Development and validation of a postpartum cardiovascular disease risk prediction model in women incorporating reproductive and pregnancy-related predictors.

Background: Each year, over 700,000 pregnancies occur in the UK, with up to 10% affected by complications such as hypertensive disorders of pregnancy and gestational diabetes mellitus. Pregnancy-related complications and reproductive factors are associated with an increased risk of cardiovascular disease (CVD) later in life. Our aim was to determine whether adding pregnancy factors to a prediction model with established CVD risk factors improves 10-year risk prediction of CVD in postpartum women, using QRISK®-3 as a benchmark model.

Oxford Classic-defined EMT risk stratification of High Grade Serous Ovarian cancer for guiding treatment decisions.

Purpose: The association between epithelial to mesenchymal transition in High Grade Serous Ovarian Cancer (HGSOC) and poor prognosis is known. However, molecularly defining a subset of tumours that reproducibly associates with poor prognosis has been an elusive goal in this disease. A molecular signature that can robustly identify patients with poor prognosis and guide treatment decisions, including surgical strategy and targeted therapies, can improve survival rates.

Incorporation of Patient and Public Involvement in Statistical Methodology Research: Summary of Workshop Proceedings.

Patient and Public Involvement (PPI) is well-established in applied health research but remains under utilised in statistical methodology research due to perceived irrelevance and communication challenges. This paper summarises a one-day workshop held in February 2024 in Leicester, organised by the University of Leicester and the NIHR Statistics Group, aimed at addressing barriers to meaningful PPI in statistical methodology. The workshop brought together statisticians and experienced public contributors to discuss strategies, share case studies, and offer practical guidance on conducting effective PPI.

GPerturb: Gaussian process modelling of single-cell perturbation data.

Single-cell RNA sequencing and CRISPR screening enable high-throughput analysis of genetic perturbations at single-cell resolution. Understanding combinatorial perturbation effects is essential but challenging due to data sparsity and complex biological mechanisms. We present GPerturb, a Gaussian process-based sparse perturbation regression model designed to estimate gene-level perturbation effects.

Refined selection of individuals for preventive cardiovascular disease treatment with a transformer-based risk model.

Background: Although statistical models have been commonly used to identify patients at risk of cardiovascular disease for preventive therapy, these models tend to over-recommend therapy. Moreover, in populations with pre-existing diseases, the current approach is to indiscriminately treat all, as modelling in this context is currently inadequate. This study aimed to develop and validate the Transformer-based Risk assessment survival (TRisk) model, a novel deep learning model, for predicting 10-year risk of cardiovascular disease in both the primary prevention population and individuals with diabetes.

Metabolic Dysfunction Associated With Alterations in Gut Microbiota in Adolescents With Obesity.

Obesity in childhood is associated with adulthood obesity, type 2 diabetes (T2D), and future metabolic complications. The gut microbiota is a modifier of host metabolic function with altered bacterial composition associated with disease risk. Few studies have investigated the relationships among metabolic disease, inflammation, and the gut microbiota in youth, in whom these connections likely originate.

Analysis of early childhood intestinal microbial dynamics in a continuous-flow bioreactor.

Background: The human gut microbiota is inoculated at birth and undergoes a process of assembly and diversification during the first few years of life. Studies in mice and humans have revealed associations between the early-life gut microbiome and future susceptibility to immune and metabolic diseases. To resolve microbe and host contributing factors to early-life development and to disease states requires experimental platforms that support reproducible, longitudinal, and high-content analyses.

Cracking the type 1 diabetes code: Genes, microbes, immunity, and the early life environment.

Type 1 diabetes (T1D) results from a complex interplay of genetic predisposition, immunological dysregulation, and environmental triggers, that culminate in the destruction of insulin-secreting pancreatic β cells. This review provides a comprehensive examination of the multiple factors underpinning T1D pathogenesis, to elucidate key mechanisms and potential therapeutic targets. Beginning with an exploration of genetic risk factors, we dissect the roles of human leukocyte antigen (HLA) haplotypes and non-HLA gene variants associated with T1D susceptibility.

Bayesian inference for identifying tumour-specific cancer dependencies through integration of ex-vivo drug response assays and drug-protein profiling.

The identification of tumor-specific molecular dependencies is essential for the development of effective cancer therapies. Genetic and chemical perturbations are powerful tools for discovering these dependencies. Even though chemical perturbations can be applied to primary cancer samples at large scale, the interpretation of experiment outcomes is often complicated by the fact that one chemical compound can affect multiple proteins.

Concordance of randomised controlled trials for artificial intelligence interventions with the CONSORT-AI reporting guidelines.

The Consolidated Standards of Reporting Trials extension for Artificial Intelligence interventions (CONSORT-AI) was published in September 2020. Since its publication, several randomised controlled trials (RCTs) of AI interventions have been published but their completeness and transparency of reporting is unknown. This systematic review assesses the completeness of reporting of AI RCTs following publication of CONSORT-AI and provides a comprehensive summary of RCTs published in recent years.

Association between pregnancy-related complications and development of type 2 diabetes and hypertension in women: an umbrella review.

Background: Despite many systematic reviews and meta-analyses examining the associations of pregnancy complications with risk of type 2 diabetes mellitus (T2DM) and hypertension, previous umbrella reviews have only examined a single pregnancy complication. Here we have synthesised evidence from systematic reviews and meta-analyses on the associations of a wide range of pregnancy-related complications with risk of developing T2DM and hypertension.

Methods: Medline, Embase and Cochrane Database of Systematic Reviews were searched from inception until 26 September 2022 for systematic reviews and meta-analysis examining the association between pregnancy complications and risk of T2DM and hypertension.

Rarity: discovering rare cell populations from single-cell imaging data.

Motivation: Cell type identification plays an important role in the analysis and interpretation of single-cell data and can be carried out via supervised or unsupervised clustering approaches. Supervised methods are best suited where we can list all cell types and their respective marker genes a priori, while unsupervised clustering algorithms look for groups of cells with similar expression properties. This property permits the identification of both known and unknown cell populations, making unsupervised methods suitable for discovery.

Completing a genomic characterisation of microscopic tumour samples with copy number.

Background: Genomic insights in settings where tumour sample sizes are limited to just hundreds or even tens of cells hold great clinical potential, but also present significant technical challenges. We previously developed the DigiPico sequencing platform to accurately identify somatic mutations from such samples.

Results: Here, we complete this genomic characterisation with copy number.

Sociodemographic characteristics and longitudinal progression of multimorbidity: A multistate modelling analysis of a large primary care records dataset in England.

Background: Multimorbidity, characterised by the coexistence of multiple chronic conditions in an individual, is a rising public health concern. While much of the existing research has focused on cross-sectional patterns of multimorbidity, there remains a need to better understand the longitudinal accumulation of diseases. This includes examining the associations between important sociodemographic characteristics and the rate of progression of chronic conditions.

Immune responses to gut bacteria associated with time to diagnosis and clinical response to T cell-directed therapy for type 1 diabetes prevention.

Immune-targeted therapies have efficacy for treatment of autoinflammatory diseases. For example, treatment with the T cell-specific anti-CD3 antibody teplizumab delayed disease onset in participants at high risk for type 1 diabetes (T1D) in the TrialNet 10 (TN-10) trial. However, heterogeneity in therapeutic responses in TN-10 and other immunotherapy trials identifies gaps in understanding disease progression and treatment responses.

Identifying factors associated with user retention and outcomes of a digital intervention for substance use disorder: a retrospective analysis of real-world data.

Objectives: Successful delivery of digital health interventions is affected by multiple real-world factors. These factors may be identified in routinely collected, ecologically valid data from these interventions. We propose ideas for exploring these data, focusing on interventions targeting complex, comorbid conditions.

On the Difficulty of Predicting Engagement with Digital Health for Substance Use.

Digital interventions can be an important instrument in treating substance use disorder. However, most digital mental health interventions suffer from early, frequent user dropout. Early prediction of engagement would allow identification of individuals whose engagement with digital interventions may be too limited to support behaviour change, and subsequently offering them support.

An atlas of genetic scores to predict multi-omic traits.

The use of omic modalities to dissect the molecular underpinnings of common diseases and traits is becoming increasingly common. But multi-omic traits can be genetically predicted, which enables highly cost-effective and powerful analyses for studies that do not have multi-omics. Here we examine a large cohort (the INTERVAL study; n = 50,000 participants) with extensive multi-omic data for plasma proteomics (SomaScan, n = 3,175; Olink, n = 4,822), plasma metabolomics (Metabolon HD4, n = 8,153), serum metabolomics (Nightingale, n = 37,359) and whole-blood Illumina RNA sequencing (n = 4,136), and use machine learning to train genetic scores for 17,227 molecular traits, including 10,521 that reach Bonferroni-adjusted significance.

Protocol for development and validation of postpartum cardiovascular disease (CVD) risk prediction model incorporating reproductive and pregnancy-related candidate predictors.

Background: Cardiovascular disease (CVD) is a leading cause of death among women. CVD is associated with reduced quality of life, significant treatment and management costs, and lost productivity. Estimating the risk of CVD would help patients at a higher risk of CVD to initiate preventive measures to reduce risk of disease.