients drawn from Changhai Hospital(52 with Pca, 44.8%). Age, body mass index (BMI), serum PSA, PSA-derived indices, several peripheral blood biomarkers, and ultrasound findings were considered as predictive factors and were analyzed by logistic regression. Significant predictors (P < 0.05) were included in five machine learning algorithm models. The performance of the models was evaluated by receiver operating characteristic curves. Decision curve analysis (DCA) was performed to estimate the clinical utility of the models. Ten-fold cross-validation was applied in the training process.

Results

Prostate-specific antigen density, alanine transaminase-to-aspartate transaminase ratio, BMI, and urine red blood cell levels were identified as independent predictors for the differential diagnosis of PCa according to multivariate logistic regression analysis. The RandomForest model exhibited the best predictive performance and had the highest net benefit when compared with the other algorithms, with an area under the curve of 0.871. In addition, DCA had the highest net benefit across the whole range of cut-off points examined.

Conclusion

The RandomForest-based model generated showed good prediction ability for the risk of PCa. Thus, this model could help urologists in the treatment decision-making process.

AST-to-ALT ratio in the first trimester and the risk of gestational diabetes mellitus

BACKGROUND

Aspartate aminotransferase-to-alanine transaminase ratio (AST/ALT) has been reported affect the risk of type 2 diabetes (T2DM), but it is uncertain if it has relationship with gestational diabetes mellitus (GDM).

OBJECTIVES

Our study aimed to investigate the association between AST/ALT ratio in the first trimester and the risk of subsequent development of GDM.

METHOD

This prospective cohort study enrolling 870 pregnant women, 204 pregnant women with missing data or liver diseases were excluded, 666 pregnant women were included in this study containing 94 GDM women. Blood samples were collected in the first trimester. Univariate analysis and multivariate logistic regression were used to evaluate the association between AST/ALT and GDM. Nomogram was established based on the results of multivariate logistic analysis. Receiver Operating Characteristic (ROC) curves and calibration curves were used to evaluate the predictive ability of this nomogram model for GDM. Decision curve analysis (DCA) was used to examine the clinical net benefit of predictive model.

RESULTS

AST/ALT ratio (RR:0.228; 95% CI:0.107-0.488) was associated with lower risk of GDM after adjusting for confounding factors. Indicators used in nomogram including AST/ALT, maternal age, preBMI, waist circumference, glucose, triglycerides, high density lipoprotein cholesterol and parity. The area under the ROC curve (AUC) value of this predictive model was 0.778, 95% CI (0.724, 0.832). Calibration curves for GDM probabilities showed acceptable agreement between nomogram predictions and observations. The DCA curve demonstrated a good positive net benefit in the predictive model.

CONCLUSIONS

The early AST/ALT level of pregnant women negatively correlated with the risk of GDM. The nomogram including AST/ALT at early pregnancy shows good predictive ability for the occurrence of GDM.