ABSTRACT
BACKGROUND: Risk factor burden and clinical characteristics of patients with coronary artery disease (CAD) differ among ethnic groups. We related biomarkers to CAD severity in Caucasians, Chinese, Indians and Malays. METHODS: In the Dutch-Singaporean UNICORN coronary angiography cohort (n = 2033) we compared levels of five cardiovascular biomarkers: N-terminal pro-brain natriuretic peptide (NTproBNP), high-sensitivity C-reactive protein (hsCRP), cystatin C (CysC), myeloperoxidase (MPO) and high-sensitivity troponin I (hsTnI). We assessed ethnicity-specific associations of biomarkers with CAD severity, quantified by the SYNTAX score. RESULTS: Adjusted for baseline differences, NTproBNP levels were significantly higher in Malays than in Chinese and Caucasians (72.1 vs. 34.4 and 41.1 pmol/l, p < 0.001 and p = 0.005, respectively). MPO levels were higher in Caucasians than in Indians (32.8 vs. 27.2 ng/ml, p = 0.026), hsTnI levels were higher in Malays than in Caucasians and Indians (33.3 vs. 16.4 and 17.8 ng/l, p < 0.001 and p = 0.029) and hsTnI levels were higher in Chinese than in Caucasians (23.3 vs. 16.4, p = 0.031). We found modifying effects of ethnicity on the association of biomarkers with SYNTAX score. NTproBNP associated more strongly with the SYNTAX score in Malays than Caucasians (ß 0.132 vs. ß 0.020 per 100 pmol/l increase in NTproBNP, p = 0.032). For MPO levels the association was stronger in Malays than Caucasians (ß 1.146 vs. ß 0.016 per 10 ng/ml increase, p = 0.017). Differing biomarker cut-off levels were found for the ethnic groups. CONCLUSION: When corrected for possible confounders we observe ethnicity-specific differences in biomarker levels. Moreover, biomarkers associated differently with CAD severity, suggesting that ethnicity-specific cut-off values should be considered.
ABSTRACT
We analyzed data from the German Asthma Genetics Group with three methods that utilize pedigree-specific nonparametric linkage scores to facilitate the search for multiple independent and interacting susceptibility loci. The three methods included a conditional analysis, logistic regression, and neural networks. Although there were differences, the three methods identified many of the same susceptibility loci. The most consistent evidence was provided for loci on chromosomes 1, 2, 6, 9, and 15. Both the conditional and the logistic regression analyses suggested an epistatic relationship between loci on chromosomes 2 and 9. The logistic regression analysis further revealed evidence for locus heterogeneity between loci on chromosomes 6 and 15. Finally, the neural network analysis identified a potential locus on chromosome 17 that was not identified in the other analyses.