Long-term interleukin-6 levels and subsequent risk of coronary heart disease: Two new prospective studies and a systematic review

Background The relevance to coronary heart disease (CHD) of cytokines that govern inflammatory cascades, such as interleukin-6 (IL-6), may be underestimated because such mediators are short acting and prone to fluctuations. We evaluated associations of long-term circulating IL-6 levels with CHD risk (defined as nonfatal myocardial infarction [MI] or fatal CHD) in two population-based cohorts, involving serial measurements to enable correction for within-person variability. We updated a systematic review to put the new findings in context. Methods and Findings Measurements were made in samples obtained at baseline from 2,138 patients who had a first-ever nonfatal MI or died of CHD during follow-up, and from 4,267 controls in two cohorts comprising 24,230 participants. Correction for within-person variability was made using data from repeat measurements taken several years apart in several hundred participants. The year-to-year variability of IL-6 values within individuals was relatively high (regression dilution ratios of 0.41, 95% confidence interval [CI] 0.28-0.53, over 4 y, and 0.35, 95% CI 0.23-0.48, over 12 y). Ignoring this variability, we found an odds ratio for CHD, adjusted for several established risk factors, of 1.46 (95% CI 1.29-1.65) per 2 standard deviation (SD) increase of baseline IL-6 values, similar to that for baseline C-reactive protein. After correction for within-person variability, the odds ratio for CHD was 2.14 (95% CI 1.45-3.15) with long-term average ("usual'') IL-6, similar to those for some established risk factors. Increasing IL-6 levels were associated with progressively increasing CHD risk. An updated systematic review of electronic databases and other sources identified 15 relevant previous population-based prospective studies of IL-6 and clinical coronary outcomes (i.e., MI or coronary death). Including the two current studies, the 17 available prospective studies gave a combined odds ratio of 1.61 (95% CI 1.42-1.83) per 2 SD increase in baseline IL-6 (corresponding to an odds ratio of 3.34 [95% CI 2.45-4.56] per 2 SD increase in usual [long-term average] IL-6 levels). Conclusions Long-term IL-6 levels are associated with CHD risk about as strongly as are some major established risk factors, but causality remains uncertain. These findings highlight the potential relevance of IL-6-mediated pathways to CH

Leucocyte telomere length and risk of cardiovascular disease: systematic review and meta-analysis.

OBJECTIVE: To assess the association between leucocyte telomere length and risk of cardiovascular disease. DESIGN: Systematic review and meta-analysis. DATA SOURCES: Studies published up to March 2014 identified through searches of Medline, Web of Science, and Embase. ELIGIBILITY CRITERIA: Prospective and retrospective studies that reported on associations between leucocyte telomere length and coronary heart disease (defined as non-fatal myocardial infarction, coronary heart disease death, or coronary revascularisation) or cerebrovascular disease (defined as non-fatal stroke or death from cerebrovascular disease) and were broadly representative of general populations--that is, they did not select cohort or control participants on the basis of pre-existing cardiovascular disease or diabetes. RESULTS: Twenty four studies involving 43,725 participants and 8400 patients with cardiovascular disease (5566 with coronary heart disease and 2834 with cerebrovascular disease) were found to be eligible. In a comparison of the shortest versus longest third of leucocyte telomere length, the pooled relative risk for coronary heart disease was 1.54 (95% confidence interval 1.30 to 1.83) in all studies, 1.40 (1.15 to 1.70) in prospective studies, and 1.80 (1.32 to 2.44) in retrospective studies. Heterogeneity between studies was moderate (I(2) = 64%, 41% to 77%, Phet<0.001) and was not significantly explained by mean age of participants (P = 0.23), the proportion of male participants (P = 0.45), or distinction between retrospective versus prospective studies (P = 0.32). Findings for coronary heart disease were similar in meta-analyses restricted to studies that adjusted for conventional vascular risk factors (relative risk 1.42, 95% confidence interval 1.17 to 1.73); studies with ≥ 200 cases (1.44, 1.20 to 1.74); studies with a high quality score (1.53, 1.22 to 1.92); and in analyses that corrected for publication bias (1.34, 1.12 to 1.60). The pooled relative risk for cerebrovascular disease was 1.42 (1.11 to 1.81), with no significant heterogeneity between studies (I(2) = 41%, 0% to 72%, Phet = 0.08). Shorter telomeres were not significantly associated with cerebrovascular disease risk in prospective studies (1.14, 0.85 to 1.54) or in studies with a high quality score (1.21, 0.83 to 1.76). CONCLUSION: Available observational data show an inverse association between leucocyte telomere length and risk of coronary heart disease independent of conventional vascular risk factors. The association with cerebrovascular disease is less certain

Meta‐analysis of non‐linear exposure‐outcome relationships using individual participant data: A comparison of two methods

Non‐linear exposure‐outcome relationships such as between body mass index (BMI) and mortality are common. They are best explored as continuous functions using individual participant data from multiple studies. We explore two two‐stage methods for meta‐analysis of such relationships, where the confounder‐adjusted relationship is first estimated in a non‐linear regression model in each study, then combined across studies. The “metacurve” approach combines the estimated curves using multiple meta‐analyses of the relative effect between a given exposure level and a reference level. The “mvmeta” approach combines the estimated model parameters in a single multivariate meta‐analysis. Both methods allow the exposure‐outcome relationship to differ across studies. Using theoretical arguments, we show that the methods differ most when covariate distributions differ across studies; using simulated data, we show that mvmeta gains precision but metacurve is more robust to model mis‐specification. We then compare the two methods using data from the Emerging Risk Factors Collaboration on BMI, coronary heart disease events, and all‐cause mortality (>80 cohorts, >18 000 events). For each outcome, we model BMI using fractional polynomials of degree 2 in each study, with adjustment for confounders. For metacurve, the powers defining the fractional polynomials may be study‐specific or common across studies. For coronary heart disease, metacurve with common powers and mvmeta correctly identify a small increase in risk in the lowest levels of BMI, but metacurve with study‐specific powers does not. For all‐cause mortality, all methods identify a steep U‐shape. The metacurve and mvmeta methods perform well in combining complex exposure‐disease relationships across studies

Equalization of four cardiovascular risk algorithms after systematic recalibration: Individual-participant meta-analysis of 86 prospective studies

© 2018 The Author(s). Published by Oxford University Press on behalf of the European Society of Cardiology. 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 \u27recalibration\u27, 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 \u27high\u27 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

World Health Organization cardiovascular disease risk charts: revised models to estimate risk in 21 global regions

© 2019 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license Background: To help adapt cardiovascular disease risk prediction approaches to low-income and middle-income countries, WHO has convened an effort to develop, evaluate, and illustrate revised risk models. Here, we report the derivation, validation, and illustration of the revised WHO cardiovascular disease risk prediction charts that have been adapted to the circumstances of 21 global regions. Methods: In this model revision initiative, we derived 10-year risk prediction models for fatal and non-fatal cardiovascular disease (ie, myocardial infarction and stroke) using individual participant data from the Emerging Risk Factors Collaboration. Models included information on age, smoking status, systolic blood pressure, history of diabetes, and total cholesterol. For derivation, we included participants aged 40–80 years without a known baseline history of cardiovascular disease, who were followed up until the first myocardial infarction, fatal coronary heart disease, or stroke event. We recalibrated models using age-specific and sex-specific incidences and risk factor values available from 21 global regions. For external validation, we analysed individual participant data from studies distinct from those used in model derivation. We illustrated models by analysing data on a further 123 743 individuals from surveys in 79 countries collected with the WHO STEPwise Approach to Surveillance. Findings: Our risk model derivation involved 376 177 individuals from 85 cohorts, and 19 333 incident cardiovascular events recorded during 10 years of follow-up. The derived risk prediction models discriminated well in external validation cohorts (19 cohorts, 1 096 061 individuals, 25 950 cardiovascular disease events), with Harrell\u27s C indices ranging from 0·685 (95% CI 0·629–0·741) to 0·833 (0·783–0·882). For a given risk factor profile, we found substantial variation across global regions in the estimated 10-year predicted risk. For example, estimated cardiovascular disease risk for a 60-year-old male smoker without diabetes and with systolic blood pressure of 140 mm Hg and total cholesterol of 5 mmol/L ranged from 11% in Andean Latin America to 30% in central Asia. When applied to data from 79 countries (mostly low-income and middle-income countries), the proportion of individuals aged 40–64 years estimated to be at greater than 20% risk ranged from less than 1% in Uganda to more than 16% in Egypt. Interpretation: We have derived, calibrated, and validated new WHO risk prediction models to estimate cardiovascular disease risk in 21 Global Burden of Disease regions. The widespread use of these models could enhance the accuracy, practicability, and sustainability of efforts to reduce the burden of cardiovascular disease worldwide. Funding: World Health Organization, British Heart Foundation (BHF), BHF Cambridge Centre for Research Excellence, UK Medical Research Council, and National Institute for Health Research

A comparison of Cox and logistic regression for use in genome-wide association studies of cohort and case-cohort design

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.This work was supported by the UK Medical Research Council (G66840) and Pfizer (G73632). EPIC-CVD was funded by grants awarded to the University of Cambridge from the EU Framework Programme 7 (HEALTH-F2-2012- 279233), the UK Medical Research Council (G0800270), British Heart Foundation (SP/09/002) and the European Research Council (268834). EPIC InterAct project was funded by the EU FP6 programme (LSHM_CT_2006_037197) and is also supported by MC_UU_12015/1 and MC_UU_12015/5

Inflammatory cytokines and risk of coronary heart disease: new prospective study and updated meta-analysis.

AIMS: Because low-grade inflammation may play a role in the pathogenesis of coronary heart disease (CHD), and pro-inflammatory cytokines govern inflammatory cascades, this study aimed to assess the associations of several pro-inflammatory cytokines and CHD risk in a new prospective study, including meta-analysis of prospective studies. METHODS AND RESULTS: Interleukin-6 (IL-6), IL-18, matrix metalloproteinase-9 (MMP-9), soluble CD40 ligand (sCD40L), and tumour necrosis factor-α (TNF-α) were measured at baseline in a case-cohort study of 1514 participants and 833 incident CHD events within population-based prospective cohorts at the Danish Research Centre for Prevention and Health. Age- and sex-adjusted hazard ratios (HRs) for CHD per 1-SD higher log-transformed baseline levels were: 1.37 (95% CI: 1.21-1.54) for IL-6, 1.26 (1.11-1.44) for IL-18, 1.30 (1.16-1.46) for MMP-9, 1.01 (0.89-1.15) for sCD40L, and 1.13 (1.01-1.27) for TNF-α. Multivariable adjustment for conventional vascular risk factors attenuated the HRs to: 1.26 (1.08-1.46) for IL-6, 1.12 (0.95-1.31) for IL-18, 1.21 (1.05-1.39) for MMP-9, 0.93 (0.78-1.11) for sCD40L, and 1.14 (1.00-1.31) for TNF-α. In meta-analysis of up to 29 population-based prospective studies, adjusted relative risks for non-fatal MI or CHD death per 1-SD higher levels were: 1.25 (1.19-1.32) for IL-6; 1.13 (1.05-1.20) for IL-18; 1.07 (0.97-1.19) for MMP-9; 1.07 (0.95-1.21) for sCD40L; and 1.17 (1.09-1.25) for TNF-α. CONCLUSIONS: Several different pro-inflammatory cytokines are each associated with CHD risk independent of conventional risk factors and in an approximately log-linear manner. The findings lend support to the inflammation hypothesis in vascular disease, but further studies are needed to assess causality.This work was supported by a grant from the British Heart Foundation (RG/08/014), the U.K. Medical Research Council, and the U.K. National Institute of Health Research Cambridge Biomedical Research Centre.This is the accepted manuscript. The final version is available from OUP at http://eurheartj.oxfordjournals.org/content/35/9/578

Assessing risk prediction models using individual participant data from multiple studies

Individual participant time-to-event data from multiple prospective epidemiologic studies enable detailed investigation into the predictive ability of risk models. Here we address the challenges in appropriately combining such information across studies. Methods are exemplified by analyses of log C-reactive protein and conventional risk factors for coronary heart disease in the Emerging Risk Factors Collaboration, a collation of individual data from multiple prospective studies with an average follow-up duration of 9.8 years (dates varied).We derive risk prediction models using Cox proportional hazards regression analysis stratified by study and obtain estimates of risk discrimination, Harrell’s concordance index, and Royston’s discrimination measure within each study; we then combine the estimates across studies using aweighted meta-analysis. Various weighting approaches are compared and lead us to recommend using the number of events in each study. We also discuss the calculation of measures of reclassification for multiple studies. We further show that comparison of differences in predictive ability across subgroups should be based only on within-study information and that combining measures of risk discrimination from casecontrol studies and prospective studies is problematic. The concordance index and discrimination measure gave qualitatively similar results throughout. While the concordance index was very heterogeneous between studies, principally because of differing age ranges, the increments in the concordance index from adding log C-reactive protein to conventional risk factors were more homogeneous.Lisa Pennells, Stephen Kaptoge, Ian R. White, Simon G. Thompson, Angela M. Wood and the Emerging Risk Factors Collaboration (Debbie A. Lawlor

Shorter leukocyte telomere length is associated with adverse COVID-19 outcomes: A cohort study in UK Biobank

Background Older age is the most powerful risk factor for adverse coronavirus disease-19 (COVID-19) outcomes. It is uncertain whether leucocyte telomere length (LTL), previously proposed as a marker of biological age, is also associated with COVID-19 outcomes. Methods We associated LTL values obtained from participants recruited into UK Biobank (UKB) during 2006–2010 with adverse COVID-19 outcomes recorded by 30 November 2020, defined as a composite of any of the following: hospital admission, need for critical care, respiratory support, or mortality. Using information on 130 LTL-associated genetic variants, we conducted exploratory Mendelian randomisation (MR) analyses in UKB to evaluate whether observational associations might reflect cause-and-effect relationships. Findings Of 6775 participants in UKB who tested positive for infection with SARS-CoV-2 in the community, there were 914 (13.5%) with adverse COVID-19 outcomes. The odds ratio (OR) for adverse COVID-19 outcomes was 1·17 (95% CI 1·05–1·30; P = 0·004) per 1-SD shorter usual LTL, after adjustment for age, sex and ethnicity. Similar ORs were observed in analyses that: adjusted for additional risk factors; disaggregated the composite outcome and reduced the scope for selection or collider bias. In MR analyses, the OR for adverse COVID-19 outcomes was directionally concordant but non-significant. Interpretation Shorter LTL is associated with higher risk of adverse COVID-19 outcomes, independent of several major risk factors for COVID-19 including age. Further data are needed to determine whether this association reflects causality. Funding UK Medical Research Council, Biotechnology and Biological Sciences Research Council and British Heart Foundation