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: Attempts to subtype, type 2 diabetes (T2D) have mostly focused on newly diagnosed European patients. In this study, our aim was to subtype T2D in a non-white Emirati ethnic population with long-standing disease, using unsupervised soft clustering, based on etiological determinants. Methods: The Auto Cluster model in the IBM SPSS Modeler was used to cluster data from 348 Emirati patients with long-standing T2D. Five predictor variables (fasting blood glucose (FBG), fasting serum insulin (FSI), body mass index (BMI), hemoglobin A1c (HbA1c) and age at diagnosis) were used to determine the appropriate number of clusters and their clinical characteristics. Multinomial logistic regression was used to validate clustering results. Results: Five clusters were identified; the first four matched Ahlqvist et al subgroups: severe insulin-resistant diabetes (SIRD), severe insulin-deficient diabetes (SIDD), mild age-related diabetes (MARD), mild obesity-related diabetes (MOD), and a fifth new subtype of mild early onset diabetes (MEOD). The Modeler algorithm allows for soft assignments, in which a data point can be assigned to multiple clusters with different probabilities. There were 151 patients (43%) with membership in cluster peaks with no overlap. The remaining 197 patients (57%) showed extensive overlap between clusters at the base of distributions. Conclusions: Despite the complex picture of long-standing T2D with comorbidities and complications, our study demonstrates the feasibility of identifying subtypes and their underlying causes. While clustering provides valuable insights into the architecture of T2D subtypes, its application to individual patient management would remain limited due to overlapping characteristics. Therefore, integrating simplified, personalized metabolic profiles with clustering holds greater promise for guiding clinical decisions than subtyping alone.

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.

Cytorace-3 is a laboratory evolved hybrid lineage of Drosophila nasuta nasuta males and Drosophila nasuta albomicans females currently passing ~850 generations. To assess interracial hybridization effects on gene expression in Cytorace-3 we profiled the transcriptomes of mature ovaries and testes by employing Illumina sequencing technology and de novo transcriptome assembling strategies. We found 26% of the ovarian, and 14% of testis genes to be differentially expressed in Cytorace-3 relative to the expressed genes in the parental gonadal transcriptomes. About 5% of genes exhibited additive gene expression pattern in the ovary and 3% in the testis, while the remaining genes were misexpressed in Cytorace-3. Nearly 772 of these misexpressed genes in the ovary and 413 in the testis were either over- or under-dominant. Genes following D. n. nasuta dominance was twice (270 genes) than D. n. albomicans dominance (133 genes) in the ovary. In contrast, only 105 genes showed D. n. nasuta dominance and 207 showed D. n. albomicans dominance in testis transcriptome. Of the six expression inheritance patterns, conserved inheritance pattern was predominant for both ovary (73%) and testis (85%) in Cytorace-3. This study is the first to provide an overview of the expression divergence and inheritance patterns of the transcriptomes in an independently evolving distinct hybrid lineage of Drosophila. This recorded expression divergence in Cytorace-3 surpasses that between parental lineages illustrating the strong impact of hybridization driving rapid gene expression changes.