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Background: The prevalence of multimorbidity in patients with acute myocardial infarction (AMI) is increasing. It is unclear whether comorbidities cluster into distinct phenogroups and whether are associated with clinical trajectories., Methods: Survey-weighted analysis of the United States Nationwide Inpatient Sample (NIS) for patients admitted with a primary diagnosis of AMI in 2018. In-hospital outcomes included mortality, stroke, bleeding, and coronary revascularisation. Latent class analysis of 21 chronic conditions was used to identify comorbidity classes. Multivariable logistic and linear regressions were fitted for associations between comorbidity classes and outcomes., Results: Among 416,655 AMI admissions included in the analysis, mean (±SD) age was 67 (±13) years, 38% were females, and 76% White ethnicity. Overall, hypertension, coronary heart disease (CHD), dyslipidaemia, and diabetes were common comorbidities, but each of the identified five classes (C) included ≥1 predominant comorbidities defining distinct phenogroups: cancer/coagulopathy/liver disease class (C1); least burdened (C2); CHD/dyslipidaemia (largest/referent group, (C3)); pulmonary/valvular/peripheral vascular disease (C4); diabetes/kidney disease/heart failure class (C5). Odds ratio (95% confidence interval [CI]) for mortality ranged between 2.11 (1.89-2.37) in C2 to 5.57 (4.99-6.21) in C1. For major bleeding, OR for C1 was 4.48 (3.78; 5.31); for acute stroke, ORs ranged between 0.75 (0.60; 0.94) in C2 to 2.76 (2.27; 3.35) in C1; for coronary revascularization, ORs ranged between 0.34 (0.32; 0.36) in C1 to 1.41 (1.30; 1.53) in C4., Conclusions: We identified distinct comorbidity phenogroups that predicted in-hospital outcomes in patients admitted with AMI. Some conditions overlapped across classes, driven by the high comorbidity burden. Our findings demonstrate the predictive value and potential clinical utility of identifying patients with AMI with specific comorbidity clustering., Competing Interests: SSZ, LYS, EK, MKR, DS, DMA MAM, MR declare no competing interests. SW is a currently an employee of GSK. This does not alter our adherence to PLOS ONE policies on sharing data and materials., (Copyright: © 2023 Zghebi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Rare variants in ten genes have been reported to cause Mendelian sleep conditions characterised by extreme sleep duration or timing. These include familial natural short sleep (ADRB1, DEC2/BHLHE41, GRM1 and NPSR1), advanced sleep phase (PER2, PER3, CRY2, CSNK1D and TIMELESS) and delayed sleep phase (CRY1). The association of variants in these genes with extreme sleep conditions were usually based on clinically ascertained families, and their effects when identified in the population are unknown. We aimed to determine the effects of these variants on sleep traits in large population-based cohorts. We performed genetic association analysis of variants previously reported to be causal for Mendelian sleep and circadian conditions. Analyses were performed using 191,929 individuals with data on sleep and whole-exome or genome-sequence data from 4 population-based studies: UK Biobank, FINRISK, Health-2000-2001, and the Multi-Ethnic Study of Atherosclerosis (MESA). We identified sleep disorders from self-report, hospital and primary care data. We estimated sleep duration and timing measures from self-report and accelerometery data. We identified carriers for 10 out of 12 previously reported pathogenic variants for 8 of the 10 genes. They ranged in frequency from 1 individual with the variant in CSNK1D to 1,574 individuals with a reported variant in the PER3 gene in the UK Biobank. No carriers for variants reported in NPSR1 or PER2 were identified. We found no association between variants analyzed and extreme sleep or circadian phenotypes. Using sleep timing as a proxy measure for sleep phase, only PER3 and CRY1 variants demonstrated association with earlier and later sleep timing, respectively; however, the magnitude of effect was smaller than previously reported (sleep midpoint ~7 mins earlier and ~5 mins later, respectively). We also performed burden tests of protein truncating (PTVs) or rare missense variants for the 10 genes. Only PTVs in PER2 and PER3 were associated with a relevant trait (for example, 64 individuals with a PTV in PER2 had an odds ratio of 4.4 for being "definitely a morning person", P = 4x10-8; and had a 57-minute earlier midpoint sleep, P = 5x10-7). Our results indicate that previously reported variants for Mendelian sleep and circadian conditions are often not highly penetrant when ascertained incidentally from the general population., Competing Interests: The authors have declared that no competing interests exist.

Background: Atrial fibrillation (AF) is an important risk factor for ischaemic stroke, and AF incidence is expected to increase. Guidelines recommend using oral anticoagulants (OACs) to prevent the development of stroke. However, studies have reported the frequent underuse of OACs in AF patients. The objective of this study is to describe nonvalvular atrial fibrillation (NVAF) incidence in England and assess the clinical and socioeconomic factors associated with the underprescribing of OACs., Methods and Findings: We conducted a population-based retrospective cohort study using the UK Clinical Practice Research Datalink (CPRD) database to identify patients with NVAF aged ≥18 years and registered in English general practices between 2009 and 2019. Annual incidence rate of NVAF by age, deprivation quintile, and region was estimated. OAC prescribing status was explored for patients at risk for stroke and classified into the following: OAC, aspirin only, or no treatment. We used a multivariable multinomial logistic regression model to estimate relative risk ratios (RRRs) and 95% confidence intervals (CIs) of the factors associated with OAC or aspirin-only prescribing compared to no treatment in patients with NVAF who are recommended to take OAC. The multivariable regression was adjusted for age, sex, comorbidities, socioeconomic status, baseline treatment, frailty, bleeding risk factors, and takes into account clustering by general practice. Between 2009 and 2019, 12,517,191 patients met the criteria for being at risk of developing NVAF. After a median follow-up of 4.6 years, 192,265 patients had an incident NVAF contributing a total of 647,876 person-years (PYR) of follow-up. The overall age-adjusted incidence of NVAF per 10,000 PYR increased from 20.8 (95% CI: 20.4; 21.1) in 2009 to 25.5 (25.1; 25.9) in 2019. Higher incidence rates were observed for older ages and males. Among NVAF patients eligible for anticoagulation, OAC prescribing rose from 59.8% (95% CI: 59.0; 60.6) in 2009 to 83.2% (95% CI: 83.0; 83.4) in 2019. Several conditions were associated with lower risk of OAC prescribing: dementia [RRR 0.52 (0.47; 0.59)], liver disease 0.58 (0.50; 0.67), malignancy 0.74 (0.72; 0.77), and history of falls 0.82 (0.78; 0.85). Compared to white ethnicity, patients from black and other ethnic minorities were less likely to receive OAC; 0.78 (0.65; 0.94) and 0.76 (0.64; 0.91), respectively. Patients living in the most deprived areas were less likely to receive OAC 0.85 (0.79; 0.91) than patients living in the least deprived areas. Practices located in the East of England were associated with higher risk of prescribing aspirin only over no treatment than practices in London (RRR 1.22; 95% CI 1.02 to 1.45). The main limitation of this study is that these findings depends on accurate recording of conditions by health professionals and the inevitable residual confounding due to lack of data on certain factors that could be associated with under-prescribing of OACs., Conclusions: The incidence of NVAF increased between 2009 and 2015, before plateauing. Underprescribing of OACs in NVAF is associated with a range of comorbidities, ethnicity, and socioeconomic factors, demonstrating the need for initiatives to reduce inequalities in the care for AF patients., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: DA received research grants from AbbVie, Almirall, Celgene, Eli Lilly, Janssen, Novartis, UCB, and the Leo Foundation.

Background: Deaths in the first year of the Coronavirus Disease 2019 (COVID-19) pandemic in England and Wales were unevenly distributed socioeconomically and geographically. However, the full scale of inequalities may have been underestimated to date, as most measures of excess mortality do not adequately account for varying age profiles of deaths between social groups. We measured years of life lost (YLL) attributable to the pandemic, directly or indirectly, comparing mortality across geographic and socioeconomic groups., Methods and Findings: We used national mortality registers in England and Wales, from 27 December 2014 until 25 December 2020, covering 3,265,937 deaths. YLLs (main outcome) were calculated using 2019 single year sex-specific life tables for England and Wales. Interrupted time-series analyses, with panel time-series models, were used to estimate expected YLL by sex, geographical region, and deprivation quintile between 7 March 2020 and 25 December 2020 by cause: direct deaths (COVID-19 and other respiratory diseases), cardiovascular disease and diabetes, cancer, and other indirect deaths (all other causes). Excess YLL during the pandemic period were calculated by subtracting observed from expected values. Additional analyses focused on excess deaths for region and deprivation strata, by age-group. Between 7 March 2020 and 25 December 2020, there were an estimated 763,550 (95% CI: 696,826 to 830,273) excess YLL in England and Wales, equivalent to a 15% (95% CI: 14 to 16) increase in YLL compared to the equivalent time period in 2019. There was a strong deprivation gradient in all-cause excess YLL, with rates per 100,000 population ranging from 916 (95% CI: 820 to 1,012) for the least deprived quintile to 1,645 (95% CI: 1,472 to 1,819) for the most deprived. The differences in excess YLL between deprivation quintiles were greatest in younger age groups; for all-cause deaths, a mean of 9.1 years per death (95% CI: 8.2 to 10.0) were lost in the least deprived quintile, compared to 10.8 (95% CI: 10.0 to 11.6) in the most deprived; for COVID-19 and other respiratory deaths, a mean of 8.9 years per death (95% CI: 8.7 to 9.1) were lost in the least deprived quintile, compared to 11.2 (95% CI: 11.0 to 11.5) in the most deprived. For all-cause mortality, estimated deaths in the most deprived compared to the most affluent areas were much higher in younger age groups, but similar for those aged 85 or over. There was marked variability in both all-cause and direct excess YLL by region, with the highest rates in the North West. Limitations include the quasi-experimental nature of the research design and the requirement for accurate and timely recording., Conclusions: In this study, we observed strong socioeconomic and geographical health inequalities in YLL, during the first calendar year of the COVID-19 pandemic. These were in line with long-standing existing inequalities in England and Wales, with the most deprived areas reporting the largest numbers in potential YLL., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: CG declares receiving support from Abbot and BMS towards this work, consulting fees from Amgen and AstraZeneca, honoraria from AstraZeneca, participation in Data Safety Monitoring Boards for several trials (TACTIC, DUAL-ACS, DANBLOCK, PROFID, RAPID NSTEMI, STEEER-AF), stock options with the European Heart Journal Quality of Care and Clinical Outcomes as Deputy Editor, and other financial interests with Wondr Medical. MKR declares Cell catapult and NovoNordisk consulting fees, unrelated to the content of the manuscript. None of the other authors have anything relevant to declare.

Over the last decade the availability of SNP-trait associations from genome-wide association studies has led to an array of methods for performing Mendelian randomization studies using only summary statistics. A common feature of these methods, besides their intuitive simplicity, is the ability to combine data from several sources, incorporate multiple variants and account for biases due to weak instruments and pleiotropy. With the advent of large and accessible fully-genotyped cohorts such as UK Biobank, there is now increasing interest in understanding how best to apply these well developed summary data methods to individual level data, and to explore the use of more sophisticated causal methods allowing for non-linearity and effect modification. In this paper we describe a general procedure for optimally applying any two sample summary data method using one sample data. Our procedure first performs a meta-analysis of summary data estimates that are intentionally contaminated by collider bias between the genetic instruments and unmeasured confounders, due to conditioning on the observed exposure. These estimates are then used to correct the standard observational association between an exposure and outcome. Simulations are conducted to demonstrate the method's performance against naive applications of two sample summary data MR. We apply the approach to the UK Biobank cohort to investigate the causal role of sleep disturbance on HbA1c levels, an important determinant of diabetes. Our approach can be viewed as a generalization of Dudbridge et al. (Nat. Comm. 10: 1561), who developed a technique to adjust for index event bias when uncovering genetic predictors of disease progression based on case-only data. Our work serves to clarify that in any one sample MR analysis, it can be advantageous to estimate causal relationships by artificially inducing and then correcting for collider bias., Competing Interests: The authors have declared that no competing interests exist.

Background: The association between psoriasis and the risk of cancer has been investigated in numerous studies utilising electronic health records (EHRs), with conflicting results in the extent of the association., Objectives: To assess concordance and timing of cancer recording between primary care, hospital and death registration data for people with and without psoriasis., Methods: Cohort studies delineated using primary care EHRs from the Clinical Practice Research Datalink (CPRD) GOLD and Aurum databases, with linkage to hospital episode statistics (HES), Office for National Statistics (ONS) mortality data and indices of multiple deprivation (IMD). People with psoriasis were matched to those without psoriasis by age, sex and general practice. Cancer recording between databases was investigated by proportion concordant, that being the presence of cancer record in both source and comparator datasets. Delay in recording cancer diagnoses between CPRD and HES records and predictors of discordance were also assessed., Results: 58,904 people with psoriasis and 350,592 comparison patients were included using CPRD GOLD; whereas 213,400 people with psoriasis and 1,268,998 comparison patients were included in CPRD Aurum. For all cancer records (excluding keratinocyte), concordance between CPRD and HES was greater than 80%. Concordance for same-site cancer records was markedly lower (<68% GOLD-linked data; <72% Aurum-linked data). Concordance of non-Hodgkin lymphoma and liver cancer recording between CPRD and HES was lower for people with psoriasis compared to those without., Conclusions: Concordance between CPRD and HES is poor when restricted to cancers of the same site, with greater discordance in people with psoriasis for some cancers of specific sites. The use of linked patient-level data is an important step in reducing misclassification of cancer outcomes in epidemiological studies using routinely collected electronic health records., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: MKR reports receiving research funding from Novo Nordisk, consultancy fees from Novo Nordisk and Roche Diabetes Care, and modest owning of shares in GlaxoSmithKline. CEMG reports receiving honorariums or research grants from AbbVie, Almirall, Celgene, Eli Lilly, Galderma, Janssen, LEO Pharma, Novartis, Pfizer, Sandoz, Sanofi, and UCB Pharma. DMA reports research grants from AbbVie, Almirall, Celgene, Eli Lilly, Janssen, Novartis, UCB, and the Leo Foundation. This does not alter our adherence to PLOS ONE policies on sharing data and materials. All other authors declare that there are no relationships or activities that might bias, or be perceived to bias, their work.