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Introduction: Increasing evidence from human and animal model studies indicates the significant role of microRNAs (miRNAs) in pancreatic beta cell function, insulin signaling, immune responses, and pathogenesis of type 1 diabetes (T1D)., Methods: We aimed, using next-generation sequencing, to screen miRNAs from peripheral blood mononuclear cells of eight independent Kuwaiti-Arab families with T1D affected siblings, consisting of 18 T1D patients and 18 unaffected members, characterized by no parent-to-child inheritance pattern., Results: Our analysis revealed 20 miRNAs that are differentially expressed in T1D patients compared with healthy controls. Module-based weighted gene co-expression network analysis prioritized key consensus miRNAs in T1D pathogenesis. These included hsa-miR-320a-3p, hsa-miR-139-3p, hsa-miR-200-3p, hsa-miR-99b-5p and hsa-miR-6808-3p. Functional enrichment analysis of differentially expressed miRNAs indicated that PI3K-AKT is one of the key pathways perturbed in T1D. Gene ontology analysis of hub miRNAs also implicated PI3K-AKT, along with mTOR, MAPK, and interleukin signaling pathways, in T1D. Using quantitative RT-PCR, we validated one of the key predicted miRNA-target gene-transcription factor networks in an extended cohort of children with new-onset T1D positive for islet autoantibodies. Our analysis revealed that hsa-miR-320a-3p and its key targets, including PTEN, AKT1, BCL2, FOXO1 and MYC , are dysregulated in T1D, along with their interacting partners namely BLIMP3, GSK3B , CAV1, CXCL3, TGFB , and IL10 . Receiver Operating Characteristic analysis highlighted the diagnostic potential of hsa-miR-320a-3p, CAV1, GSK3B and MYC for T1D., Discussion: Our study presents a novel link between hsa-miR-320a-3p and T1D, and highlights its key regulatory role in the network of mRNA markers and transcription factors involved in T1D pathogenesis., Competing Interests: JT is a stock owner in Orion Pharma. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be constructed as a potential conflict of interest., (Copyright © 2024 Nizam, Malik, Jacob, Alsmadi, Koistinen, Tuomilehto, Alkandari, Al-Mulla and Thanaraj.)

Type 1 diabetes (T1D) incidence has increased globally over the last decades, alongside other autoimmune diseases. Early screening of individuals at risk of developing T1D is vital to facilitate appropriate interventions and improve patient outcomes. This is particularly important to avoid life-threatening diabetic ketoacidosis and hospitalization associated with T1D diagnosis. Additionally, considering that new therapies have been developed for T1D, screening the population and individuals at high risk would be of great benefit. However, adopting such screening approaches may not be feasible due to limitations, such as cost, adaptation of such programs, and sample processing. In this perspective, we explore and highlight the use of multiplexing chemiluminescent assays for T1D screening and emphasize on their advantages in detecting multiple autoantibodies simultaneously, maximizing efficiency, and minimizing sample volume requirements. These assays could be extremely valuable for pediatric populations and large-scale screening initiatives, providing a cost-efficient solution with increased diagnostic accuracy and deeper insights into T1D pathogenesis. Eventually, the adoption of such screening methods can help transform T1D diagnosis, especially in countries with high T1D prevalence, such as Kuwait, which will contribute to the development of novel therapeutic interventions, positively impacting the lives of those affected by T1D and other autoimmune diseases., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision., (Copyright © 2023 Al-Mulla, Alhomaidah, Abu-Farha, Hasan, Al-Khairi, Nizam, Alqabandi, Alkandari and Abubaker.)

Objective: In recent decades, the Arab population has experienced an increase in the prevalence of type 2 diabetes (T2DM), particularly within the Gulf Cooperation Council. In this context, early intervention programmes rely on an ability to identify individuals at risk of T2DM. We aimed to build prognostic models for the risk of T2DM in the Arab population using machine-learning algorithms vs. conventional logistic regression (LR) and simple non-invasive clinical markers over three different time scales (3, 5, and 7 years from the baseline). Design: This retrospective cohort study used three models based on LR, k -nearest neighbours (k-NN), and support vector machines (SVM) with five-fold cross-validation. The models included the following baseline non-invasive parameters: age, sex, body mass index (BMI), pre-existing hypertension, family history of hypertension, and T2DM. Setting: This study was based on data from the Kuwait Health Network (KHN), which integrated primary health and hospital laboratory data into a single system. Participants: The study included 1,837 native Kuwaiti Arab individuals (equal proportion of men and women) with mean age as 59.5 ± 11.4 years. Among them, 647 developed T2DM within 7 years of the baseline non-invasive measurements. Analytical methods: The discriminatory power of each model for classifying people at risk of T2DM within 3, 5, or 7 years and the area under the receiver operating characteristic curve (AUC) were determined. Outcome measures: Onset of T2DM at 3, 5, and 7 years. Results: The k-NN machine-learning technique, which yielded AUC values of 0.83, 0.82, and 0.79 for 3-, 5-, and 7-year prediction horizons, respectively, outperformed the most commonly used LR method and other previously reported methods. Comparable results were achieved using the SVM and LR models with corresponding AUC values of (SVM: 0.73, LR: 0.74), (SVM: 0.68, LR: 0.72), and (SVM: 0.71, LR: 0.70) for 3-, 5-, and 7-year prediction horizons, respectively. For all models, the discriminatory power decreased as the prediction horizon increased from 3 to 7 years. Conclusions: Machine-learning techniques represent a useful addition to the commonly reported LR technique. Our prognostic models for the future risk of T2DM could be used to plan and implement early prevention programmes for at risk groups in the Arab population., (Copyright © 2019 Farran, AlWotayan, Alkandari, Al-Abdulrazzaq, Channanath and Thanaraj.)