Conectar
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.
Retrospective, cross-sectional, non-interventional study.
Primary healthcare centres in Dubai, UAE.
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.
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.
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.
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.
Background
Despite the effect of maternal breast cancer on many children, there is no valid or reliable quantitative measure of the concern that children attribute to their mothers' disease, which constrains both science and clinical practice.
Objectives
This study aimed to develop and psychometrically evaluate the initial measures of child-reported, illness-related concerns associated with maternal cancer.
Methods
The study was conducted in three phases: scoping review, item extraction from a battery of items obtained from school-aged children about general issues related to their mothers' breast cancer, and testing of the three proposed structural models of these extracted items using confirmatory factor analysis. The scoping review yielded five categories of illness-related concerns: altered family routines, uncertainty, concerns about illness contagion, maternal death, and maternal well-being. To reflect these five categories, 18 items were extracted from a 93-item questionnaire completed by 202 school-aged children regarding their mothers' breast cancer. Next, three structural models were hypothesized to assess the construct validity of illness-related concerns: five-, three-, and one-factor models. Confirmatory factor analysis was used to test and compare the models.
Results
The five-factor model best fit the data, and each factor showed adequate internal consistency reliability. These findings align with the a priori five-factor model informed by the scoping review.
Conclusion
The results provide initial evidence of the construct validity of the 18-item Children's Illness-Related Concerns Scale, which can be used to assess children's concerns and inform future intervention studies. 
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