| 1. |
- Mishra, A., et al.
(author)
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Stroke genetics informs drug discovery and risk prediction across ancestries
- 2022
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In: Nature. - : Springer Science and Business Media LLC. - 0028-0836 .- 1476-4687. ; 611, s. 115-123
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Journal article (peer-reviewed)abstract
- Previous genome-wide association studies (GWASs) of stroke - the second leading cause of death worldwide - were conducted predominantly in populations of European ancestry(1,2). Here, in cross-ancestry GWAS meta-analyses of 110,182 patients who have had a stroke (five ancestries, 33% non-European) and 1,503,898 control individuals, we identify association signals for stroke and its subtypes at 89 (61 new) independent loci: 60 in primary inverse-variance-weighted analyses and 29 in secondary meta-regression and multitrait analyses. On the basis of internal cross-ancestry validation and an independent follow-up in 89,084 additional cases of stroke (30% non-European) and 1,013,843 control individuals, 87% of the primary stroke risk loci and 60% of the secondary stroke risk loci were replicated (P < 0.05). Effect sizes were highly correlated across ancestries. Cross-ancestry fine-mapping, in silico mutagenesis analysis(3), and transcriptome-wide and proteome-wide association analyses revealed putative causal genes (such as SH3PXD2A and FURIN) and variants (such as at GRK5 and NOS3). Using a three-pronged approach(4), we provide genetic evidence for putative drug effects, highlighting F11, KLKB1, PROC, GP1BA, LAMC2 and VCAM1 as possible targets, with drugs already under investigation for stroke for F11 and PROC. A polygenic score integrating cross-ancestry and ancestry-specific stroke GWASs with vascular-risk factor GWASs (integrative polygenic scores) strongly predicted ischaemic stroke in populations of European, East Asian and African ancestry(5). Stroke genetic risk scores were predictive of ischaemic stroke independent of clinical risk factors in 52,600 clinical-trial participants with cardiometabolic disease. Our results provide insights to inform biology, reveal potential drug targets and derive genetic risk prediction tools across ancestries.
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| 2. |
- Jiang, X., et al.
(author)
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Genome-wide association study in 79,366 European-ancestry individuals informs the genetic architecture of 25-hydroxyvitamin D levels
- 2018
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In: Nature Communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 9:1
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Journal article (peer-reviewed)abstract
- Vitamin D is a steroid hormone precursor that is associated with a range of human traits and diseases. Previous GWAS of serum 25-hydroxyvitamin D concentrations have identified four genome-wide significant loci (GC, NADSYN1/DHCR7, CYP2R1, CYP24A1). In this study, we expand the previous SUNLIGHT Consortium GWAS discovery sample size from 16,125 to 79,366 (all European descent). This larger GWAS yields two additional loci harboring genome-wide significant variants (P = 4.7x10(-9) at rs8018720 in SEC23A, and P = 1.9x10(-14) at rs10745742 in AMDHD1). The overall estimate of heritability of 25-hydroxyvitamin D serum concentrations attributable to GWAS common SNPs is 7.5%, with statistically significant loci explaining 38% of this total. Further investigation identifies signal enrichment in immune and hematopoietic tissues, and clustering with autoimmune diseases in cell-type-specific analysis. Larger studies are required to identify additional common SNPs, and to explore the role of rare or structural variants and gene-gene interactions in the heritability of circulating 25-hydroxyvitamin D levels.
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| 3. |
- Hageman, S., et al.
(author)
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SCORE2 risk prediction algorithms: new models to estimate 10-year risk of cardiovascular disease in Europe
- 2021
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In: European Heart Journal. - : Oxford University Press (OUP). - 0195-668X .- 1522-9645. ; 42:25, s. 2439-2454
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Journal article (peer-reviewed)abstract
- Aims The aim of this study was to develop, validate, and illustrate an updated prediction model (SCORE2) to estimate 10-year fatal and non-fatal cardiovascular disease (CVD) risk in individuals without previous CVD or diabetes aged 40-69 years in Europe. Methods and results We derived risk prediction models using individual-participant data from 45 cohorts in 13 countries (677 684 individuals, 30 121 CVD events). We used sex-specific and competing risk-adjusted models, including age, smoking status, systolic blood pressure, and total- and HDL-cholesterol. We defined four risk regions in Europe according to country-specific CVD mortality, recalibrating models to each region using expected incidences and risk factor distributions. Region-specific incidence was estimated using CVD mortality and incidence data on 10 776 466 individuals. For external validation, we analysed data from 25 additional cohorts in 15 European countries (1 133 181 individuals, 43 492 CVD events). After applying the derived risk prediction models to external validation cohorts, C-indices ranged from 0.67 (0.65-0.68) to 0.81 (0.76-0.86). Predicted CVD risk varied several-fold across European regions. For example, the estimated 10-year CVD risk for a 50-year-old smoker, with a systolic blood pressure of 140 mmHg, total cholesterol of 5.5 mmol/L, and HDL-cholesterol of 1.3 mmol/L, ranged from 5.9% for men in low- risk countries to 14.0% for men in very high-risk countries, and from 4.2% for women in low-risk countries to 13.7% for women in very high-risk countries. Conclusion SCORE2-a new algorithm derived, calibrated, and validated to predict 10-year risk of first-onset CVD in European populations-enhances the identification of individuals at higher risk of developing CVD across Europe.
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| 6. |
- Burgess, S., et al.
(author)
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Bayesian methods for meta-analysis of causal relationships estimated using genetic instrumental variables
- 2010
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In: Statistics in medicine. - : Wiley. - 1097-0258 .- 0277-6715. ; 29:12, s. 1298-311
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Journal article (peer-reviewed)abstract
- 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.
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| 7. |
- Di Angelantonio, E., et al.
(author)
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Glycated Hemoglobin Measurement and Prediction of Cardiovascular Disease
- 2014
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In: Jama-Journal of the American Medical Association. - : American Medical Association (AMA). - 0098-7484 .- 1538-3598. ; 311:12, s. 1225-1233
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Journal article (peer-reviewed)abstract
- IMPORTANCE The value of measuring levels of glycated hemoglobin (HbA(1c)) for the prediction of first cardiovascular events is uncertain. OBJECTIVE To determine whether adding information on HbA(1c) values to conventional cardiovascular risk factors is associated with improvement in prediction of cardiovascular disease (CVD) risk. DESIGN, SETTING, AND PARTICIPANTS Analysis of individual-participant data available from 73 prospective studies involving 294 998 participants without a known history of diabetes mellitus or CVD at the baseline assessment. MAIN OUTCOMES AND MEASURES Measures of risk discrimination for CVD outcomes (eg, C-index) and reclassification (eg, net reclassification improvement) of participants across predicted 10-year risk categories of low (<5%), intermediate (5% to <7.5%), and high (>= 7.5%) risk. RESULTS During a median follow-up of 9.9 (interquartile range, 7.6-13.2) years, 20 840 incident fatal and nonfatal CVD outcomes (13 237 coronary heart disease and 7603 stroke outcomes) were recorded. In analyses adjusted for several conventional cardiovascular risk factors, there was an approximately J-shaped association between HbA(1c) values and CVD risk. The association between HbA(1c) values and CVD risk changed only slightly after adjustment for total cholesterol and triglyceride concentrations or estimated glomerular filtration rate, but this association attenuated somewhat after adjustment for concentrations of high-density lipoprotein cholesterol and C-reactive protein. The C-index for a CVD risk prediction model containing conventional cardiovascular risk factors alone was 0.7434 (95% CI, 0.7350 to 0.7517). The addition of information on HbA(1c) was associated with a C-index change of 0.0018 (0.0003 to 0.0033) and a net reclassification improvement of 0.42 (-0.63 to 1.48) for the categories of predicted 10-year CVD risk. The improvement provided by HbA(1c) assessment in prediction of CVD risk was equal to or better than estimated improvements for measurement of fasting, random, or postload plasma glucose levels. CONCLUSIONS AND RELEVANCE In a study of individuals without known CVD or diabetes, additional assessment of HbA(1c) values in the context of CVD risk assessment provided little incremental benefit for prediction of CVD risk.
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| 8. |
- Di Angelantonio, E., et al.
(author)
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Association of Cardiometabolic Multimorbidity With Mortality
- 2015
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In: JAMA. - : American Medical Association (AMA). - 0098-7484 .- 1538-3598. ; 314:1, s. 52-60
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Journal article (peer-reviewed)abstract
- 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.
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| 9. |
- Di Angelantonio, E., et al.
(author)
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Lipid-related markers and cardiovascular disease prediction
- 2012
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In: JAMA : the journal of the American Medical Association. - : American Medical Association (AMA). - 1538-3598 .- 0098-7484. ; 307:23, s. 2499-506
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Journal article (peer-reviewed)abstract
- CONTEXT: The value of assessing various emerging lipid-related markers for prediction of first cardiovascular events is debated. OBJECTIVE: To determine whether adding information on apolipoprotein B and apolipoprotein A-I, lipoprotein(a), or lipoprotein-associated phospholipase A2 to total cholesterol and high-density lipoprotein cholesterol (HDL-C) improves cardiovascular disease (CVD) risk prediction. DESIGN, SETTING, AND PARTICIPANTS: Individual records were available for 165,544 participants without baseline CVD in 37 prospective cohorts (calendar years of recruitment: 1968-2007) with up to 15,126 incident fatal or nonfatal CVD outcomes (10,132 CHD and 4994 stroke outcomes) during a median follow-up of 10.4 years (interquartile range, 7.6-14 years). MAIN OUTCOME MEASURES: Discrimination of CVD outcomes and reclassification of participants across predicted 10-year risk categories of low (<10%), intermediate (10%-<20%), and high (>/=20%) risk. RESULTS: The addition of information on various lipid-related markers to total cholesterol, HDL-C, and other conventional risk factors yielded improvement in the model's discrimination: C-index change, 0.0006 (95% CI, 0.0002-0.0009) for the combination of apolipoprotein B and A-I; 0.0016 (95% CI, 0.0009-0.0023) for lipoprotein(a); and 0.0018 (95% CI, 0.0010-0.0026) for lipoprotein-associated phospholipase A2 mass. Net reclassification improvements were less than 1% with the addition of each of these markers to risk scores containing conventional risk factors. We estimated that for 100,000 adults aged 40 years or older, 15,436 would be initially classified at intermediate risk using conventional risk factors alone. Additional testing with a combination of apolipoprotein B and A-I would reclassify 1.1%; lipoprotein(a), 4.1%; and lipoprotein-associated phospholipase A2 mass, 2.7% of people to a 20% or higher predicted CVD risk category and, therefore, in need of statin treatment under Adult Treatment Panel III guidelines. CONCLUSION: In a study of individuals without known CVD, the addition of information on the combination of apolipoprotein B and A-I, lipoprotein(a), or lipoprotein-associated phospholipase A2 mass to risk scores containing total cholesterol and HDL-C led to slight improvement in CVD prediction.
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| 10. |
- Emerging Risk Factors, Collaboration, et al.
(author)
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The Emerging Risk Factors Collaboration: analysis of individual data on lipid, inflammatory and other markers in over 1.1 million participants in 104 prospective studies of cardiovascular diseases
- 2007
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In: Eur J Epidemiol. - 0393-2990. ; 22:12, s. 839-69
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Journal article (peer-reviewed)abstract
- Many long-term prospective studies have reported on associations of cardiovascular diseases with circulating lipid markers and/or inflammatory markers. Studies have not, however, generally been designed to provide reliable estimates under different circumstances and to correct for within-person variability. The Emerging Risk Factors Collaboration has established a central database on over 1.1 million participants from 104 prospective population-based studies, in which subsets have information on lipid and inflammatory markers, other characteristics, as well as major cardiovascular morbidity and cause-specific mortality. Information on repeat measurements on relevant characteristics has been collected in approximately 340,000 participants to enable estimation of and correction for within-person variability. Re-analysis of individual data will yield up to approximately 69,000 incident fatal or nonfatal first ever major cardiovascular outcomes recorded during about 11.7 million person years at risk. The primary analyses will involve age-specific regression models in people without known baseline cardiovascular disease in relation to fatal or nonfatal first ever coronary heart disease outcomes. This initiative will characterize more precisely and in greater detail than has previously been possible the shape and strength of the age- and sex-specific associations of several lipid and inflammatory markers with incident coronary heart disease outcomes (and, secondarily, with other incident cardiovascular outcomes) under a wide range of circumstances. It will, therefore, help to determine to what extent such associations are independent from possible confounding factors and to what extent such markers (separately and in combination) provide incremental predictive value.
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