Prediction of sudden cardiac death with automated high-throughput analysis of heterogeneity in standard resting 12-lead electrocardiograms.

BACKGROUND: Heterogeneity of depolarization and repolarization underlies the development of lethal arrhythmias. OBJECTIVE: We investigated whether quantification of spatial depolarization and repolarization heterogeneity identifies individuals at risk for sudden cardiac death (SCD). METHODS: Spatial R-, J-, and T-wave heterogeneity (RWH, JWH, and TWH, respectively) was analyzed using automated second central moment analysis of standard digital 12-lead electrocardiograms in 5618 adults (2588, 46% men; mean ± SEM age 50.9 ± 0.2 years), who took part in the epidemiological Health 2000 Survey as representative of the entire Finnish adult population. RESULTS: During the follow-up period of 7.7 ± 0.2 years, a total of 72 SCDs occurred (1.3%), with an average yearly incidence rate of 0.17% per year. Increased RWH, JWH, and TWH in left precordial leads (V4-V6) were univariately associated with SCD (P < .001 for each). When adjusted with standard clinical risk markers, JWH and TWH remained independent predictors of SCD. Increased TWH (≥102 µV) was associated with a 1.7-fold adjusted relative risk for SCD (95% confidence interval [CI] 1.0-2.9; P = .048) and increased JWH (≥123 µV) with a 2.0-fold adjusted relative risk for SCD (95% CI 1.2-3.3; P = .006). When both TWH and JWH were above the threshold, the adjusted relative risk for SCD was 2.9-fold (95% CI 1.5-5.7; P = .002). When RWH (≥470 µV), JWH, and TWH were all above the threshold, the adjusted relative risk for SCD was 3.2-fold (95% CI 1.4-7.1; P = .009). CONCLUSION: Second central moment analysis of standard resting 12-lead electrocardiographic morphology provides an ultrarapid means for the automated measurement of spatial RWH, JWH, and TWH, enabling analysis of high subject volumes and screening for SCD risk in the general population.

Common variation in fatty acid metabolic genes and risk of incident sudden cardiac arrest.

BACKGROUND: There is limited information on genetic factors associated with sudden cardiac arrest (SCA). OBJECTIVE: To assess the association of common variation in genes in fatty acid pathways with SCA risk. METHODS: We selected 85 candidate genes and 1155 single nucleotide polymorphisms (SNPs) tagging common variation in each gene. We investigated the SNP associations with SCA in a population-based case-control study. Cases (n = 2160) were from a repository of SCA in the greater Seattle area. Controls (n = 2615), frequency-matched on age and sex, were from the same area. We used linear logistic regression to examine SNP associations with SCA. We performed permutation-based p-min tests to account for multiple comparisons within each gene. The SNP associations with a corrected P value of <.05 were then examined in a meta-analysis of these SNP associations in 9 replication studies totaling 2129 SCA cases and 23,833 noncases. RESULTS: Eight SNPs in or near 8 genes were associated with SCA risk in the discovery study, one of which was nominally significant in the replication phase (rs7737692, minor allele frequency 36%, near the LPCAT1 gene). For each copy of the minor allele, rs7737692 was associated with 13% lower SCA risk (95% confidence interval -21% to -5%) in the discovery phase and 9% lower SCA risk (95% confidence interval -16% to -1%) in the replication phase. CONCLUSIONS: While none of the associations reached significance with Bonferroni correction, a common genetic variant near LPCAT1, a gene involved in the remodeling of phospholipids, was nominally associated with incident SCA risk. Further study is needed to validate this observation.

The use of genome-wide eQTL associations in lymphoblastoid cell lines to identify novel genetic pathways involved in complex traits.

The integrated analysis of genotypic and expression data for association with complex traits could identify novel genetic pathways involved in complex traits. We profiled 19,573 expression probes in Epstein-Barr virus-transformed lymphoblastoid cell lines (LCLs) from 299 twins and correlated these with 44 quantitative traits (QTs). For 939 expressed probes correlating with more than one QT, we investigated the presence of eQTL associations in three datasets of 57 CEU HapMap founders and 86 unrelated twins. Genome-wide association analysis of these probes with 2.2 m SNPs revealed 131 potential eQTLs (1,989 eQTL SNPs) overlapping between the HapMap datasets, five of which were in cis (58 eQTL SNPs). We then tested 535 SNPs tagging the eQTL SNPs, for association with the relevant QT in 2,905 twins. We identified nine potential SNP-QT associations (P<0.01) but none significantly replicated in five large consortia of 1,097-16,129 subjects. We also failed to replicate previous reported eQTL associations with body mass index, plasma low-density lipoprotein cholesterol, high-density lipoprotein cholesterol and triglycerides levels derived from lymphocytes, adipose and liver tissue. Our results and additional power calculations suggest that proponents may have been overoptimistic in the power of LCLs in eQTL approaches to elucidate regulatory genetic effects on complex traits using the small datasets generated to date. Nevertheless, larger tissue-specific expression data sets relevant to specific traits are becoming available, and should enable the adoption of similar integrated analyses in the near future.