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BACKGROUND: Telomere length is associated with risk of several age-related diseases and cancers. We aimed to investigate the extent to which telomere length might be modifiable through lifestyle and behaviour, and whether such modification has any clinical consequences. METHODS: In this population-based study, we included participants from UK Biobank who had leukocyte telomere length (LTL) measurement, ethnicity, and white blood cell count data. We investigated associations of LTL with 117 potentially modifiable traits, as well as two indices of healthy behaviours incorporating between them smoking, physical activity, diet, maintenance of a healthy bodyweight, and alcohol intake, using both available and imputed data. To help interpretation, associations were summarised as the number of equivalent years of age-related change in LTL by dividing the trait β coefficients with the age β coefficient. We used mendelian randomisation to test causality of selected associations. We investigated whether the associations of LTL with 22 diseases were modified by the number of healthy behaviours and the extent to which the associations of more healthy behaviours with greater life expectancy and lower risk of coronary artery disease might be mediated through LTL. FINDINGS: 422 797 participants were available for the analysis (227 620 [53·8%] were women and 400 036 [94·6%] were White). 71 traits showed significant (p

Aims: This study aims to develop the first race‐specific and sex‐specific risk prediction models for heart failure with preserved (HFpEF) and reduced ejection fraction (HFrEF). Methods and results: We created a cohort of 1.8 million individuals who had an outpatient clinic visit between 2002 and 2007 within the Veterans Affairs (VA) Healthcare System and obtained information on HFpEF, HFrEF, and several risk factors from electronic health records (EHR). Variables were selected for the risk prediction models in a ‘derivation cohort’ that consisted of individuals with baseline date in 2002, 2003, or 2004 using a forward stepwise selection based on a change in C‐index threshold. Discrimination and calibration were assessed in the remaining participants (internal ‘validation cohort’). A total of 66 831 individuals developed HFpEF, and 92 233 developed HFrEF (52 679 and 71 463 in the derivation cohort) over a median of 11.1 years of follow‐up. The HFpEF risk prediction model included age, diabetes, BMI, COPD, previous MI, antihypertensive treatment, SBP, smoking status, atrial fibrillation, and estimated glomerular filtration rate (eGFR), while the HFrEF model additionally included previous CAD. For the HFpEF model, C‐indices were 0.74 (SE = 0.002) for white men, 0.76 (0.005) for black men, 0.79 (0.015) for white women, and 0.77 (0.026) for black women, compared with 0.72 (0.002), 0.72 (0.004), 0.77 (0.017), and 0.75 (0.028), respectively, for the HFrEF model. These risk prediction models were generally well calibrated in each race‐specific and sex‐specific stratum of the validation cohort. Conclusions: Our race‐specific and sex‐specific risk prediction models, which used easily obtainable clinical variables, can be a useful tool to implement preventive strategies or subtype‐specific prevention trials in the nine million users of the VA healthcare system and the general population after external validation.

BACKGROUND: Frailty is a multidimensional syndrome of decline that affects multiple systems and predisposes to adverse health outcomes. Although chronological age is the major risk factor, inter-individual variation in risk is not fully understood. Leucocyte telomere length (LTL), a proposed marker of biological age, has been associated with risk of many diseases. We sought to determine whether LTL is associated with risk of frailty. METHODS: We utilized cross-sectional data from 441 781 UK Biobank participants (aged 40-69 years), with complete data on frailty indicators and LTL. Frailty was defined as the presence of at least three of five indicators: weaker grip strength, slower walking pace, weight loss in the past year, lower physical activity, and exhaustion in the past 2 weeks. LTL was measured using a validated qPCR method and reported as a ratio of the telomere repeat number (T) to a single-copy gene (S) (T/S ratio). Association of LTL with frailty was evaluated using adjusted (chronological age, sex, deprivation, smoking, alcohol intake, body mass index, and multimorbidity) multinomial and ordinal regression models, and results are presented as relative risk (RRR) or odds ratios (OR), respectively, alongside the 95% confidence interval (CI). Mendelian randomization (MR), using 131 genetic variants associated with LTL, was used to assess if the association of LTL with frailty was causal. RESULTS: Frail participants (4.6%) were older (median age difference (95% CI): 3 (2.5; 3.5) years, P = 2.73 × 10-33 ), more likely to be female (61%, P = 1.97 × 10-129 ), and had shorter LTL (-0.13SD vs. 0.03SD, P = 5.43 × 10-111 ) than non-frail. In adjusted analyses, both age and LTL were associated with frailty (RRR = 1.03 (95% CI: 1.02; 1.04) per year of older chronological age, P = 3.99 × 10-12 ; 1.10 (1.08; 1.11) per SD shorter LTL, P = 1.46 × 10-30 ). Within each age group (40-49, 50-59, 60-69 years), the prevalence of frailty was about 33% higher in participants with shorter (-2SD) versus longer telomeres (+2SD). MR analysis showed an association of LTL with frailty that was directionally consistent with the observational association, but not statistically significant (MR-Median: OR (95% CI): 1.08 (0.98; 1.19) per SD shorter LTL, P = 0.13). CONCLUSIONS: Inter-individual variation in LTL is associated with the risk of frailty independently of chronological age and other risk factors. Our findings provide evidence for an additional biological determinant of frailty., This research has been conducted using the UK Biobank Resource under Application Number 6077. Generation of the LTL measurements was funded by the UK Medical Research Council (MRC), Biotechnology and Biological Sciences Research Council and British Heart Foundation (BHF) through MRC grant MR/M012816/1.

Cardiovascular disease (CVD) risk-prediction models are used to identify high-risk individuals and guide statin initiation. However, these models are usually derived from individuals who might initiate statins during follow-up. We present a simple approach to address statin initiation to predict “statin-naive” CVD risk. We analyzed primary care data (2004–2017) from the UK Clinical Practice Research Datalink for 1,678,727 individuals (aged 40–85 years) without CVD or statin treatment history at study entry. We derived age- and sex-specific prediction models including conventional risk factors and a time-dependent effect of statin initiation constrained to 25% risk reduction (from trial results). We compared predictive performance and measures of public-health impact (e.g., number needed to screen to prevent 1 event) against models ignoring statin initiation. During a median follow-up of 8.9 years, 103,163 individuals developed CVD. In models accounting for (versus ignoring) statin initiation, 10-year CVD risk predictions were slightly higher; predictive performance was moderately improved. However, few individuals were reclassified to a high-risk threshold, resulting in negligible improvements in number needed to screen to prevent 1 event. In conclusion, incorporating statin effects from trial results into risk-prediction models enables statin-naive CVD risk estimation and provides moderate gains in predictive ability but had a limited impact on treatment decision-making under current guidelines in this population.

Funder: Wellcome Trust, BACKGROUND: Thromboses in unusual locations after the Coronavirus Disease 2019 (COVID-19) vaccine ChAdOx1-S have been reported, although their frequency with vaccines of different types is uncertain at a population level. The aim of this study was to estimate the population-level risks of hospitalised thrombocytopenia and major arterial and venous thromboses after COVID-19 vaccination. METHODS AND FINDINGS: In this whole-population cohort study, we analysed linked electronic health records from adults living in England, from 8 December 2020 to 18 March 2021. We estimated incidence rates and hazard ratios (HRs) for major arterial, venous, and thrombocytopenic outcomes 1 to 28 and >28 days after first vaccination dose for ChAdOx1-S and BNT162b2 vaccines. Analyses were performed separately for ages 28 days post-ChAdOx1-S, 359 (338 to 382), ≤28 days post-BNT162b2-S, 241 (229 to 253); >28 days post-BNT162b2-S 277 (263 to 291). The crude incidence rates (per 100,000 person-years) of all arterial events were as follows: prevaccination, 546 (95% CI: 541 to 555); ≤28 days post-ChAdOx1-S, 1,211 (1,185 to 1,237); >28 days post-ChAdOx1-S, 1678 (1,630 to 1,726), ≤28 days post-BNT162b2-S, 1,242 (1,214 to 1,269); >28 days post-BNT162b2-S, 1,539 (1,507 to 1,572). Adjusted HRs (aHRs) 1 to 28 days after ChAdOx1-S, compared with unvaccinated rates, at ages

Funder: Health Data Research UK EU/EFPIA Innovative Medicines Initiative Joint Undertaking BigData@Heart (11607)., Funder: Health Data Research UK, Telomeres, the end fragments of chromosomes, play key roles in cellular proliferation and senescence. Here we characterize the genetic architecture of naturally occurring variation in leukocyte telomere length (LTL) and identify causal links between LTL and biomedical phenotypes in 472,174 well-characterized UK Biobank participants. We identified 197 independent sentinel variants associated with LTL at 138 genomic loci (108 new). Genetically determined differences in LTL were associated with multiple biological traits, ranging from height to bone marrow function, as well as several diseases spanning neoplastic, vascular and inflammatory pathologies. Finally, we estimated that, at the age of 40 years, people with an LTL >1 s.d. shorter than the population mean had a 2.5-year-lower life expectancy compared with the group with ≥1 s.d. longer LDL. Overall, we furnish new insights into the genetic regulation of LTL, reveal wide-ranging influences of LTL on physiological traits, diseases and longevity, and provide a powerful resource available to the global research community.

Background Genetic, lifestyle, and environmental factors can lead to perturbations in circulating lipid levels and increase the risk of cardiovascular and metabolic diseases. However, how changes in individual lipid species contribute to disease risk is often unclear. Moreover, little is known about the role of lipids on cardiovascular disease in Pakistan, a population historically underrepresented in cardiovascular studies. Methods We characterised the genetic architecture of the human blood lipidome in 5662 hospital controls from the Pakistan Risk of Myocardial Infarction Study (PROMIS) and 13,814 healthy British blood donors from the INTERVAL study. We applied a candidate causal gene prioritisation tool to link the genetic variants associated with each lipid to the most likely causal genes, and Gaussian Graphical Modelling network analysis to identify and illustrate relationships between lipids and genetic loci. Results We identified 253 genetic associations with 181 lipids measured using direct infusion high-resolution mass spectrometry in PROMIS, and 502 genetic associations with 244 lipids in INTERVAL. Our analyses revealed new biological insights at genetic loci associated with cardiometabolic diseases, including novel lipid associations at the LPL, MBOAT7, LIPC, APOE-C1-C2-C4, SGPP1, and SPTLC3 loci. Conclusions Our findings, generated using a distinctive lipidomics platform in an understudied South Asian population, strengthen and expand the knowledge base of the genetic determinants of lipids and their association with cardiometabolic disease-related loci.

BackgroundPre-term pre-eclampsia is a major cause of maternal and perinatal morbidity and mortality worldwide. A multi-centre randomized–controlled trial has shown that first-trimester screening followed by treatment of high-risk women with aspirin reduces the risk of pre-term pre-eclampsia. However, the biomarkers currently employed in risk prediction are only weakly associated with the outcome.MethodsWe conducted a case–cohort study within the Pregnancy Outcome Prediction study to analyse untargeted maternal serum metabolomics in samples from 12, 20, 28 and 36 weeks of gestational age (wkGA) in women with pre-eclampsia delivering at term (n = 165) and pre-term (n = 29), plus a random sample of the cohort (n = 325). We used longitudinal linear mixed models to identify candidate metabolites at 20/28 wkGA that differed by term pre-eclampsia status. Candidates were validated using measurements at 36 wkGA in the same women. We then tested the association between the 12-, 20- and 28-wkGA measurements and pre-term pre-eclampsia. We externally validated the association using 24- to 28-wkGA samples from the Born in Bradford study (25 cases and 953 controls).ResultsWe identified 100 metabolites that differed most at 20/28 wkGA in term pre-eclampsia. Thirty-three of these were validated (P Conclusions4-hydroxyglutamate is a novel biochemical predictor of pre-eclampsia that provides better first-trimester prediction of pre-term disease than currently employed protein biomarkers.

Objective To describe a novel England-wide electronic health record (EHR) resource enabling whole population research on covid-19 and cardiovascular disease while ensuring data security and privacy and maintaining public trust. Design Data resource comprising linked person level records from national healthcare settings for the English population, accessible within NHS Digital’s new trusted research environment. Setting EHRs from primary care, hospital episodes, death registry, covid-19 laboratory test results, and community dispensing data, with further enrichment planned from specialist intensive care, cardiovascular, and covid-19 vaccination data. Participants 54.4 million people alive on 1 January 2020 and registered with an NHS general practitioner in England. Main outcome measures Confirmed and suspected covid-19 diagnoses, exemplar cardiovascular conditions (incident stroke or transient ischaemic attack and incident myocardial infarction) and all cause mortality between 1 January and 31 October 2020. Results The linked cohort includes more than 96% of the English population. By combining person level data across national healthcare settings, data on age, sex, and ethnicity are complete for around 95% of the population. Among 53.3 million people with no previous diagnosis of stroke or transient ischaemic attack, 98 721 had a first ever incident stroke or transient ischaemic attack between 1 January and 31 October 2020, of which 30% were recorded only in primary care and 4% only in death registry records. Among 53.2 million people with no previous diagnosis of myocardial infarction, 62 966 had an incident myocardial infarction during follow-up, of which 8% were recorded only in primary care and 12% only in death registry records. A total of 959 470 people had a confirmed or suspected covid-19 diagnosis (714 162 in primary care data, 126 349 in hospital admission records, 776 503 in covid-19 laboratory test data, and 50 504 in death registry records). Although 58% of these were recorded in both primary care and covid-19 laboratory test data, 15% and 18%, respectively, were recorded in only one. Conclusions This population-wide resource shows the importance of linking person level data across health settings to maximise completeness of key characteristics and to ascertain cardiovascular events and covid-19 diagnoses. Although this resource was initially established to support research on covid-19 and cardiovascular disease to benefit clinical care and public health and to inform healthcare policy, it can broaden further to enable a wide range of research.

Background Polygenic risk scores (PRSs) can stratify populations into cardiovascular disease (CVD) risk groups. We aimed to quantify the potential advantage of adding information on PRSs to conventional risk factors in the primary prevention of CVD. Methods and findings Using data from UK Biobank on 306,654 individuals without a history of CVD and not on lipid-lowering treatments (mean age [SD]: 56.0 [8.0] years; females: 57%; median follow-up: 8.1 years), we calculated measures of risk discrimination and reclassification upon addition of PRSs to risk factors in a conventional risk prediction model (i.e., age, sex, systolic blood pressure, smoking status, history of diabetes, and total and high-density lipoprotein cholesterol). We then modelled the implications of initiating guideline-recommended statin therapy in a primary care setting using incidence rates from 2.1 million individuals from the Clinical Practice Research Datalink. The C-index, a measure of risk discrimination, was 0.710 (95% CI 0.703–0.717) for a CVD prediction model containing conventional risk predictors alone. Addition of information on PRSs increased the C-index by 0.012 (95% CI 0.009–0.015), and resulted in continuous net reclassification improvements of about 10% and 12% in cases and non-cases, respectively. If a PRS were assessed in the entire UK primary care population aged 40–75 years, assuming that statin therapy would be initiated in accordance with the UK National Institute for Health and Care Excellence guidelines (i.e., for persons with a predicted risk of ≥10% and for those with certain other risk factors, such as diabetes, irrespective of their 10-year predicted risk), then it could help prevent 1 additional CVD event for approximately every 5,750 individuals screened. By contrast, targeted assessment only among people at intermediate (i.e., 5% to, Luanluan Sun and colleagues investigate whether adding polygenic risk scores to conventional risk factors of cardiovascular disease helps predict disease risk., Author summary Why was this study done? Application of polygenic risk scores (PRSs) has opened opportunities to enhance risk stratification and prevention for common diseases. The clinical utility of PRSs in cardiovascular disease (CVD) risk prediction is, however, uncertain. Previous analyses have generally focused only on coronary heart disease (CHD) rather than the composite outcome of CHD and stroke, and have often lacked modelling of clinical implications of initiating guideline-recommended interventions (e.g., statin therapy). What did the researchers do and find? We quantified the incremental predictive gain with PRSs on top of conventional risk factors using data on 306,654 individuals from UK Biobank. We modelled the population health implications of initiating statin therapy as recommended by current guidelines using data from 2.1 million individuals from the Clinical Practice Research Datalink. Addition of information on PRSs to a conventional risk prediction model increased the C-index (a measure of risk discrimination) and improved risk classification of cases and non-cases. We estimated that targeted assessment of PRSs among people at intermediate (i.e., 5% to

Funder: University of Cambridge, The COVID-19 global pandemic is a threat not only to the health of millions of individuals, but also to the stability of infrastructure and economies around the world. The disease will inevitably place an overwhelming burden on healthcare systems that cannot be effectively dealt with by existing facilities or responses based on conventional approaches. We believe that a rigorous clinical and societal response can only be mounted by using intelligence derived from a variety of data sources to better utilize scarce healthcare resources, provide personalized patient management plans, inform policy, and expedite clinical trials. In this paper, we introduce five of the most important challenges in responding to COVID-19 and show how each of them can be addressed by recent developments in machine learning (ML) and artificial intelligence (AI). We argue that the integration of these techniques into local, national, and international healthcare systems will save lives, and propose specific methods by which implementation can happen swiftly and efficiently. We offer to extend these resources and knowledge to assist policymakers seeking to implement these techniques.

In cross-platform analyses of 174 metabolites, we identify 499 associations (P < 4.9 × 10-10) characterized by pleiotropy, allelic heterogeneity, large and nonlinear effects and enrichment for nonsynonymous variation. We identify a signal at GLP2R (p.Asp470Asn) shared among higher citrulline levels, body mass index, fasting glucose-dependent insulinotropic peptide and type 2 diabetes, with β-arrestin signaling as the underlying mechanism. Genetically higher serine levels are shown to reduce the likelihood (by 95%) and predict development of macular telangiectasia type 2, a rare degenerative retinal disease. Integration of genomic and small molecule data across platforms enables the discovery of regulators of human metabolism and translation into clinical insights.

Iron is essential for many biological functions and iron deficiency and overload have major health implications. We performed a meta-analysis of three genome-wide association studies from Iceland, the UK and Denmark of blood levels of ferritin (N=246,139), total iron binding capacity (N=135,430), iron (N=163,511) and transferrin saturation (N=131,471). We found 62 independent sequence variants associating with iron homeostasis parameters at 56 loci, including 46 novel loci. Variants at DUOX2, F5, SLC11A2 and TMPRSS6 associate with iron deficiency anemia, while variants at TF, HFE, TFR2 and TMPRSS6 associate with iron overload. A HBS1L-MYB intergenic region variant associates both with increased risk of iron overload and reduced risk of iron deficiency anemia. The DUOX2 missense variant is present in 14% of the population, associates with all iron homeostasis biomarkers, and increases the risk of iron deficiency anemia by 29%. The associations implicate proteins contributing to the main physiological processes involved in iron homeostasis: iron sensing and storage, inflammation, absorption of iron from the gut, iron recycling, erythropoiesis and bleeding/menstruation.

RATIONALE: chronic obstructive pulmonary disease (COPD) is a leading cause of mortality and common in older adults. The BODE Index is the most recognised mortality risk score in COPD but includes a 6-minute walk test (6MWT) that is seldom available in practise; the BODE Index may be better adopted if the 6MWT was replaced. OBJECTIVES: we investigated whether a modified BODE Index in which 6MWT was replaced by an alternative measure of physical capacity, specifically the short physical performance battery (SPPB) or components, retained its predictive ability for mortality in individuals with COPD. METHODS: we analysed 630 COPD patients from the ERICA cohort study for whom UK Office for National Statistics verified mortality data were available. Variables tested at baseline included spirometry, 6MWT, SPPB and its components (4-m gait speed test [4MGS], chair stand and balance). Predictive models were developed using stratified multivariable Cox regression, and assessed by C-indices and calibration plots with 10-fold cross-validation and replication. RESULTS: during median 2 years of follow-up, 60 (10%) individuals died. There was no significant difference between the discriminative ability of BODE6MWT (C-index 0.709, 95% confidence interval [CI], 0.680-0.737), BODESPPB (C-index 0.683, 95% CI, 0.647-0.712), BODE4MGS (C-index 0.676, 95% CI, 0.643-0.700) and BODEBALANCE (C-index 0.686, 95% CI, 0.651-0.713) for predicting mortality. CONCLUSIONS: the SPPB, and its 4MGS and balance components, can potentially be used as an alternative to the 6MWT in the BODE Index without significant loss of predictive ability in all-cause mortality.

Aim To investigate the associations between major foods and dietary fibre with subtypes of stroke in a large prospective cohort. Methods and results We analysed data on 418 329 men and women from nine European countries, with an average of 12.7 years of follow-up. Diet was assessed using validated country-specific questionnaires which asked about habitual intake over the past year, calibrated using 24-h recalls. Multivariable-adjusted Cox regressions were used to estimate hazard ratios (HRs) for ischaemic and haemorrhagic stroke associated with consumption of red and processed meat, poultry, fish, dairy foods, eggs, cereals, fruit and vegetables, legumes, nuts and seeds, and dietary fibre. For ischaemic stroke (4281 cases), lower risks were observed with higher consumption of fruit and vegetables combined (HR; 95% CI per 200 g/day higher intake, 0.87; 0.82–0.93, P-trend Conclusion Risk of ischaemic stroke was inversely associated with consumption of fruit and vegetables, dietary fibre, and dairy foods, while risk of haemorrhagic stroke was positively associated with egg consumption. The apparent differences in the associations highlight the importance of examining ischaemic and haemorrhagic stroke subtypes separately.

Blood donors attending a donation session may be deferred from donating blood due to a failure to meet low hemoglobin (Hb) thresholds. This costs the blood donor service and donors valuable time and resources. In addition, donors who are deferred may have more symptoms, and as a direct and/or indirect effect of their experience, return rates of donors deferred for low Hb are reduced, even in repeat donors. It is therefore vital that low Hb deferral (LHD) is minimized. The aim of this updated systematic review is to expand the evidence base for factors which affect a donor's risk of deferral due to low Hb. Studies were identified by searching MEDLINE, Embase, The Cochrane Library, and the WHO International Clinical Trials Registry to March 2019. Demographic data, donor history, hematological/biological factors, and the primary outcome of deferral due to low Hb were extracted. Our primary outcome was deferral due to low Hb. Analyses were descriptive and quantitative; pooled odds ratios (ORs) and 95% confidence intervals (CIs) were obtained by meta-analysis using random-effects models. A total of 116 studies met the inclusion criteria. Meta-analysis showed a significantly greater risk of LHD in females compared with males in studies applying universal Hb thresholds for males and females (OR 14.62 95% CI 12.43-17.19) and in those which used sex-specific thresholds (OR 5.73, 95% CI 4.36-7.53). Higher rates of LHD were also associated with increasing age in men, low body weight, shorter interdonation interval, donors of Hispanic or African descent, higher ambient temperature, donors with low ferritin levels, and donation in a fixed donor center. There was conflicting evidence on the effect of new and repeat donor status, and blood group. This work has strengthened the evidence of the previous review in identifying factors that should be considered in studies of donor deferral and highlighting areas in need of further study, including ABO and Rh blood groups, previous platelet donation, diet, smoking, time of day, and genetic data. These factors may lead to individually tailored donation criteria for safe and efficient donation in the future., This research is supported by core funding from NHS Blood and Transplant, the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre Programme (SF, CD), the UK Medical Research Council (MR/L003120/1), the British Heart Foundation (RG/13/13/30194; RG/18/13/33946), and the National Institute for Health Research [Cambridge Biomedical Research Centre at the Cambridge University Hospitals NHS Foundation Trust]. AB, TB, and KM are funded by the NIHR Blood and Transplant Research Unit in Donor Health and Genomics (NIHR BTRU-2014-10024). SAR is funded by the National Institute for Health Research [Cambridge Biomedical Research Centre at the Cambridge University Hospitals NHS Foundation Trust].

Analyses were supported by the UK Medical Research Council (MR/M012190/1), Cancer Research UK (C8221/A19170 and 570/A16491) and the Wellcome Trust (Our Planet Our Health, Livestock Environment and People 205212/Z/16/Z). A.P.C. is supported by a Cancer Research UK Population Research Fellowship (C60192/A28516) and by the World Cancer Research Fund (WCRF UK), as part of the Word Cancer Research Fund International grant programme (2019/1953). EPIC-CVD has been supported by the European Union Framework 7 (HEALTH-F2-2012-279233), the European Research Council (268834), the UK Medical Research Council (G0800270 and MR/L003120/1), the British Heart Foundation (SP/09/002 and RG/08/014 and RG13/13/30194) and the UK National Institute of Health Research. The InterAct project was funded by the EU FP6 programme (grant number LSHM_CT_2006_037197) and provided the biomarker data in the sub-cohort that was used in the current study. The coordination of EPIC is financially supported by the European Commission (DGSANCO) and the International Agency for Research on Cancer. The national cohorts are supported by: Danish Cancer Society (Denmark); Ligue Contre le Cancer, Institut Gustave Roussy, Mutuelle Ge ' ne ' rale de l'Education Nationale, Institut National de la Sante ' et de la Recherche Me ' dicale (INSERM) (France); German Cancer Aid, German Cancer Research Center (DKFZ), Federal Ministry of Education and Research (BMBF) (Germany); the Hellenic Health Foundation (Greece); Associazione Italiana per la Ricerca sul Cancro-AIRC-Italy and National Research Council (Italy); Dutch Ministry of Public Health, Welfare and Sports (VWS), Netherlands Cancer Registry (NKR), LK Research Funds, Dutch Prevention Funds, Dutch ZON (Zorg Onderzoek Nederland), World Cancer Research Fund (WCRF); Health Research Fund (FIS), PI13/00061 to Granada, PI13/01162 to EPIC-Murcia, Regional Governments of Andaluci ' a, Asturias, Basque Country, Murcia (no. 6236) and Navarra, ISCIII RETIC (RD06/0020) (Spain); Swedish Cancer Society, Swedish Research Council and County Councils of Ska degrees ne and Va sterbotten (Sweden); Cancer Research UK (14136 to EPIC-Norfolk; C570/A16491 and C8221/A19170 to EPIC-Oxford), UK Medical Research Council [1000143 to EPIC-Norfolk, MR/M012190/1 to EPIC-Oxford, MC_UU_12015/1 (N.J.W.), MC_UU_12015/5 (NGF), and MC_UU_12015/520], and NIHR Biomedical Research Centre Cambridge: Nutrition, Diet, and Lifestyle Research Theme (IS-BRC-1215-20014) to the MRC Epidemiology Unit Cambridge (N.J.W., N.G.F.). J.D. holds a British Heart Foundation Chair and an NIHR Senior Investigator Award. K.E.B. holds a Girdlers' New Zealand Health Research Council Fellowship. J.D. is funded by the National Institute for Health Research [Senior Investigator Award] [*]. A.W is supported by a BHF-Turing Cardiovascular Data Science Award and by the ECInnovative Medicines Initiative (BigData@Heart).M.S.reports funding from the Alpro Foundation whilst at the Cardiovascular Epidemiology Unit (up to 15-08-2016), and Core MRC Unit support through the Nutritional Epidemiology Programme (MC_UU_12015/5) whilst at the MRC Epidemiology Unit (15-08-2016 until now), during the conduct of the study., The authors thank all participants in the EPIC cohort for their invaluable contribution to the study., Epidemiological evidence indicates that diets rich in plant foods are associated with a lower risk of ischaemic heart disease (IHD), but there is sparse information on fruit and vegetable subtypes and sources of dietary fibre. This study examined the associations of major plant foods, their subtypes and dietary fibre with risk of IHD in the European Prospective Investigation into Cancer and Nutrition (EPIC). Methods We conducted a prospective analysis of 490 311 men and women without a history of myocardial infarction or stroke at recruitment (12.6 years of follow-up, n cases = 8504), in 10 European countries. Dietary intake was assessed using validated questionnaires, calibrated with 24-h recalls. Multivariable Cox regressions were used to estimate hazard ratios (HR) of IHD. Results There was a lower risk of IHD with a higher intake of fruit and vegetables combined [HR per 200 g/day higher intake 0.94, 95% confidence interval (CI): 0.90–0.99, P-trend = 0.009], and with total fruits (per 100 g/day 0.97, 0.95–1.00, P-trend = 0.021). There was no evidence for a reduced risk for fruit subtypes, except for bananas. Risk was lower with higher intakes of nuts and seeds (per 10 g/day 0.90, 0.82–0.98, P-trend = 0.020), total fibre (per 10 g/day 0.91, 0.85–0.98, P-trend = 0.015), fruit and vegetable fibre (per 4 g/day 0.95, 0.91–0.99, P-trend = 0.022) and fruit fibre (per 2 g/day 0.97, 0.95–1.00, P-trend = 0.045). No associations were observed between vegetables, vegetables subtypes, legumes, cereals and IHD risk. Conclusions In this large prospective study, we found some small inverse associations between plant foods and IHD risk, with fruit and vegetables combined being the most strongly inversely associated with risk. Whether these small associations are causal remains unclear., UK Research & Innovation (UKRI) Medical Research Council UK (MRC) MR/M012190/1 G0800270 MR/L003120/1 1000143 MC_UU_12015/1 MC_UU_12015/5 MC_UU_12015/520, Cancer Research UK C8221/A19170 570/A16491 14136 C570/A16491, Wellcome Trust (Our Planet Our Health, Livestock Environment and People) 205212/Z/16/Z, Cancer Research UK Population Research Fellowship C60192/A28516, World Cancer Research Fund (WCRF UK), as part of the Word Cancer Research Fund International grant programme 2019/1953, European Commission HEALTH-F2-2012-279233, European Research Council (ERC) European Commission 268834, British Heart Foundation SP/09/002 RG/08/014 RG13/13/30194, National Institute for Health Research (NIHR), European Commission LSHM_CT_2006_037197, European Commission European Commission Joint Research Centre, International Agency for Research on Cancer, Danish Cancer Society, Ligue Contre le Cancer (France), Institut Gustave Roussy (France), Mutuelle Generale de l'Education Nationale (France), Institut National de la Sante et de la Recherche Medicale (Inserm), Deutsche Krebshilfe, German Cancer Research Center (DKFZ) (Germany), Federal Ministry of Education & Research (BMBF), Hellenic Health Foundation (Greece), Associazione Italiana per la Ricerca sul Cancro (AIRC), Consiglio Nazionale delle Ricerche (CNR), Dutch Ministry of Public Health, Welfare and Sports (VWS), Netherlands Cancer Registry (NKR), LK Research Funds, Dutch Prevention Funds, Netherlands Organization for Scientific Research (NWO), World Cancer Research Fund International (WCRF), Instituto de Salud Carlos III PI13/00061 PI13/01162, Junta de Andalucia, Regional Government of Asturias (Spain), Regional Government of Basque Country (Spain), Regional Government of Murcia (Spain) 6236, Regional Government of Navarra (Spain), Instituto de Salud Carlos III RD06/0020, Swedish Cancer Society, Swedish Research Council, County Council of Skane (Sweden), County Council of Vasterbotten (Sweden), NIHR Biomedical Research Centre Cambridge: Nutrition, Diet, and Lifestyle Research Theme IS-BRC-1215-20014, British Heart Foundation, NIHR Senior Investigator Award, Girdlers' New Zealand Health Research Council Fellowship, BHF-Turing Cardiovascular Data Science Award, ECInnovative Medicines Initiative (BigData@Heart), Alpro Foundation 15-08-2016, Core MRC Unit support through the Nutritional Epidemiology Programme MC_UU_12015/5

Supplemental Digital Content is available in the text., Background: There is uncertainty about the relevance of animal foods to the pathogenesis of ischemic heart disease (IHD). We examined meat, fish, dairy products, and eggs and risk for IHD in the pan-European EPIC cohort (European Prospective Investigation Into Cancer and Nutrition). Methods: In this prospective study of 409 885 men and women in 9 European countries, diet was assessed with validated questionnaires and calibrated with 24-hour recalls. Lipids and blood pressure were measured in a subsample. During a mean of 12.6 years of follow-up, 7198 participants had a myocardial infarction or died of IHD. The relationships of animal foods with risk were examined with Cox regression with adjustment for other animal foods and relevant covariates. Results: The hazard ratio (HR) for IHD was 1.19 (95% CI, 1.06–1.33) for a 100-g/d increment in intake of red and processed meat, and this remained significant after exclusion of the first 4 years of follow-up (HR, 1.25 [95% CI, 1.09–1.42]). Risk was inversely associated with intakes of yogurt (HR, 0.93 [95% CI, 0.89–0.98] per 100-g/d increment), cheese (HR, 0.92 [95% CI, 0.86–0.98] per 30-g/d increment), and eggs (HR, 0.93 [95% CI, 0.88–0.99] per 20-g/d increment); the associations with yogurt and eggs were attenuated and nonsignificant after exclusion of the first 4 years of follow-up. Risk was not significantly associated with intakes of poultry, fish, or milk. In analyses modeling dietary substitutions, replacement of 100 kcal/d from red and processed meat with 100 kcal/d from fatty fish, yogurt, cheese, or eggs was associated with ≈20% lower risk of IHD. Consumption of red and processed meat was positively associated with serum non–high-density lipoprotein cholesterol concentration and systolic blood pressure, and consumption of cheese was inversely associated with serum non–high-density lipoprotein cholesterol. Conclusions: Risk for IHD was positively associated with consumption of red and processed meat and inversely associated with consumption of yogurt, cheese, and eggs, although the associations with yogurt and eggs may be influenced by reverse causation bias. It is not clear whether the associations with red and processed meat and cheese reflect causality, but they were consistent with the associations of these foods with plasma non–high-density lipoprotein cholesterol and for red and processed meat with systolic blood pressure, which could mediate such effects.

Background Earlier age at menopause has been associated with increased risk of coronary heart disease (CHD), but the shape of association and role of established cardiovascular risk factors remain unclear. Therefore, we examined the associations between menopausal characteristics and CHD risk; the shape of the association between age at menopause and CHD risk; and the extent to which these associations are explained by established cardiovascular risk factors. Methods We used data from EPIC-CVD, a case–cohort study, which includes data from 23 centres from 10 European countries. We included only women, of whom 10 880 comprise the randomly selected sub-cohort, supplemented with 4522 cases outside the sub-cohort. We conducted Prentice-weighted Cox proportional hazards regressions with age as the underlying time scale, stratified by country and adjusted for relevant confounders. Results After confounder and intermediate adjustment, post-menopausal women were not at higher CHD risk compared with pre-menopausal women. Among post-menopausal women, earlier menopause was linearly associated with higher CHD risk [HRconfounder and intermediate adjusted per-year decrease = 1.02, 95% confidence interval (CI) = 1.01–1.03, p = 0.001]. Women with a surgical menopause were at higher risk of CHD compared with those with natural menopause (HRconfounder-adjusted = 1.25, 95% CI = 1.10–1.42, p Conclusions Earlier and surgical menopause were associated with higher CHD risk. These associations could partially be explained by differences in conventional cardiovascular risk factors. These women might benefit from close monitoring of cardiovascular risk factors and disease.

Importance It is uncertain to what extent established cardiovascular risk factors are associated with venous thromboembolism (VTE). Objective To estimate associations between major cardiovascular risk factors and VTE, i.e., deep-vein thrombosis (DVT) and pulmonary embolism (PE). Design Analysis of individual-participant data from the Emerging Risk Factors Collaboration (ERFC; 731,728 participants; 75 cohorts; latest date of follow-up 2015), and UK Biobank (UKBB; 421,537 participants; latest date of follow-up 2016). Setting Approximately population-based prospective cohort studies. Participants Individuals without cardiovascular disease at baseline. Exposures A panel of several established cardiovascular risk factors. Main Outcomes and Measures Hazard ratios (HRs) per 1-SD higher risk factor levels (or presence/absence). Incident fatal outcomes in ERFC (n=1041 VTE, n=25,131 CHD) and incident fatal/non-fatal outcomes in UKBB (n=2321 VTE, n=3385 CHD). HRs were adjusted for age, sex, smoking status, diabetes mellitus, and body-mass index. Results Adjusted HRs for VTE were: 2.67 (2.45-2.91) in ERFC and 1.81 (1.71-1.92) in UKBB per decade older age; 1.38 (1.20-1.58) in ERFC and 1.23 (1.08-1.40) in UKBB with smoking; 1.43 (1.35-1.50) in ERFC and 1.37 (1.32-1.41) in UKBB per 1-SD higher body-mass index; and 0.75 (0.61-0.93) in ERFC and 0.82 (0.71-0.94) in UKBB with current alcohol consumption. For the preceding factors, there were similar HRs for pulmonary embolism versus deep vein thrombosis in UKBB (except adiposity was more strongly associated with PE; P, A study website (http://www.phpc.cam.ac.uk/ceu/erfc/list-of-studies/) includes a list that investigators have provided of funding agencies that have supported individual cohorts in the ERFC contributing to the present consortium. This research has been conducted using the UK Biobank resource (application 26865).

Importance: Triglycerides and cholesterol are both carried in plasma by apolipoprotein B (ApoB)-containing lipoprotein particles. It is unknown whether lowering plasma triglyceride levels reduces the risk of cardiovascular events to the same extent as lowering low-density lipoprotein cholesterol (LDL-C) levels. Objective: To compare the association of triglyceride-lowering variants in the lipoprotein lipase (LPL) gene and LDL-C-lowering variants in the LDL receptor gene (LDLR) with the risk of cardiovascular disease per unit change in ApoB. Design, Setting, and Participants: Mendelian randomization analyses evaluating the associations of genetic scores composed of triglyceride-lowering variants in the LPL gene and LDL-C-lowering variants in the LDLR gene, respectively, with the risk of cardiovascular events among participants enrolled in 63 cohort or case-control studies conducted in North America or Europe between 1948 and 2017. Exposures: Differences in plasma triglyceride, LDL-C, and ApoB levels associated with the LPL and LDLR genetic scores. Main Outcomes and Measures: Odds ratio (OR) for coronary heart disease (CHD) - defined as coronary death, myocardial infarction, or coronary revascularization - per 10-mg/dL lower concentration of ApoB-containing lipoproteins. Results: A total of 654783 participants, including 91129 cases of CHD, were included (mean age, 62.7 years; 51.4% women). For each 10-mg/dL lower level of ApoB-containing lipoproteins, the LPL score was associated with 69.9-mg/dL (95% CI, 68.1-71.6; P = 7.1 × 10 -1363 ) lower triglyceride levels and 0.7-mg/dL (95% CI, 0.03-1.4; P =.04) higher LDL-C levels; while the LDLR score was associated with 14.2-mg/dL (95% CI, 13.6-14.8; P = 1.4 × 10 -465 ) lower LDL-C and 1.9-mg/dL (95% CI, 0.1-3.9; P =.04) lower triglyceride levels. Despite these differences in associated lipid levels, the LPL and LDLR scores were associated with similar lower risk of CHD per 10-mg/dL lower level of ApoB-containing lipoproteins (OR, 0.771 [95% CI, 0.741-0.802], P = 3.9 × 10 -38 and OR, 0.773 [95% CI, 0.747-0.801], P = 1.1 × 10 -46 , respectively). In multivariable mendelian randomization analyses, the associations between triglyceride and LDL-C levels with the risk of CHD became null after adjusting for differences in ApoB (triglycerides: OR, 1.014 [95% CI, 0.965-1.065], P =.19; LDL-C: OR, 1.010 [95% CI, 0.967-1.055], P =.19; ApoB: OR, 0.761 [95% CI, 0.723-0.798], P = 7.51 × 10 -20 ). Conclusions and Relevance: Triglyceride-lowering LPL variants and LDL-C-lowering LDLR variants were associated with similar lower risk of CHD per unit difference in ApoB. Therefore, the clinical benefit of lowering triglyceride and LDL-C levels may be proportional to the absolute change in ApoB.

Publisher's version (útgefin grein), Aims: There is debate about the optimum algorithm for cardiovascular disease (CVD) risk estimation. We conducted head-to-head comparisons of four algorithms recommended by primary prevention guidelines, before and after 'recalibration', a method that adapts risk algorithms to take account of differences in the risk characteristics of the populations being studied. Methods and results: Using individual-participant data on 360 737 participants without CVD at baseline in 86 prospective studies from 22 countries, we compared the Framingham risk score (FRS), Systematic COronary Risk Evaluation (SCORE), pooled cohort equations (PCE), and Reynolds risk score (RRS). We calculated measures of risk discrimination and calibration, and modelled clinical implications of initiating statin therapy in people judged to be at 'high' 10 year CVD risk. Original risk algorithms were recalibrated using the risk factor profile and CVD incidence of target populations. The four algorithms had similar risk discrimination. Before recalibration, FRS, SCORE, and PCE over-predicted CVD risk on average by 10%, 52%, and 41%, respectively, whereas RRS under-predicted by 10%. Original versions of algorithms classified 29-39% of individuals aged ≥40 years as high risk. By contrast, recalibration reduced this proportion to 22-24% for every algorithm. We estimated that to prevent one CVD event, it would be necessary to initiate statin therapy in 44-51 such individuals using original algorithms, in contrast to 37-39 individuals with recalibrated algorithms. Conclusion: Before recalibration, the clinical performance of four widely used CVD risk algorithms varied substantially. By contrast, simple recalibration nearly equalized their performance and improved modelled targeting of preventive action to clinical need., The work of the co-ordinating centre was funded by the UK Medical Research Council (G0800270), British Heart Foundation (SP/09/ 002), British Heart Foundation Cambridge Cardiovascular Centre of Excellence, UK National Institute for Health Research Cambridge Biomedical Research Centre, European Research Council (268834), and European Commission Framework Programme 7 (HEALTH-F2-2012-279233). The Emerging Risk Factor Collaboration’s website https://www.phpc.cam.ac.uk/ceu/erfc/list-of-studies/ has compiled a list provided by investigators of some of the funders of the component studies in this analysis. I.W. was supported by the Medical Research Council Unit Programme MC_UU_12023/21. M.K. is supported by the Netherlands Organization for Scientific Research (NWO) Veni grant (Veni, 91616079). J.P. is supported by Erasmus Mundus Western Balkans (ERAWEB), a project funded by the European Commission.

Prediction models in health care use predictors to estimate for an individual the probability that a condition or disease is already present (diagnostic model) or will occur in the future (prognostic model). Publications on prediction models have become more common in recent years, and competing prediction models frequently exist for the same outcome or target population. Health care providers, guideline developers, and policymakers are often unsure which model to use or recommend, and in which persons or settings. Hence, systematic reviews of these studies are increasingly demanded, required, and performed. A key part of a systematic review of prediction models is examination of risk of bias and applicability to the intended population and setting. To help reviewers with this process, the authors developed PROBAST (Prediction model Risk Of Bias ASsessment Tool) for studies developing, validating, or updating (for example, extending) prediction models, both diagnostic and prognostic. PROBAST was developed through a consensus process involving a group of experts in the field. It includes 20 signaling questions across 4 domains (participants, predictors, outcome, and analysis). This explanation and elaboration document describes the rationale for including each domain and signaling question and guides researchers, reviewers, readers, and guideline developers in how to use them to assess risk of bias and applicability concerns. All concepts are illustrated with published examples across different topics. The latest version of the PROBAST checklist, accompanying documents, and filled-in examples can be downloaded from www.probast.org.

To help prevent anaemia, it is a requisite for blood donors to undergo a haemoglobin test to ensure levels are not too low before donation. It is therefore important to have an accurate testing device and strategy to ensure donors are not being inappropriately bled. A recent study in blood donors used a selective testing strategy where if a donor’s haemoglobin level is below the level required for donation, then another reading is taken and if this occurs again, a third and final reading is used. This strategy can reduce the average number of readings required per donor compared to taking three measurements for all donors. However, the final decision-making measurement will on average be higher than a single measurement. In this paper, a selective testing strategy is compared against other strategies. Individual-level biases are derived for the selective strategy and are shown to depend on how close a donor’s true haemoglobin level is to the donation threshold and the magnitude of error in the testing device. A simulation study was conducted using the distribution of haemoglobin levels from a large donor population to investigate the effects different strategies have on population performance. We consider scenarios based on varying the measurement device bias and error, including differential biases that depend on the underlying haemoglobin level. Discriminatory performance is shown to be affected when using the selective testing strategies, especially when measurement error is large and when differential bias is present in the device. We recommend that the average of a number of readings should be used in preference to selective testing strategies if multiple measurements are available.

Key Points Question To what extent are established cardiovascular risk factors associated with risk of venous thromboembolism (VTE)? Findings In this analysis of individual participant data from the Emerging Risk Factors Collaboration and the UK Biobank including 1.1 million participants, among a panel of several established cardiovascular risk factors, older age, smoking, and greater adiposity were consistently associated with higher VTE risk. Meaning There is overlap in at least some of the major population determinants of important venous and arterial thrombotic diseases., This analysis of data from the Emerging Risk Factors Collaboration and the UK Biobank estimates the associations of major cardiovascular risk factors with venous thromboembolism., Importance It is uncertain to what extent established cardiovascular risk factors are associated with venous thromboembolism (VTE). Objective To estimate the associations of major cardiovascular risk factors with VTE, ie, deep vein thrombosis and pulmonary embolism. Design, Setting, and Participants This study included individual participant data mostly from essentially population-based cohort studies from the Emerging Risk Factors Collaboration (ERFC; 731 728 participants; 75 cohorts; years of baseline surveys, February 1960 to June 2008; latest date of follow-up, December 2015) and the UK Biobank (421 537 participants; years of baseline surveys, March 2006 to September 2010; latest date of follow-up, February 2016). Participants without cardiovascular disease at baseline were included. Data were analyzed from June 2017 to September 2018. Exposures A panel of several established cardiovascular risk factors. Main Outcomes and Measures Hazard ratios (HRs) per 1-SD higher usual risk factor levels (or presence/absence). Incident fatal outcomes in ERFC (VTE, 1041; coronary heart disease [CHD], 25 131) and incident fatal/nonfatal outcomes in UK Biobank (VTE, 2321; CHD, 3385). Hazard ratios were adjusted for age, sex, smoking status, diabetes, and body mass index (BMI). Results Of the 731 728 participants from the ERFC, 403 396 (55.1%) were female, and the mean (SD) age at the time of the survey was 51.9 (9.0) years; of the 421 537 participants from the UK Biobank, 233 699 (55.4%) were female, and the mean (SD) age at the time of the survey was 56.4 (8.1) years. Risk factors for VTE included older age (ERFC: HR per decade, 2.67; 95% CI, 2.45-2.91; UK Biobank: HR, 1.81; 95% CI, 1.71-1.92), current smoking (ERFC: HR, 1.38; 95% CI, 1.20-1.58; UK Biobank: HR, 1.23; 95% CI, 1.08-1.40), and BMI (ERFC: HR per 1-SD higher BMI, 1.43; 95% CI, 1.35-1.50; UK Biobank: HR, 1.37; 95% CI, 1.32-1.41). For these factors, there were similar HRs for pulmonary embolism and deep vein thrombosis in UK Biobank (except adiposity was more strongly associated with pulmonary embolism) and similar HRs for unprovoked vs provoked VTE. Apart from adiposity, these risk factors were less strongly associated with VTE than CHD. There were inconsistent associations of VTEs with diabetes and blood pressure across ERFC and UK Biobank, and there was limited ability to study lipid and inflammation markers. Conclusions and Relevance Older age, smoking, and adiposity were consistently associated with higher VTE risk.

Importance: It is uncertain whether depressive symptoms are independently associated with subsequent risk of cardiovascular diseases (CVD). Objective: To characterize the association between depressive symptoms and CVD incidence across the spectrum of lower mood. Design, setting and participants: A pooled analysis of individual-participant-data from the Emerging Risk Factors Collaboration (ERFC; 162,036 participants; 21 cohorts; baseline surveys, 1960-2008; latest follow-up, March 2020) and UK Biobank (UKB; 401,219 participants; baseline surveys, 2006-2010; latest follow-up, March 2020). Eligible participants had information about self-reported depressive symptoms and no CVD history at baseline. Exposure: Depressive symptoms were recorded using validated instruments. ERFC scores were harmonized across studies to a scale representative of the Centre for Epidemiological Studies Depression scale (CES-D; range 0-60; ≥16 indicates possible depressive disorder). UKB recorded the Patient Health Questionnaire-2 (PHQ-2; range 0-6; ≥3 indicates possible depressive disorder). Main Outcomes and Measures: Primary outcomes were incident fatal/nonfatal coronary heart disease (CHD), stroke and CVD (composite of CHD and stroke). Hazard ratios (HRs) per 1-SD higher log-CES-D or PHQ-2 adjusted for age, sex, smoking and diabetes were reported. Results: Among 162,036 participants from the ERFC, 73% were female, mean (SD) age at baseline was 63 (9) years, and 5,078 CHD and 3,932 stroke events were recorded (median follow-up, 9.5-years). Associations with CHD, stroke and CVD were log-linear. HRs (95%CI) per 1SD higher depression score for CHD, stroke and CVD respectively were 1.07 (1.03-1.11), 1.05 (1.01-1.10), and 1.06 (1.04-1.08). This reflects, 36 versus 29 CHD events, 28 versus 25 stroke events, and 63 versus 54 CVD events per 1000 individuals over 10 years in the highest versus lowest quintile of CES-D (geometric mean CES-D score, 19 versus 1). Among 401,219 participants from the UKB, 55% were female, mean baseline age was 56 (8) years, and 4607 CHD and 3253 stroke events were recorded (median follow-up, 8.1-years). HRs per 1SD higher depression score for CHD, stroke and CVD respectively were 1.11 (1.08-1.14), 1.10 (1.06-1.14) and 1.10 (1.08-1.13). This reflects, 21 versus 14 CHD events, 15 versus 10 stroke events, and 36 versus 25 CVD events per 1000 individuals over 10 years in those with PHQ2 ≥4 versus 0. The magnitude and statistical significance of the HRs were not materially changed after adjustment for additional risk factors. Conclusions and Relevance: In a pooled analysis of 563,255 participants in 22 cohorts, baseline depressive symptoms were associated with CVD incidence, including at symptom levels below the threshold indicative of a depressive disorder. However, the magnitude of associations was modest., Lisa Pennells, Stephen Kaptoge and Sarah Spackman are funded by a British Heart Foundation Programme Grant (RG/18/13/33946). Steven Bell was funded by the National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics (NIHR BTRU-2014-10024). Tom Bolton is funded by the National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics (NIHR BTRU-2014-10024). Angela Wood is supported by a BHF-Turing Cardiovascular Data Science Award and by the EC-Innovative Medicines Initiative (BigData@Heart). John Danesh holds a British Heart Foundation Professorship and a National Institute for Health Research Senior Investigator Award.* *The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care.

Background Coronary artery disease (CAD) has substantial heritability and a polygenic architecture. However, the potential of genomic risk scores to help predict CAD outcomes has not been evaluated comprehensively, because available studies have involved limited genomic scope and limited sample sizes. Objectives This study sought to construct a genomic risk score for CAD and to estimate its potential as a screening tool for primary prevention. Methods Using a meta-analytic approach to combine large-scale, genome-wide, and targeted genetic association data, we developed a new genomic risk score for CAD (metaGRS) consisting of 1.7 million genetic variants. We externally tested metaGRS, both by itself and in combination with available data on conventional risk factors, in 22,242 CAD cases and 460,387 noncases from the UK Biobank. Results The hazard ratio (HR) for CAD was 1.71 (95% confidence interval [CI]: 1.68 to 1.73) per SD increase in metaGRS, an association larger than any other externally tested genetic risk score previously published. The metaGRS stratified individuals into significantly different life course trajectories of CAD risk, with those in the top 20% of metaGRS distribution having an HR of 4.17 (95% CI: 3.97 to 4.38) compared with those in the bottom 20%. The corresponding HR was 2.83 (95% CI: 2.61 to 3.07) among individuals on lipid-lowering or antihypertensive medications. The metaGRS had a higher C-index (C = 0.623; 95% CI: 0.615 to 0.631) for incident CAD than any of 6 conventional factors (smoking, diabetes, hypertension, body mass index, self-reported high cholesterol, and family history). For men in the top 20% of metaGRS with >2 conventional factors, 10% cumulative risk of CAD was reached by 48 years of age. Conclusions The genomic score developed and evaluated here substantially advances the concept of using genomic information to stratify individuals with different trajectories of CAD risk and highlights the potential for genomic screening in early life to complement conventional risk prediction.

In the last decade, over 175 genetic loci have robustly been associated to levels of major circulating blood lipids. Most loci are specific to one or two lipids, whereas some (SUGP1, ZPR1, TRIB1, HERPUD1, and FADS1) are associated to all. While exposing the polygenic architecture of circulating lipids and the underpinnings of dyslipidaemia, these genome-wide association studies (GWAS) have provided further evidence of the critical role that lipids play in coronary heart disease (CHD) risk, as indicated by the 2.7-fold enrichment for macrophage gene expression in atherosclerotic plaques and the association of 25 loci (such as PCSK9, APOB, ABCG5-G8, KCNK5, LPL, HMGCR, NPC1L1, CETP, TRIB1, ABO, PMAIP1-MC4R, and LDLR) with CHD. These GWAS also confirmed known and commonly used therapeutic targets, including HMGCR (statins), PCSK9 (antibodies), and NPC1L1 (ezetimibe). As we head into the post-GWAS era, we offer suggestions for how to move forward beyond genetic risk loci, towards refining the biology behind the associations and identifying causal genes and therapeutic targets. Deep phenotyping through lipidomics and metabolomics will refine and increase the resolution to find causal and druggable targets, and studies aimed at demonstrating gene transcriptional and regulatory effects of lipid associated loci will further aid in identifying these targets. Thus, we argue the need for deeply phenotyped, large genetic association studies to reduce costs and failures and increase the efficiency of the drug discovery pipeline. We conjecture that in the next decade a paradigm shift will tip the balance towards a data-driven approach to therapeutic target development and the application of precision medicine where human genomics takes centre stage.

Preeclampsia and fetal growth restriction (FGR) are major causes of the more than 5 million perinatal and infant deaths occurring globally each year, and both are associated with placental dysfunction. The risk of perinatal and infant death is greater in males, but the mechanisms are unclear. We studied data and biological samples from the Pregnancy Outcome Prediction (POP) study, a prospective cohort study that followed 4,212 women having first pregnancies from their dating ultrasound scan through delivery. We tested the hypothesis that fetal sex would be associated with altered placental function using multiomic and targeted analyses. We found that spermine synthase (SMS) escapes X-chromosome inactivation (XCI) in the placenta and is expressed at lower levels in male primary trophoblast cells, and male cells were more sensitive to polyamine depletion. The spermine metabolite N1,N12-diacetylspermine (DiAcSpm) was higher in the female placenta and in the serum of women pregnant with a female fetus. Higher maternal serum levels of DiAcSpm increased the risk of preeclampsia but decreased the risk of FGR. To our knowledge, DiAcSpm is the first maternal biomarker to demonstrate opposite associations with preeclampsia and FGR, and this is the first evidence to implicate polyamine metabolism in sex-related differences in placentally related complications of human pregnancy., The human female placenta has enhanced spermine synthesis which protects fetal growth but predisposes to preeclampsia.

The C statistic is a commonly reported measure of screening test performance. Optimistic estimation of the C statistic is a frequent problem because of overfitting of statistical models in small data sets, and methods exist to correct for this issue. However, many studies do not use such methods, and those that do correct for optimism use diverse methods, some of which are known to be biased. We used clinical data sets (United Kingdom Down syndrome screening data from Glasgow (1991-2003), Edinburgh (1999-2003), and Cambridge (1990-2006), as well as Scottish national pregnancy discharge data (2004-2007)) to evaluate different approaches to adjustment for optimism. We found that sample splitting, cross-validation without replication, and leave-1-out cross-validation produced optimism-adjusted estimates of the C statistic that were biased and/or associated with greater absolute error than other available methods. Cross-validation with replication, bootstrapping, and a new method (leave-pair-out cross-validation) all generated unbiased optimism-adjusted estimates of the C statistic and had similar absolute errors in the clinical data set. Larger simulation studies confirmed that all 3 methods performed similarly with 10 or more events per variable, or when the C statistic was 0.9 or greater. However, with lower events per variable or lower C statistics, bootstrapping tended to be optimistic but with lower absolute and mean squared errors than both methods of cross-validation.

Although plasma proteins have important roles in biological processes and are the direct targets of many drugs, the genetic factors that control inter-individual variation in plasma protein levels are not well understood. Here we characterize the genetic architecture of the human plasma proteome in healthy blood donors from the INTERVAL study. We identify 1,927 genetic associations with 1,478 proteins, a fourfold increase on existing knowledge, including trans associations for 1,104 proteins. To understand the consequences of perturbations in plasma protein levels, we apply an integrated approach that links genetic variation with biological pathway, disease, and drug databases. We show that protein quantitative trait loci overlap with gene expression quantitative trait loci, as well as with disease-associated loci, and find evidence that protein biomarkers have causal roles in disease using Mendelian randomization analysis. By linking genetic factors to diseases via specific proteins, our analyses highlight potential therapeutic targets, opportunities for matching existing drugs with new disease indications, and potential safety concerns for drugs under development.

Objective To investigate the association between alcohol consumption (at baseline and over lifetime) and non-fatal and fatal coronary heart disease (CHD) and stroke. Design Multicentre case-cohort study. Setting A study of cardiovascular disease (CVD) aetiology (EPIC-CVD) within the European Prospective Investigation into Cancer and nutrition cohort from 8 European countries. Participants A case-cohort study of 32 549 participants without baseline CVD, comprised of incident CVD cases and a subcohort for comparison. Main outcome measure Non-fatal and fatal CHD and stroke (including ischaemic and haemorrhagic). Results There were 9307 non-fatal CHD, 1699 fatal CHD, 5855 non-fatal stroke and 733 fatal stroke events. Baseline alcohol intake was inversely associated with non-fatal CHD, with a hazard ratio of 0.94 (95% confidence interval 0.92 to 0.96) per 12 g/day higher intake. There was a J-shaped association between baseline alcohol intake and risk of fatal CHD (hazard ratios=0.83 [95% confidence interval 0.70 to 0.98], 0.65 [0.53 to 0.81], and 0.82 [0.65 to 1.03] for categories 5.0-14.9 g/day, 15.0-29.9 g/day, and 30.0-59.9 g/day, respectively, compared with 0.1-4.9 g/day. In contrast, hazard ratios for non-fatal and fatal stroke risk were 1.04 (95% confidence interval 1.02 to 1.07), and 1.05 (0.98 to 1.13) per 12 g/day increase in baseline alcohol intake, respectively, including broadly similar findings for ischaemic and haemorrhagic stroke. Associations with cardiovascular outcomes were broadly similar with average lifetime alcohol consumption as for baseline alcohol intake, and across the eight countries we studied. There was no strong evidence for interactions of alcohol consumption with smoking status on the risk of CVD events. Conclusions Alcohol intake was inversely associated with non-fatal CHD risk but positively associated with risk of different stroke subtypes, highlighting the opposing associations of alcohol intake with different cardiovascular disease types and strengthening the evidence for policies to reduce alcohol consumption., This work was supported by the Direction Générale de la Santé (French Ministry of Health) (grant GR-IARC-2003-09-12-01). EPIC-CVD has been supported by the European Union Framework 7 (HEALTH-F2-2012-279233), the European Research Council (268834), the UK Medical Research Council (G0800270 and MR/L003120/1), the British Heart Foundation (SP/09/002 and RG/08/014 and RG13/13/30194), and the UK National Institute of Health Research. The establishment of the random subcohort was supported by the EU Sixth Framework Programme (FP6) (grant LSHM_CT_2006_037197 to the InterAct project) and the Medical Research Council Epidemiology Unit (grants MC_UU_12015/1 and MC_UU_12015/5).

The benefits of using electronic health records (EHRs) for disease risk screening and personalized health-care decisions are being increasingly recognized. Here we present a computationally feasible statistical approach with which to address the methodological challenges involved in utilizing historical repeat measures of multiple risk factors recorded in EHRs to systematically identify patients at high risk of future disease. The approach is principally based on a 2-stage dynamic landmark model. The first stage estimates current risk factor values from all available historical repeat risk factor measurements via landmark-age-specific multivariate linear mixed-effects models with correlated random intercepts, which account for sporadically recorded repeat measures, unobserved data, and measurement errors. The second stage predicts future disease risk from a sex-stratified Cox proportional hazards model, with estimated current risk factor values from the first stage. We exemplify these methods by developing and validating a dynamic 10-year cardiovascular disease risk prediction model using primary-care EHRs for age, diabetes status, hypertension treatment, smoking status, systolic blood pressure, total cholesterol, and high-density lipoprotein cholesterol in 41,373 persons from 10 primary-care practices in England and Wales contributing to The Health Improvement Network (1997-2016). Using cross-validation, the model was well-calibrated (Brier score = 0.041, 95% confidence interval: 0.039, 0.042) and had good discrimination (C-index = 0.768, 95% confidence interval: 0.759, 0.777).

Quantitative trait locus (QTL) mapping of molecular phenotypes such as metabolites, lipids, and proteins through genome-wide association studies (GWAS) represents a powerful means of highlighting molecular mechanisms relevant to human diseases. However, a major challenge of this approach is to identify the causal gene(s) at the observed QTLs. Here we present a framework for the “Prioritisation of candidate causal Genes at Molecular QTLs” (ProGeM), which incorporates biological domain-specific annotation data alongside genome annotation data from multiple repositories. We assessed the performance of ProGeM using a reference set of 227 previously reported and extensively curated metabolite QTLs. For 98% of these loci, the expert-curated gene was one of the candidate causal genes prioritised by ProGeM. Benchmarking analyses revealed that 69% of the causal candidates were nearest to the sentinel variant at the investigated molecular QTLs, indicating that genomic proximity is the most reliable indicator of “true positive” causal genes. In contrast, cis-gene expression QTL data led to three false positive candidate causal gene assignments for every one true positive assignment. We provide evidence that these conclusions also apply to other molecular phenotypes, suggesting that ProGeM is a powerful and versatile tool for annotating molecular QTLs. ProGeM is freely available via GitHub.

Logistic regression is often used instead of Cox regression to analyse genome-wide association studies (GWAS) of single nucleotide polymorphisms (SNPs) and disease outcomes with cohort and case-cohort designs, as it is less computationally expensive. Although Cox and logistic regression models have been compared previously in cohort studies, this work does not completely cover the GWAS setting nor extend to the case-cohort study design. Here, we evaluated Cox and logistic regression applied to cohort and case-cohort genetic association studies using simulated data and genetic data from the EPIC-CVD study. In the cohort setting, there was a modest improvement in power to detect SNP–disease associations using Cox regression compared with logistic regression, which increased as the disease incidence increased. In contrast, logistic regression had more power than (Prentice weighted) Cox regression in the case-cohort setting. Logistic regression yielded inflated effect estimates (assuming the hazard ratio is the underlying measure of association) for both study designs, especially for SNPs with greater effect on disease. Given logistic regression is substantially more computationally efficient than Cox regression in both settings, we propose a two-step approach to GWAS in cohort and case-cohort studies. First to analyse all SNPs with logistic regression to identify associated variants below a pre-defined P-value threshold, and second to fit Cox regression (appropriately weighted in case-cohort studies) to those identified SNPs to ensure accurate estimation of association with disease.

The added value of incorporating information from repeated blood pressure and cholesterol measurements to predict cardiovascular disease (CVD) risk has not been rigorously assessed. We used data on 191,445 adults from the Emerging Risk Factors Collaboration (38 cohorts from 17 countries with data encompassing 1962-2014) with more than 1 million measurements of systolic blood pressure, total cholesterol, and high-density lipoprotein cholesterol. Over a median 12 years of follow-up, 21,170 CVD events occurred. Risk prediction models using cumulative mean values of repeated measurements and summary measures from longitudinal modeling of the repeated measurements were compared with models using measurements from a single time point. Risk discrimination (C-index) and net reclassification were calculated, and changes in C-indices were meta-analyzed across studies. Compared with the single-time-point model, the cumulative means and longitudinal models increased the C-index by 0.0040 (95% confidence interval (CI): 0.0023, 0.0057) and 0.0023 (95% CI: 0.0005, 0.0042), respectively. Reclassification was also improved in both models; compared with the single-time-point model, overall net reclassification improvements were 0.0369 (95% CI: 0.0303, 0.0436) for the cumulative-means model and 0.0177 (95% CI: 0.0110, 0.0243) for the longitudinal model. In conclusion, incorporating repeated measurements of blood pressure and cholesterol into CVD risk prediction models slightly improves risk prediction.

BACKGROUND: Guidelines for primary prevention of cardiovascular diseases focus on prediction of coronary heart disease and stroke. We assessed whether or not measurement of N-terminal-pro-B-type natriuretic peptide (NT-proBNP) concentration could enable a more integrated approach than at present by predicting heart failure and enhancing coronary heart disease and stroke risk assessment.METHODS: In this individual-participant-data meta-analysis, we generated and harmonised individual-participant data from relevant prospective studies via both de-novo NT-proBNP concentration measurement of stored samples and collection of data from studies identified through a systematic search of the literature (PubMed, Scientific Citation Index Expanded, and Embase) for articles published up to Sept 4, 2014, using search terms related to natriuretic peptide family members and the primary outcomes, with no language restrictions. We calculated risk ratios and measures of risk discrimination and reclassification across predicted 10 year risk categories (ie, FINDINGS: We recorded 5500 coronary heart disease, 4002 stroke, and 2212 heart failure outcomes among 95 617 participants without a history of cardiovascular disease in 40 prospective studies. Risk ratios (for a comparison of the top third vs bottom third of NT-proBNP concentrations, adjusted for conventional risk factors) were 1·76 (95% CI 1·56-1·98) for the combination of coronary heart disease and stroke and 2·00 (1·77-2·26) for the combination of coronary heart disease, stroke, and heart failure. Addition of information about NT-proBNP concentration to a model containing conventional risk factors was associated with a C-index increase of 0·012 (0·010-0·014) and a net reclassification improvement of 0·027 (0·019-0·036) for the combination of coronary heart disease and stroke and a C-index increase of 0·019 (0·016-0·022) and a net reclassification improvement of 0·028 (0·019-0·038) for the combination of coronary heart disease, stroke, and heart failure.INTERPRETATION: In people without baseline cardiovascular disease, NT-proBNP concentration assessment strongly predicted first-onset heart failure and augmented coronary heart disease and stroke prediction, suggesting that NT-proBNP concentration assessment could be used to integrate heart failure into cardiovascular disease primary prevention.FUNDING: British Heart Foundation, Austrian Science Fund, UK Medical Research Council, National Institute for Health Research, European Research Council, and European Commission Framework Programme 7.

Hepcidin, the master regulator of bioavailable iron, is a key mediator of anemia and also plays a central role in host defense against infection. We hypothesized that measuring hepcidin levels in cord blood could provide an early indication of interindividual differences in iron regulation with quantifiable implications for anemia, malaria, and mortality-related risk. Hepcidin concentrations were measured in cord plasma from a birth cohort (N = 710), which was followed for up to 4 years in a region of perennial malaria transmission in Muheza, Tanzania (2002-2006). At the time of delivery, cord hepcidin levels were correlated with inflammatory mediators, iron markers, and maternal health conditions. Hepcidin levels were 30% (95% confidence interval [CI]: 12%, 44%) lower in children born to anemic mothers and 48% (95% CI: 11%, 97%) higher in placental malaria-exposed children. Relative to children in the lowest third, children in the highest third of cord hepcidin had on average 2.5 g/L (95% CI: 0.1, 4.8) lower hemoglobin levels over the duration of follow-up, increased risk of anemia and severe anemia (adjusted hazard ratio [HR] [95% CI]: 1.18 [1.03, 1.36] and 1.34 [1.08, 1.66], respectively), and decreased risk of malaria and all-cause mortality (adjusted HR [95% CI]: 0.78 [0.67, 0.91] and 0.34 [0.14, 0.84], respectively). Although longitudinal measurements of hepcidin and iron stores are required to strengthen causal inference, these results suggest that hepcidin may have utility as a biomarker indicating children's susceptibility to anemia and infection in early life.

Objective: There is uncertainty about the direction and magnitude of the associations between parity, breastfeeding and the risk of coronary heart disease (CHD). We examined the separate and combined associations of parity and breastfeeding practices with the incidence of CHD later in life among women in a large, pan-European cohort study. Methods: Data were used from European Prospective Investigation into Cancer and Nutrition (EPIC)-CVD, a case–cohort study nested within the EPIC prospective study of 520,000 participants from 10 countries. Information on reproductive history was available for 14,917 women, including 5138 incident cases of CHD. Using Prentice-weighted Cox regression separately for each country followed by a random-effects meta-analysis, we calculated hazard ratios (HRs) and 95% confidence intervals (CIs) for CHD, after adjustment for age, study centre and several socioeconomic and biological risk factors. Results: Compared with nulliparous women, the adjusted HR was 1.19 (95% CI: 1.01–1.41) among parous women; HRs were higher among women with more children (e.g., adjusted HR: 1.95 (95% CI: 1.19–3.20) for women with five or more children). Compared with women who did not breastfeed, the adjusted HR was 0.71 (95% CI: 0.52–0.98) among women who breastfed. For childbearing women who never breastfed, the adjusted HR was 1.58 (95% CI: 1.09–2.30) compared with nulliparous women, whereas for childbearing women who breastfed, the adjusted HR was 1.19 (95% CI: 0.99–1.43). Conclusion: Having more children was associated with a higher risk of CHD later in life, whereas breastfeeding was associated with a lower CHD risk. Women who both had children and breastfed did have a non-significantly higher risk of CHD.

Objective:\ud To determine whether the relationship between previous miscarriage and risk of preterm birth changed over the period 1980–2008, and to determine whether the pattern varied according to the cause of the preterm birth.\ud \ud Design:\ud Linked birth databases.\ud \ud Setting:\ud All Scottish NHS hospitals.\ud \ud Population:\ud A total of 732 719 nulliparous women with a first live birth between 1980 and 2008.\ud \ud Methods:\ud Risk was estimated using logistic regression.\ud \ud Main outcome measures:\ud Preterm birth, subdivided by cause (spontaneous, induced with a diagnosis of pre‐eclampsia, or induced without a diagnosis of pre‐eclampsia) and severity [extreme (24–28 weeks of gestation), moderate (29–32 weeks of gestation), and mild (33–36 weeks of gestation)].\ud \ud Results:\ud Consistent with previous studies, previous miscarriage was associated with an increased risk of all‐cause preterm birth (adjusted odds ratio, aOR 1.26; 95% confidence interval, 95% CI 1.22–1.29). This arose from associations with all subtypes. The strongest association was found with extreme preterm birth (aOR 1.73; 95% CI 1.57–1.90). Risk increased with the number of miscarriages. Women with three or more miscarriages had the greatest risk of all‐cause preterm birth (aOR 2.14; 95% CI 1.93–2.38), and the strongest association was with extreme preterm birth (aOR 3.87; 95% CI 2.85–5.26). The strength of the association between miscarriage and preterm birth decreased from 1980 to 2008. This was because of weakening associations with spontaneous preterm birth and induced preterm birth without a diagnosis of pre‐eclampsia.\ud \ud Conclusions:\ud The association between a prior history of miscarriage and the risk of preterm birth declined in Scotland over the period 1980–2008. We speculate that changes in the methods of managing incomplete termination of pregnancy might explain the trend, through reduced cervical damage.

Multiple imputation can be used as a tool in the process of constructing prediction models in medical and epidemiological studies with missing covariate values. Such models can be used to make predictions for model performance assessment, but the task is made more complicated by the multiple imputation structure. We summarize various predictions constructed from covariates, including multiply imputed covariates, and either the set of imputation-specific prediction model coefficients or the pooled prediction model coefficients. We further describe approaches for using the predictions to assess model performance. We distinguish between ideal model performance and pragmatic model performance, where the former refers to the model's performance in an ideal clinical setting where all individuals have fully observed predictors and the latter refers to the model's performance in a real-world clinical setting where some individuals have missing predictors. The approaches are compared through an extensive simulation study based on the UK700 trial. We determine that measures of ideal model performance can be estimated within imputed datasets and subsequently pooled to give an overall measure of model performance. Alternative methods to evaluate pragmatic model performance are required and we propose constructing predictions either from a second set of covariate imputations which make no use of observed outcomes, or from a set of partial prediction models constructed for each potential observed pattern of covariate. Pragmatic model performance is generally lower than ideal model performance. We focus on model performance within the derivation data, but describe how to extend all the methods to a validation dataset.

BACKGROUND: To investigate potential cardiovascular and other effects of long-term pharmacological interleukin 1 (IL-1) inhibition, we studied genetic variants that produce inhibition of IL-1, a master regulator of inflammation. METHODS: We created a genetic score combining the effects of alleles of two common variants (rs6743376 and rs1542176) that are located upstream of IL1RN, the gene encoding the IL-1 receptor antagonist (IL-1Ra; an endogenous inhibitor of both IL-1α and IL-1β); both alleles increase soluble IL-1Ra protein concentration. We compared effects on inflammation biomarkers of this genetic score with those of anakinra, the recombinant form of IL-1Ra, which has previously been studied in randomised trials of rheumatoid arthritis and other inflammatory disorders. In primary analyses, we investigated the score in relation to rheumatoid arthritis and four cardiometabolic diseases (type 2 diabetes, coronary heart disease, ischaemic stroke, and abdominal aortic aneurysm; 453,411 total participants). In exploratory analyses, we studied the relation of the score to many disease traits and to 24 other disorders of proposed relevance to IL-1 signalling (746,171 total participants). FINDINGS: For each IL1RN minor allele inherited, serum concentrations of IL-1Ra increased by 0.22 SD (95% CI 0.18-0.25; 12.5%; p = 9.3 × 10(-33)), concentrations of interleukin 6 decreased by 0.02 SD (-0.04 to -0.01; -1.7%; p = 3.5 × 10(-3)), and concentrations of C-reactive protein decreased by 0.03 SD (-0.04 to -0.02; -3.4%; p = 7.7 × 10(-14)). We noted the effects of the genetic score on these inflammation biomarkers to be directionally concordant with those of anakinra. The allele count of the genetic score had roughly log-linear, dose-dependent associations with both IL-1Ra concentration and risk of coronary heart disease. For people who carried four IL-1Ra-raising alleles, the odds ratio for coronary heart disease was 1.15 (1.08-1.22; p = 1.8 × 10(-6)) compared with people who carried no IL-1Ra-raising alleles; the per-allele odds ratio for coronary heart disease was 1.03 (1.02-1.04; p = 3.9 × 10(-10)). Per-allele odds ratios were 0.97 (0.95-0.99; p = 9.9 × 10(-4)) for rheumatoid arthritis, 0.99 (0.97-1.01; p = 0.47) for type 2 diabetes, 1.00 (0.98-1.02; p = 0.92) for ischaemic stroke, and 1.08 (1.04-1.12; p = 1.8 × 10(-5)) for abdominal aortic aneurysm. In exploratory analyses, we observed per-allele increases in concentrations of proatherogenic lipids, including LDL-cholesterol, but no clear evidence of association for blood pressure, glycaemic traits, or any of the 24 other disorders studied. Modelling suggested that the observed increase in LDL-cholesterol could account for about a third of the association observed between the genetic score and increased coronary risk. INTERPRETATION: Human genetic data suggest that long-term dual IL-1α/β inhibition could increase cardiovascular risk and, conversely, reduce the risk of development of rheumatoid arthritis. The cardiovascular risk might, in part, be mediated through an increase in proatherogenic lipid concentrations.

BACKGROUND: The detection of bias due to cryptic population structure is an important step in the evaluation of findings of genetic association studies. The standard method of measuring this bias in a genetic association study is to compare the observed median association test statistic to the expected median test statistic. This ratio is inflated in the presence of cryptic population structure. However, inflation may also be caused by the properties of the association test itself particularly in the analysis of rare variants. We compared the properties of the three most commonly used association tests: the likelihood ratio test, the Wald test and the score test when testing rare variants for association using simulated data. RESULTS: We found evidence of inflation in the median test statistics of the likelihood ratio and score tests for tests of variants with less than 20 heterozygotes across the sample, regardless of the total sample size. The test statistics for the Wald test were under-inflated at the median for variants below the same minor allele frequency. CONCLUSIONS: In a genetic association study, if a substantial proportion of the genetic variants tested have rare minor allele frequencies, the properties of the association test may mask the presence or absence of bias due to population structure. The use of either the likelihood ratio test or the score test is likely to lead to inflation in the median test statistic in the absence of population structure. In contrast, the use of the Wald test is likely to result in under-inflation of the median test statistic which may mask the presence of population structure.

IMPORTANCE: The prevalence of cardiometabolic multimorbidity is increasing. OBJECTIVE: To estimate reductions in life expectancy associated with cardiometabolic multimorbidity. DESIGN, SETTING, AND PARTICIPANTS: Age- and sex-adjusted mortality rates and hazard ratios (HRs) were calculated using individual participant data from the Emerging Risk Factors Collaboration (689 300 participants; 91 cohorts; years of baseline surveys: 1960-2007; latest mortality follow-up: April 2013; 128 843 deaths). The HRs from the Emerging Risk Factors Collaboration were compared with those from the UK Biobank (499 808 participants; years of baseline surveys: 2006-2010; latest mortality follow-up: November 2013; 7995 deaths). Cumulative survival was estimated by applying calculated age-specific HRs for mortality to contemporary US age-specific death rates. EXPOSURES: A history of 2 or more of the following: diabetes mellitus, stroke, myocardial infarction (MI). MAIN OUTCOMES AND MEASURES: All-cause mortality and estimated reductions in life expectancy. RESULTS: In participants in the Emerging Risk Factors Collaboration without a history of diabetes, stroke, or MI at baseline (reference group), the all-cause mortality rate adjusted to the age of 60 years was 6.8 per 1000 person-years. Mortality rates per 1000 person-years were 15.6 in participants with a history of diabetes, 16.1 in those with stroke, 16.8 in those with MI, 32.0 in those with both diabetes and MI, 32.5 in those with both diabetes and stroke, 32.8 in those with both stroke and MI, and 59.5 in those with diabetes, stroke, and MI. Compared with the reference group, the HRs for all-cause mortality were 1.9 (95% CI, 1.8-2.0) in participants with a history of diabetes, 2.1 (95% CI, 2.0-2.2) in those with stroke, 2.0 (95% CI, 1.9-2.2) in those with MI, 3.7 (95% CI, 3.3-4.1) in those with both diabetes and MI, 3.8 (95% CI, 3.5-4.2) in those with both diabetes and stroke, 3.5 (95% CI, 3.1-4.0) in those with both stroke and MI, and 6.9 (95% CI, 5.7-8.3) in those with diabetes, stroke, and MI. The HRs from the Emerging Risk Factors Collaboration were similar to those from the more recently recruited UK Biobank. The HRs were little changed after further adjustment for markers of established intermediate pathways (eg, levels of lipids and blood pressure) and lifestyle factors (eg, smoking, diet). At the age of 60 years, a history of any 2 of these conditions was associated with 12 years of reduced life expectancy and a history of all 3 of these conditions was associated with 15 years of reduced life expectancy. CONCLUSIONS AND RELEVANCE: Mortality associated with a history of diabetes, stroke, or MI was similar for each condition. Because any combination of these conditions was associated with multiplicative mortality risk, life expectancy was substantially lower in people with multimorbidity.

OBJECTIVE: To determine whether caesarean delivery in the first pregnancy is a risk factor for unexplained antepartum stillbirth in a second pregnancy. DESIGN: A population-based retrospective cohort study and meta-analysis. SETTING: All maternity units in Scotland. PARTICIPANTS: A cohort of 128 585 second births, 1999-2008. METHODS: Time-to-event analysis and random-effects meta-analysis. MAIN OUTCOME MEASURE: Risk of unexplained antepartum stillbirth in a second pregnancy. RESULTS: There were 88 stillbirths among 23 688 women with a previous caesarean delivery (2.34 per 10 000 women per week) and 288 stillbirths in 104 897 women who had previously delivered vaginally (1.67 per 10 000 women per week, P = 0.002). When analysed by cause, women with a previous caesarean delivery had an increased risk of unexplained stillbirth (hazard ratio, HR 1.47; 95% confidence interval, 95% CI 1.12-1.94; P = 0.006) and, as previously observed, the excess risk was apparent from 34 weeks of gestation onwards. The risk did not differ in relation to the indication of the caesarean delivery, and was independent of maternal characteristics and previous obstetric complications. We identified three other comparable studies (two in North America and one in Europe), and meta-analysis of these studies showed a statistically significant association between previous caesarean delivery and the risk of antepartum stillbirth in the second pregnancy (pooled HR 1.40; 95% CI 1.10-1.77; P = 0.006). CONCLUSIONS: Women who have had a previous caesarean delivery are at increased risk of unexplained stillbirth in the second pregnancy. TWEETABLE ABSTRACT: Caesarean first delivery is associated with an increased risk of unexplained stillbirth in the next pregnancy.

OBJECTIVE: To estimate the association between birth weight percentile and the risk of perinatal death at term in relation to the cause of death. METHODS: We performed a retrospective cohort study of all term singleton births in delivery units in Scotland between 1992 and 2008 (n=784,576), excluding perinatal deaths ascribed to congenital anomaly. RESULTS: There were 1,700 perinatal deaths in the cohort, which were not the result of congenital anomaly (21.7/10,000 women at term). We observed a reversed J-shaped association between birth weight percentile and the risk of antepartum stillbirth in all women, but the associations significantly differed (P

We are concerned with multiple imputation of the ratio of two variables, which is to be used as a covariate in a regression analysis. If the numerator and denominator are not missing simultaneously, it seems sensible to make use of the observed variable in the imputation model. One such strategy is to impute missing values for the numerator and denominator, or the log-transformed numerator and denominator, and then calculate the ratio of interest; we call this ‘passive’ imputation. Alternatively, missing ratio values might be imputed directly, with or without the numerator and/or the denominator in the imputation model; we call this ‘active’ imputation. In two motivating datasets, one involving body mass index as a covariate and the other involving the ratio of total to high-density lipoprotein cholesterol, we assess the sensitivity of results to the choice of imputation model and, as an alternative, explore fully Bayesian joint models for the outcome and incomplete ratio. Fully Bayesian approaches using Winbugs were unusable in both datasets because of computational problems. In our first dataset, multiple imputation results are similar regardless of the imputation model; in the second, results are sensitive to the choice of imputation model. Sensitivity depends strongly on the coefficient of variation of the ratio's denominator. A simulation study demonstrates that passive imputation without transformation is risky because it can lead to downward bias when the coefficient of variation of the ratio's denominator is larger than about 0.1. Active imputation or passive imputation after log-transformation is preferable. © 2013 The Authors. Statistics in Medicine published by John Wiley & Sons, Ltd.

The case-cohort study design combines the advantages of a cohort study with the efficiency of a nested case-control study. However, unlike more standard observational study designs, there are currently no guidelines for reporting results from case-cohort studies. Our aim was to review recent practice in reporting these studies, and develop recommendations for the future. By searching papers published in 24 major medical and epidemiological journals between January 2010 and March 2013 using PubMed, Scopus and Web of Knowledge, we identified 32 papers reporting case-cohort studies. The median subcohort sampling fraction was 4.1% (interquartile range 3.7% to 9.1%). The papers varied in their approaches to describing the numbers of individuals in the original cohort and the subcohort, presenting descriptive data, and in the level of detail provided about the statistical methods used, so it was not always possible to be sure that appropriate analyses had been conducted. Based on the findings of our review, we make recommendations about reporting of the study design, subcohort definition, numbers of participants, descriptive information and statistical methods, which could be used alongside existing STROBE guidelines for reporting observational studies.