Evaluation of a machine learning-based metabolic marker for coronary artery disease in the UK Biobank.

Background And Aims: An in silico quantitative score of coronary artery disease (ISCAD), built using machine learning and clinical data from electronic health records, has been shown to result in gradations of risk of subclinical atherosclerosis, coronary artery disease (CAD) sequelae, and mortality. Large-scale metabolite biomarker profiling provides increased portability and objectivity in machine learning for disease prediction and gradation. However, these models have not been fully leveraged.

Assessing Lung Ventilation and Bronchodilator Response in Asthma and Chronic Obstructive Pulmonary Disease with F MRI.

Background Pulmonary function tests are central to diagnosis and monitoring of respiratory diseases but do not provide information on regional lung function heterogeneity. Fluorine 19 (F) MRI of inhaled perfluoropropane permits quantitative and spatially localized assessment of pulmonary ventilation properties without tracer gas hyperpolarization. Purpose To assess regional lung ventilation properties using F MRI of inhaled perfluoropropane in participants with asthma, participants with chronic obstructive pulmonary disease (COPD), and healthy participants, including quantitative evaluation of bronchodilator response in participants with respiratory disease.

Muesli Intake May Protect Against Coronary Artery Disease: Mendelian Randomization on 13 Dietary Traits.

Background: Diet is a key modifiable risk factor of coronary artery disease (CAD). However, the causal effects of specific dietary traits on CAD risk remain unclear. With the expansion of dietary data in population biobanks, Mendelian randomization (MR) could help enable the efficient estimation of causality in diet-disease associations.

Eating and drinking experience in patients with idiopathic pulmonary fibrosis: a qualitative study.

Objective: To explore eating and drinking experiences of patients with idiopathic pulmonary fibrosis (IPF), the impact of any changes associated with their diagnosis and any coping mechanisms developed by patients.

Setting: Pulmonary fibrosis support groups around the UK and the regional Interstitial Lung Diseases Clinic, Newcastle upon Tyne.

Participants: 15 patients with IPF (9 men, 6 women), median age 71 years, range (54-92) years, were interviewed.

Historical Mortality Dynamics on the Baja California Peninsula : Marriage, Mining Booms, Migration, and Infectious Disease.

Historical demographic research shows that the factors influencing mortality risk are labile across time and space. This is particularly true for datasets that span societal transitions. Here, we seek to understand how marriage, migration, and the local economy influenced mortality dynamics in a rapidly changing environment characterized by high in-migration and male-biased sex ratios.

Real-world experience of nintedanib for progressive fibrosing interstitial lung disease in the UK.

Background: Nintedanib slows progression of lung function decline in patients with progressive fibrosing (PF) interstitial lung disease (ILD) and was recommended for this indication within the United Kingdom (UK) National Health Service in Scotland in June 2021 and in England, Wales and Northern Ireland in November 2021. To date, there has been no national evaluation of the use of nintedanib for PF-ILD in a real-world setting.

Methods: 26 UK centres were invited to take part in a national service evaluation between 17 November 2021 and 30 September 2022.

Proteome-Wide Fragment-Based Ligand and Target Discovery.

Chemical probes are invaluable tools to investigate biological processes and can serve as lead molecules for the development of new therapies. However, despite their utility, only a fraction of human proteins have selective chemical probes, and more generally, our knowledge of the "chemically-tractable" proteome is limited, leaving many potential therapeutic targets unexploited. To help address these challenges, powerful chemical proteomic approaches have recently been developed to globally survey the ability of proteins to bind small molecules (i.

Development of a human genetics-guided priority score for 19,365 genes and 399 drug indications.

Studies have shown that drug targets with human genetic support are more likely to succeed in clinical trials. Hence, a tool integrating genetic evidence to prioritize drug target genes is beneficial for drug discovery. We built a genetic priority score (GPS) by integrating eight genetic features with drug indications from the Open Targets and SIDER databases.

Deep learning on electronic medical records identifies distinct subphenotypes of diabetic kidney disease driven by genetic variations in the pathway.

Kidney disease affects 50% of all diabetic patients; however, prediction of disease progression has been challenging due to inherent disease heterogeneity. We use deep learning to identify novel genetic signatures prognostically associated with outcomes. Using autoencoders and unsupervised clustering of electronic health record data on 1,372 diabetic kidney disease patients, we establish two clusters with differential prevalence of end-stage kidney disease.

Cholesterol Contributes to Risk, Severity, and Machine Learning-Driven Diagnosis of Lyme Disease.

Background: Lyme disease is the most prevalent vector-borne disease in the US, yet its host factors are poorly understood and diagnostic tests are limited. We evaluated patients in a large health system to uncover cholesterol's role in the susceptibility, severity, and machine learning-based diagnosis of Lyme disease.

Methods: A longitudinal health system cohort comprised 1 019 175 individuals with electronic health record data and 50 329 with linked genetic data.

A machine learning model identifies patients in need of autoimmune disease testing using electronic health records.

Systemic autoimmune rheumatic diseases (SARDs) can lead to irreversible damage if left untreated, yet these patients often endure long diagnostic journeys before being diagnosed and treated. Machine learning may help overcome the challenges of diagnosing SARDs and inform clinical decision-making. Here, we developed and tested a machine learning model to identify patients who should receive rheumatological evaluation for SARDs using longitudinal electronic health records of 161,584 individuals from two institutions.

Phenome-wide Mendelian randomization study of plasma triglyceride levels and 2600 disease traits.

Background: Causality between plasma triglyceride (TG) levels and atherosclerotic cardiovascular disease (ASCVD) risk remains controversial despite more than four decades of study and two recent landmark trials, STRENGTH, and REDUCE-IT. Further unclear is the association between TG levels and non-atherosclerotic diseases across organ systems.

Methods: Here, we conducted a phenome-wide, two-sample Mendelian randomization (MR) analysis using inverse-variance weighted (IVW) regression to systematically infer the causal effects of plasma TG levels on 2600 disease traits in the European ancestry population of UK Biobank.

Machine learning-based marker for coronary artery disease: derivation and validation in two longitudinal cohorts.

Background: Binary diagnosis of coronary artery disease does not preserve the complexity of disease or quantify its severity or its associated risk with death; hence, a quantitative marker of coronary artery disease is warranted. We evaluated a quantitative marker of coronary artery disease derived from probabilities of a machine learning model.

Methods: In this cohort study, we developed and validated a coronary artery disease-predictive machine learning model using 95 935 electronic health records and assessed its probabilities as in-silico scores for coronary artery disease (ISCAD; range 0 [lowest probability] to 1 [highest probability]) in participants in two longitudinal biobank cohorts.

Oropharyngeal swallowing physiology and safety in patients with Idiopathic Pulmonary Fibrosis: a consecutive descriptive case series.

Introduction: Dysphagia occurs in multiple respiratory pathophysiologies, increasing the risk of pulmonary complications secondary to aspiration. Reflux associated aspiration and a dysregulated lung microbiome is implicated in Idiopathic Pulmonary Fibrosis (IPF), but swallowing dysfunction has not been described. We aimed to explore oropharyngeal swallowing in IPF patients, without known swallowing dysfunction.

Safety, tolerability, and efficacy of pirfenidone in patients with rheumatoid arthritis-associated interstitial lung disease: a randomised, double-blind, placebo-controlled, phase 2 study.

Background: Interstitial lung disease is a known complication of rheumatoid arthritis, with a lifetime risk of developing the disease in any individual of 7·7%. We aimed to assess the safety, tolerability, and efficacy of pirfenidone for the treatment of patients with rheumatoid arthritis-associated interstitial lung disease (RA-ILD).

Methods: TRAIL1 was a randomised, double-blind, placebo-controlled, phase 2 trial done in 34 academic centres specialising in interstitial lung disease in four countries (the UK, the USA, Australia, and Canada).

A tissue-level phenome-wide network map of colocalized genes and phenotypes in the UK Biobank.

Phenome-wide association studies identified numerous loci associated with traits and diseases. To help interpret these associations, we constructed a phenome-wide network map of colocalized genes and phenotypes. We generated colocalized signals using the Genotype-Tissue Expression data and genome-wide association results in UK Biobank.

Exposure to bile and gastric juice can impact the aerodigestive microbiome in people with cystic fibrosis.

Studies of microbiota reveal inter-relationships between the microbiomes of the gut and lungs. This relationship may influence the progression of lung disease, particularly in patients with cystic fibrosis (CF), who often experience extraoesophageal reflux (EOR). Despite identifying this relationship, it is not well characterised.

When should palliative care be introduced for people with progressive fibrotic interstitial lung disease? A meta-ethnography of the experiences of people with end-stage interstitial lung disease and their family carers.

Background: Little is currently known about the perspectives of people with interstitial lung disease and their carers in relation to the timing of palliative care conversations.

Aim: To establish patients' and carers' views on palliative care in interstitial lung disease and identify an optimum time to introduce the concept of palliative care.

Design: Meta-ethnography of qualitative evidence.