Liver enzymes and type 2 diabetes: a complex two-way relationship

Examining dyslipidaemia, metabolic syndrome and liver enzyme levels in patients with prediabetes and type 2 diabetes in population from Hoveyzeh cohort study: A case-control study in Iran

INTRODUCTION

Type 2 diabetes mellitus (T2DM) is among the world's top 10 leading causes of death. Additionally, prediabetes is a major risk factor for diabetes. Identifying diabetes co-occurring disorders can aid in reducing adverse effects and facilitating early detection. In this study, we evaluated dyslipidaemia, metabolic syndrome (MetS), and liver enzyme levels in pre-diabetic and T2DM patients in the Persian cohort compared to a control group.

MATERIALS AND METHODS

In this cross-sectional study, 2259 pre-diabetes, 1664 T2DM and 5840 controls (35-70 years) who were selected from the Hoveyzeh cohort centre were examined. Body mass index, blood pressure, fasting blood glucose (FBG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), triglyceride (TG) and liver enzymes: γ-glutamyltransferase (GGT), alanine aminotransferase (ALT) and aspartate aminotransferase (AST) were determined using the standard protocols. MetS subjects were also identified based on the National Cholesterol Education Program guidelines.

RESULTS

Prediabetes and T2MD were closely correlated with the lipid profile, MetS, and liver enzymes (ALT, GGT, ALT/AST). MetS increases the risk of T2DM by 12.45 [95% CI: 10.88-14.24] fold, while an increase in ALT/AST ratio increases the risk of T2DM by 3.68 [95% CI: 3.159-4.154] fold. ROC curve analysis also revealed the diagnostic roles of GGT, ALT, AST and the ALT/AST ratio among pre-diabetics, diabetics and the control group. The GGT level corresponds to the highest AUCs (0.685) with the highest sensitivity (70.25%).

CONCLUSIONS

Our results indicated a significant increase in liver enzymes, lipid profile and MetS status in both pre-diabetic and T2MD subjects, with the differences being more pronounced in diabetic individuals. Consequently, on the one hand, these variables may be considered predictive risk factors for diabetes, and on the other hand, they may be used as diagnostic factors. In order to confirm the clinical applications of these variables, additional research is required.

Determinants of Longitudinal Change of Glycated Hemoglobin in a Large Non-Diabetic Population

Although many cross-section studies have assessed the determinants of glycated hemoglobin (HbA_1c), there have been limited studies designed to evaluate the temporal correlates of HbA_1c in non-diabetic patients. This study aimed to identify the major determinants of longitudinal change of HbA_1c in non-diabetic patients. This study included subjects from the 104,451 participants enrolled between 2012 and 2018 in the Taiwan Biobank. We only included participants with complete data at baseline and follow-up (n = 27,209). Patients with diabetes at baseline or follow-up (n = 3983) were excluded. Finally, 23,226 participants without diabetes at baseline and follow-up were selected in this study. △Parameters was defined as the difference between the measurement baseline and follow-up. Multivariable linear regression analysis was used to identify the major determinants of HbA_1c longitudinal change (△HbA_1c). During a mean 3.8 year follow-up, after multivariable analysis, new-onset hypertension (coefficient β: 0.014, p < 0.001), high △heart rate (coefficient β: 0.020, p = 0.002), high △BMI (coefficient β: 0.171, p = 0.028), high △fasting glucose (coefficient β: 0.107, p < 0.001), low △creatinine (coefficient β: −0.042, p < 0.001), high △total cholesterol (coefficient β: 0.040, p < 0.001), high △hemoglobin (coefficient β: 0.062, p < 0.001), high △GPT (coefficient β: 0.041, p = 0.001), and low △albumin (coefficient β: −0.070, p < 0.001) were significantly associated with high △HbA_1c. In non-diabetic population, strategies to decrease the development of new-onset hypertension, resting heart rate, body mass index, fasting glucose, total cholesterol, and GPT and increase serum albumin level might be helpful in slowing the longitudinal change of HbA_1c. In addition, increased hemoglobin and decreased serum creatinine over time also had an impact on the HbA_1c elevation over time in non-diabetic population.

Establishment of a Risk Prediction Model for Non-alcoholic Fatty Liver Disease in Type 2 Diabetes

INTRODUCTION

Non-alcoholic fatty liver disease (NAFLD) is becoming more prevalent in patients with type 2 diabetes mellitus (T2DM) and can contribute to serious liver damage in this patient population. The aim of this study was to develop a risk nomogram for NAFLD in a Chinese population with T2DM.

METHODS

A questionnaire survey, physical examination and biochemical indicator testing were performed on 874 patients with T2DM, and the collected data were used to evaluate the risk to develop NAFLD in T2DM patients. The least absolute shrinkage and selection operator (LASSO) regression analysis method was used to optimize variable selection by running cyclic coordinate descent with k-fold (tenfold in this case) cross-validation. Multivariable logistic regression analysis was applied to build a predictive model by introducing the predictors selected from the LASSO regression analysis. The nomogram was developed based on the selected variables visually. A calibration plot, receiver operating characteristic curve (ROC) and decision curve analysis (DCA) were used to validate the model, with further assessment by external validation.

RESULTS

A total of nine predictors, namely sex, age, total cholesterol (TC), body mass index (BMI), waistline, diastolic blood pressure (DBP), serum uric acid (SUA), course of disease and high-density lipoprotein-cholesterol (HDL-C), were identified by LASSO regression analysis from a total of 24 variables studied. The model constructed using these nine predictors displayed medium prediction ability, with an area under the ROC of 0.848 in the training set and 0.809 in the validation set. The DCA curve showed that the nomogram could be applied clinically if the risk threshold was between 48 and 91%, which was found to be between 44 and 82% in the external validation.

CONCLUSION

Introducing sex, age, TC, BMI, waistline, DBP, SUA, course of disease and HDL-C into the risk nomogram increased its usefulness for predicting NAFLD risk in patients with T2DM.

Incidence and causes of mildly to moderately elevated aminotransferase in Japanese patients with type 2 diabetes

Objectives

To identify the association of type 2 diabetes (T2DM) and liver disease with elevated ALT and factors associated with increased ALT in patients with and without T2DM.

Design and setting

We performed a retrospective study in adults with ≥ 2 claims for blood tests recorded in the Medical Data Vision claims database between 2010 and 2016.

Participants

Patients were entered into T2DM and non-DM groups based on diagnosis and medication claim codes.

Primary outcome measure

The primary endpoint was the first follow-up ALT elevation over three times the normal value, and etiologies were categorized by subsequent diagnoses. We estimated the ALT elevation incidence and association with T2DM using Poisson regression and Cox proportional hazard models.

Results

We identified 3161 cases of elevated ALT in 104,903 patients (follow-up, 280,659 patient-years). The age- and sex-adjusted incidence of elevated ALT in the T2DM group (13.47 per 1000 patient-years; 95% confidence interval (CI) 12.53-14.48) was significantly higher than that in the non-DM group (8.43 per 1000 patient-years; 95% CI 7.72-9.20, p < 0.0001). Compared to the non-DM group, the T2DM group had an approximately 3.5 times higher risk of fatty liver-related ALT elevation (adjusted hazard ratio (HR), 3.54; 95% CI 1.90-6.58). T2DM was not associated with an increased incidence of jointly elevated ALT and total bilirubin (adjusted HR, 0.94; 95% CI 0.77-1.15).

Conclusion

T2DM is strongly associated with increased liver enzymes secondary to fatty liver. The causes of liver enzyme abnormalities were not fully characterized due to a high proportion of unexplained ALT elevation.