{
  "abstract": "Objectives Type 2 diabetes (T2D) is a complex disease with a heterogeneous clinical presentation. Recently, five distinct clusters of T2D have been identified in the Emirati population of long-standing T2D with complications. This study aimed to validate these clusters in newly diagnosed T2D patients without any complications and determine whether severe and mild phenotypes are detectable early in the disease course.Design Retrospective, cross-sectional, non-interventional study.Setting Primary healthcare centres in Dubai, UAE.Participants A total of 451 adults, including both Emiratis and expatriates, diagnosed with T2D in the last 5 years and without T2D-related complications at the time of visit, were enrolled. Patients with complications, incomplete clinical data or higher duration of T2D were excluded from the study.Outcome measures Identification of distinct T2D clusters using machine learning-based clustering analysis. Five clinical variables: age at diagnosis, body mass index, glycated haemoglobin, fasting serum insulin and fasting blood glucose served as predictors. Overlap between clusters was assessed via the Silhouette Index and Bayesian probability.Results Five clusters were identified, replicating prior findings: 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). As confirmed by a Silhouette Index and Bayesian probability of 1, 55.43% of the patients showed cluster-exclusiveness, while 44.56% of the cohort showed overlap between clusters. The highest overlap was recorded for mild forms of T2D in the order MOD>MARD>MEOD.Conclusions The study confirms that both severe and mild T2D phenotypes are present in newly diagnosed, complication-free patients, supporting the applicability of cluster-based classification early in disease. These results highlight the potential for personalised treatment strategies to optimise management and prevent complications. Future studies should investigate longitudinal outcomes and therapeutic response across clusters.",
  "authors": [
    {
      "affiliations": [
        "College of Medicine, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai, UAE"
      ],
      "name": "Stafny Melony Dsouza"
    },
    {
      "affiliations": [
        "College of Medicine, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai, UAE"
      ],
      "name": "Fatima Sulaiman"
    },
    {
      "affiliations": [
        "College of Medicine, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai, UAE"
      ],
      "name": "Fatima Abdul"
    },
    {
      "affiliations": [
        "College of Medicine, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai, UAE"
      ],
      "name": "Fahad Mulla"
    },
    {
      "affiliations": [
        "Pathology and Genetics Department, Dubai Hospital, Dubai Health, Dubai, UAE"
      ],
      "name": "Fayha Salah Ahmed"
    },
    {
      "affiliations": [
        "Pathology and Genetics Department, Dubai Hospital, Dubai Health, Dubai, UAE"
      ],
      "name": "Mouza AlSharhan"
    },
    {
      "affiliations": [
        "Primary Healthcare Centre, Dubai Health, Dubai, UAE"
      ],
      "name": "Ayesha AlOlama"
    },
    {
      "affiliations": [
        "Primary Healthcare Centre, Dubai Health, Dubai, UAE"
      ],
      "name": "Noorah Ali"
    },
    {
      "affiliations": [
        "Primary Healthcare Centre, Dubai Health, Dubai, UAE"
      ],
      "name": "Aaesha Abdulaziz"
    },
    {
      "affiliations": [
        "Primary Healthcare Centre, Dubai Health, Dubai, UAE"
      ],
      "name": "Alia Mohammad Rafie"
    },
    {
      "affiliations": [
        "College of Medicine, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai, UAE"
      ],
      "name": "Suhaila Alnuaimi"
    },
    {
      "affiliations": [
        "College of Medicine, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai, UAE"
      ],
      "name": "Nandu Goswami"
    },
    {
      "affiliations": [
        "Hamdan Bin Mohammed College of Dental Medicine, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai, UAE"
      ],
      "name": "Amar Hassan Khamis"
    },
    {
      "affiliations": [
        "College of Medicine, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai, UAE"
      ],
      "name": "Riad Abdel Latif Bayoumi"
    }
  ],
  "title": "Aetiological clustering of newly diagnosed type 2 diabetes using machine learning: a retrospective cross-sectional study in Dubai, UAE",
  "uid": "b7b03ae8-18e6-5a51-b443-9f085a2ec087"
}