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Background The UK abdominal aortic aneurysm (AAA) screening programmes currently invite only men for screening because the benefit in women is uncertain. Perioperative risk is critical in determining the effectiveness of screening, and contemporary estimates of these risks in women are lacking. The aim of this study was to compare mortality following AAA repair between women and men in the UK. Methods Anonymized data from the UK National Vascular Registry (NVR) for patients undergoing AAA repair (January 2010 to December 2014) were analysed. Co‐variables were extracted for analysis by sex. The primary outcome measure was in‐hospital mortality. Secondary outcome measures included mortality by 5‐year age groups and duration of hospital stay. Logistic regression was performed to adjust for age, calendar time, AAA diameter and smoking status. NVR‐based outcomes were checked against Hospital Episode Statistics (HES) data. Results A total of 23 245 patients were included (13·0 per cent women). Proportionally, more women than men underwent open repair. For elective open AAA repair, the in‐hospital mortality rate was 6·9 per cent in women and 4·0 per cent in men (odds ratio (OR) 1·48, 95 per cent c.i. 1·08 to 2·02; P = 0·014), whereas for elective endovascular AAA repair it was 1·8 per cent in women and 0·7 per cent in men (OR 2·86, 1·72 to 4·74; P < 0·001); the results in HES were similar. For ruptured AAA, there was no sex difference in mortality within the NVR; however, in HES, for ruptured open AAA repair, the in‐hospital mortality rate was higher in women (33·6 versus 27·1 per cent; OR 1·36, 1·16 to 1·59; P < 0·001). Conclusion Women have a higher in‐hospital mortality rate than men after elective AAA repair even after adjustment. This higher mortality may have an impact on the benefit offered by any screening programme offered to women., Mortality double in women

Background Abdominal aortic aneurysm (AAA) screening programmes have been established for men in the UK to reduce deaths from AAA rupture. Whether or not screening should be extended to women is uncertain. Objective To evaluate the cost-effectiveness of population screening for AAAs in women and compare a range of screening options. Design A discrete event simulation (DES) model was developed to provide a clinically realistic model of screening, surveillance, and elective and emergency AAA repair operations. Input parameters specifically for women were employed. The model was run for 10 million women, with parameter uncertainty addressed by probabilistic and deterministic sensitivity analyses. Setting Population screening in the UK. Participants Women aged ≥ 65 years, followed up to the age of 95 years. Interventions Invitation to ultrasound screening, followed by surveillance for small AAAs and elective surgical repair for large AAAs. Main outcome measures Number of operations undertaken, AAA-related mortality, quality-adjusted life-years (QALYs), NHS costs and cost-effectiveness with annual discounting. Data sources AAA surveillance data, National Vascular Registry, Hospital Episode Statistics, trials of elective and emergency AAA surgery, and the NHS Abdominal Aortic Aneurysm Screening Programme (NAAASP). Review methods Systematic reviews of AAA prevalence and, for elective operations, suitability for endovascular aneurysm repair, non-intervention rates, operative mortality and literature reviews for other parameters. Results The prevalence of AAAs (aortic diameter of ≥ 3.0 cm) was estimated as 0.43% in women aged 65 years and 1.15% at age 75 years. The corresponding attendance rates following invitation to screening were estimated as 73% and 62%, respectively. The base-case model adopted the same age at screening (65 years), definition of an AAA (diameter of ≥ 3.0 cm), surveillance intervals (1 year for AAAs with diameter of 3.0–4.4 cm, 3 months for AAAs with diameter of 4.5–5.4 cm) and AAA diameter for consideration of surgery (5.5 cm) as in NAAASP for men. Per woman invited to screening, the estimated gain in QALYs was 0.00110, and the incremental cost was £33.99. This gave an incremental cost-effectiveness ratio (ICER) of £31,000 per QALY gained. The corresponding incremental net monetary benefit at a threshold of £20,000 per QALY gained was –£12.03 (95% uncertainty interval –£27.88 to £22.12). Almost no sensitivity analyses brought the ICER below £20,000 per QALY gained; an exception was doubling the AAA prevalence to 0.86%, which resulted in an ICER of £13,000. Alternative screening options (increasing the screening age to 70 years, lowering the threshold for considering surgery to diameters of 5.0 cm or 4.5 cm, lowering the diameter defining an AAA in women to 2.5 cm and lengthening the surveillance intervals for the smallest AAAs) did not bring the ICER below £20,000 per QALY gained when considered either singly or in combination. Limitations The model for women was not directly validated against empirical data. Some parameters were poorly estimated, potentially lacking relevance or unavailable for women. Conclusion The accepted criteria for a population-based AAA screening programme in women are not currently met. Future work A large-scale study is needed of the exact aortic size distribution for women screened at relevant ages. The DES model can be adapted to evaluate screening options in men. Study registration This study is registered as PROSPERO CRD42015020444 and CRD42016043227. Funding The National Institute for Health Research Health Technology Assessment programme.

Objective To assess the three year clinical outcomes and cost effectiveness of a strategy of endovascular repair (if aortic morphology is suitable, open repair if not) versus open repair for patients with suspected ruptured abdominal aortic aneurysm. Design Randomised controlled trial. Participants 613 eligible patients (480 men) with a clinical diagnosis of ruptured aneurysm, of whom 502 underwent emergency repair for rupture. Interventions 316 patients were randomised to an endovascular strategy (275 with confirmed rupture) and 297 to open repair (261 with confirmed rupture). Main outcome measures Mortality, with reinterventions after aneurysm repair, quality of life, and hospital costs to three years as secondary measures. Results The maximum follow-up for mortality was 7.1 years, with two patients in each group lost to follow-up by three years. After similar mortality by 90 days, in the mid-term (three months to three years) there were fewer deaths in the endovascular than the open repair group (hazard ratio 0.57, 95% confidence interval 0.36 to 0.90), but by seven years mortality was about 60% in each group (hazard ratio 0.92, 0.75 to 1.13). Results for the 502 patients with repaired ruptures were more pronounced: three year mortality was lower in the endovascular strategy group (42% v 54%; odds ratio 0.62, 0.43 to 0.88), but after seven years there was no clear difference between the groups (hazard ratio 0.86, 0.68 to 1.08). Reintervention rates up to three years were not significantly different between the randomised groups (hazard ratio 1.02, 0.79 to 1.32); the initial rapid rate of reinterventions was followed by a much slower mid-term reintervention rate in both groups. The early higher average quality of life in the endovascular strategy versus open repair group, coupled with the lower mortality at three years, led to a gain in average quality adjusted life years (QALYs) at three years of 0.17 (95% confidence interval 0.00 to 0.33). The endovascular strategy group spent fewer days in hospital and had lower average costs of −£2605 (€2813; $3439) (95% confidence interval −£5966 to £702). The probability that the endovascular strategy is cost effective was >90% at all levels of willingness to pay for a QALY gain. Conclusions At three years, compared with open repair, an endovascular strategy for suspected ruptured abdominal aortic aneurysm was associated with a survival advantage, a gain in QALYs, similar levels of reintervention, and reduced costs, and this strategy was cost effective. These findings support the increasing use of an endovascular strategy, with wider availability of emergency endovascular repair. Trial registration Current Controlled Trials ISRCTN48334791; ClinicalTrials NCT00746122

Mendelian randomization investigations are becoming more powerful and simpler to perform, due to the increasing size and coverage of genome-wide association studies and the increasing availability of summarized data on genetic associations with risk factors and disease outcomes. However, when using multiple genetic variants from different gene regions in a Mendelian randomization analysis, it is highly implausible that all the genetic variants satisfy the instrumental variable assumptions. This means that a simple instrumental variable analysis alone should not be relied on to give a causal conclusion. In this paper, we discuss a range of sensitivity analyses that will either support or question the validity of causal inference from a Mendelian randomization analysis with multiple genetic variants. We focus on sensitivity analyses of greatest practical relevance for ensuring robust causal inferences, and those that can be undertaken using summarized data. Aside from cases in which the justification of the instrumental variable assumptions is supported by strong biological understanding, a Mendelian randomization analysis in which no assessment of the robustness of the findings to violations of the instrumental variable assumptions has been made should be viewed as speculative and incomplete. In particular, Mendelian randomization investigations with large numbers of genetic variants without such sensitivity analyses should be treated with skepticism.This is an open access article distributed under the Creative Commons Attribution License 4.0 (CCBY), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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 Strategies for screening and intervening to reduce the risk of cardiovascular disease (CVD) in primary care settings need to be assessed in terms of both their costs and long-term health effects. We undertook a literature review to investigate the methodologies used. Methods In a framework of developing a new health-economic model for evaluating different screening strategies for primary prevention of CVD in Europe (EPIC-CVD project), we identified seven key modeling issues and reviewed papers published between 2000 and 2013 to assess how they were addressed. Results We found 13 relevant health-economic modeling studies of screening to prevent CVD in primary care. The models varied in their degree of complexity, with between two and 33 health states. Programmes that screen the whole population by a fixed cut-off (e.g., predicted 10-year CVD risk >20 %) identify predominantly elderly people, who may not be those most likely to benefit from long-term treatment. Uncertainty and model validation were generally poorly addressed. Few studies considered the disutility of taking drugs in otherwise healthy individuals or the budget impact of the programme. Conclusions Model validation, incorporation of parameter uncertainty, and sensitivity analyses for assumptions made are all important components of model building and reporting, and deserve more attention. Complex models may not necessarily give more accurate predictions. Availability of a large enough source dataset to reliably estimate all relevant input parameters is crucial for achieving credible results. Decision criteria should consider budget impact and the medicalization of the population as well as cost-effectiveness thresholds. Electronic supplementary material The online version of this article (doi:10.1007/s10198-015-0753-2) contains supplementary material, which is available to authorized users.

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.

Aims Darapladib, a potent inhibitor of lipoprotein-associated phospholipase A javax.xml.bind.JAXBElement@3ed2b341 (Lp-PLA javax.xml.bind.JAXBElement@3d94c871 ), has not reduced risk of cardiovascular disease outcomes in recent randomized trials. We aimed to test whether Lp-PLA javax.xml.bind.JAXBElement@ab2bb75 enzyme activity is causally relevant to coronary heart disease. Methods In 72,657 patients with coronary heart disease and 110,218 controls in 23 epidemiological studies, we genotyped five functional variants: four rare loss-of-function mutations (c.109+2T > C (rs142974898), Arg82His (rs144983904), Val279Phe (rs76863441), Gln287Ter (rs140020965)) and one common modest-impact variant (Val379Ala (rs1051931)) in PLA2G7, the gene encoding Lp-PLA javax.xml.bind.JAXBElement@77eb0872 . We supplemented de-novo genotyping with information on a further 45,823 coronary heart disease patients and 88,680 controls in publicly available databases and other previous studies. We conducted a systematic review of randomized trials to compare effects of darapladib treatment on soluble Lp-PLA javax.xml.bind.JAXBElement@5c08e575 activity, conventional cardiovascular risk factors, and coronary heart disease risk with corresponding effects of Lp-PLA javax.xml.bind.JAXBElement@474479c5 -lowering alleles. Results Lp-PLA javax.xml.bind.JAXBElement@58b21b4b activity was decreased by 64% ( p = 2.4 × 10 javax.xml.bind.JAXBElement@77b520b7 ) with carriage of any of the four loss-of-function variants, by 45% ( p < 10 javax.xml.bind.JAXBElement@6c8d5079 ) for every allele inherited at Val279Phe, and by 2.7% ( p = 1.9 × 10 javax.xml.bind.JAXBElement@8e3967c ) for every allele inherited at Val379Ala. Darapladib 160 mg once-daily reduced Lp-PLA javax.xml.bind.JAXBElement@4043bc68 activity by 65% ( p < 10 javax.xml.bind.JAXBElement@1ead8c4f ). Causal risk ratios for coronary heart disease per 65% lower Lp-PLA javax.xml.bind.JAXBElement@1825097b activity were: 0.95 (0.88-1.03) with Val279Phe; 0.92 (0.74-1.16) with carriage of any loss-of-function variant; 1.01 (0.68-1.51) with Val379Ala; and 0.95 (0.89-1.02) with darapladib treatment. Conclusions In a large-scale human genetic study, none of a series of Lp-PLA javax.xml.bind.JAXBElement@c612c75 -lowering alleles was related to coronary heart disease risk, suggesting that Lp-PLA javax.xml.bind.JAXBElement@511c716d is unlikely to be a causal risk factor.

BACKGROUND: Overweight and obesity are increasing worldwide. To help assess their relevance to mortality in different populations we conducted individual-participant data meta-analyses of prospective studies of body-mass index (BMI), limiting confounding and reverse causality by restricting analyses to never-smokers and excluding pre-existing disease and the first 5 years of follow-up. METHODS: Of 10 625 411 participants in Asia, Australia and New Zealand, Europe, and North America from 239 prospective studies (median follow-up 13·7 years, IQR 11·4-14·7), 3 951 455 people in 189 studies were never-smokers without chronic diseases at recruitment who survived 5 years, of whom 385 879 died. The primary analyses are of these deaths, and study, age, and sex adjusted hazard ratios (HRs), relative to BMI 22·5

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: A case-cohort study is an efficient epidemiological study design for estimating exposure-outcome associations. When sampling of the subcohort is stratified, several methods of analysis are possible, but it is unclear how they compare. Our objective was to compare five analysis methods using Cox regression for this type of data, ranging from a crude model that ignores the stratification to a flexible one that allows nonproportional hazards and varying covariate effects across the strata. STUDY DESIGN AND SETTING: We applied the five methods to estimate the association between physical activity and incident type 2 diabetes using data from a stratified case-cohort study and also used artificial data sets to exemplify circumstances in which they can give different results. RESULTS: In the diabetes study, all methods except the method that ignores the stratification gave similar results for the hazard ratio associated with physical activity. In the artificial data sets, the more flexible methods were shown to be necessary when certain assumptions of the simpler models failed. The most flexible method gave reliable results for all the artificial data sets. CONCLUSION: The most flexible method is computationally straightforward, and appropriate whether or not key assumptions made by the simpler models are valid.

Finding individual-level data for adequately-powered Mendelian randomization analyses may be problematic. As publicly-available summarized data on genetic associations with disease outcomes from large consortia are becoming more abundant, use of published data is an attractive analysis strategy for obtaining precise estimates of the causal effects of risk factors on outcomes. We detail the necessary steps for conducting Mendelian randomization investigations using published data, and present novel statistical methods for combining data on the associations of multiple (correlated or uncorrelated) genetic variants with the risk factor and outcome into a single causal effect estimate. A two-sample analysis strategy may be employed, in which evidence on the gene-risk factor and gene-outcome associations are taken from different data sources. These approaches allow the efficient identification of risk factors that are suitable targets for clinical intervention from published data, although the ability to assess the assumptions necessary for causal inference is diminished. Methods and guidance are illustrated using the example of the causal effect of serum calcium levels on fasting glucose concentrations. The estimated causal effect of a 1 standard deviation (0.13 mmol/L) increase in calcium levels on fasting glucose (mM) using a single lead variant from the CASR gene region is 0.044 (95 % credible interval -0.002, 0.100). In contrast, using our method to account for the correlation between variants, the corresponding estimate using 17 genetic variants is 0.022 (95 % credible interval 0.009, 0.035), a more clearly positive causal effect.

Background: Mendelian randomization uses genetic variants, assumed to be instrumental variables for a particular exposure, to estimate the causal effect of that exposure on an outcome. If the instrumental variable criteria are satisfied, the resulting estimator is consistent even in the presence of unmeasured confounding and reverse causation.\ud \ud Methods: We extend the Mendelian randomization paradigm to investigate more complex networks of relationships between variables, in particular where some of the effect of an exposure on the outcome may operate through an intermediate variable (a mediator). If instrumental variables for the exposure and mediator are available, direct and indirect effects of the exposure on the outcome can be estimated, for example using either a regression-based method or structural equation models. The direction of effect between the exposure and a possible mediator can also be assessed. Methods are illustrated in an applied example considering causal relationships between body mass index, C-reactive protein and uric acid.\ud \ud Results: These estimators are consistent in the presence of unmeasured confounding if, in addition to the instrumental variable assumptions, the effects of both the exposure on the mediator and the mediator on the outcome are homogeneous across individuals and linear without interactions. Nevertheless, a simulation study demonstrates that even considerable heterogeneity in these effects does not lead to bias in the estimates.\ud \ud Conclusions: These methods can be used to estimate direct and indirect causal effects in a mediation setting, and have potential for the investigation of more complex networks between multiple interrelated exposures and disease outcomes.

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.

A conventional Mendelian randomization analysis assesses the causal effect of a risk factor on an outcome by using genetic variants that are solely associated with the risk factor of interest as instrumental variables. However, in some cases, such as the case of triglyceride level as a risk factor for cardiovascular disease, it may be difficult to find a relevant genetic variant that is not also associated with related risk factors, such as other lipid fractions. Such a variant is known as pleiotropic. In this paper, we propose an extension of Mendelian randomization that uses multiple genetic variants associated with several measured risk factors to simultaneously estimate the causal effect of each of the risk factors on the outcome. This "multivariable Mendelian randomization" approach is similar to the simultaneous assessment of several treatments in a factorial randomized trial. In this paper, methods for estimating the causal effects are presented and compared using real and simulated data, and the assumptions necessary for a valid multivariable Mendelian randomization analysis are discussed. Subject to these assumptions, we demonstrate that triglyceride-related pathways have a causal effect on the risk of coronary heart disease independent of the effects of low-density lipoprotein cholesterol and high-density lipoprotein cholesterol.

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.

BACKGROUND: Instrumental variable methods can estimate the causal effect of an exposure on an outcome using observational data. Many instrumental variable methods assume that the exposure-outcome relation is linear, but in practice this assumption is often in doubt, or perhaps the shape of the relation is a target for investigation. We investigate this issue in the context of Mendelian randomization, the use of genetic variants as instrumental variables. METHODS: Using simulations, we demonstrate the performance of a simple linear instrumental variable method when the true shape of the exposure-outcome relation is not linear. We also present a novel method for estimating the effect of the exposure on the outcome within strata of the exposure distribution. This enables the estimation of localized average causal effects within quantile groups of the exposure or as a continuous function of the exposure using a sliding window approach. RESULTS: Our simulations suggest that linear instrumental variable estimates approximate a population-averaged causal effect. This is the average difference in the outcome if the exposure for every individual in the population is increased by a fixed amount. Estimates of localized average causal effects reveal the shape of the exposure-outcome relation for a variety of models. These methods are used to investigate the relations between body mass index and a range of cardiovascular risk factors. CONCLUSIONS: Nonlinear exposure-outcome relations should not be a barrier to instrumental variable analyses. When the exposure-outcome relation is not linear, either a population-averaged causal effect or the shape of the exposure-outcome relation can be estimated.

BACKGROUND: Ageing populations may demand more blood transfusions, but the blood supply could be limited by difficulties in attracting and retaining a decreasing pool of younger donors. One approach to increase blood supply is to collect blood more frequently from existing donors. If more donations could be safely collected in this manner at marginal cost, then it would be of considerable benefit to blood services. National Health Service (NHS) Blood and Transplant in England currently allows men to donate up to every 12 weeks and women to donate up to every 16 weeks. In contrast, some other European countries allow donations as frequently as every 8 weeks for men and every 10 weeks for women. The primary aim of the INTERVAL trial is to determine whether donation intervals can be safely and acceptably decreased to optimise blood supply whilst maintaining the health of donors. METHODS/DESIGN: INTERVAL is a randomised trial of whole blood donors enrolled from all 25 static centres of NHS Blood and Transplant. Recruitment of about 50,000 male and female donors started in June 2012 and was completed in June 2014. Men have been randomly assigned to standard 12-week versus 10-week versus 8-week inter-donation intervals, while women have been assigned to standard 16-week versus 14-week versus 12-week inter-donation intervals. Sex-specific comparisons will be made by intention-to-treat analysis of outcomes assessed after two years of intervention. The primary outcome is the number of blood donations made. A key secondary outcome is donor quality of life, assessed using the Short Form Health Survey. Additional secondary endpoints include the number of 'deferrals' due to low haemoglobin (and other factors), iron status, cognitive function, physical activity, and donor attitudes. A comprehensive health economic analysis will be undertaken. DISCUSSION: The INTERVAL trial should yield novel information about the effect of inter-donation intervals on blood supply, acceptability, and donors' physical and mental well-being. The study will generate scientific evidence to help formulate blood collection policies in England and elsewhere. TRIAL REGISTRATION: Current Controlled Trials ISRCTN24760606, 25 January 2012.

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.

Background Dietary studies differ in design and quality making it difficult to compare results. This study quantifies the prospective association between dietary energy density (DED) and adiposity in children using a meta-analysis method that adjusts for differences in design and quality through eliciting and incorporating expert opinion on the biases and their uncertainty. Method Six prospective studies identified by a previous systematic literature search were included. Differences in study quality and design were considered respectively as internal and external biases and captured in bias checklists. Study results were converted to correlation coefficients; biases were considered either additive or proportional on this scale. The extent and uncertainty of the internal and external biases in each study were elicited in a formal process by five quantitatively-trained assessors and five subject-matter specialists. Biases for each study were combined across assessors using median pooling and results combined across studies by random-effects meta-analysis. Results The unadjusted combined correlation between DED and adiposity change was 0.06 (95%CI 0.01, 0.11; p = 0.013), but with considerable heterogeneity (I2 = 52%). After bias-adjustment the pooled correlation was 0.17 (95%CI - 0.11, 0.45; p = 0.24), and the studies were apparently compatible (I2 = 0%). Conclusions This method allowed quantitative synthesis of the prospective association between DED and adiposity change in children, which is important for the development of evidence-informed policy. Bias adjustment increased the magnitude of the positive association but the widening confidence interval reflects the uncertainty of the assessed biases and implies that higher quality studies are required.

Genetic markers can be used as instrumental variables, in an analogous way to randomization in a clinical trial, to estimate the causal relationship between a phenotype and an outcome variable. Our purpose is to extend the existing methods for such Mendelian randomization studies to the context of multiple genetic markers measured in multiple studies, based on the analysis of individual participant data. First, for a single genetic marker in one study, we show that the usual ratio of coefficients approach can be reformulated as a regression with heterogeneous error in the explanatory variable. This can be implemented using a Bayesian approach, which is next extended to include multiple genetic markers. We then propose a hierarchical model for undertaking a meta-analysis of multiple studies, in which it is not necessary that the same genetic markers are measured in each study. This provides an overall estimate of the causal relationship between the phenotype and the outcome, and an assessment of its heterogeneity across studies. As an example, we estimate the causal relationship of blood concentrations of C-reactive protein on fibrinogen levels using data from 11 studies. These methods provide a flexible framework for efficient estimation of causal relationships derived from multiple studies. Issues discussed include weak instrument bias, analysis of binary outcome data such as disease risk, missing genetic data, and the use of haplotypes.

Background The interpretation of trial results can be helped by understanding how generalisable they are to the target population for which inferences are intended. INTERVAL, a large pragmatic randomised trial of blood donors in England, is assessing the effectiveness and safety of reducing inter-donation intervals. The trial recruited mainly from the blood service’s static centres, which collect only about 10 % of whole-blood donations. Hence, the extent to which the trial’s participants are representative of the general blood donor population is uncertain. We compare these groups in detail. Methods We present the CONSORT flowchart from participant invitation to randomisation in INTERVAL. We compare the characteristics of those eligible and consenting to participate in INTERVAL with the general donor population, using the national blood supply ’PULSE’ database for the period of recruitment. We compare the characteristics of specific groups of trial participants recruited from different sources, as well as those who were randomised versus those not randomised. Results From a total of 540,459 invitations, 48,725 donors were eligible and consented to participate in INTERVAL. The proportion of such donors varied from 1–22 % depending on the source of recruitment. The characteristics of those consenting were similar to those of the general population of 1.3 million donors in terms of ethnicity, blood group distribution and recent deferral rates from blood donation due to low haemoglobin. However, INTERVAL participants included more men (50 % versus 44 %), were slightly older (mean age 43.1 versus 42.3 years), included fewer new donors (3 % versus 22 %) and had given more donations over the previous 2 years (mean 3.3 versus 2.2) than the general donor population. Of the consenting participants, 45,263 (93 %) donors were randomised. Compared to those not randomised, the randomised donors showed qualitatively similar differences to those described above. Conclusions There was broad similarity of participants in INTERVAL with the general blood donor population of England, notwithstanding some differences in age, sex and donation history. Any heterogeneity of the trial’s results according to these characteristics will need to be studied to ensure its generalisability to the general donor population. Trial registration Current Controlled Trials ISRCTN24760606. Registered on 25 January 2012. Electronic supplementary material The online version of this article (doi:10.1186/s13063-016-1579-7) contains supplementary material, which is available to authorized users.