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: 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.

Background: Obesity's global rise has outpaced the effectiveness of “one‐size‐fits‐all” prevention and treatment strategies. The promise of precision medicine lies in addressing the etiological and biological diversity of obesity and transforming it into actionable endotypes. Objectives: In a narrative review, we analyze “obesityomics,” looking at the contributions each omics layer makes to understanding and managing obesity. The areas we evaluate are genomics (heritability, polygenic risk, and monogenic forms), epigenomics (development and environment imprinting), transcriptomics (tissue‐specific programs of adipose, liver, muscle, and hypothalamus), proteomics (signaling networks and adipokines), metabolomics and lipidomics (fuel partitioning, lipotoxicity, and metabolic inflection points), and microbiome science (metabolites, host–microbe interactions, and microbial ecology). Our domain‐by‐domain (single‐omics) approach demonstrates how disease taxonomy, causal pathways, and biomarkers can be refined and targeted therapeutically. Discussion: We argue that “obesityomics” is a pragmatic approach to aligning omics modalities to the right questions at the right time, not a wholesale merger of datasets. Artificial intelligence (AI)—including machine learning and deep learning—should be used in conjunction with quality control, feature selection, modest data integration, and obesity intervention prediction. Among the key challenges in managing sensitive multilayer data are harmonization, standardization, reproducibility, equitable representation, and governance. The translation priorities include prospective studies related to patient‐centered outcomes, biomarker panels that are clinic‐ready, interoperability standards, and cost‐effectiveness evaluations. Conclusion: The review maps concrete steps to assist people with obesity in making better choices by identifying what each omics domain excels at and introducing a brief AI perspective.