Deriving research-quality phenotypes from national electronic health records to advance precision medicine: a UK Biobank case-study

High-throughput genotyping and increased availability of electronic health records (EHR) are giving scientists the unprecedented opportunity to exploit routinely generated clinical data to advance precision medicine. The extent to which national structured EHR in the United Kingdom can be utilized in genome-wide association studies (GWAS) has not been systematically examined. In this study, we evaluate the performance of an EHR-derived acute myocardial infarction phenotype (AMI) for performing GWAS in the UK Biobank

Discovering and validating disease subtypes for heart failure using unsupervised machine learning methods

Notable heterogeneity exists in the clinical presentation of heart failure (HF) patients. Current subtype classifications are based on ejection fraction may not fully capture the aetiological and prognostic heterogeneity of HF. The use of unsupervised machine learning (ML) approaches, such as cluster analysis, on large-scale observational data from electronic health records (EHR), can enable the discovery of novel subtypes and guide the characterization of their clinical manifestation. Clustering methods can group HF patients based on similarities between their clinical features without making a priori assumptions about the distribution of the data. We sought to discover, characterize and replicate HF subtypes by applying a clustering method on a heterogeneous HF population derived from phenotypically rich EHR. Characterization of HF subtypes using EHR derived variable may enable more precise large-scale genomic analysis to inform better prevention, diagnostic and treatment strategies

A semi-supervised approach for rapidly creating clinical biomarker phenotypes in the UK Biobank using different primary care EHR and clinical terminology systems

Objectives: The UK Biobank (UKB) is making primary care electronic health records (EHRs) for 500 000 participants available for COVID-19-related research. Data are extracted from four sources, recorded using five clinical terminologies and stored in different schemas. The aims of our research were to: (a) develop a semi-supervised approach for bootstrapping EHR phenotyping algorithms in UKB EHR, and (b) to evaluate our approach by implementing and evaluating phenotypes for 31 common biomarkers. Materials and Methods: We describe an algorithmic approach to phenotyping biomarkers in primary care EHR involving (a) bootstrapping definitions using existing phenotypes, (b) excluding generic, rare, or semantically distant terms, (c) forward-mapping terminology terms, (d) expert review, and (e) data extraction. We evaluated the phenotypes by assessing the ability to reproduce known epidemiological associations with all-cause mortality using Cox proportional hazards models. Results: We created and evaluated phenotyping algorithms for 31 biomarkers many of which are directly related to COVID-19 complications, for example diabetes, cardiovascular disease, respiratory disease. Our algorithm identified 1651 Read v2 and Clinical Terms Version 3 terms and automatically excluded 1228 terms. Clinical review excluded 103 terms and included 44 terms, resulting in 364 terms for data extraction (sensitivity 0.89, specificity 0.92). We extracted 38 190 682 events and identified 220 978 participants with at least one biomarker measured. Discussion and conclusion: Bootstrapping phenotyping algorithms from similar EHR can potentially address pre-existing methodological concerns that undermine the outputs of biomarker discovery pipelines and provide research-quality phenotyping algorithms

The Relationship Between Glycaemia, Cognitive Function, Structural Brain Outcomes and Dementia: A Mendelian Randomisation Study in the UK Biobank

We investigated the relationship between glycaemia and cognitive function, brain structure and incident dementia using bidirectional Mendelian randomisation (MR). Data were from UK Biobank (n∼500,000). Our exposures were genetic instruments for type-2 diabetes (157 variants) and HbA1c (51 variants) and our outcomes were reaction time (RT), visual memory, hippocampal and white matter hyperintensity volumes, Alzheimer’s dementia (AD). We also investigated associations between genetic variants for RT (43 variants) and, diabetes and HbA1c. We used conventional inverse-variance weighted (IVW) MR, alongside MR sensitivity analyses. Using IVW, genetic liability to type-2 diabetes was not associated with reaction time (exponentiated ß=1.00, 95%CI=1.00; 1.00), visual memory (expß=1.00, 95%CI=0.99; 1.00), white matter hyperintensity volume (WMHV) (expß=0.99, 95%CI=0.97; 1.01), hippocampal volume (HV) (ß coefficient mm3=4.56, 95%CI=-3.98; 13.09) or AD (OR 0.89, 95%CI=0.78; 1.01). HbA1c was not associated with RT (expß=1.01, 95%CI=1.00; 1.01), WMHV (expß=0.94, 95%CI=0.81; 1.08), HV (ß=7.21, 95%CI=-54.06; 68.48), or risk of AD (OR 0.94, 95%CI=0.47; 1.86), but HbA1c was associated with visual memory (expß=1.06, 95%CI=1.05; 1.07) using a weighted median. IVW showed that reaction time was not associated with diabetes risk (OR 0.96, 95%CI=0.63; 1.46) or with HbA1c (ß coefficient mmol/mol=-0.08, 95%CI=-0.57; 0.42). Overall, we observed little evidence of causal association between genetic instruments for T2D or peripheral glycaemia and some measures of cognition and brain structure in midlife

Evaluation of the Association of IGF2BP2 Variants With Type 2 Diabetes in French Caucasians

OBJECTIVE—We performed a comprehensive genetic association study of common variation spanning the IGF2BP2 locus in order to replicate the association of the “confirmed” type 2 diabetes susceptibility variants rs4402960 and rs1470579 in the French Caucasian population and to further characterize the susceptibility variants at this novel locus

Metabolomic Profiling of Statin Use and Genetic Inhibition of HMG-CoA Reductase

Background Statins are first-line therapy for cardiovascular disease prevention, but their systemic effects across lipoprotein subclasses, fatty acids, and circulating metabolites remain incompletely characterized. Objectives This study sought to determine the molecular effects of statin therapy on multiple metabolic pathways. Methods Metabolic profiles based on serum nuclear magnetic resonance metabolomics were quantified at 2 time points in 4 population-based cohorts from the United Kingdom and Finland (N = 5,590; 2.5 to 23.0 years of follow-up). Concentration changes in 80 lipid and metabolite measures during follow-up were compared between 716 individuals who started statin therapy and 4,874 persistent nonusers. To further understand the pharmacological effects of statins, we used Mendelian randomization to assess associations of a genetic variant known to mimic inhibition of HMG-CoA reductase (the intended drug target) with the same lipids and metabolites for 27,914 individuals from 8 population-based cohorts. Results Starting statin therapy was associated with numerous lipoprotein and fatty acid changes, including substantial lowering of remnant cholesterol (80% relative to low-density lipoprotein cholesterol [LDL-C]), but only modest lowering of triglycerides (25% relative to LDL-C). Among fatty acids, omega-6 levels decreased the most (68% relative to LDL-C); other fatty acids were only modestly affected. No robust changes were observed for circulating amino acids, ketones, or glycolysis-related metabolites. The intricate metabolic changes associated with statin use closely matched the association pattern with rs12916 in the HMGCR gene (R2 = 0.94, slope 1.00 ± 0.03). Conclusions Statin use leads to extensive lipid changes beyond LDL-C and appears efficacious for lowering remnant cholesterol. Metabolomic profiling, however, suggested minimal effects on amino acids. The results exemplify how detailed metabolic characterization of genetic proxies for drug targets can inform indications, pleiotropic effects, and pharmacological mechanisms

The influence of CYP2D6 and CYP2C19 genetic variation on diabetes mellitus risk in people taking antidepressants and antipsychotics

CYP2D6 and CYP2C19 enzymes are essential in the metabolism of antidepressants and antipsychotics. Genetic variation in these genes may increase risk of adverse drug reactions. Antidepressants and antipsychotics have previously been associated with risk of diabetes. We examined whether individual genetic differences in CYP2D6 and CYP2C19 contribute to these effects. We identified 31,579 individuals taking antidepressants and 2699 taking antipsychotics within UK Biobank. Participants were classified as poor, intermediate, or normal metabolizers of CYP2D6, and as poor, intermediate, normal, rapid, or ultra-rapid metabolizers of CYP2C19. Risk of diabetes mellitus represented by HbA1c level was examined in relation to the metabolic phenotypes. CYP2D6 poor metabolizers taking paroxetine had higher Hb1Ac than normal metabolizers (mean difference: 2.29 mmol/mol; p < 0.001). Among participants with diabetes who were taking venlafaxine, CYP2D6 poor metabolizers had higher HbA1c levels compared to normal metabolizers (mean differences: 10.15 mmol/mol; p < 0.001. Among participants with diabetes who were taking fluoxetine, CYP2D6 intermediate metabolizers and decreased HbA1c, compared to normal metabolizers (mean difference −7.74 mmol/mol; p = 0.017). We did not observe any relationship between CYP2D6 or CYP2C19 metabolic status and HbA1c levels in participants taking antipsychotic medication. Our results indicate that the impact of genetic variation in CYP2D6 differs depending on diabetes status. Although our findings support existing clinical guidelines, further research is essential to inform pharmacogenetic testing for people taking antidepressants and antipsychotics

Associations Between Measures of Sarcopenic Obesity and Risk of Cardiovascular Disease and Mortality: A Cohort Study and Mendelian Randomization Analysis Using the UK Biobank

Background The "healthy obese" hypothesis suggests the risks associated with excess adiposity are reduced in those with higher muscle quality (mass/strength). Alternative possibilities include loss of muscle quality as people become unwell (reverse causality) or unmeasured confounding. Methods and Results We conducted a cohort study using the UK Biobank (n=452 931). Baseline body mass index ( BMI) was used to quantify adiposity and handgrip strength ( HGS ) used for muscle quality. Outcomes were fatal and non-fatal cardiovascular disease, and mortality. As a secondary analysis we used waist-hip-ratio or fat mass percentage instead of BMI , and skeletal muscle mass index instead of HGS . In a subsample, we used gene scores for BMI , waist-hip-ratio and HGS in a Mendelian randomization ( MR ). BMI defined obesity was associated with an increased risk of all outcomes (hazard ratio [ HR ] range 1.10-1.82). Low HGS was associated with increased risks of cardiovascular and all-cause mortality ( HR range 1.39-1.72). HR s for the association between low HGS and cardiovascular disease events were smaller ( HR range 1.05-1.09). There was no suggestion of an interaction between HGS and BMI to support the healthy obese hypothesis. Results using other adiposity metrics were similar. There was no evidence of an association between skeletal muscle mass index and any outcome. Factorial Mendelian randomization confirmed no evidence for an interaction. Low genetically predicted HGS was associated with an increased risk of mortality ( HR range 1.08-1.19). Conclusions Our analyses do not support the healthy obese concept, with no evidence that the adverse effect of obesity on outcomes was reduced by improved muscle quality. Lower HGS was associated with increased risks of mortality in both observational and MR analyses, suggesting reverse causality may not be the sole explanation

Replication and Characterization of Association between ABO SNPs and Red Blood Cell Traits by Meta-Analysis in Europeans.

Red blood cell (RBC) traits are routinely measured in clinical practice as important markers of health. Deviations from the physiological ranges are usually a sign of disease, although variation between healthy individuals also occurs, at least partly due to genetic factors. Recent large scale genetic studies identified loci associated with one or more of these traits; further characterization of known loci and identification of new loci is necessary to better understand their role in health and disease and to identify potential molecular mechanisms. We performed meta-analysis of Metabochip association results for six RBC traits-hemoglobin concentration (Hb), hematocrit (Hct), mean corpuscular hemoglobin (MCH), mean corpuscular hemoglobin concentration (MCHC), mean corpuscular volume (MCV) and red blood cell count (RCC)-in 11 093 Europeans from seven studies of the UCL-LSHTM-Edinburgh-Bristol (UCLEB) Consortium. We identified 394 non-overlapping SNPs in five loci at genome-wide significance: 6p22.1-6p21.33 (with HFE among others), 6q23.2 (with HBS1L among others), 6q23.3 (contains no genes), 9q34.3 (only ABO gene) and 22q13.1 (with TMPRSS6 among others), replicating previous findings of association with RBC traits at these loci and extending them by imputation to 1000 Genomes. We further characterized associations between ABO SNPs and three traits: hemoglobin, hematocrit and red blood cell count, replicating them in an independent cohort. Conditional analyses indicated the independent association of each of these traits with ABO SNPs and a role for blood group O in mediating the association. The 15 most significant RBC-associated ABO SNPs were also associated with five cardiometabolic traits, with discordance in the direction of effect between groups of traits, suggesting that ABO may act through more than one mechanism to influence cardiometabolic risk.British Heart Foundation (Grant ID: RG/10/12/28456, RG/08/013/25942, RG/13/16/30528, RG/98002, RG/07/008/23674); Medical Research Council (Grant ID: G0000934, G0500877, MC_UU_12019/1, K013351); Wellcome Trust (Grant ID: 068545/Z/02, 097451/Z/11/Z); European Commission Framework Programme 6 (Grant ID: 018996); French Ministry of Research; Department of Health Policy Research Programme (England); Chief Scientist Office of Scotland (Grant ID: CZB/4/672, CZQ/1/38); National Institute on Ageing (NIA) (Grant ID: AG1764406S1, 5RO1AG13196); Pfizer plc (Unrestricted Investigator Led Grant); Diabetes UK (Clinical Research Fellowship 10/0003985); Stroke Association; National Heart Lung and Blood Institute (5RO1HL036310); Agency for Health Care Policy Research (HS06516); John D. and Catherine T. MacArthur Foundation Research Networks on Successful Midlife Development and Socio-economic Status and Health; Swiss National Science Foundation (33CSCO-122661); GlaxoSmithKline. Faculty of Biology and Medicine of Lausanne,Switzerland.This is the final version of the article. It first appeared from Public Library of Science (PLOS) via http://dx.doi.org/10.1371/journal.pone.015691