The evolution of diagnostic term from non-alcoholic fatty liver disease (NAFLD) to the metabolic dysfunction-associated fatty liver disease (MAFLD) aims to highlight the critical role of "metabolic dysfunction" in the development and progression of fatty liver diseases. As one of the most common metabolic disorders globally, type 2 diabetes mellitus (T2DM) is now considered as the most common complication of MAFLD. The two diseases interact in ways that adversely affect various systems within the body. With regard to this situation, it is urgently to fully implement the "holistic medical approach" and explore a full-cycle and comprehensive management model for patients with both T2DM and MAFLD, which is of great significant for improving their prognosis. This article summarizes the epidemiology and pathogenesis of patients with MAFLD and T2DM, and shared the innovative practices of the new multidisciplinary management model, aiming to provide more support for the co-management of MAFLD and T2DM.
Metabolic dysfunction-associated fatty liver disease (MAFLD) and type 2 diabetes mellitus (T2DM) are the two most common metabolic diseases worldwide. The coexistence of MAFLD and T2DM has a high prevalence rate and accelerates disease progression, imposing a significant disease burden on patients and posing a major public health challenge. MAFLD and T2DM mutually influence each other, sharing common pathogenic mechanisms. Developing effective co-management strategies for MAFLD and T2DM is a critical clinical priority. This review elaborates on recent advances in the epidemiology, pathogenesis, screening, monitoring, and treatment of T2DM combined with MAFLD. It highlights that the co-existence of T2DM and MAFLD has become a common clinical phenomenon with each condition exacerbating the development and progression of the other. Screening for MAFLD should be implemented in T2DM patients. Non-invasive diagnostic tools such as the Fibrosis 4 Index and NAFLD Fbrosis Score can be used for routine screening, though their accuracy requires further validation. Additionally, medications like sodium-glucose cotransporter 2 inhibitors and glucagon-like peptide 1 receptor agonists have been shown to improve outcomes in patients with T2DM and MAFLD, effectively preventing cardiovascular events. This review provides reference for the optimization of clinical diagnosis and treatment strategy of T2DM combined with MAFLD and the formulation of clinical "glycohepatic co-management" strategy.
Type 2 diabetes mellitus (T2DM) is the most common type of diabetes. The incidence of metabolic associated fatty liver disease (MAFLD) in T2DM patients is higher than that in non - diabetic patients. Therefore, it is of great significance to find effective indicators for predicting the occurrence of MAFLD in T2DM patients.
This study aims to explore the predictive value of the ratio of fasting C-peptide to diabetes duration (FCP/DD) for the occurrence of MAFLD in patients with T2DM, providing a potential indicator for the early prevention and management of MAFLD.
This study enrolled 532 patients diagnosed with T2DM at the Department of Endocrinology, Hebei General Hospital from September 2018 to December 2021. Demographic data were collected, and fasting blood samples were obtained to assess biochemical parameters. The FCP/DD was computed using a predefined formula. Participants were stratified into MAFLD (n=359) and non-MAFLD (n=173) groups based on the presence or absence of MAFLD. Further classification into low FCP/DD (n=266) and high FCP/DD (n=266) groups was performed according to the median FCP/DD ratio. The relationship between the FCP/DD ratio and MAFLD incidence in T2DM patients were examined using Spearman rank correlation and Logistic regression analyses. The predictive efficacy of the FCP/DD ratio for MAFLD was evaluated by constructing receiver operating characteristic (ROC) curves and calculating the area under the curve (AUC) .
The FCP/DD in the MAFLD group was higher than that in the non-MAFLD group (P<0.05). The incidence of MAFLD in the high FCP/DD group was higher than that in the low FCP/DD group (P<0.05). The Spearman rank correlation analysis results showed that in patients with T2DM and MAFLD, FCP/DD was negatively correlated with age and high-density lipoprotein cholesterol (HDL-C), and positively correlated with BMI, fasting blood glucose (FBG), glycated hemoglobin (HbA1c), total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), serum uric acid (SUA), aspartate aminotransferase (AST), alanine aminotransferase (ALT), and triglyceride-glucose index (TyG) (P<0.05). The results of multivariate Logistic regression analysis showed that after adjusting for confounding factors, a high level of FCP/DD was an independent risk factor for the occurrence of MAFLD in T2DM patients (P<0.05). The ROC curve results showed that the AUC of FCP/DD for predicting the occurrence of MAFLD in T2DM patients was 0.829 (95%CI=0.791-0.867), the AUC of FCP was 0.758 (95%CI=0.711-0.805), the AUC of HbA1c was 0.525 (95%CI=0.471-0.578), and the AUC of TyG was 0.733 (95%CI=0.689-0.778) .
The level of FCP/DD was significantly increased. T2DM patients with high levels of FCP/DD had a higher risk of developing MAFLD. FCP/DD ratio has better predictive value than FCP, HbA1c, TyG for combined MAFLD in T2DM patients.
The global prevalence and incidence of metabolic associated fatty liver disease (MAFLD) co-occurring with type 2 diabetes mellitus (T2DM) are increasing, significantly elevating the risk of liver-related adverse outcomes. In clinical practice, early screening and diagnosis of high-risk MAFLD patients with hyperglycemia are crucial to slowing disease progression.
Based on the relationship between T2DM and MAFLD, this study evaluates the impact of hyperglycemia on hepatic steatosis and liver fibrosis in MAFLD using large-scale health examination data and aims to identify key factors influencing the development of MAFLD with hyperglycemia.
Data from 18 286 individuals who underwent health examinations at the First Affiliated Hospital of Soochow University from March to July 2024 were analyzed. The dataset included demographic information, medical history, abdominal ultrasound results, biochemical markers, and routine blood tests. Individuals meeting the MAFLD diagnostic criteria were classified into the MAFLD group, which was further stratified into three subgroups according to the Fibrosis-4 index (FIB-4) scores: T1 (FIB-4<1.30, n=4 275), T2 (1.30≤FIB-4≤2.67, n=924), and T3 (FIB-4>2.67, n=59). Clinical indicators among these subgroups were compared. Additionally, the MAFLD group was divided into two subgroups: MAFLD with hyperglycemia (n=752) and MAFLD without hyperglycemia (n=4 506), based on a history of diabetes, fasting blood glucose (FBG) ≥7.0 mmol/L, or glycated hemoglobin A1c (HbA1c) ≥6.5% (meeting any one criterion). Differences in hepatic steatosis and liver fibrosis-related indicators between these subgroups were analyzed. Univariate and multivariate Logistic regression analyses were performed to identify key factors associated with MAFLD with hyperglycemia. The predictive performance of a combined model for MAFLD with hyperglycemia was evaluated using the receiver operating characteristic (ROC) curve analysis.
Among the T1, T2, and T3 groups, significant differences (P<0.05) were observed in clinical indicators, including smoking, hypertension, diabetes, hyperlipidemia, hyperuricemia, coronary heart disease, age, BMI, FBG, HbA1c, platelet count (PLT), white blood cell count (WBC), red blood cell count (RBC), hemoglobin (Hb), red blood cell distribution width (RDW), neutrophil count (NEUT), lymphocyte count (LYM), monocyte count (MONO), alanine aminotransferase (ALT), aspartate aminotransferase (AST), total cholesterol (TC), triglycerides (TG), high density lipoprotein cholesterol (HDL-C), low density lipoprotein cholesterol (LDL-C), albumin (ALB), glutamyl transferase (GGT), uric acid (UA), creatinine (Cr), blood urea nitrogen (BUN), total bilirubin (TBIL), direct bilirubin (DBIL), indirect bilirubin (IBIL), alkaline phosphatase (ALP), and estimated glucose processing rate (eGDR). Moreover, the fatty liver index (FLI), hepatic steatosis index (HSI), and ZJU index were significantly higher in the MAFLD with hyperglycemia group compared to the MAFLD without hyperglycemia group (P<0.05). Additionally, the FIB-4, AST/PLT ratio index (APRI), non-alcoholic fatty liver disease fibrosis score (NFS), and BMI, AST/ALT and diabetes score (BARD) were also higher in the MAFLD with hyperglycemia group (P<0.05). The samples of MAFLD with hyperglycemia and MAFLD without hyperglycemia groups were randomly divided into training and validation sets at 1∶1 ratio respectively. In the training set, univariate and multivariate Logistic regression analyses identified age, waist circumference (WC), hypertension, hyperlipidemia, TG, GGT, UA and BUN as key influencing factors associated with MAFLD with hyperglycemia (P<0.05). Further ROC analysis of these factors demonstrated moderate predictive accuracy for MAFLD with hyperglycemia (0.53≤AUC≤0.75). A predictive model incorporating these eight key factors achieved an AUC of 0.805 (95%CI=0.781-0.828), with a sensitivity of 75.8% and specificity of 72.6%. Validation of this combined model in the validation set yielded a positive predictive value of 70.5%, a negative predictive value of 73.1%, and an overall predictive accuracy of 72.7%.
Among MAFLD patients stratified by FIB-4, significant differences in hypertension, FBG, HbA1c, PLT, WBC, RBC, LYM, AST, and eGDR were observed across the three subgroups. Hyperglycemia exacerbates hepatic steatosis and liver fibrosis in MAFLD. Furthermore, age, WC, hypertension, hyperlipidemia, TG, GGT, UA and BUN were identified as significant risk factors for the progression of MAFLD to MAFLD with hyperglycemia. The predictive model incorporating these eight indicators enhances the accuracy of assessing hyperglycemia risk in MAFLD, potentially providing a reference for the early differential diagnosis in clinical practice.
