Returning research results that indicate risk of Alzheimer disease (AD) dementia—a disease for which no meaningful treatments or cure exist—to cognitively normal participants is controversial. AD is thought to begin many years before clinical signs and symptoms begin. During this time, individuals are cognitively normal but have biomarkers that indicate pathophysiological changes in the brain. With this study, we aim to evaluate the impact of returning research results on cognitively normal participants recruited from a longitudinal observational cohort on ageing at the Knight Alzheimer Disease Research Centre (Knight ADRC) at Washington University in St. Louis.

Our study uses a 2-year, delayed-start randomised clinical trial design. Participants are randomised to receive their research results either 2 weeks or 1 year after informed consent. This study was approved to recruit up to 450 participants with existing genetic and biomarker testing results from the Knight ADRC. During the study period, 260 individuals were eligible and approached for entry into the study. The primary cognitive outcomes are 1-year change in subjective cognitive score on the clinical dementia rating sum of box scores and the objective cognitive score on cognitive composite score. The primary psychosocial outcome is change in geriatric depression scale score 1 year after return of research results. The study was powered to answer primary outcomes with 140 participants (70 per study arm).

This study has been approved by the Washington University School of Medicine (WUSM) Institutional Review Board and the Human Research Protection Office. Results from these trials are shared through conferences and publications.

NCT04699786.

Prescribing high-dose antipsychotics is typically reserved for individuals with treatment-resistant severe mental illnesses, such as schizophrenia, bipolar disorder and psychotic depression. It carries an increased risk of adverse drug effects, necessitating regular monitoring. Non-mental health specialist clinicians may not always be aware when the maximum recommended dose of antipsychotics is exceeded, leading to unintentional high-dose prescribing without recognising the need for additional monitoring or understanding the associated risks. Therefore, providing clinical decision support (CDS) tools to support clinicians and improve the appropriate prescribing of antipsychotics is important. The aim of this study is to understand current prescribing practices and assess the impact of high-dose antipsychotic prescribing on clinical outcomes among hospitalised patients. The findings from this study will shape a future project focused on developing an integrated computerised CDS tool.

This retrospective cohort study will examine antipsychotic prescribing among hospitalised patients using Hospital Electronic Prescribing and Medicines Administration data in Scotland from 2019 to 2023, in linkage with hospital records, Scottish Morbidity Records and primary care prescribing (Prescribing Information System). Patients will be grouped into those prescribed high-dose (exposed), defined as exceeding the 100% maximum recommended British National Formulary dose and normal-dose (unexposed) antipsychotics, followed from their first ever antipsychotic prescription date (index date) until the end of the study, study outcomes or death, whichever happens first. We will quantify high-dose antipsychotic prescribing, profile patient characteristics and use machine learning techniques to assess associations of high-dose antipsychotic prescribing with clinical outcomes, including harms and benefits, but will not attempt to establish causality.

The Health and Social Care Public Benefit and Privacy Policy Panel (HSC-PBPP) has granted ethical approval (ref. 2024-0239) following a Data Protection Impact Assessment, with data securely held and accessed in the National Safe Haven. The results will be published in international peer-reviewed journals and will be shared with clinicians.

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.