Simvastatin is associated with superior lipid and glycaemic control to atorvastatin and reduced levels of incident Type 2 diabetes, in men and women, in the UK Biobank

INTRODUCTION: Cardiovascular disease (CVD) is the leading cause of mortality in people with Type 2 diabetes mellitus (T2DM). Statins reduce low‐density lipoproteins and positively affect CVD outcomes. Statin type and dose have differential effects on glycaemia and risk of incident T2DM; however, the impact of gender, and of individual drugs within the statin class, remains unclear. AIM: To compare effects of simvastatin and atorvastatin on lipid and glycaemic control in men and women with and without T2DM, and their association with incident T2DM. METHODS: The effect of simvastatin and atorvastatin on lipid and glycaemic control was assessed in the T2DM DiaStrat cohort. Prescribed medications, gender, age, BMI, diabetes duration, blood lipid profile and HbA1c were extracted from Electronic Care Record, and compared in men and women prescribed simvastatin and atorvastatin. Analyses were replicated in the UKBiobank in those with and without T2DM. The association of simvastatin and atorvastatin with incident T2DM was also investigated in the UKBiobank. Cohorts where matched for age, BMI and diabetes duration in men and women, in the UKBioBank analysis, where possible. RESULTS: Simvastatin was associated with better LDL (1.6 ± 0.6 vs 2.1 ± 0.9 mmol/L, p < .01) and total cholesterol (3.6 ± 0.7 vs 4.2 ± 1.0 mmol/L, p < .05), and glycaemic control (62 ± 17 vs 67 ± 19 mmol/mol, p < .059) than atorvastatin specifically in women in the DiaStrat cohort. In the UKBiobank, both men and women prescribed simvastatin had better LDL (Women: 2.6 ± 0.6 vs 2.6 ± 0.7 mmol/L, p < .05; Men: 2.4 ± 0.6 vs 2.4 ± 0.6, p < .01) and glycaemic control (Women:54 ± 14 vs 56 ± 15mmol/mol, p < .05; Men, 54 ± 14 vs 55 ± 15 mmol/mol, p < .01) than those prescribed atorvastatin. Simvastatin was also associated with reduced risk of incident T2DM in both men and women (p < .0001) in the UKBiobank. CONCLUSIONS: Simvastatin is associated with superior lipid and glycaemic control to atorvastatin in those with and without T2DM, and with fewer incident T2DM cases. Given the importance of lipid and glycaemic control in preventing secondary complications of T2DM, these findings may help inform prescribing practices

Co-Clustering Multi-View Data Using the Latent Block Model

The Latent Block Model (LBM) is a prominent model-based co-clustering method, returning parametric representations of each block cluster and allowing the use of well-grounded model selection methods. The LBM, while adapted in literature to handle different feature types, cannot be applied to datasets consisting of multiple disjoint sets of features, termed views, for a common set of observations. In this work, we introduce the multi-view LBM, extending the LBM method to multi-view data, where each view marginally follows an LBM. In the case of two views, the dependence between them is captured by a cluster membership matrix, and we aim to learn the structure of this matrix. We develop a likelihood-based approach in which parameter estimation uses a stochastic EM algorithm integrating a Gibbs sampler, and an ICL criterion is derived to determine the number of row and column clusters in each view. To motivate the application of multi-view methods, we extend recent work developing hypothesis tests for the null hypothesis that clusters of observations in each view are independent of each other. The testing procedure is integrated into the model estimation strategy. Furthermore, we introduce a penalty scheme to generate sparse row clusterings. We verify the performance of the developed algorithm using synthetic datasets, and provide guidance for optimal parameter selection. Finally, the multi-view co-clustering method is applied to a complex genomics dataset, and is shown to provide new insights for high-dimension multi-view problems

Analysis of Risk Factors and Diagnosis for Anxiety Disorder in Older People with the Aid of Artificial Intelligence: Observational Study

Anxiety disorders are the most common mental health problems particularly in older people who suffer from loneliness and social isolation, chronic health conditions, financial insecurity and other factors that can lead to anxiety disorders. The high prevalence and health risks of anxiety disorders, and the requirement for effective mental care, coupled with recent advances in artificial intelligence, has resulted in an increase exploration of how machine learning can aid the diagnosis and prediction of mental health problems. Data from the Trinity-Ulster-Department of Agriculture (TUDA) study will be utilized to identify risk factors for anxiety in community dwelling older adults using machine learning techniques. The TUDA study includes detailed information on biochemical, clinical, nutritional, lifestyle, and sociodemographic factors in 5186 older people recruited from the Republic of Ireland and Northern Ireland. These characteristics could foster the prediction of anxiety disorders using supervised machine learning methods. Biomarker risk factor analysis was conducted to facilitate feature engineering. In this observational study, several classical machine learning models have been trained to predict anxiety disorders. Comparing the accuracy results and determining the impact of features on the predictions of each method. The models' performance was assessed on a held-out test set and achieved an accuracy of 85.4% (sensitivity: 67.0%, specificity: 90.3%) and 83.4% (sensitivity: 81.5%, specificity: 83.9%) for two best performing methods i.e., random forest and support vector machine respectively, using the standard Synthetic Minority Oversampling Technique. Risk factors such as female sex, loneliness, separated/divorced conditions, lifestyle-related, socio-economic low status, chronic diseases, and family related diseases were identified. These results will aid in the early detection of anxiety disorder in future studies

Homocysteine, MTHFR 677C->T polymorphism and B vitamin status in patients with premature cardiovascular disease

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