Multilocus heterozygosity and coronary heart disease: nested case-control studies in men and women.
Ontology highlight
ABSTRACT:
SUBMITTER: Mukamal KJ
PROVIDER: S-EPMC4430477 | biostudies-other | 2015
REPOSITORIES: biostudies-other
Similar Datasets
Project description:Comparison of patients with coronary heart disease and controls in genome-wide association studies has revealed several single nucleotide polymorphisms (SNPs) associated with coronary heart disease. We aimed to establish the external validity of these findings and to obtain more precise risk estimates using a prospective cohort design.We tested 13 recently discovered SNPs for association with coronary heart disease in a case-control design including participants differing from those in the discovery samples (3829 participants with prevalent coronary heart disease and 48,897 controls free of the disease) and a prospective cohort design including 30,725 participants free of cardiovascular disease from Finland and Sweden. We modelled the 13 SNPs as a multilocus genetic risk score and used Cox proportional hazards models to estimate the association of genetic risk score with incident coronary heart disease. For case-control analyses we analysed associations between individual SNPs and quintiles of genetic risk score using logistic regression.In prospective cohort analyses, 1264 participants had a first coronary heart disease event during a median 10·7 years' follow-up (IQR 6·7-13·6). Genetic risk score was associated with a first coronary heart disease event. When compared with the bottom quintile of genetic risk score, participants in the top quintile were at 1·66-times increased risk of coronary heart disease in a model adjusting for traditional risk factors (95% CI 1·35-2·04, p value for linear trend=7·3×10(-10)). Adjustment for family history did not change these estimates. Genetic risk score did not improve C index over traditional risk factors and family history (p=0·19), nor did it have a significant effect on net reclassification improvement (2·2%, p=0·18); however, it did have a small effect on integrated discrimination index (0·004, p=0·0006). Results of the case-control analyses were similar to those of the prospective cohort analyses.Using a genetic risk score based on 13 SNPs associated with coronary heart disease, we can identify the 20% of individuals of European ancestry who are at roughly 70% increased risk of a first coronary heart disease event. The potential clinical use of this panel of SNPs remains to be defined.The Wellcome Trust; Academy of Finland Center of Excellence for Complex Disease Genetics; US National Institutes of Health; the Donovan Family Foundation.
| S-EPMC2965351 | biostudies-literature
Project description:C-reactive protein (CRP), an acute phase reactant and marker of inflammation, has been shown to predict risk of incident cardiovascular events. However, few studies have comprehensively examined six common single-nucleotide polymorphisms (SNPs) in the CRP gene, haplotypes, and plasma CRP levels with risk of coronary heart disease (CHD).We conducted parallel nested case-control studies within two ongoing, prospective cohort studies of U.S. women (Nurses' Health Study) and men (Health Professionals Follow-up Study). Blood samples were available in a subset of 32,826 women and 18,225 men for biomarker and DNA analyses. During 8 and 6 years of follow-up, 249 women and 266 men developed incident nonfatal myocardial infarction or fatal CHD, and controls (498 women, 531 men) were matched 2:1 on age, smoking, and date of blood draw from participants free of cardiovascular disease at the time the case was diagnosed. Among both women and men, minor alleles were significantly associated with higher CRP levels for SNPs 1919A>T and 4741G>C, but associated with lower CRP levels for SNPs 2667G>C and 3872C>T. SNP 2667G>C was individually associated with increased risk of CHD in both women [OR 1.57 (95% CI 1.01-2.44); p = 0.047] and men [1.93 (95% CI 1.30-2.88); p = 0.001]. Two of the five common haplotypes were associated with lower CRP levels, and Haplotype 4 which included minor alleles for 2667 and 3872 was associated with significantly lower CRP levels and an elevated risk of CHD. The remaining SNPs or haplotypes were not associated with CHD in both populations.Common variation in the CRP gene was significantly associated with plasma CRP levels; however, the association between common SNPs and CRP levels did not correspond to a predicted change in CHD risk. The underlying inflammatory processes which predict coronary events cannot be captured solely by variation in the CRP gene.
| S-EPMC2148071 | biostudies-literature
Project description:Identifying environmentally responsive genetic loci where DNA methylation is associated with coronary heart disease (CHD) may reveal novel pathways or therapeutic targets for CHD. We conducted the first prospective epigenome-wide analysis of DNA methylation in relation to incident CHD in the Asian population. We did a nested case-control study comprising incident CHD cases and 1:1 matched controls who were identified from the 10 year follow-up of the China Kadoorie Biobank. Methylation level of baseline blood leukocyte DNA was measured by Infinium Methylation EPIC BeadChip. We performed the single cytosine-phosphate-guanine (CpG) site association analysis and network approach to identify CHD-associated CpG sites and co-methylation gene module. After quality control, 982 participants (mean age 50.1 years) were retained. Methylation level at 25 CpG sites across the genome was associated with incident CHD (genome-wide false discovery rate [FDR] < 0.05 or module-specific FDR < 0.01). One SD increase in methylation level of identified CpGs was associated with differences in CHD risk, ranging from a 47 % decrease to a 118 % increase. Mediation analyses revealed 28.5 % of the excessed CHD risk associated with smoking was mediated by methylation level at the promoter region of ANKS1A gene (P for mediation effect = 0.036). Methylation level at the promoter region of SNX30 was associated with blood pressure and subsequent risk of CHD, with the mediating proportion to be 7.7 % (P = 0.003) via systolic blood pressure and 6.4 % (P = 0.006) via diastolic blood pressure. Network analysis revealed a co-methylation module associated with CHD. We identified novel blood methylation alterations associated with incident CHD in the Asian population and provided evidence of the possible role of epigenetic regulations in the smoking- and blood pressure-related pathways to CHD risk. This work was supported by National Natural Science Foundation of China (81390544 and 91846303). The CKB baseline survey and the first re-survey were supported by a grant from the Kadoorie Charitable Foundation in Hong Kong. The long-term follow-up is supported by grants from the UK Wellcome Trust (202922/Z/16/Z, 088158/Z/09/Z, 104085/Z/14/Z), grant (2016YFC0900500, 2016YFC0900501, 2016YFC0900504, 2016YFC1303904) from the National Key R&D Program of China, and Chinese Ministry of Science and Technology (2011BAI09B01).
| S-EPMC8585480 | biostudies-literature
Project description:BACKGROUND AND AIM:Coronary heart disease (CHD) is a chronic complex disease caused by a combination of factors such as lifestyle behaviors and environmental and genetic factors. We conducted this study to evaluate the risk factors affecting the development of CHD in Xinjiang, and to obtain valuable information for formulating appropriate local public health policies. METHOD:We conducted a nested case-control study with 277 confirmed CHD cases and 554 matched controls. The association of the risk factors with the risk of CHD was assessed using the multivariate Cox proportional hazard model. Multiplicative interactions were evaluated by entering interaction terms in the Cox proportional hazard model. The additive interactions among the risk factors were assessed by the index of additive interaction. RESULTS:The risk of CHD increased with frequent high-fat food consumption, dyslipidemia, obesity, and family history of CHD after adjustment for drinking, smoking status, hypertension, diabetes, family history of hypertension, and family history of diabetes. We noted consistent interactions between family history of CHD and frequent high-fat food consumption, family history of CHD and obesity, frequent high-fat food consumption and obesity, frequent high-fat food consumption and dyslipidemia, and obesity and dyslipidemia. The risk of CHD events increased with the presence of the aforementioned interactions. CONCLUSIONS:Frequent high-fat food consumption, family history of CHD, dyslipidemia and obesity were independent risk factors for CHD, and their interactions are important for public health interventions in patients with CHD in Xinjiang.
| S-EPMC7055895 | biostudies-literature
Project description:The nested case-control (NCC) design have been widely adopted as a cost-effective solution in many large cohort studies for risk assessment with expensive markers, such as the emerging biologic and genetic markers. To analyze data from NCC studies, conditional logistic regression (Goldstein and Langholz, 1992; Borgan et al., 1995) and maximum likelihood (Scheike and Juul, 2004; Zeng et al., 2006) based methods have been proposed. However, most of these methods either cannot be easily extended beyond the Cox model (Cox, 1972) or require additional modeling assumptions. More generally applicable approaches based on inverse probability weighting (IPW) have been proposed as useful alternatives (Samuelsen, 1997; Chen, 2001; Samuelsen et al., 2007). However, due to the complex correlation structure induced by repeated finite risk set sampling, interval estimation for such IPW estimators remain challenging especially when the estimation involves non-smooth objective functions or when making simultaneous inferences about functions. Standard resampling procedures such as the bootstrap cannot accommodate the correlation and thus are not directly applicable. In this paper, we propose a resampling procedure that can provide valid estimates for the distribution of a broad class of IPW estimators. Simulation results suggest that the proposed procedures perform well in settings when analytical variance estimator is infeasible to derive or gives less optimal performance. The new procedures are illustrated with data from the Framingham Offspring Study to characterize individual level cardiovascular risks over time based on the Framingham risk score, C-reactive protein (CRP) and a genetic risk score.
| S-EPMC3891801 | biostudies-literature
Project description:ObjectiveEvaluate the risk of pre-existing comorbidities on COVID-19 mortality, and provide clinical suggestions accordingly.SettingA nested case-control design using confirmed case reports released from the news or the national/provincial/municipal health commissions of China between 18 December 2019 and 8 March 2020.ParticipantsPatients with confirmed SARS-CoV-2 infection, excluding asymptomatic patients, in mainland China outside of Hubei Province.Outcome measuresPatient demographics, survival time and status, and history of comorbidities.MethodA total of 94 publicly reported deaths in locations outside of Hubei Province, mainland China, were included as cases. Each case was matched with up to three controls, based on gender and age ±1 year old (94 cases and 181 controls). The inverse probability-weighted Cox proportional hazard model was performed, controlling for age, gender and the early period of the outbreak.ResultsOf the 94 cases, the median age was 72.5 years old (IQR=16), and 59.6% were men, while in the control group the median age was 67 years old (IQR=22), and 64.6% were men. Adjusting for age, gender and the early period of the outbreak, poor health conditions were associated with a higher risk of COVID-19 mortality (HR of comorbidity score, 1.31 [95% CI 1.11 to 1.54]; p=0.001). The estimated mortality risk in patients with pre-existing coronary heart disease (CHD) was three times that of those without CHD (p<0.001). The estimated 30-day survival probability for a profile patient with pre-existing CHD (65-year-old woman with no other comorbidities) was 0.53 (95% CI 0.34 to 0.82), while it was 0.85 (95% CI 0.79 to 0.91) for those without CHD. Older age was also associated with increased mortality risk: every 1-year increase in age was associated with a 4% increased risk of mortality (p<0.001).ConclusionExtra care and early medical interventions are needed for patients with pre-existing comorbidities, especially CHD.
| S-EPMC7499679 | biostudies-literature
Project description:BACKGROUND: Epistasis, i.e., the interaction of alleles at different loci, is thought to play a central role in the formation and progression of complex diseases. The complexity of disease expression should arise from a complex network of epistatic interactions involving multiple genes. METHODOLOGY: We develop a general model for testing high-order epistatic interactions for a complex disease in a case-control study. We incorporate the quantitative genetic theory of high-order epistasis into the setting of cases and controls sampled from a natural population. The new model allows the identification and testing of epistasis and its various genetic components. CONCLUSIONS: Simulation studies were used to examine the power and false positive rates of the model under different sampling strategies. The model was used to detect epistasis in a case-control study of inflammatory bowel disease, in which five SNPs at a candidate gene were typed, leading to the identification of a significant three-locus epistasis.
| S-EPMC2909900 | biostudies-literature
Project description:BACKGROUND:Moderately elevated blood levels of homocysteine are weakly correlated with coronary heart disease (CHD) risk, but causality remains uncertain. When folate levels are low, the TT genotype of the common C677T polymorphism (rs1801133) of the methylene tetrahydrofolate reductase gene (MTHFR) appreciably increases homocysteine levels, so "Mendelian randomization" studies using this variant as an instrumental variable could help test causality. METHODS AND FINDINGS:Nineteen unpublished datasets were obtained (total 48,175 CHD cases and 67,961 controls) in which multiple genetic variants had been measured, including MTHFR C677T. These datasets did not include measurements of blood homocysteine, but homocysteine levels would be expected to be about 20% higher with TT than with CC genotype in the populations studied. In meta-analyses of these unpublished datasets, the case-control CHD odds ratio (OR) and 95% CI comparing TT versus CC homozygotes was 1.02 (0.98-1.07; p?=?0.28) overall, and 1.01 (0.95-1.07) in unsupplemented low-folate populations. By contrast, in a slightly updated meta-analysis of the 86 published studies (28,617 CHD cases and 41,857 controls), the OR was 1.15 (1.09-1.21), significantly discrepant (p?=?0.001) with the OR in the unpublished datasets. Within the meta-analysis of published studies, the OR was 1.12 (1.04-1.21) in the 14 larger studies (those with variance of log OR<0.05; total 13,119 cases) and 1.18 (1.09-1.28) in the 72 smaller ones (total 15,498 cases). CONCLUSIONS:The CI for the overall result from large unpublished datasets shows lifelong moderate homocysteine elevation has little or no effect on CHD. The discrepant overall result from previously published studies reflects publication bias or methodological problems.
| S-EPMC3283559 | biostudies-literature
Project description:The nested case-control and case-cohort designs are two main approaches for carrying out a substudy within a prospective cohort. This article adapts multiple imputation (MI) methods for handling missing covariates in full-cohort studies for nested case-control and case-cohort studies. We consider data missing by design and data missing by chance. MI analyses that make use of full-cohort data and MI analyses based on substudy data only are described, alongside an intermediate approach in which the imputation uses full-cohort data but the analysis uses only the substudy. We describe adaptations to two imputation methods: the approximate method (MI-approx) of White and Royston (2009) and the "substantive model compatible" (MI-SMC) method of Bartlett et al. (2015). We also apply the "MI matched set" approach of Seaman and Keogh (2015) to nested case-control studies, which does not require any full-cohort information. The methods are investigated using simulation studies and all perform well when their assumptions hold. Substantial gains in efficiency can be made by imputing data missing by design using the full-cohort approach or by imputing data missing by chance in analyses using the substudy only. The intermediate approach brings greater gains in efficiency relative to the substudy approach and is more robust to imputation model misspecification than the full-cohort approach. The methods are illustrated using the ARIC Study cohort. Supplementary Materials provide R and Stata code.
| S-EPMC6481559 | biostudies-literature
Project description:Nested case-control (NCC) design is used frequently in epidemiological studies as a cost-effective subcohort sampling strategy to conduct biomarker research. Sampling strategy, on the other hoand, creates challenges for data analysis because of outcome-dependent missingness in biomarker measurements. In this paper, we propose inverse probability weighted (IPW) methods for making inference about the prognostic accuracy of a novel biomarker for predicting future events with data from NCC studies. The consistency and asymptotic normality of these estimators are derived using the empirical process theory and convergence theorems for sequences of weakly dependent random variables. Simulation and analysis using Framingham Offspring Study data suggest that the proposed methods perform well in finite samples.
| S-EPMC3276269 | biostudies-other