Pleiotropy among common genetic loci identified for cardiometabolic disorders and C-reactive protein.

Pleiotropic genetic variants have independent effects on different phenotypes. C-reactive protein (CRP) is associated with several cardiometabolic phenotypes. Shared genetic backgrounds may partially underlie these associations. We conducted a genome-wide analysis to identify the shared genetic background of inflammation and cardiometabolic phenotypes using published genome-wide association studies (GWAS). We also evaluated whether the pleiotropic effects of such loci were biological or mediated in nature. First, we examined whether 283 common variants identified for 10 cardiometabolic phenotypes in GWAS are associated with CRP level. Second, we tested whether 18 variants identified for serum CRP are associated with 10 cardiometabolic phenotypes. We used a Bonferroni corrected p-value of 1.1×10-04 (0.05/463) as a threshold of significance. We evaluated the independent pleiotropic effect on both phenotypes using individual level data from the Women Genome Health Study. Evaluating the genetic overlap between inflammation and cardiometabolic phenotypes, we found 13 pleiotropic regions. Additional analyses showed that 6 regions (APOC1, HNF1A, IL6R, PPP1R3B, HNF4A and IL1F10) appeared to have a pleiotropic effect on CRP independent of the effects on the cardiometabolic phenotypes. These included loci where individuals carrying the risk allele for CRP encounter higher lipid levels and risk of type 2 diabetes. In addition, 5 regions (GCKR, PABPC4, BCL7B, FTO and TMEM18) had an effect on CRP largely mediated through the cardiometabolic phenotypes. In conclusion, our results show genetic pleiotropy among inflammation and cardiometabolic phenotypes. In addition to reverse causation, our data suggests that pleiotropic genetic variants partially underlie the association between CRP and cardiometabolic phenotypes

Excess mortality from avoidable and non-avoidable causes in men of low socioeconomic status: a prospective study in Korea

STUDY OBJECTIVE—The objective of this study was to evaluate the magnitude and contributory factors of socioeconomic differentials in mortality in a cohort of Korean male civil servants.
DESIGN—A prospective observational study of male civil servants followed up for five years after baseline measurement.
SETTING—All civil service offices in Korea.
PARTICIPANTS AND MEASUREMENTS—The study was conducted on 759 665 Korean male public servants aged 30-64 at baseline examination in 1992. The grade of monthly salary of these participants divided into four groups, a proxy indicator of socioeconomic status (SES), was the main predictive variable. Mortality of the participants was followed up from 1992( )to 1996. The causes of deaths were categorised into four groups according to the medical amenability: avoidable, partly avoidable, non-avoidable, and external causes of death. The risk of mortality associated with SES was estimated using the Cox proportional hazard model.
MAIN RESULTS—Lowest SES group had significantly higher risk of mortality from most causes compared with the highest SES group in the order of external cause (relative risk (RR): 2.26), avoidable (RR: 1.65), all cause (RR: 1.59), and non-avoidable mortality (RR: 1.54). With the adjustment of known risk factors, significantly higher risks of mortality in lowest SES group were attenuated but persisted. Looking at the deaths from partly avoidable causes, significantly higher risks of mortality in the lowest SES group was observed from cerebrovascular disease but not from coronary heart disease.
CONCLUSIONS—Socioeconomic differentials in non-avoidable as well as avoidable mortality, persisting even under the control of risk factors, suggest that mortality is influenced not only by the quality of health care and different distribution of risk factors but also by other aspects of SES that are yet unknown.


Genetic variants associated with cardiac structure and function: A meta-analysis and replication of genome-wide association data

Context: Echocardiographic measures of left ventricular (LV) structure and function are heritable phenotypes of cardiovascular disease. Objective: To identify common genetic variants associated with cardiac structure and function by conducting a meta-analysis of genome-wide association data in 5 population-based cohort studies (stage 1) with replication (stage 2) in 2 other community-based samples. Design, Setting, and Participants: Within each of 5 community-based cohorts comprising the EchoGen consortium (stage 1; n=12 612 individuals of European ancestry; 55% women, aged 26-95 years; examinations between 1978-2008), we estimated the association between approximately 2.5 million single-nucleotide polymorphisms (SNPs; imputed to the HapMap CEU panel) and echocardiographic traits. In stage 2, SNPs significantly associated with traits in stage 1 were tested for association in 2 other cohorts (n=4094 people of European ancestry). Using a prespecified P value threshold of 5 x 10-7to indicate genome-wide significance, we performed an inverse variance-weighted fixed-effects meta-analysis of genome-wide association data from each cohort. Main Outcome Measures: Echocardiographic traits: LV mass, internal dimensions, wall thickness, systolic dysfunction, aortic root, and left atrial size. Results: In stage 1, 16 genetic loci were associated with 5 echocardiographic traits: 1 each with LV internal dimensions and systolic dysfunction, 3 each with LV mass and wall thickness, and 8 with aortic root size. In stage 2, 5 loci replicated (6q22 locus associated with LV diastolic dimensions, explaining <1%of trait variance; 5q23, 12p12, 12q14, and 17p13 associated with aortic root size, explaining 1%-3% of trait variance). Conclusions: We identified 5 genetic loci harboring common variants that were associated with variation in LV diastolic dimensions and aortic root size, but such findings explained a very small proportion of variance. Further studies are required to replicate these findings, identify the causal variants at or near these loci, characterize their functional significance, and determine whether they are related to overt cardiovascular disease