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- Furberg, Helena, et al.
(author)
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Genome-wide meta-analyses identify multiple loci associated with smoking behavior
- 2010
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In: Nature Genetics. - : Springer Science and Business Media LLC. - 1546-1718 .- 1061-4036. ; 42:5, s. 134-441
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Journal article (peer-reviewed)abstract
- Consistent but indirect evidence has implicated genetic factors in smoking behavior1,2. We report meta-analyses of several smoking phenotypes within cohorts of the Tobacco and Genetics Consortium (n = 74,053). We also partnered with the European Network of Genetic and Genomic Epidemiology (ENGAGE) and Oxford-GlaxoSmithKline (Ox-GSK) consortia to follow up the 15 most significant regions (n > 140,000). We identified three loci associated with number of cigarettes smoked per day. The strongest association was a synonymous 15q25 SNP in the nicotinic receptor gene CHRNA3 (rs1051730[A], b = 1.03, standard error (s.e.) = 0.053, beta = 2.8 x 10(-73)). Two 10q25 SNPs (rs1329650[G], b = 0.367, s. e. = 0.059, beta = 5.7 x 10(-10); and rs1028936[A], b = 0.446, s. e. = 0.074, beta = 1.3 x 10(-9)) and one 9q13 SNP in EGLN2 (rs3733829[G], b = 0.333, s. e. = 0.058, P = 1.0 x 10(-8)) also exceeded genome-wide significance for cigarettes per day. For smoking initiation, eight SNPs exceeded genome-wide significance, with the strongest association at a nonsynonymous SNP in BDNF on chromosome 11 (rs6265[C], odds ratio (OR) = 1.06, 95% confidence interval (Cl) 1.04-1.08, P = 1.8 x 10(-8)). One SNP located near DBH on chromosome 9 (rs3025343[G], OR = 1.12, 95% Cl 1.08-1.18, P = 3.6 x 10(-8)) was significantly associated with smoking cessation.
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- Thankappan, K R, et al.
(author)
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Risk factor profile for chronic non-communicable diseases : results of a community-based study in Kerala, India.
- 2010
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In: Indian Journal of Medical Research (IJMR). - 0971-5916. ; 131, s. 53-63
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Journal article (peer-reviewed)abstract
- BACKGROUND & OBJECTIVES: Kerala State is a harbinger of what will happen in future to the rest of India in chronic non-communicable diseases (NCD). We assessed: (i) the burden of NCD risk factors; (ii) estimated the relations of behavioural risk factors to socio-demographic correlates, anthropometric risk factors with behavioural risk factors; (iii) evaluated if socio-demographic, behavioural and anthropometric risk factors predicted biochemical risk factors; and (iv) estimated awareness, treatment and adequacy of control of hypertension and diabetes, in Kerala state.METHODS: A total of 7449 individuals (51% women) stratified by age group, sex and place of residence were selected and information on behavioural risk factors; tobacco use, diet, physical activity, alcohol use, measured anthropometry, blood pressure was collected. Fasting blood samples were analysed for blood glucose, total cholesterol, high density lipoprotein cholesterol and triglycerides in a sample subset.Using multiple logistic regression models the associations between socio-demographic and anthropometric variables with biochemical risk factors were estimated.RESULTS: The burden of NCD risk factors was high in our sample. Prevalence of behavioural and each of the biochemical risk factors increased with age, adjusting for other factors including sex and the place of residence. The odds ratios relating anthropometric variables to biochemical variables were modest, suggesting that anthropometric variables may not be useful surrogates for biochemical risk factors for population screening purposes.INTERPRETATION & CONCLUSIONS: In this large study of community-based sample in Kerala, high burden of NCD risk factors was observed, comparable to that in the United States. These data may serve to propel multisectoral efforts to lower the community burden of NCD risk factors in India in general, and in Kerala, in particular.
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