Type 2 diabetes (T2D) is a heterogeneous disease influenced by both genetic and environmental factors. Recent studies suggest that T2D subtypes may exhibit distinct gene expression profiles. In this study, we aimed to identify T2D cluster-specific miRNA expression signatures for the previously reported five clinical subtypes that characterize the underlying pathophysiology of long-standing T2D: severe insulin-resistant diabetes (SIRD), severe insulin-deficient diabetes (SIDD), mild age-related diabetes (MARD), mild obesity-related diabetes (MOD), and mild early-onset diabetes (MEOD). We analyzed the circulating microRNAs (miRNAs) in 45 subjects representing the five T2D clusters and 7 non-T2D healthy controls by single-end small RNA sequencing. Bioinformatic analyses identified a total of 430 known circulating miRNAs and 13 previously unreported novel miRNAs. Of these, 71 were upregulated and 37 were downregulated in either controls or individual clusters. Each T2D subtype was associated with a specific dysregulated miRNA profile, distinct from that of healthy controls. Specifically, 3 upregulated miRNAs were unique to SIRD, 1 to MARD, 9 to MOD, and 18 to MEOD. Among the downregulated miRNAs, 11 were specific to SIRD, 9 to SIDD, 2 to MARD, and 1 to MEOD. Our study confirms the heterogeneity of T2D, represented by distinguishable subtypes both clinically and epigenetically and highlights the potential of miRNAs as markers for distinguishing the pathophysiology of T2D subtypes.

Background: Type 2 diabetes mellitus (T2DM) and metabolic dysfunction–associated steatotic liver disease (MASLD), which have a reciprocal relationship compounded by obesity, are highly prevalent in the Middle East affecting morbidity, mortality, and healthcare costs. Objective: This study aimed to assess the severity of MASLD and liver fibrosis among adult Emirati patients with long-standing T2DM. Design and Participants: This cross-sectional study used noninvasive methods to assess the severity of MASLD and fibrosis progression in an adult cohort of Emirati patients (N = 546) with a mean T2DM duration of 16 years. Main Measures: Fatty liver infiltration was assessed by hepatic steatosis index (HSI), while fibrosis was assessed by the fibrosis-4 (FIB-4) index and aspartate aminotransferase/platelet ratio index (APRI). Of those, 108 patients were randomly subjected to ultrasound-based FibroScan^® to assess controlled attenuation parameter (CAP) and liver stiffness measurement (LSM). Key Results: All patients had fatty liver with ~ 83% being categorized as having severe steatosis. Serum-based fibrosis biomarker panels detected significant liver fibrosis in ~ 2.5% of these patients. The APRI appeared to be more restrictive in detecting moderate fibrosis (1.5%) than the FIB-4 index (25.5%). CAP significantly correlated with the LSM, indicating that the two methods contributed to the same underlying pathophysiology. Liver steatosis was more severe in female patients, who were older and had a higher body mass index (BMI) than those with moderate or no significant fibrosis. They also had higher serum liver enzymes and were more likely to have age-related changes in kidney function. Interestingly, severity of both steatosis and fibrosis remained unaffected by age and duration of T2D except for fibrosis severity detected by FibroScan^®. Conclusions: This study highlights the critical need for routine screening of MASLD among Emirati patients with long-standing T2DM, given the high point prevalence of severe steatosis (~ 83%), predominantly among women in this population.

Background/Objectives: In type 2 diabetes (T2D), collective damage to the eyes, kidneys, and peripheral nerves constitutes microvascular complications, which significantly affect patients’ quality of life. This study aimed to prospectively evaluate the risk of microvascular complications in newly diagnosed T2D patients in Dubai, UAE. Methods: Supervised automated machine learning in the Auto-Classifier model of the IBM SPSS Modeler package was used to predict microvascular complications in a training data set of 348 long-term T2D patients with complications using 24 independent variables as predictors and complications as targets. Three automated model scenarios were tested: Full All-Variable Model; Univariate-Selected Model, and Backward Stepwise Logistic Regression Model. An independent cohort of 338 newly diagnosed T2D patients with no complications was used for the model validation. Results: Long-term T2D patients with complications (duration = ~14.5 years) were significantly older (mean age = 56.3 ± 10.9 years) than the newly diagnosed patients without complications (duration = ~2.5 years; mean age = 48.9 ± 9.6 years). The Bayesian Network was the most reliable algorithm for predicting microvascular complications in all three scenarios with an area under the curve (AUC) of 77–87%, accuracy of 68–75%, sensitivity of 86–95%, and specificity of 53–75%. Among newly diagnosed T2D patients, 22.5% were predicted positive and 49.1% negative across all models. Logistic regression applied to the 16 significant predictors between the two sub-groups showed that BMI, HDL, adjusted for age at diagnosis of T2D, age at visit, and urine albumin explained >90% of the variation in microvascular measures. Conclusions: the Bayesian Network model effectively predicts microvascular complications in newly diagnosed T2D patients, highlighting the significant roles of BMI, HDL, age at diagnosis, age at visit, and urine albumin.

Background: Clustering type 2 diabetes (T2D) remains a challenge due to its clinical heterogeneity and multifactorial nature. We aimed to evaluate the validity and robustness of the clinical variables in defining T2D subtypes using a discovery-to-prediction design. Methods: Five explanatory clinical aetiology variables (fasting serum insulin, fasting blood glucose, body mass index, age at diagnosis and HbA1c) were assessed for clustering T2D subtypes using two independent patient datasets. Clustering was performed using the IBM-Modeler Auto-Cluster. The resulting cluster validity was tested by multinomial logistic regression. The variables’ validity for direct unsupervised clustering was compared with machine learning (ML) predictive models. Results: Five distinct subtypes were consistently identified: severe insulin-resistant diabetes (SIRD), severe insulin-deficient diabetes (SIDD), mild obesity-related diabetes (MOD), mild age-related diabetes (MARD), and mild early-onset diabetes (MEOD). Using all five variables yielded the highest concordance between clustering methods. Concordance was strongest for SIRD and SIDD, reflecting their distinct clinical signatures in contrast to that in MARD, MOD and MEOD. Conclusions: These findings support the robustness of clinically defined T2D subtypes and demonstrate the value of probabilistic clustering combined with ML for advancing precision diabetes care.