501
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Marques-Vidal P, Bochud M, Bastardot F, Lüscher T, Ferrero F, Gaspoz JM, Paccaud F, Urwyler A, von Känel R, Hock C, Waeber G, Preisig M, Vollenweider P. Levels and determinants of inflammatory biomarkers in a Swiss population-based sample (CoLaus study). PLoS One 2011; 6:e21002. [PMID: 21695270 PMCID: PMC3111463 DOI: 10.1371/journal.pone.0021002] [Citation(s) in RCA: 58] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2011] [Accepted: 05/16/2011] [Indexed: 02/08/2023] Open
Abstract
Objective to assess the levels and determinants of interleukin (IL)-1β, IL-6, tumour necrosis factor (TNF)-α and C-reactive protein (CRP) in a healthy Caucasian population. Methods population sample of 2884 men and 3201 women aged 35 to 75. IL-1β, IL-6 and TNF-α were assessed by a multiplexed particle-based flow cytometric assay and CRP by an immunometric assay. Results Spearman rank correlations between duplicate cytokine measurements (N = 80) ranged between 0.89 and 0.96; intra-class correlation coefficients ranged between 0.94 and 0.97, indicating good reproducibility. Among the 6085 participants, 2289 (37.6%), 451 (7.4%) and 43 (0.7%) had IL-1β, IL-6 and TNF-α levels below detection limits, respectively. Median (interquartile range) for participants with detectable values were 1.17 (0.48–3.90) pg/ml for IL-1β; 1.47 (0.71–3.53) pg/ml for IL-6; 2.89 (1.82–4.53) pg/ml for TNF-α and 1.3 (0.6–2.7) ng/ml for CRP. On multivariate analysis, greater age was the only factor inversely associated with IL-1β levels. Male sex, increased BMI and smoking were associated with greater IL-6 levels, while no relationship was found for age and leisure-time PA. Male sex, greater age, increased BMI and current smoking were associated with greater TNF-α levels, while no relationship was found with leisure-time PA. CRP levels were positively related to age, BMI and smoking, and inversely to male sex and physical activity. Conclusion Population-based levels of several cytokines were established. Increased age and BMI, and to a lesser degree sex and smoking, significantly and differentially impact cytokine levels, while leisure-time physical activity has little effect.
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Affiliation(s)
- Pedro Marques-Vidal
- Institute of Social and Preventive Medicine, CHUV and Faculty of Biology and Medicine, Lausanne, Switzerland.
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502
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Song K, Nelson MR, Aponte J, Manas ES, Bacanu SA, Yuan X, Kong X, Cardon L, Mooser VE, Whittaker JC, Waterworth DM. Sequencing of Lp-PLA2-encoding PLA2G7 gene in 2000 Europeans reveals several rare loss-of-function mutations. THE PHARMACOGENOMICS JOURNAL 2011; 12:425-31. [PMID: 21606947 PMCID: PMC3449231 DOI: 10.1038/tpj.2011.20] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Elevated plasma levels of lipoprotein-associated phospholipase A2 (Lp-PLA2) activity have been shown to be associated with increased risk of coronary heart disease and an inhibitor of this enzyme is under development for the treatment of that condition. A Val279Phe null allele in this gene, that may influence patient eligibility for treatment, is relatively common in East Asians but has not been observed in Europeans. We investigated the existence and functional effects of low frequency alleles in a Western European population by re-sequencing the exons of PLA2G7 in 2000 samples. In all, 19 non-synonymous single-nucleotide polymorphisms (nsSNPs) were found, 14 in fewer than four subjects (minor allele frequency <0.1%). Lp-PLA2 activity was significantly lower in rare nsSNP carriers compared with non-carriers (167.8±63.2 vs 204.6±41.8, P=0.01) and seven variants had enzyme activities consistent with a null allele. The cumulative frequency of these null alleles was 0.25%, so <1 in 10 000 Europeans would be expected to be homozygous, and thus not potentially benefit from treatment with an Lp-PLA2 inhibitor.
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Affiliation(s)
- K Song
- Department of Genetics, GlaxoSmithKline, Upper Merion, PA, USA
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503
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Elevated serum uric acid is associated with high circulating inflammatory cytokines in the population-based Colaus study. PLoS One 2011; 6:e19901. [PMID: 21625475 PMCID: PMC3098830 DOI: 10.1371/journal.pone.0019901] [Citation(s) in RCA: 171] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2011] [Accepted: 04/06/2011] [Indexed: 12/19/2022] Open
Abstract
Background The relation of serum uric acid (SUA) with systemic inflammation has been
little explored in humans and results have been inconsistent. We analyzed
the association between SUA and circulating levels of interleukin-6 (IL-6),
interleukin-1β (IL-1β), tumor necrosis factor- α (TNF-α) and
C-reactive protein (CRP). Methods and Findings This cross-sectional population-based study conducted in Lausanne,
Switzerland, included 6085 participants aged 35 to 75 years. SUA was
measured using uricase-PAP method. Plasma TNF-α, IL-1β and IL-6 were
measured by a multiplexed particle-based flow cytometric assay and hs-CRP by
an immunometric assay. The median levels of SUA, IL-6, TNF-α, CRP and
IL-1β were 355 µmol/L, 1.46 pg/mL, 3.04 pg/mL, 1.2 mg/L and 0.34
pg/mL in men and 262 µmol/L, 1.21 pg/mL, 2.74 pg/mL, 1.3 mg/L and 0.45
pg/mL in women, respectively. SUA correlated positively with IL-6, TNF-α
and CRP and negatively with IL-1β (Spearman r: 0.04, 0.07, 0.20 and 0.05
in men, and 0.09, 0.13, 0.30 and 0.07 in women, respectively, P<0.05). In
multivariable analyses, SUA was associated positively with CRP (β
coefficient ± SE = 0.35±0.02,
P<0.001), TNF-α (0.08±0.02, P<0.001) and IL-6
(0.10±0.03, P<0.001), and negatively with IL-1β
(−0.07±0.03, P = 0.027). Upon further
adjustment for body mass index, these associations were substantially
attenuated. Conclusions SUA was associated positively with IL-6, CRP and TNF-α and negatively
with IL-1β, particularly in women. These results suggest that uric acid
contributes to systemic inflammation in humans and are in line with
experimental data showing that uric acid triggers sterile inflammation.
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504
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Marques-Vidal P, Ross A, Wynn E, Rezzi S, Paccaud F, Decarli B. Reproducibility and relative validity of a food-frequency questionnaire for French-speaking Swiss adults. Food Nutr Res 2011; 55:5905. [PMID: 21562629 PMCID: PMC3091846 DOI: 10.3402/fnr.v55i0.5905] [Citation(s) in RCA: 40] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2010] [Revised: 04/07/2011] [Accepted: 04/08/2011] [Indexed: 11/19/2022] Open
Abstract
Background Due to the distinct cultural and language differences that exist in Switzerland, there is little information on the dietary intake among the general Swiss population. Adequately assessing dietary intake is thus paramount if nutritional epidemiological studies are to be conducted. Objective To assess the reproducibility and validity of a food-frequency questionnaire (FFQ) developed for French-speaking Swiss adults. Design A total of 23 men and 17 women (43.1±2.0 years) filled out one FFQ and completed one 24-hour dietary recall at baseline and 1 month afterward. Results Crude Pearson's correlation coefficients between the first and the second FFQ ranged from 0.58 to 0.90, intraclass correlation coefficient (ICC) ranged between 0.53 and 0.92. Lin's concordance coefficients ranged between 0.55 and 0.87. Over 80% of participants were classified in the same or adjacent tertile using each FFQ. Macronutrient intakes estimated by both FFQs were significantly higher than those estimated from the 24-hour recall for protein and water, while no significant differences were found for energy, carbohydrate, fats (five groups), and alcohol. De-attenuated Pearson's correlation coefficients between the 24-hour recall and the first FFQ ranged between 0.31 and 0.49, while for the second FFQ the values ranged between 0.38 and 0.59. Over 40 and 95% of participants fell into the same or the adjacent energy and nutrient tertiles, respectively, using the FFQs and the 24-hour recall. Conclusions This FFQ shows good reproducibility and can be used determining macronutrient intake in a French-speaking Swiss population in an epidemiological setting.
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Affiliation(s)
- Pedro Marques-Vidal
- Institute of Social and Preventive Medicine (IUMSP), University Hospital Center and Faculty of Biology and Medicine, Lausanne, Switzerland
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505
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Prevalence, treatment and control of dyslipidaemia in Switzerland: still a long way to go. ACTA ACUST UNITED AC 2011; 17:682-7. [PMID: 20700055 DOI: 10.1097/hjr.0b013e32833a09ab] [Citation(s) in RCA: 13] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
BACKGROUND There is little information regarding the prevalence and management of dyslipidaemia in Switzerland. DESIGN Cross-sectional population-based study of 3238 women and 2846 men aged 35-75. METHODS Dyslipidaemia prevalence, treatment and control were defined according to PROCAM guidelines adapted to Switzerland. RESULTS About 29% of the overall sample presented with dyslipidaemia, of which 39% were treated and 58% of those treated were controlled. Among the 710 patients with personal history of cardiovascular disease (CVD) and/or diabetes, 632 (89%) presented with dyslipidaemia, of which 278 (44%) and 134 (21%) patients were treated and adequately controlled, respectively. On multivariate analysis, hypolipidaemic drug treatment was positively related with age and body mass index (P for trend <0.001), and negatively related with smoking status (P for trend <0.002), whereas personal history of CVD and/or diabetes had no effect [odds ratio (OR)=1.12, 95% confidence interval (CI): 0.90-1.38]. Adequate control of lipid levels was negatively related with female sex (OR=0.65, 95% CI: 0.45-0.94) and personal history of CVD and/or diabetes (OR=0.42, 95% CI: 0.30-0.59). When personal history of CVD and/or diabetes was replaced by PROCAM risk categories, patients in the highest risk were also less well controlled. CONCLUSION In this population-based study, one-third of the participants was dyslipidaemic, but less than half was treated and only one-fifth was adequately controlled. The low treatment and control levels among individuals at high risk for CVD calls for a better application of recommendations regarding personal preventive measures.
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506
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Marques-Vidal P, Kutalik Z, Paccaud F, Bergmann S, Waeber G, Vollenweider P, Cornuz J. Variant within the promoter region of the CHRNA3 gene associated with FTN dependence is not related to self-reported willingness to quit smoking. Nicotine Tob Res 2011; 13:833-9. [PMID: 21511889 DOI: 10.1093/ntr/ntr084] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
INTRODUCTION Common variation in the CHRNA5-CHRNA3-CHRNB4 gene region is robustly associated with smoking quantity. Conversely, the association between one of the most significant single nucleotide polymorphisms (SNPs; rs1051730 within the CHRNA3 gene) with perceived difficulty or willingness to quit smoking among current smokers is unknown. METHODS Cross-sectional study including current smokers, 502 women, and 552 men. Heaviness of smoking index (HSI), difficulty, attempting, and intention to quit smoking were assessed by questionnaire. RESULTS The rs1051730 SNP was associated with increased HSI (age, gender, and education-adjusted mean ± SE: 2.6 ± 0.1, 2.2 ± 0.1, and 2.0 ± 0.1 for AA, AG, and GG genotypes, respectively, p < .01). Multivariate logistic regression adjusting for gender, age, education, leisure-time physical activity, and personal history of cardiovascular or lung disease showed rs1051730 to be associated with higher smoking dependence (odds ratio [OR] and 95% CI for each additional A-allele: 1.38 [1.11-1.72] for smoking more than 20 cigarette equivalents/day; 1.31 [1.00-1.71] for an HSI ≥5 and 1.32 [1.05-1.65] for smoking 5 min after waking up) and borderline associated with difficulty to quit (OR = 1.29 [0.98-1.70]), but this relationship was no longer significant after adjusting for nicotine dependence. Also, no relationship was found with willingness (OR = 1.03 [0.85-1.26]), attempt (OR = 1.00 [0.83-1.20]), or preparation (OR = 0.95 [0.38-2.38]) to quit. Similar findings were obtained for other SNPs, but their effect on nicotine dependence was no longer significant after adjusting for rs1051730. CONCLUSIONS These data confirm the effect of rs1051730 on nicotine dependence but failed to find any relationship with difficulty, willingness, and motivation to quit.
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Affiliation(s)
- Pedro Marques-Vidal
- Institute of Social and Preventive Medicine, University Hospital and University of Lausanne, Lausanne, Switzerland.
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507
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Guessous I, Bonny O, Paccaud F, Mooser V, Waeber G, Vollenweider P, Bochud M. Serum calcium levels are associated with novel cardiometabolic risk factors in the population-based CoLaus study. PLoS One 2011; 6:e18865. [PMID: 21533040 PMCID: PMC3080882 DOI: 10.1371/journal.pone.0018865] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2010] [Accepted: 03/20/2011] [Indexed: 01/19/2023] Open
Abstract
BACKGROUND Associations of serum calcium levels with the metabolic syndrome and other novel cardio-metabolic risk factors not classically included in the metabolic syndrome, such as those involved in oxidative stress, are largely unexplored. We analyzed the association of albumin-corrected serum calcium levels with conventional and non-conventional cardio-metabolic risk factors in a general adult population. METHODOLOGY/PRINCIPAL FINDINGS The CoLaus study is a population-based study including Caucasians from Lausanne, Switzerland. The metabolic syndrome was defined using the Adult Treatment Panel III criteria. Non-conventional cardio-metabolic risk factors considered included: fat mass, leptin, LDL particle size, apolipoprotein B, fasting insulin, adiponectin, ultrasensitive CRP, serum uric acid, homocysteine, and gamma-glutamyltransferase. We used adjusted standardized multivariable regression to compare the association of each cardio-metabolic risk factor with albumin-corrected serum calcium. We assessed associations of albumin-corrected serum calcium with the cumulative number of non-conventional cardio-metabolic risk factors. We analyzed 4,231 subjects aged 35 to 75 years. Corrected serum calcium increased with both the number of the metabolic syndrome components and the number of non-conventional cardio-metabolic risk factors, independently of the metabolic syndrome and BMI. Among conventional and non-conventional cardio-metabolic risk factors, the strongest positive associations were found for factors related to oxidative stress (uric acid, homocysteine and gamma-glutamyltransferase). Adiponectin had the strongest negative association with corrected serum calcium. CONCLUSIONS/SIGNIFICANCE Serum calcium was associated with the metabolic syndrome and with non-conventional cardio-metabolic risk factors independently of the metabolic syndrome. Associations with uric acid, homocysteine and gamma-glutamyltransferase were the strongest. These novel findings suggest that serum calcium levels may be associated with cardiovascular risk via oxidative stress.
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Affiliation(s)
- Idris Guessous
- Community Prevention Unit, Institute of Social and Preventive Medicine, Centre Hospitalier Universitaire Vaudois, University of Lausanne, Lausanne, Switzerland.
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508
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Marques-Vidal P, Melich-Cerveira J, Paccaud F, Waeber G, Vollenweider P, Cornuz J. Prevalence and factors associated with difficulty and intention to quit smoking in Switzerland. BMC Public Health 2011; 11:227. [PMID: 21489259 PMCID: PMC3095559 DOI: 10.1186/1471-2458-11-227] [Citation(s) in RCA: 41] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2010] [Accepted: 04/13/2011] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Recent data indicate a slight decrease in the prevalence of smoking in Switzerland, but little is known regarding the intention and difficulty to quit smoking among current smokers. Hence, we aimed to quantify the difficulty and intention to quit smoking among current smokers in Switzerland. METHODS Cross-sectional study including 607 female and 658 male smokers. Difficulty, intention and motivation to quit smoking were assessed by questionnaire. RESULTS 90% of women and 85% of men reported being "very difficult" or "difficult" to quit smoking. Almost three quarters of smokers (73% of women and 71% of men) intended to quit; however, less than 20% of them were in the preparation stage and 40% were in the precontemplation stage. On multivariate analysis, difficulty to quit was lower among men (Odds ratio and 95% [confidence interval]: 0.51 [0.35-0.74]) and increased with nicotine dependence and number of previous quitting attempts (OR=3.14 [1.75-5.63] for 6+ attempts compared to none). Intention to quit decreased with increasing age (OR=0.48 [0.30-0.75] for ≥65 years compared to <45 years) and increased with nicotine dependence, the number of previous quitting attempts (OR=4.35 [2.76-6.83] for 6+ attempts compared to none) and among non-cigarette smokers (OR=0.51 [0.28-0.92]). Motivation to quit was inversely associated with nicotine dependence and positively associated with the number of previous quitting attempts and personal history of lung disease. CONCLUSION Over two thirds of Swiss smokers want to quit. However, only a small fraction wishes to do so in the short term. Nicotine dependence, previous attempts to quit or previous history of lung disease are independently associated with difficulty and intention to quit.
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Affiliation(s)
- Pedro Marques-Vidal
- Institute of Social and Preventive Medicine (IUMSP), Centre Hospitalier Universitaire Vaudois and University of Lausanne, Bugnon 17, 1005 Lausanne, Switzerland.
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509
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Kutalik Z, Whittaker J, Waterworth D, Beckmann JS, Bergmann S. Novel method to estimate the phenotypic variation explained by genome-wide association studies reveals large fraction of the missing heritability. Genet Epidemiol 2011; 35:341-9. [PMID: 21465548 DOI: 10.1002/gepi.20582] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2010] [Revised: 02/15/2011] [Accepted: 03/01/2011] [Indexed: 12/12/2022]
Abstract
Genome-wide association studies (GWAS) are conducted with the promise to discover novel genetic variants associated with diverse traits. For most traits, associated markers individually explain just a modest fraction of the phenotypic variation, but their number can well be in the hundreds. We developed a maximum likelihood method that allows us to infer the distribution of associated variants even when many of them were missed by chance. Compared to previous approaches, the novelty of our method is that it (a) does not require having an independent (unbiased) estimate of the effect sizes; (b) makes use of the complete distribution of P-values while allowing for the false discovery rate; (c) takes into account allelic heterogeneity and the SNP pruning strategy. We applied our method to the latest GWAS meta-analysis results of the GIANT consortium. It revealed that while the explained variance of genome-wide (GW) significant SNPs is around 1% for waist-hip ratio (WHR), the observed P-values provide evidence for the existence of variants explaining 10% (CI=[8.5-11.5%]) of the phenotypic variance in total. Similarly, the total explained variance likely to exist for height is estimated to be 29% (CI=[28-30%]), three times higher than what the observed GW significant SNPs give rise to. This methodology also enables us to predict the benefit of future GWA studies that aim to reveal more associated genetic markers via increased sample size.
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Affiliation(s)
- Zoltán Kutalik
- Department of Medical Genetics, University of Lausanne, Lausanne, Switzerland.
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510
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Böger CA, Chen MH, Tin A, Olden M, Köttgen A, de Boer IH, Fuchsberger C, O'Seaghdha CM, Pattaro C, Teumer A, Liu CT, Glazer NL, Li M, O'Connell JR, Tanaka T, Peralta CA, Kutalik Z, Luan J, Zhao JH, Hwang SJ, Akylbekova E, Kramer H, van der Harst P, Smith AV, Lohman K, de Andrade M, Hayward C, Kollerits B, Tönjes A, Aspelund T, Ingelsson E, Eiriksdottir G, Launer LJ, Harris TB, Shuldiner AR, Mitchell BD, Arking DE, Franceschini N, Boerwinkle E, Egan J, Hernandez D, Reilly M, Townsend RR, Lumley T, Siscovick DS, Psaty BM, Kestenbaum B, Haritunians T, Bergmann S, Vollenweider P, Waeber G, Mooser V, Waterworth D, Johnson AD, Florez JC, Meigs JB, Lu X, Turner ST, Atkinson EJ, Leak TS, Aasarød K, Skorpen F, Syvänen AC, Illig T, Baumert J, Koenig W, Krämer BK, Devuyst O, Mychaleckyj JC, Minelli C, Bakker SJ, Kedenko L, Paulweber B, Coassin S, Endlich K, Kroemer HK, Biffar R, Stracke S, Völzke H, Stumvoll M, Mägi R, Campbell H, Vitart V, Hastie ND, Gudnason V, Kardia SL, Liu Y, Polasek O, Curhan G, Kronenberg F, Prokopenko I, Rudan I, Ärnlöv J, Hallan S, Navis G, the CKDGen Consortium, Parsa A, Ferrucci L, Coresh J, Shlipak MG, et alBöger CA, Chen MH, Tin A, Olden M, Köttgen A, de Boer IH, Fuchsberger C, O'Seaghdha CM, Pattaro C, Teumer A, Liu CT, Glazer NL, Li M, O'Connell JR, Tanaka T, Peralta CA, Kutalik Z, Luan J, Zhao JH, Hwang SJ, Akylbekova E, Kramer H, van der Harst P, Smith AV, Lohman K, de Andrade M, Hayward C, Kollerits B, Tönjes A, Aspelund T, Ingelsson E, Eiriksdottir G, Launer LJ, Harris TB, Shuldiner AR, Mitchell BD, Arking DE, Franceschini N, Boerwinkle E, Egan J, Hernandez D, Reilly M, Townsend RR, Lumley T, Siscovick DS, Psaty BM, Kestenbaum B, Haritunians T, Bergmann S, Vollenweider P, Waeber G, Mooser V, Waterworth D, Johnson AD, Florez JC, Meigs JB, Lu X, Turner ST, Atkinson EJ, Leak TS, Aasarød K, Skorpen F, Syvänen AC, Illig T, Baumert J, Koenig W, Krämer BK, Devuyst O, Mychaleckyj JC, Minelli C, Bakker SJ, Kedenko L, Paulweber B, Coassin S, Endlich K, Kroemer HK, Biffar R, Stracke S, Völzke H, Stumvoll M, Mägi R, Campbell H, Vitart V, Hastie ND, Gudnason V, Kardia SL, Liu Y, Polasek O, Curhan G, Kronenberg F, Prokopenko I, Rudan I, Ärnlöv J, Hallan S, Navis G, the CKDGen Consortium, Parsa A, Ferrucci L, Coresh J, Shlipak MG, Bull SB, Paterson AD, on behalf of DCCT/EDIC, Wichmann HE, Wareham NJ, Loos RJ, Rotter JI, Pramstaller PP, Cupples LA, Beckmann JS, Yang Q, Heid IM, Rettig R, Dreisbach AW, Bochud M, Fox CS, Kao W. CUBN is a gene locus for albuminuria. J Am Soc Nephrol 2011; 22:555-70. [PMID: 21355061 PMCID: PMC3060449 DOI: 10.1681/asn.2010060598] [Show More Authors] [Citation(s) in RCA: 183] [Impact Index Per Article: 13.1] [Reference Citation Analysis] [Collaborators] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2010] [Accepted: 10/19/2010] [Indexed: 11/03/2022] Open
Abstract
Identification of genetic risk factors for albuminuria may alter strategies for early prevention of CKD progression, particularly among patients with diabetes. Little is known about the influence of common genetic variants on albuminuria in both general and diabetic populations. We performed a meta-analysis of data from 63,153 individuals of European ancestry with genotype information from genome-wide association studies (CKDGen Consortium) and from a large candidate gene study (CARe Consortium) to identify susceptibility loci for the quantitative trait urinary albumin-to-creatinine ratio (UACR) and the clinical diagnosis microalbuminuria. We identified an association between a missense variant (I2984V) in the CUBN gene, which encodes cubilin, and both UACR (P = 1.1 × 10(-11)) and microalbuminuria (P = 0.001). We observed similar associations among 6981 African Americans in the CARe Consortium. The associations between this variant and both UACR and microalbuminuria were significant in individuals of European ancestry regardless of diabetes status. Finally, this variant associated with a 41% increased risk for the development of persistent microalbuminuria during 20 years of follow-up among 1304 participants with type 1 diabetes in the prospective DCCT/EDIC Study. In summary, we identified a missense CUBN variant that associates with levels of albuminuria in both the general population and in individuals with diabetes.
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Affiliation(s)
- Carsten A. Böger
- Department of Internal Medicine II, University Medical Center Regensburg, Regensburg, Germany
| | - Ming-Huei Chen
- Department of Neurology, Boston University School of Medicine, Boston, Massachusetts
| | - Adrienne Tin
- Department of Epidemiology and the Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, Maryland
| | - Matthias Olden
- Department of Internal Medicine II, University Medical Center Regensburg, Regensburg, Germany
- Department of Epidemiology and Preventive Medicine, Regensburg University Medical Center, Regensburg, Germany
| | - Anna Köttgen
- Department of Epidemiology and the Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, Maryland
- Renal Division, University Hospital of Freiburg, Freiburg, Germany
| | - Ian H. de Boer
- Division of Nephrology, University of Washington, Seattle, Washington
| | - Christian Fuchsberger
- Institute of Genetic Medicine, European Academy of Bolzano/Bozen (EURAC), Italy and Affiliated Institute of the University of Lübeck, Lübeck, Germany
| | - Conall M. O'Seaghdha
- Division of Nephrology, Brigham and Women's Hospital and Harvard Medical School, Boston Massachusetts
| | - Cristian Pattaro
- Institute of Genetic Medicine, European Academy of Bolzano/Bozen (EURAC), Italy and Affiliated Institute of the University of Lübeck, Lübeck, Germany
| | - Alexander Teumer
- Interfaculty Institute for Genetics and Functional Genomics, University of Greifswald, Greifswald, Germany
| | - Ching-Ti Liu
- Department of Biostatistics, Boston University School of Public Health and NHLBI's Framingham Heart Study, Boston Massachusetts
| | - Nicole L. Glazer
- Cardiovascular Health Research Unit and Department of Biostatistics, University of Washington, Seattle, Washington
| | - Man Li
- Department of Epidemiology and the Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, Maryland
| | | | - Toshiko Tanaka
- Medstar Research Institute, Baltimore, Maryland
- Clinical Research Branch, National Institute on Aging, Baltimore, Maryland
| | - Carmen A. Peralta
- Division of Nephrology, University of California, San Francisco Medical School and San Francisco VA Medical Center, San Francisco, California
| | - Zoltán Kutalik
- Department of Medical Genetics, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Jian'an Luan
- MRC Epidemiology Unit, Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, United Kingdom
| | - Jing Hua Zhao
- MRC Epidemiology Unit, Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, United Kingdom
| | - Shih-Jen Hwang
- NHLBI's Framingham Heart Study and the Center for Population Studies, Framingham, Massachusetts
| | | | | | - Pim van der Harst
- Department of Cardiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Albert V. Smith
- University of Iceland, Reykjavik, Iceland
- Icelandic Heart Association, Hjartavernd, Holtasmara, Kopavogur, Iceland
| | - Kurt Lohman
- Department of Biostatistical Sciences, Wake Forest University, Division of Public Health Sciences, Winston-Salem, North Carolina
| | - Mariza de Andrade
- Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota
| | - Caroline Hayward
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, Western General Hospital, Crewe Road, Edinburgh, Scotland
| | - Barbara Kollerits
- Innsbruck Medical University, Division of Genetic Epidemiology, Innsbruck, Austria
| | - Anke Tönjes
- Department of Medicine, University of Leipzig, Leipzig, Germany
| | - Thor Aspelund
- University of Iceland, Reykjavik, Iceland
- Icelandic Heart Association, Hjartavernd, Holtasmara, Kopavogur, Iceland
| | - Erik Ingelsson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Gudny Eiriksdottir
- Icelandic Heart Association, Hjartavernd, Holtasmara, Kopavogur, Iceland
| | - Lenore J. Launer
- Laboratory of Epidemiology, Demography, and Biometry, NIA, Bethesda, Maryland
| | - Tamara B. Harris
- Laboratory of Epidemiology, Demography, and Biometry, NIA, Bethesda, Maryland
| | - Alan R. Shuldiner
- University of Maryland School of Medicine, Geriatric Research and Education Clinical Center, Veterans Administration Medical Center, Baltimore, Maryland
| | | | - Dan E. Arking
- McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins Medical Institutions, Baltimore, Maryland
| | - Nora Franceschini
- University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Eric Boerwinkle
- Human Genetics Center, University of Texas Health Science Center, Houston, Texas
| | - Josephine Egan
- Laboratory of Clinical Investigation, National Institute on Aging, Baltimore, Maryland
| | - Dena Hernandez
- Laboratory of Neurogenetics, National Institute on Aging, Bethesda, Maryland
| | - Muredach Reilly
- University of Pennsylvania Division of Cardiology, Perelman Center for Advanced Medicine, Philadelphia, Pennsylvania
| | - Raymond R. Townsend
- University of Pennsylvania Renal Electrolyte and Hypertension Division, Philadelphia, Pennsylvania
| | - Thomas Lumley
- Cardiovascular Health Research Unit and Department of Biostatistics, University of Washington, Seattle, Washington
| | - David S. Siscovick
- Departments of Epidemiology and Medicine, University of Washington, Seattle, Washington
| | - Bruce M. Psaty
- Cardiovascular Health Research Unit, Departments of Medicine, Epidemiology, and Health Services and Group Health Research Institute, Group Health Cooperative, Seattle, Washington
| | - Bryan Kestenbaum
- Division of Nephrology, University of Washington, Seattle, Washington
| | - Talin Haritunians
- Medical Genetics Institute, Cedars-Sinai Medical Center, Los Angeles, California
| | - Sven Bergmann
- Department of Medical Genetics, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Peter Vollenweider
- Department of Internal Medicine, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
| | - Gerard Waeber
- Department of Internal Medicine, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
| | - Vincent Mooser
- Genetics Division, GlaxoSmithKline, King of Prussia, Pennsylvania
| | - Dawn Waterworth
- Genetics Division, GlaxoSmithKline, King of Prussia, Pennsylvania
| | - Andrew D. Johnson
- NHLBI's Framingham Heart Study and the Center for Population Studies, Framingham, Massachusetts
| | - Jose C. Florez
- Center for Human Genetic Research and Diabetes Research Center (Diabetes Unit), Massachusetts General Hospital, Boston, Massachusetts, Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachussetts, and Department of Medicine, Harvard Medical School, Boston, Massachusetts
| | - James B. Meigs
- Department of General Internal Medicine, Massachussetts General Hospital, Boston, Massachusetts
| | - Xiaoning Lu
- Department of Biostatistics, Boston University School of Public Health and NHLBI's Framingham Heart Study, Boston Massachusetts
| | - Stephen T. Turner
- Department of Internal Medicine, Division of Nephrology and Hypertension, Mayo Clinic, Rochester, Minnesota
| | - Elizabeth J. Atkinson
- Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota
| | - Tennille S. Leak
- Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Knut Aasarød
- St Olav University Hospital, Trondheim, Norway
- Faculty of Medicine, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Frank Skorpen
- Faculty of Medicine, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Ann-Christine Syvänen
- Molecular Medicine, Department of Medical Sciences, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Thomas Illig
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Jens Baumert
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Wolfgang Koenig
- Zentrum für Innere Medizin, Klinik für Innere Medizin II - Kardiologie, Universitätsklinikum Ulm, Ulm, Germany
| | - Bernhard K. Krämer
- University Medical Centre Mannheim, 5th Department of Medicine, Mannheim, Germany
| | - Olivier Devuyst
- NEFR Unit Université Catholique de Louvain Medical School, Brussels, Belgium
| | | | - Cosetta Minelli
- Institute of Genetic Medicine, European Academy of Bolzano/Bozen (EURAC), Italy and Affiliated Institute of the University of Lübeck, Lübeck, Germany
| | - Stephan J.L. Bakker
- Department of Internal Medicine, University Medical Center, Groningen, University of Groningen, Groningen, The Netherlands
| | - Lyudmyla Kedenko
- First Department of Internal Medicine, Paracelsus Medical University, Salzburg, Austria
| | - Bernhard Paulweber
- First Department of Internal Medicine, Paracelsus Medical University, Salzburg, Austria
| | - Stefan Coassin
- Innsbruck Medical University, Division of Genetic Epidemiology, Innsbruck, Austria
| | - Karlhans Endlich
- Institute of Anatomy and Cell Biology, University of Greifswald, Greifswald, Germany
| | - Heyo K. Kroemer
- Institute of Pharmacology, University of Greifswald, Greifswald, Germany
| | - Reiner Biffar
- Clinic for Prosthodontic Dentistry, Gerostomatology and Material Science, University of Greifswald, Greifswald, Germany
| | - Sylvia Stracke
- Nephrology Clinic for Internal Medicine A, University of Greifswald, Greifswald, Germany
| | - Henry Völzke
- Institute for Community Medicine, University of Greifswald, Greifswald, Germany
| | | | - Reedik Mägi
- Wellcome Trust Centre for Human Genetics, and Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, United Kingdom
| | - Harry Campbell
- Centre for Population Health Sciences, University of Edinburgh, Edinburgh, Scotland
| | - Veronique Vitart
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, Western General Hospital, Crewe Road, Edinburgh, Scotland
| | - Nicholas D. Hastie
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, Western General Hospital, Crewe Road, Edinburgh, Scotland
| | - Vilmundur Gudnason
- University of Iceland, Reykjavik, Iceland
- Icelandic Heart Association, Hjartavernd, Holtasmara, Kopavogur, Iceland
| | - Sharon L.R. Kardia
- University of Michigan School of Public Health, Department of Epidemiology, University of Michigan, Ann Arbor, Michigan
| | - Yongmei Liu
- Department of Biostatistical Sciences, Wake Forest University, Division of Public Health Sciences, Winston-Salem, North Carolina
| | | | - Gary Curhan
- Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Florian Kronenberg
- Innsbruck Medical University, Division of Genetic Epidemiology, Innsbruck, Austria
| | - Inga Prokopenko
- Wellcome Trust Centre for Human Genetics, and Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, United Kingdom
| | - Igor Rudan
- Center for Population Health Sciences, University of Edinburgh Medical School, Edinburgh, Scotland
| | - Johan Ärnlöv
- Department of Public Health and Caring Sciences, Uppsala University, Uppsala, Sweden
| | - Stein Hallan
- St Olav University Hospital, Trondheim, Norway
- Faculty of Medicine, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Gerjan Navis
- Department of Internal Medicine, University Medical Center, Groningen, University of Groningen, Groningen, The Netherlands
| | - the CKDGen Consortium
- Department of Internal Medicine II, University Medical Center Regensburg, Regensburg, Germany
- Department of Neurology, Boston University School of Medicine, Boston, Massachusetts
- Department of Epidemiology and the Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, Maryland
- Department of Epidemiology and Preventive Medicine, Regensburg University Medical Center, Regensburg, Germany
- Renal Division, University Hospital of Freiburg, Freiburg, Germany
- Division of Nephrology, University of Washington, Seattle, Washington
- Institute of Genetic Medicine, European Academy of Bolzano/Bozen (EURAC), Italy and Affiliated Institute of the University of Lübeck, Lübeck, Germany
- Division of Nephrology, Brigham and Women's Hospital and Harvard Medical School, Boston Massachusetts
- Interfaculty Institute for Genetics and Functional Genomics, University of Greifswald, Greifswald, Germany
- Department of Biostatistics, Boston University School of Public Health and NHLBI's Framingham Heart Study, Boston Massachusetts
- Cardiovascular Health Research Unit and Department of Biostatistics, University of Washington, Seattle, Washington
- University of Maryland School of Medicine, Baltimore, Maryland
- Medstar Research Institute, Baltimore, Maryland
- Clinical Research Branch, National Institute on Aging, Baltimore, Maryland
- Division of Nephrology, University of California, San Francisco Medical School and San Francisco VA Medical Center, San Francisco, California
- Department of Medical Genetics, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- MRC Epidemiology Unit, Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, United Kingdom
- NHLBI's Framingham Heart Study and the Center for Population Studies, Framingham, Massachusetts
- Jackson State University, Jackson, Mississippi
- Loyola University, Maywood, Illinois
- Department of Cardiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
- University of Iceland, Reykjavik, Iceland
- Icelandic Heart Association, Hjartavernd, Holtasmara, Kopavogur, Iceland
- Department of Biostatistical Sciences, Wake Forest University, Division of Public Health Sciences, Winston-Salem, North Carolina
- Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, Western General Hospital, Crewe Road, Edinburgh, Scotland
- Innsbruck Medical University, Division of Genetic Epidemiology, Innsbruck, Austria
- Department of Medicine, University of Leipzig, Leipzig, Germany
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Laboratory of Epidemiology, Demography, and Biometry, NIA, Bethesda, Maryland
- University of Maryland School of Medicine, Geriatric Research and Education Clinical Center, Veterans Administration Medical Center, Baltimore, Maryland
- University of Maryland School of Medicine, Baltimore, Maryland
- McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins Medical Institutions, Baltimore, Maryland
- University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
- Human Genetics Center, University of Texas Health Science Center, Houston, Texas
- Laboratory of Clinical Investigation, National Institute on Aging, Baltimore, Maryland
- Laboratory of Neurogenetics, National Institute on Aging, Bethesda, Maryland
- University of Pennsylvania Division of Cardiology, Perelman Center for Advanced Medicine, Philadelphia, Pennsylvania
- University of Pennsylvania Renal Electrolyte and Hypertension Division, Philadelphia, Pennsylvania
- Departments of Epidemiology and Medicine, University of Washington, Seattle, Washington
- Cardiovascular Health Research Unit, Departments of Medicine, Epidemiology, and Health Services and Group Health Research Institute, Group Health Cooperative, Seattle, Washington
- Medical Genetics Institute, Cedars-Sinai Medical Center, Los Angeles, California
- Department of Internal Medicine, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
- Genetics Division, GlaxoSmithKline, King of Prussia, Pennsylvania
- Center for Human Genetic Research and Diabetes Research Center (Diabetes Unit), Massachusetts General Hospital, Boston, Massachusetts, Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachussetts, and Department of Medicine, Harvard Medical School, Boston, Massachusetts
- Department of General Internal Medicine, Massachussetts General Hospital, Boston, Massachusetts
- Department of Internal Medicine, Division of Nephrology and Hypertension, Mayo Clinic, Rochester, Minnesota
- Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania
- St Olav University Hospital, Trondheim, Norway
- Faculty of Medicine, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
- Molecular Medicine, Department of Medical Sciences, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Zentrum für Innere Medizin, Klinik für Innere Medizin II - Kardiologie, Universitätsklinikum Ulm, Ulm, Germany
- University Medical Centre Mannheim, 5th Department of Medicine, Mannheim, Germany
- NEFR Unit Université Catholique de Louvain Medical School, Brussels, Belgium
- Center for Public Health Genomics, Charlottesville, Virginia
- Department of Internal Medicine, University Medical Center, Groningen, University of Groningen, Groningen, The Netherlands
- First Department of Internal Medicine, Paracelsus Medical University, Salzburg, Austria
- Institute of Anatomy and Cell Biology, University of Greifswald, Greifswald, Germany
- Institute of Pharmacology, University of Greifswald, Greifswald, Germany
- Clinic for Prosthodontic Dentistry, Gerostomatology and Material Science, University of Greifswald, Greifswald, Germany
- Nephrology Clinic for Internal Medicine A, University of Greifswald, Greifswald, Germany
- Institute for Community Medicine, University of Greifswald, Greifswald, Germany
- Wellcome Trust Centre for Human Genetics, and Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, United Kingdom
- Centre for Population Health Sciences, University of Edinburgh, Edinburgh, Scotland
- University of Michigan School of Public Health, Department of Epidemiology, University of Michigan, Ann Arbor, Michigan
- Gen-Info Ltd., Zagreb, Croatia
- Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
- Center for Population Health Sciences, University of Edinburgh Medical School, Edinburgh, Scotland
- Department of Public Health and Caring Sciences, Uppsala University, Uppsala, Sweden
- University of Maryland School of Medicine, Baltimore, Maryland
- Welch Center for Prevention, Epidemiology & Clinical Research, Johns Hopkins University, Baltimore, Maryland
- General Internal Medicine, University of California, San Francisco, San Francisco, California
- Samuel Lunenfeld Research Institute of Mount Sinai Hospital, Prosserman Centre for Health Research, Toronto, Ontario, Canada
- The Hospital for Sick Children, Toronto, Ontario, Canada
- Institute of Medical Informatics, Biometry and Epidemiology, Ludwig-Maximilians-Universität, Munich, Germany
- Klinikum Grosshadern, Munich, Germany
- Service of Medical Genetics, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts
- Institute of Physiology, University of Greifswald, Greifswald, Germany
- University of Mississippi Division of Nephrology, University of Mississippi, Jackson, Mississippi
- University Institute of Social and Preventive Medicine, Centre Hospitalier Universitaire Vaudois and University of Lausanne, IUMSP, Lausanne, Switzerland; and
- Division of Endocrinology, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
| | - Afshin Parsa
- University of Maryland School of Medicine, Baltimore, Maryland
| | - Luigi Ferrucci
- Clinical Research Branch, National Institute on Aging, Baltimore, Maryland
| | - Josef Coresh
- Welch Center for Prevention, Epidemiology & Clinical Research, Johns Hopkins University, Baltimore, Maryland
| | - Michael G. Shlipak
- General Internal Medicine, University of California, San Francisco, San Francisco, California
| | - Shelley B. Bull
- Samuel Lunenfeld Research Institute of Mount Sinai Hospital, Prosserman Centre for Health Research, Toronto, Ontario, Canada
| | | | - on behalf of DCCT/EDIC
- Department of Internal Medicine II, University Medical Center Regensburg, Regensburg, Germany
- Department of Neurology, Boston University School of Medicine, Boston, Massachusetts
- Department of Epidemiology and the Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, Maryland
- Department of Epidemiology and Preventive Medicine, Regensburg University Medical Center, Regensburg, Germany
- Renal Division, University Hospital of Freiburg, Freiburg, Germany
- Division of Nephrology, University of Washington, Seattle, Washington
- Institute of Genetic Medicine, European Academy of Bolzano/Bozen (EURAC), Italy and Affiliated Institute of the University of Lübeck, Lübeck, Germany
- Division of Nephrology, Brigham and Women's Hospital and Harvard Medical School, Boston Massachusetts
- Interfaculty Institute for Genetics and Functional Genomics, University of Greifswald, Greifswald, Germany
- Department of Biostatistics, Boston University School of Public Health and NHLBI's Framingham Heart Study, Boston Massachusetts
- Cardiovascular Health Research Unit and Department of Biostatistics, University of Washington, Seattle, Washington
- University of Maryland School of Medicine, Baltimore, Maryland
- Medstar Research Institute, Baltimore, Maryland
- Clinical Research Branch, National Institute on Aging, Baltimore, Maryland
- Division of Nephrology, University of California, San Francisco Medical School and San Francisco VA Medical Center, San Francisco, California
- Department of Medical Genetics, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- MRC Epidemiology Unit, Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, United Kingdom
- NHLBI's Framingham Heart Study and the Center for Population Studies, Framingham, Massachusetts
- Jackson State University, Jackson, Mississippi
- Loyola University, Maywood, Illinois
- Department of Cardiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
- University of Iceland, Reykjavik, Iceland
- Icelandic Heart Association, Hjartavernd, Holtasmara, Kopavogur, Iceland
- Department of Biostatistical Sciences, Wake Forest University, Division of Public Health Sciences, Winston-Salem, North Carolina
- Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, Western General Hospital, Crewe Road, Edinburgh, Scotland
- Innsbruck Medical University, Division of Genetic Epidemiology, Innsbruck, Austria
- Department of Medicine, University of Leipzig, Leipzig, Germany
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Laboratory of Epidemiology, Demography, and Biometry, NIA, Bethesda, Maryland
- University of Maryland School of Medicine, Geriatric Research and Education Clinical Center, Veterans Administration Medical Center, Baltimore, Maryland
- University of Maryland School of Medicine, Baltimore, Maryland
- McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins Medical Institutions, Baltimore, Maryland
- University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
- Human Genetics Center, University of Texas Health Science Center, Houston, Texas
- Laboratory of Clinical Investigation, National Institute on Aging, Baltimore, Maryland
- Laboratory of Neurogenetics, National Institute on Aging, Bethesda, Maryland
- University of Pennsylvania Division of Cardiology, Perelman Center for Advanced Medicine, Philadelphia, Pennsylvania
- University of Pennsylvania Renal Electrolyte and Hypertension Division, Philadelphia, Pennsylvania
- Departments of Epidemiology and Medicine, University of Washington, Seattle, Washington
- Cardiovascular Health Research Unit, Departments of Medicine, Epidemiology, and Health Services and Group Health Research Institute, Group Health Cooperative, Seattle, Washington
- Medical Genetics Institute, Cedars-Sinai Medical Center, Los Angeles, California
- Department of Internal Medicine, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
- Genetics Division, GlaxoSmithKline, King of Prussia, Pennsylvania
- Center for Human Genetic Research and Diabetes Research Center (Diabetes Unit), Massachusetts General Hospital, Boston, Massachusetts, Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachussetts, and Department of Medicine, Harvard Medical School, Boston, Massachusetts
- Department of General Internal Medicine, Massachussetts General Hospital, Boston, Massachusetts
- Department of Internal Medicine, Division of Nephrology and Hypertension, Mayo Clinic, Rochester, Minnesota
- Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania
- St Olav University Hospital, Trondheim, Norway
- Faculty of Medicine, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
- Molecular Medicine, Department of Medical Sciences, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Zentrum für Innere Medizin, Klinik für Innere Medizin II - Kardiologie, Universitätsklinikum Ulm, Ulm, Germany
- University Medical Centre Mannheim, 5th Department of Medicine, Mannheim, Germany
- NEFR Unit Université Catholique de Louvain Medical School, Brussels, Belgium
- Center for Public Health Genomics, Charlottesville, Virginia
- Department of Internal Medicine, University Medical Center, Groningen, University of Groningen, Groningen, The Netherlands
- First Department of Internal Medicine, Paracelsus Medical University, Salzburg, Austria
- Institute of Anatomy and Cell Biology, University of Greifswald, Greifswald, Germany
- Institute of Pharmacology, University of Greifswald, Greifswald, Germany
- Clinic for Prosthodontic Dentistry, Gerostomatology and Material Science, University of Greifswald, Greifswald, Germany
- Nephrology Clinic for Internal Medicine A, University of Greifswald, Greifswald, Germany
- Institute for Community Medicine, University of Greifswald, Greifswald, Germany
- Wellcome Trust Centre for Human Genetics, and Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, United Kingdom
- Centre for Population Health Sciences, University of Edinburgh, Edinburgh, Scotland
- University of Michigan School of Public Health, Department of Epidemiology, University of Michigan, Ann Arbor, Michigan
- Gen-Info Ltd., Zagreb, Croatia
- Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
- Center for Population Health Sciences, University of Edinburgh Medical School, Edinburgh, Scotland
- Department of Public Health and Caring Sciences, Uppsala University, Uppsala, Sweden
- University of Maryland School of Medicine, Baltimore, Maryland
- Welch Center for Prevention, Epidemiology & Clinical Research, Johns Hopkins University, Baltimore, Maryland
- General Internal Medicine, University of California, San Francisco, San Francisco, California
- Samuel Lunenfeld Research Institute of Mount Sinai Hospital, Prosserman Centre for Health Research, Toronto, Ontario, Canada
- The Hospital for Sick Children, Toronto, Ontario, Canada
- Institute of Medical Informatics, Biometry and Epidemiology, Ludwig-Maximilians-Universität, Munich, Germany
- Klinikum Grosshadern, Munich, Germany
- Service of Medical Genetics, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts
- Institute of Physiology, University of Greifswald, Greifswald, Germany
- University of Mississippi Division of Nephrology, University of Mississippi, Jackson, Mississippi
- University Institute of Social and Preventive Medicine, Centre Hospitalier Universitaire Vaudois and University of Lausanne, IUMSP, Lausanne, Switzerland; and
- Division of Endocrinology, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
| | - H.-Erich Wichmann
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Medical Informatics, Biometry and Epidemiology, Ludwig-Maximilians-Universität, Munich, Germany
- Klinikum Grosshadern, Munich, Germany
| | - Nicholas J. Wareham
- MRC Epidemiology Unit, Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, United Kingdom
| | - Ruth J.F. Loos
- MRC Epidemiology Unit, Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, United Kingdom
| | - Jerome I. Rotter
- Medical Genetics Institute, Cedars-Sinai Medical Center, Los Angeles, California
| | - Peter P. Pramstaller
- Institute of Genetic Medicine, European Academy of Bolzano/Bozen (EURAC), Italy and Affiliated Institute of the University of Lübeck, Lübeck, Germany
| | - L. Adrienne Cupples
- Department of Biostatistics, Boston University School of Public Health and NHLBI's Framingham Heart Study, Boston Massachusetts
| | - Jacques S. Beckmann
- Service of Medical Genetics, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland
| | - Qiong Yang
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts
| | - Iris M. Heid
- Department of Epidemiology and Preventive Medicine, Regensburg University Medical Center, Regensburg, Germany
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Rainer Rettig
- Institute of Physiology, University of Greifswald, Greifswald, Germany
| | - Albert W. Dreisbach
- University of Mississippi Division of Nephrology, University of Mississippi, Jackson, Mississippi
| | - Murielle Bochud
- University Institute of Social and Preventive Medicine, Centre Hospitalier Universitaire Vaudois and University of Lausanne, IUMSP, Lausanne, Switzerland; and
| | - Caroline S. Fox
- NHLBI's Framingham Heart Study and the Center for Population Studies, Framingham, Massachusetts
- Division of Endocrinology, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
| | - W.H.L. Kao
- Department of Epidemiology and the Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, Maryland
| |
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Collaborators
Anna Köttgen, Cristian Pattaro, Carsten A Böger, Christian Fuchsberger, Matthias Olden, Nicole L Glazer, Afshin Parsa, Xiaoyi Gao, Qiong Yang, Albert V Smith, Jeffrey R O'Connell, Man Li, Helena Schmidt, Toshiko Tanaka, Aaron Isaacs, Shamika Ketkar, Shih-Jen Hwang, Andrew D Johnson, Abbas Dehghan, Alexander Teumer, Guillaume Paré, Thor Aspelund, Gudny Eiriksdottir, Lenore J Launer, Tamara B Harris, Evadnie Rampersaud, Braxton D Mitchell, Eric Boerwinkle, Maksim Struchalin, Margherita Cavalieri, Andrew Singleton, Francesco Giallauria, Jeffery Metter, Ian de Boer, Talin Haritunians, Thomas Lumley, David Siscovick, Bruce M Psaty, M Carola Zillikens, Ben A Oostra, Mary Feitosa, Michael Province, Thomas Illig, Norman Klopp, Christa Meisinger, H-Erich Wichmann, Wolfgang Koenig, Lina Zgaga, Tatijana Zemunik, Ivana Kolcic, Cosetta Minelli, Åsa Johansson, Wilmar Igl, Ghazal Zaboli, Sarah H Wild, Alan F Wright, Harry Campbell, David Ellinghaus, Stefan Schreiber, Yurii S Aulchenko, Janine F Felix, Fernando Rivadeneira, Andre G Uitterlinden, Albert Hofman, Medea Imboden, Mladen Boban, Susan Campbell, Karlhans Endlich, Henry Völzke, Heyo K Kroemer, Matthias Nauck, Uwe Völker, Ozren Polasek, Veronique Vitart, Sunita Badola, Alexander N Parker, Paul M Ridker, Stefan Blankenberg, Vilmundur Gudnason, Alan R Shuldiner, Josef Coresh, Reinhold Schmidt, Luigi Ferrucci, Michael G Shlipak, Cornelia M van Duijn, Ingrid Borecki, Bernhard K Krämer, Igor Rudan, Ulf Gyllensten, James F Wilson, Jacqueline C Witteman, Peter P Pramstaller, Rainer Rettig, Nick D Hastie, Daniel I Chasman, W H Kao, Iris M Heid, Caroline S Fox,
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511
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Prevalence of overweight and obesity among migrants in Switzerland: association with country of origin. Public Health Nutr 2011; 14:1148-56. [DOI: 10.1017/s1368980011000103] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
AbstractObjectiveMigrants tend to present higher overweight and obesity levels, but whether this relationship applies to all nationalities has seldom been studied. The present study aimed to assess the prevalence of overweight and obesity according to nationality in adults.DesignCross-sectional population-based samples.SettingFive-year nationwide interview surveys (Swiss Health Surveys – SHS) from 1992 to 2007 (n 63 766) and a local examination survey (CoLaus Study in Lausanne 2004–2006, n 6743).SubjectsParticipants were separated into Swiss, French, German, Italian, Portuguese, Spanish nationals, those from the former Republic of Yugoslavia and from other European and other countries.ResultsCompared with Swiss nationals, German and French nationals presented a lower prevalence of overweight and obesity, whereas nationals from Italy, Spain, Portugal and the former Republic of Yugoslavia presented higher levels. Adjusting the SHS data for age, gender, education, smoking, leisure-time physical activity and survey year, a lower risk for overweight and obesity was found for German (OR = 0·80, 95 % CI 0·70, 0·92) and French (OR = 0·74, 95 % CI 0·61, 0·89) nationals, whereas higher risks were found for participants from Italy (OR = 1·45, 95 % CI 1·33, 1·58), Spain (OR = 1·36, 95 % CI 1·15, 1·61), Portugal (OR = 1·25, 95 % CI 1·06, 1·47) and the former Republic of Yugoslavia (OR = 1·98, 95 % CI 1·69, 2·32). Similar findings were observed in the CoLaus Study for Italian (OR = 1·63, 95 % CI 1·29, 2·06), Spanish (OR = 1·54, 95 % CI 1·17, 2·04) and Portuguese (OR = 1·49, 95 % CI 1·16, 1·91) participants and for those from the former Republic of Yugoslavia (OR = 5·34, 95 % CI 3·00, 9·50).ConclusionsOverweight and obesity are unevenly distributed among migrants in Switzerland. Migrants from Southern Europe and from the former Republic of Yugoslavia present higher prevalence rates. This suggests that preventive messages should be tailored to these specific populations.
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512
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Nanchen D, Pletcher MJ, Cornuz J, Marques-Vidal PM, Paccaud F, Waeber G, Vollenweider P, Rodondi N. Public health impact of statin prescribing strategies based on JUPITER. Prev Med 2011; 52:159-63. [PMID: 21130802 DOI: 10.1016/j.ypmed.2010.11.018] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/23/2010] [Revised: 11/18/2010] [Accepted: 11/21/2010] [Indexed: 10/18/2022]
Abstract
OBJECTIVE To evaluate the public health impact of statin prescribing strategies based on the Justification for the Use of Statins in Primary Prevention: an Intervention Trial Evaluating Rosuvastatin Study (JUPITER). METHODS We studied 2268 adults aged 35-75 without cardiovascular disease in a population-based study in Switzerland in 2003-2006. We assessed the eligibility for statins according to the Adult Treatment Panel III (ATPIII) guidelines, and by adding "strict" (hs-CRP≥2.0 mg/L and LDL-cholesterol <3.4 mmol/L), and "extended" (hs-CRP≥2.0 mg/L alone) JUPITER-like criteria. We estimated the proportion of CHD deaths potentially prevented over 10 years in the Swiss population. RESULTS Fifteen percent were already taking statins, 42% were eligible by ATPIII guidelines, 53% by adding "strict," and 62% by adding "extended" criteria, with a total of 19% newly eligible. The number needed to treat with statins to avoid one CHD death over 10 years was 38 for ATPIII, 84 for "strict" and 92 for "extended" JUPITER-like criteria. ATPIII would prevent 17% of CHD deaths, compared with 20% for ATPIII+"strict" and 23% for ATPIII + "extended" criteria (+6%). CONCLUSION Implementing JUPITER-like strategies would make statin prescribing for primary prevention more common and less efficient than it is with current guidelines.
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Affiliation(s)
- David Nanchen
- Department of Ambulatory Care and Community Medicine, University of Lausanne, Lausanne, Switzerland.
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513
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Clair C, Chiolero A, Faeh D, Cornuz J, Marques-Vidal P, Paccaud F, Mooser V, Waeber G, Vollenweider P. Dose-dependent positive association between cigarette smoking, abdominal obesity and body fat: cross-sectional data from a population-based survey. BMC Public Health 2011; 11:23. [PMID: 21223575 PMCID: PMC3025841 DOI: 10.1186/1471-2458-11-23] [Citation(s) in RCA: 118] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2010] [Accepted: 01/11/2011] [Indexed: 02/07/2023] Open
Abstract
Background Although smokers tend to have a lower body-mass index than non-smokers, smoking may favour abdominal body fat accumulation. To our knowledge, no population-based studies have assessed the relationship between smoking and body fat composition. We assessed the association between cigarette smoking and waist circumference, body fat, and body-mass index. Methods Height, weight, and waist circumference were measured among 6,123 Caucasians (ages 35-75) from a cross-sectional population-based study in Switzerland. Abdominal obesity was defined as waist circumference ≥102 cm for men and ≥88 cm for women. Body fat (percent total body weight) was measured by electrical bioimpedance. Age- and sex-specific body fat cut-offs were used to define excess body fat. Cigarettes smoked per day were assessed by self-administered questionnaire. Age-adjusted means and odds ratios were calculated using linear and logistic regression. Results Current smokers (29% of men and 24% of women) had lower mean waist circumference, body fat percentage, and body-mass index compared with non-smokers. Age-adjusted mean waist circumference and body fat increased with cigarettes smoked per day among smokers. The association between cigarettes smoked per day and body-mass index was non-significant. Compared with light smokers, the adjusted odds ratio (OR) for abdominal obesity in men was 1.28 (0.78-2.10) for moderate smokers and 1.94 (1.15-3.27) for heavy smokers (P = 0.03 for trend), and 1.07 (0.72-1.58) and 2.15 (1.26-3.64) in female moderate and heavy smokers, respectively (P < 0.01 for trend). Compared with light smokers, the OR for excess body fat in men was 1.05 (95% CI: 0.58-1.92) for moderate smokers and 1.15 (0.60-2.20) for heavy smokers (P = 0.75 for trend) and 1.34 (0.89-2.00) and 2.11 (1.25-3.57), respectively in women (P = 0.07 for trend). Conclusion Among smokers, cigarettes smoked per day were positively associated with central fat accumulation, particularly in women.
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Affiliation(s)
- Carole Clair
- Department of Ambulatory Care and Community Medicine, University of Lausanne, Lausanne, Switzerland.
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514
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Bovet P, Hirsiger P, Emery F, De Bernardini J, Rossier C, Trebeljahr J, Hagon-Traub I. Impact and cost of a 2-week community-based screening and awareness program for diabetes and cardiovascular risk factors in a Swiss canton. Diabetes Metab Syndr Obes 2011; 4:213-23. [PMID: 21760738 PMCID: PMC3131802 DOI: 10.2147/dmso.s20649] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/15/2011] [Indexed: 12/01/2022] Open
Abstract
BACKGROUND Community-based diabetes screening programs can help sensitize the population and identify new cases. However, the impact of such programs is rarely assessed in high-income countries, where concurrent health information and screening opportunities are common place. INTERVENTION AND METHODS A 2-week screening and awareness campaign was organized as part of a new diabetes program in the canton of Vaud (population of 697,000) in Switzerland. Screening was performed without appointment in 190 out of 244 pharmacies in the canton at the subsidized cost of 10 Swiss Francs per participant. Screening included questions on risk behaviors, measurement of body mass index, blood pressure, blood cholesterol, random blood glucose (RBG), and A1c if RBG was ≥7.0 mmol/L. A mass media campaign promoting physical activity and a healthy diet was channeled through several media, eg, 165 spots on radio, billboards in 250 public places, flyers in 360 public transport vehicles, and a dozen articles in several newspapers. A telephone survey in a representative sample of the population of the canton was performed after the campaign to evaluate the program. RESULTS A total of 4222 participants (0.76% of all persons aged ≥18 years) underwent the screening program (median age: 53 years, 63% females). Among participants not treated for diabetes, 3.7% had RBG ≥ 7.8 mmol/L and 1.8% had both RBG ≥ 7.0 mmol/L and A1c ≥ 6.5. Untreated blood pressure ≥140/90 mmHg and/or untreated cholesterol ≥5.2 mmol/L were found in 50.5% of participants. One or several treated or untreated modifiable risk factors were found in 78% of participants. The telephone survey showed that 53% of all adults in the canton were sensitized by the campaign. Excluding fees paid by the participants, the program incurred a cost of CHF 330,600. CONCLUSION A community-based screening program had low efficiency for detecting new cases of diabetes, but it identified large numbers of persons with elevated other cardiovascular risk factors. Our findings suggest the convenience of A1c for mass screening of diabetes, the usefulness of extending diabetes screening to other cardiovascular risk factors, and the importance of a robust background communication campaign.
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Affiliation(s)
- Pascal Bovet
- Institute of Social and Preventive Medicine (IUMSP), University of Lausanne and Centre Universitaire Hospitalier Vaudois (CHUV), Lausanne, Switzerland
- Correspondence: Pascal Bovet, Institute of Social and Preventive Medicine, Biopôle 1, Route de la Corniche 2, CH-1066 Epalinges, Switzerland, Tel +41 21 314 7272, Fax +41 21 314 7373, Email
| | - Philippe Hirsiger
- Public Health Service, Department of Health and Social Action, Canton of Vaud, Lausanne, Switzerland
| | - Frédéric Emery
- Association of Pharmacists, Canton of Vaud, Lausanne, Switzerland
| | - Jessica De Bernardini
- Public Health Service, Department of Health and Social Action, Canton of Vaud, Lausanne, Switzerland
| | | | - Josefine Trebeljahr
- Public Health Service, Department of Health and Social Action, Canton of Vaud, Lausanne, Switzerland
| | - Isabelle Hagon-Traub
- Public Health Service, Department of Health and Social Action, Canton of Vaud, Lausanne, Switzerland
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515
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Conen D, Vollenweider P, Rousson V, Marques-Vidal P, Paccaud F, Waeber G, Bochud M. Use of a Mendelian randomization approach to assess the causal relation of gamma-Glutamyltransferase with blood pressure and serum insulin levels. Am J Epidemiol 2010; 172:1431-41. [PMID: 21044991 DOI: 10.1093/aje/kwq308] [Citation(s) in RCA: 29] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
Elevated levels of γ-glutamyltransferase (GGT) have been associated with elevated blood pressure (BP) and diabetes. However, the causality of these relations has not been addressed. The authors performed a cross-sectional analysis (2003-2006) among 4,360 participants from the population-based Cohorte Lausannoise (CoLaus) Study (Lausanne, Switzerland). The rs2017869 variant of the γ-glutamyltransferase 1 (GGT1) gene, which explained 1.6% of the variance in GGT levels, was used as an instrument for Mendelian randomization (MR). Sex-specific GGT quartiles were strongly associated with both systolic and diastolic BP (all P's < 0.0001). After multivariable adjustment, these relations were attenuated but remained significant. Using MR, the authors observed no positive association of GGT with BP (systolic: β -5.68, 95% confidence interval (CI): -11.51, 0.16 (P = 0.06); diastolic: β = -2.24, 95% CI: -5.98, 1.49 (P = 0.24)). The association of GGT with insulin was also attenuated after multivariable adjustment but persisted in the fully adjusted model (β = 0.07, 95% CI: 0.04, 0.09; P < 0.0001). Using MR, the authors also observed a positive association of GGT with insulin (β = 0.19, 95% CI: 0.01, 0.37; P = 0.04). In conclusion, the authors found evidence for a direct causal relation of GGT with fasting insulin but not with BP.
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Affiliation(s)
- David Conen
- Department of Medicine, University Hospital Basel, Basel, Switzerland
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516
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Kaess BM, Barnes TA, Stark K, Charchar FJ, Waterworth D, Song K, Wang WYS, Vollenweider P, Waeber G, Mooser V, Zukowska-Szczechowska E, Samani NJ, Hengstenberg C, Tomaszewski M. FGF21 signalling pathway and metabolic traits - genetic association analysis. Eur J Hum Genet 2010; 18:1344-8. [PMID: 20717167 PMCID: PMC2988092 DOI: 10.1038/ejhg.2010.130] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2010] [Revised: 07/02/2010] [Accepted: 07/07/2010] [Indexed: 11/09/2022] Open
Abstract
Fibroblast growth factor 21 (FGF21) is a novel master regulator of metabolic profile. The biological actions of FGF21 are elicited upon its klotho beta (KLB)-facilitated binding to FGF receptor 1 (FGFR1), FGFR2 and FGFR3. We hypothesised that common polymorphisms in the FGF21 signalling pathway may be associated with metabolic risk. At the screening stage, we examined associations between 63 common single-nucleotide polymorphisms (SNPs) in five genes of this pathway (FGF21, KLB, FGFR1, FGFR2, FGFR3) and four metabolic phenotypes (LDL cholesterol - LDL-C, HDL-cholesterol - HDL-C, triglycerides and body mass index) in 629 individuals from Silesian Hypertension Study (SHS). Replication analyses were performed in 5478 unrelated individuals of the Swiss CoLaus cohort (imputed genotypes) and in 3030 directly genotyped individuals of the German Myocardial Infarction Family Study (GerMIFS). Of 54 SNPs that met quality control criteria after genotyping in SHS, 4 (rs4733946 and rs7012413 in FGFR1; rs2071616 in FGFR2 and rs7670903 in KLB) showed suggestive association with LDL-C (P=0.0006, P=0.0013, P=0.0055, P=0.011, respectively) and 1 (rs2608819 in KLB) was associated with body mass index (P=0.011); all with false discovery rate q<0.5. Of these, only one FGFR2 polymorphism (rs2071616) showed replicated association with LDL-C in both CoLaus (P=0.009) and men from GerMIFS (P=0.017). The direction of allelic effect of rs2071616 upon LDL-C was consistent in all examined populations. These data show that common genetic variations in FGFR2 may be associated with LDL-C in subjects of white European ancestry.
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Affiliation(s)
- Bernhard M Kaess
- Department of Cardiovascular Sciences, University of Leicester, Glenfield General Hospital, Leicester, UK
- Klinik und Poliklinik für Innere Medizin II, University of Regensburg, Regensburg, Germany
| | - Timothy A Barnes
- Department of Cardiovascular Sciences, University of Leicester, Glenfield General Hospital, Leicester, UK
| | - Klaus Stark
- Klinik und Poliklinik für Innere Medizin II, University of Regensburg, Regensburg, Germany
| | - Fadi J Charchar
- School of Science and Engineering, University of Ballarat, Ballarat, Victoria, Australia
| | | | | | - William Y S Wang
- Department of Cardiovascular Sciences, University of Leicester, Glenfield General Hospital, Leicester, UK
- School of Medicine, University of Queensland, Brisbane, Queensland, Australia
| | | | - Gerard Waeber
- Department of Medicine, Internal Medicine, CHUV, Lausanne, Switzerland
| | | | - Ewa Zukowska-Szczechowska
- Department of Internal Medicine, Diabetology, and Nephrology, Medical University of Silesia, Zabrze, Poland
| | - Nilesh J Samani
- Department of Cardiovascular Sciences, University of Leicester, Glenfield General Hospital, Leicester, UK
- Leicester NIHR Biomedical Research Unit in Cardiovascular Disease, Glenfield Hospital, Leicester, UK
| | - Christian Hengstenberg
- Klinik und Poliklinik für Innere Medizin II, University of Regensburg, Regensburg, Germany
| | - Maciej Tomaszewski
- Department of Cardiovascular Sciences, University of Leicester, Glenfield General Hospital, Leicester, UK
- Leicester NIHR Biomedical Research Unit in Cardiovascular Disease, Glenfield Hospital, Leicester, UK
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517
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Tomaszewski M, Debiec R, Braund PS, Nelson CP, Hardwick R, Christofidou P, Denniff M, Codd V, Rafelt S, van der Harst P, Waterworth D, Song K, Vollenweider P, Waeber G, Zukowska-Szczechowska E, Burton PR, Mooser V, Charchar FJ, Thompson JR, Tobin MD, Samani NJ. Genetic architecture of ambulatory blood pressure in the general population: insights from cardiovascular gene-centric array. Hypertension 2010; 56:1069-76. [PMID: 21060006 PMCID: PMC3035934 DOI: 10.1161/hypertensionaha.110.155721] [Citation(s) in RCA: 59] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2010] [Accepted: 10/06/2010] [Indexed: 01/11/2023]
Abstract
Genetic determinants of blood pressure are poorly defined. We undertook a large-scale, gene-centric analysis to identify loci and pathways associated with ambulatory systolic and diastolic blood pressure. We measured 24-hour ambulatory blood pressure in 2020 individuals from 520 white European nuclear families (the Genetic Regulation of Arterial Pressure of Humans in the Community Study) and genotyped their DNA using the Illumina HumanCVD BeadChip array, which contains ≈ 50 000 single nucleotide polymorphisms in >2000 cardiovascular candidate loci. We found a strong association between rs13306560 polymorphism in the promoter region of MTHFR and CLCN6 and mean 24-hour diastolic blood pressure; each minor allele copy of rs13306560 was associated with 2.6 mm Hg lower mean 24-hour diastolic blood pressure (P = 1.2 × 10⁻⁸). rs13306560 was also associated with clinic diastolic blood pressure in a combined analysis of 8129 subjects from the Genetic Regulation of Arterial Pressure of Humans in the Community Study, the CoLaus Study, and the Silesian Cardiovascular Study (P=5.4 × 10⁻⁶). Additional analysis of associations between variants in gene ontology-defined pathways and mean 24-hour blood pressure in the Genetic Regulation of Arterial Pressure of Humans in the Community Study showed that cell survival control signaling cascades could play a role in blood pressure regulation. There was also a significant overrepresentation of rare variants (minor allele frequency: < 0.05) among polymorphisms showing at least nominal association with mean 24-hour blood pressure indicating that a considerable proportion of its heritability may be explained by uncommon alleles. Through a large-scale gene-centric analysis of ambulatory blood pressure, we identified an association of a novel variant at the MTHFR/CLNC6 locus with diastolic blood pressure and provided new insights into the genetic architecture of blood pressure.
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Affiliation(s)
- Maciej Tomaszewski
- Department of Cardiovascular Sciences, University of Leicester, Clinical Sciences Wing, Glenfield Hospital, Leicester, UK.
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518
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Marques-Vidal P, Pécoud A, Hayoz D, Paccaud F, Mooser V, Waeber G, Vollenweider P. Normal weight obesity: relationship with lipids, glycaemic status, liver enzymes and inflammation. Nutr Metab Cardiovasc Dis 2010; 20:669-675. [PMID: 19748248 DOI: 10.1016/j.numecd.2009.06.001] [Citation(s) in RCA: 96] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/19/2008] [Revised: 03/24/2009] [Accepted: 06/02/2009] [Indexed: 02/06/2023]
Abstract
BACKGROUND AND AIMS Normal weight obesity (NWO) is defined as an excessive body fat associated with a normal body mass index (BMI) and has been associated with early inflammation, but its relationship with cardiovascular risk factors await investigation. METHODS AND RESULTS Cross-sectional study including 3213 women and 2912 men aged 35-75 years to assess the clinical characteristics of NWO in Lausanne, Switzerland. Body fat was assessed by bioimpedance. NWO was defined as a BMI<25 kg/m(2) and a % body fat ≥66(th) gender-specific percentiles. The prevalence of NWO was 5.4% in women and less than 3% in men, so the analysis was restricted to women. NWO women had a higher % of body fat than overweight women. After adjusting for age, smoking, educational level, physical activity and alcohol consumption, NWO women had higher blood pressure and lipid levels and a higher prevalence of dyslipidaemia (odds-ratio=1.90 [1.34-2.68]) and fasting hyperglycaemia (odds-ratio=1.63 [1.10-2.42]) than lean women, whereas no differences were found between NWO and overweight women. Conversely, no differences were found between NWO and lean women regarding levels of CRP, adiponectin and liver markers (alanine aminotransferase, aspartate aminotransferase and gamma glutamyl transferase). Using other definitions of NWO led to similar conclusions, albeit some differences were no longer significant. CONCLUSION NWO is almost nonexistent in men. Women with NWO present with higher cardiovascular risk factors than lean women, while no differences were found for liver or inflammatory markers. Specific screening of NWO might be necessary in order to implement cardiovascular prevention.
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519
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Clerc O, Nanchen D, Cornuz J, Marques-Vidal P, Gmel G, Daeppen JB, Paccaud F, Mooser V, Waeber G, Vollenweider P, Rodondi N. Alcohol drinking, the metabolic syndrome and diabetes in a population with high mean alcohol consumption. Diabet Med 2010; 27:1241-9. [PMID: 20950381 DOI: 10.1111/j.1464-5491.2010.03094.x] [Citation(s) in RCA: 36] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
AIMS To investigate the relationship of alcohol consumption with the metabolic syndrome and diabetes in a population-based study with high mean alcohol consumption. Few data exist on these conditions in high-risk drinkers. METHODS In 6172 adults aged 35-75 years, alcohol consumption was categorized as 0, 1-6, 7-13, 14-20, 21-27, 28-34 and ≥ 35 drinks/week or as non-drinkers (0), low-risk (1-13), medium-to-high-risk (14-34) and very-high-risk (≥ 35) drinkers. Alcohol consumption was objectively confirmed by biochemical tests. In multivariate analysis, we assessed the relationship of alcohol consumption with adjusted prevalence of the metabolic syndrome, diabetes and insulin resistance, determined with the homeostasis model assessment of insulin resistance (HOMA-IR). RESULTS Seventy-three per cent of participants consumed alcohol, 16% were medium-to-high-risk drinkers and 2% very-high-risk drinkers. In multivariate analysis, the prevalence of the metabolic syndrome, diabetes and mean HOMA-IR decreased with low-risk drinking and increased with high-risk drinking. Adjusted prevalence of the metabolic syndrome was 24% in non-drinkers, 19% in low-risk (P<0.001 vs. non-drinkers), 20% in medium-to-high-risk and 29% in very-high-risk drinkers (P=0.005 vs. low-risk). Adjusted prevalence of diabetes was 6.0% in non-drinkers, 3.6% in low-risk (P<0.001 vs. non-drinkers), 3.8% in medium-to-high-risk and 6.7% in very-high-risk drinkers (P=0.046 vs. low-risk). Adjusted HOMA-IR was 2.47 in non-drinkers, 2.14 in low-risk (P<0.001 vs. non-drinkers), 2.27 in medium-to-high-risk and 2.53 in very-high-risk drinkers (P=0.04 vs. low-risk). These relationships did not differ according to beverage types. CONCLUSIONS Alcohol has a U-shaped relationship with the metabolic syndrome, diabetes and HOMA-IR, without differences between beverage types.
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Affiliation(s)
- O Clerc
- Department of Ambulatory Care and Community Medicine Institute of Social and Preventive Medicine, Lausanne University Hospital, Rue du Bugnon 44, Lausanne, Switzerland
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520
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Aparicio E, Parera M, Franco S, Pérez-Alvarez N, Tural C, Clotet B, Martínez MA. IL28B SNP rs8099917 is strongly associated with pegylated interferon-α and ribavirin therapy treatment failure in HCV/HIV-1 coinfected patients. PLoS One 2010; 5:e13771. [PMID: 21048934 PMCID: PMC2966433 DOI: 10.1371/journal.pone.0013771] [Citation(s) in RCA: 65] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2010] [Accepted: 10/06/2010] [Indexed: 12/12/2022] Open
Abstract
Recent genome-wide association studies report that the SNP rs8099917, located 8.9 kb upstream of the start codon of IL28B, is associated with both disease chronicity and therapeutic response to pegIFN-α and RBV in patients infected with genotype 1 HCV. To determine the effect of rs8099917 variation on the response of HCV to therapy, we genotyped this variant in a cohort of 160 HCV/HIV-1 coinfected patients in our clinic unit who received combined peg-IFN-α/RBV therapy. The rs8099917 T/G or G/G genotypes were observed in 56 patients (35%). Treatment failure occurred in 80% of G-allele carriers versus 48% of non-carriers (P<0.0001). This result reveals that the G allele was strongly associated with treatment failure in this patient cohort. Importantly, a highly significant association was found between the G-allele and response to therapy in HCV genotype 1-infected patients (P<0.0001) but not in HCV genotype 3-infected patients. Multivariate analysis (odds ratio; 95% confidence interval; P value) indicated that the rs8099917 TT genotype was a strong predictor of treatment success (5.83; 1.26-26.92; P = 0.021), independent of baseline plasma HCV-RNA load less than 500 000 IU/ml (4.85; 1.18-19.95; P = 0.025) and absence of advanced liver fibrosis (5.24; 1.20-22.91; P = 0.025). These results reveal the high prevalence of the rs8099917 G allele in HCV/HIV-1 coinfected patients as well as its strong association with treatment failure in HCV genotype 1-infected patients. rs8099917 SNP genotyping may be a valid pre-treatment predictor of which patients are likely to respond to treatment in this group of difficult-to-treat HCV/HIV-infected patients.
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Affiliation(s)
- Ester Aparicio
- Fundació irsiCaixa, Hospital Universitari Germans Trias i Pujol, Universitat Autonoma de Barcelona (UAB), Badalona, Barcelona, Spain
| | - Mariona Parera
- Fundació irsiCaixa, Hospital Universitari Germans Trias i Pujol, Universitat Autonoma de Barcelona (UAB), Badalona, Barcelona, Spain
| | - Sandra Franco
- Fundació irsiCaixa, Hospital Universitari Germans Trias i Pujol, Universitat Autonoma de Barcelona (UAB), Badalona, Barcelona, Spain
| | - Nuria Pérez-Alvarez
- Fundació de la Lluita contra la Sida, Hospital Universitari Germans Trias i Pujol, Badalona, Spain
- Statistics and Operations Research Department, Technical University of Catalonia, Barcelona, Spain
| | - Cristina Tural
- Fundació de la Lluita contra la Sida, Hospital Universitari Germans Trias i Pujol, Badalona, Spain
| | - Bonaventura Clotet
- Fundació irsiCaixa, Hospital Universitari Germans Trias i Pujol, Universitat Autonoma de Barcelona (UAB), Badalona, Barcelona, Spain
- Fundació de la Lluita contra la Sida, Hospital Universitari Germans Trias i Pujol, Badalona, Spain
| | - Miguel Angel Martínez
- Fundació irsiCaixa, Hospital Universitari Germans Trias i Pujol, Universitat Autonoma de Barcelona (UAB), Badalona, Barcelona, Spain
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521
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Kussmann M, Krause L, Siffert W. Nutrigenomics: where are we with genetic and epigenetic markers for disposition and susceptibility? Nutr Rev 2010; 68 Suppl 1:S38-47. [DOI: 10.1111/j.1753-4887.2010.00326.x] [Citation(s) in RCA: 39] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
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522
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Current cardiovascular risk management patterns with special focus on lipid lowering in daily practice in Switzerland. ACTA ACUST UNITED AC 2010; 17:363-72. [PMID: 20168234 DOI: 10.1097/hjr.0b013e328333c1d9] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
BACKGROUND There may be a considerable gap between LDL cholesterol (LDL-C) and blood pressure (BP) goal values recommended by the guidelines and results achieved in daily practice. DESIGN Prospective cross-sectional survey of cardiovascular disease risk profiles and management with focus on lipid lowering and BP lowering in clinical practice. METHODS In phase 1, the cardiovascular risk of patients with known lipid profile visiting their general practitioner was anonymously assessed in accordance to the PROCAM-score. In phase 2, high-risk patients who did not achieve LDL-C goal less than 2.6 mmol/l in phase 1 could be further documented. RESULTS Six hundred thirty-five general practitioners collected the data of 23 892 patients with known lipid profile. Forty percent were high-risk patients (diabetes mellitus or coronary heart disease or PROCAM-score >20%), compared with 27% estimated by the physicians. Goal attainment rate was almost double for BP than for LDL-C in high-risk patients (62 vs. 37%). Both goals were attained by 25%. LDL-C values in phase 1 and 2 were available for 3097 high-risk patients not at LDL-C goal in phase 1; 32% of patients achieved LDL-C goal of less than 2.6 mmol/l after a mean of 17 weeks. The most successful strategies for LDL-C reduction were implemented in only 22% of the high-risk patients. CONCLUSION Although patients at high cardiovascular risk were treated more intensively than low or medium risk patients, the majority remained insufficiently controlled, which is an incentive for intensified medical education. Adequate implementation of Swiss and International guidelines would expectedly contribute to improved achievement of LDL-C and BP goal values in daily practice.
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523
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Rietschel M, Mattheisen M, Frank J, Treutlein J, Degenhardt F, Breuer R, Steffens M, Mier D, Esslinger C, Walter H, Kirsch P, Erk S, Schnell K, Herms S, Wichmann HE, Schreiber S, Jöckel KH, Strohmaier J, Roeske D, Haenisch B, Gross M, Hoefels S, Lucae S, Binder EB, Wienker TF, Schulze TG, Schmäl C, Zimmer A, Juraeva D, Brors B, Bettecken T, Meyer-Lindenberg A, Müller-Myhsok B, Maier W, Nöthen MM, Cichon S. Genome-wide association-, replication-, and neuroimaging study implicates HOMER1 in the etiology of major depression. Biol Psychiatry 2010; 68:578-85. [PMID: 20673876 DOI: 10.1016/j.biopsych.2010.05.038] [Citation(s) in RCA: 149] [Impact Index Per Article: 9.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/12/2010] [Revised: 05/26/2010] [Accepted: 05/27/2010] [Indexed: 11/18/2022]
Abstract
BACKGROUND Genome-wide association studies are a powerful tool for unravelling the genetic background of complex disorders such as major depression. METHODS We conducted a genome-wide association study of 604 patients with major depression and 1364 population based control subjects. The top hundred findings were followed up in a replication sample of 409 patients and 541 control subjects. RESULTS Two SNPs showed nominally significant association in both the genome-wide association study and the replication samples: 1) rs9943849 (p(combined) = 3.24E-6) located upstream of the carboxypeptidase M (CPM) gene and 2) rs7713917 (p(combined) = 1.48E-6), located in a putative regulatory region of HOMER1. Further evidence for HOMER1 was obtained through gene-wide analysis while conditioning on the genotypes of rs7713917 (p(combined) = 4.12E-3). Homer1 knockout mice display behavioral traits that are paradigmatic of depression, and transcriptional variants of Homer1 result in the dysregulation of cortical-limbic circuitry. This is consistent with the findings of our subsequent human imaging genetics study, which revealed that variation in single nucleotide polymorphism rs7713917 had a significant influence on prefrontal activity during executive cognition and anticipation of reward. CONCLUSION Our findings, combined with evidence from preclinical and animal studies, suggest that HOMER1 plays a role in the etiology of major depression and that the genetic variation affects depression via the dysregulation of cognitive and motivational processes.
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Affiliation(s)
- Marcella Rietschel
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Mannheim, University of Heidelberg, Germany.
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524
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Genome-wide association study identifies new HLA class II haplotypes strongly protective against narcolepsy. Nat Genet 2010; 42:786-9. [PMID: 20711174 DOI: 10.1038/ng.647] [Citation(s) in RCA: 138] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2010] [Accepted: 07/21/2010] [Indexed: 11/08/2022]
Abstract
Narcolepsy is a rare sleep disorder with the strongest human leukocyte antigen (HLA) association ever reported. Since the associated HLA-DRB1*1501-DQB1*0602 haplotype is common in the general population (15-25%), it has been suggested that it is almost necessary but not sufficient for developing narcolepsy. To further define the genetic basis of narcolepsy risk, we performed a genome-wide association study (GWAS) in 562 European individuals with narcolepsy (cases) and 702 ethnically matched controls, with independent replication in 370 cases and 495 controls, all heterozygous for DRB1*1501-DQB1*0602. We found association with a protective variant near HLA-DQA2 (rs2858884; P < 3 x 10(-8)). Further analysis revealed that rs2858884 is strongly linked to DRB1*03-DQB1*02 (P < 4 x 10(-43)) and DRB1*1301-DQB1*0603 (P < 3 x 10(-7)). Cases almost never carried a trans DRB1*1301-DQB1*0603 haplotype (odds ratio = 0.02; P < 6 x 10(-14)). This unexpected protective HLA haplotype suggests a virtually causal involvement of the HLA region in narcolepsy susceptibility.
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525
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First nationwide survey on cardiovascular risk factors in Grand-Duchy of Luxembourg (ORISCAV-LUX). BMC Public Health 2010; 10:468. [PMID: 20698957 PMCID: PMC2925827 DOI: 10.1186/1471-2458-10-468] [Citation(s) in RCA: 66] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2010] [Accepted: 08/10/2010] [Indexed: 11/10/2022] Open
Abstract
Background The ORISCAV-LUX study is the first baseline survey of an on-going cardiovascular health monitoring programme in Grand-Duchy of Luxembourg. The main objectives of the present manuscript were 1) to describe the study design and conduct, and 2) to present the salient outcomes of the study, in particular the prevalence of the potentially modifiable and treatable cardiovascular disease risk factors in the adult population residing in Luxembourg. Method ORISCAV-LUX is a cross-sectional study based on a random sample of 4496 subjects, stratified by gender, age categories and district, drawn from the national insurance registry of 18-69 years aged Luxembourg residents, assuming a response rate of 30% and a proportion of 5% of institutionalized subjects in each stratum. The cardiovascular health status was assessed by means of a self-administered questionnaire, clinical and anthropometric measures, as well as by blood, urine and hair examinations. The potentially modifiable and treatable risk factors studied included smoking, hypertension, dyslipidemia, diabetes mellitus, and obesity. Both univariate and multivariate statistical analyses used weighted methods to account for the stratified sampling scheme. Results A total of 1432 subjects took part in the survey, yielding a participation rate of 32.2%. This figure is higher than the minimal sample size of 1285 subjects as estimated by power calculation. The most predominant cardiovascular risk factors were dyslipidemia (69.9%), hypertension (34.5%), smoking (22.3%), and obesity (20.9%), while diabetes amounted 4.4%. All prevalence rates increased with age (except smoking) with marked gender differences (except diabetes). There was a significant difference in the prevalence of hypertension and of lipid disorders by geographic region of birth. The proportion of subjects cumulating two or more cardiovascular risk factors increased remarkably with age and was more predominant in men than in women (P<0.0001). Only 14.7% of men and 23.1% of women were free of any cardiovascular risk factor. High prevalence of non-treated CVRF, notably for hypertension and dyslipidemia, were observed in the study population. Conclusion The population-based ORISCAV-LUX survey revealed a high prevalence of potentially modifiable and treatable cardiovascular risk factors among apparently healthy subjects; significant gender and age-specific differences were seen not only for single but also for combined risk factors. From a public health perspective, these preliminary findings stress the urgent need for early routine health examinations, preventive interventions and lifestyle behavioural changes, even in young asymptomatic adults, to decrease cardiovascular morbidity and mortality in Luxembourg.
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526
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Marques-Vidal P, Melich-Cerveira J, Paccaud F, Waeber G, Vollenweider P, Cornuz J. Opinions on tobacco control policies in Lausanne, Switzerland, 2003-2006. Prev Med 2010; 51:193-4. [PMID: 20576540 DOI: 10.1016/j.ypmed.2010.05.004] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/12/2010] [Revised: 05/10/2010] [Accepted: 05/16/2010] [Indexed: 11/28/2022]
Abstract
OBJECTIVE To assess the determinants of opinions regarding tobacco control policies in the Swiss general population. METHODS Cross-sectional study conducted between 2003 and 2006 on a random sample of adult residents of Lausanne, Switzerland, aged 35-75 years (2601 women and 2398 men). Nine questions on smoking policies were applied. RESULTS Ninety-five percent of responders supported policies that would help smokers to quit, 92% no selling of tobacco to subjects aged less than 16 years, 87% a smoking ban in public places and 86% a national campaign against smoking. A further 77% supported a total ban on tobacco advertising, 74% the reimbursement of nicotine replacement therapies and 70% an increase in the price of cigarettes. A lower support was found for two non-evidence-based interventions total ban of tobacco sales (35%) and promotion of light cigarettes (22%). Never smokers, women, physically active subjects, teetotallers and subjects with lower educational level were more likely to favour stronger measures while no differences were found between age groups. Reimbursement of nicotine replacement therapies was favoured more by current smokers and inactive subjects. CONCLUSION The vast majority of responders supported the recommended tobacco control policies. Opinions regarding specific interventions vary according to the policy and subjects' characteristics.
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527
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Kapur K, Johnson T, Beckmann ND, Sehmi J, Tanaka T, Kutalik Z, Styrkarsdottir U, Zhang W, Marek D, Gudbjartsson DF, Milaneschi Y, Holm H, DiIorio A, Waterworth D, Li Y, Singleton AB, Bjornsdottir US, Sigurdsson G, Hernandez DG, DeSilva R, Elliott P, Eyjolfsson GI, Guralnik JM, Scott J, Thorsteinsdottir U, Bandinelli S, Chambers J, Stefansson K, Waeber G, Ferrucci L, Kooner JS, Mooser V, Vollenweider P, Beckmann JS, Bochud M, Bergmann S. Genome-wide meta-analysis for serum calcium identifies significantly associated SNPs near the calcium-sensing receptor (CASR) gene. PLoS Genet 2010; 6:e1001035. [PMID: 20661308 PMCID: PMC2908705 DOI: 10.1371/journal.pgen.1001035] [Citation(s) in RCA: 68] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2009] [Accepted: 06/17/2010] [Indexed: 12/24/2022] Open
Abstract
Calcium has a pivotal role in biological functions, and serum calcium levels have been associated with numerous disorders of bone and mineral metabolism, as well as with cardiovascular mortality. Here we report results from a genome-wide association study of serum calcium, integrating data from four independent cohorts including a total of 12,865 individuals of European and Indian Asian descent. Our meta-analysis shows that serum calcium is associated with SNPs in or near the calcium-sensing receptor (CASR) gene on 3q13. The top hit with a p-value of 6.3 x 10(-37) is rs1801725, a missense variant, explaining 1.26% of the variance in serum calcium. This SNP had the strongest association in individuals of European descent, while for individuals of Indian Asian descent the top hit was rs17251221 (p = 1.1 x 10(-21)), a SNP in strong linkage disequilibrium with rs1801725. The strongest locus in CASR was shown to replicate in an independent Icelandic cohort of 4,126 individuals (p = 1.02 x 10(-4)). This genome-wide meta-analysis shows that common CASR variants modulate serum calcium levels in the adult general population, which confirms previous results in some candidate gene studies of the CASR locus. This study highlights the key role of CASR in calcium regulation.
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Affiliation(s)
- Karen Kapur
- Department of Medical Genetics, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Toby Johnson
- Department of Medical Genetics, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Institute of Social and Preventive Medicine, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland
| | - Noam D. Beckmann
- Department of Medical Genetics, University of Lausanne, Lausanne, Switzerland
| | - Joban Sehmi
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
| | - Toshiko Tanaka
- Clinical Research Branch, National Institute on Aging, Baltimore, Maryland, United States of America
| | - Zoltán Kutalik
- Department of Medical Genetics, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | | | - Weihua Zhang
- Department of Epidemiology and Public Health, Imperial College London, London, United Kingdom
| | - Diana Marek
- Department of Medical Genetics, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | | | - Yuri Milaneschi
- Clinical Research Branch, National Institute on Aging, Baltimore, Maryland, United States of America
| | | | - Angelo DiIorio
- Department of Medicine and Sciences of Aging, Laboratory of Clinical Epidemiology, University G. d'Annunzio, Chieti, Italy
| | - Dawn Waterworth
- Division of Genetics, GlaxoSmithKline, King of Prussia, Pennsylvania, United States of America
| | - Yun Li
- Department of Genetics and Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Andrew B. Singleton
- Laboratory of Neurogenetics, National Institute on Aging, Bethesda, Maryland, United States of America
| | | | - Gunnar Sigurdsson
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
- Department of Endocrinology and Metabolism, University Hospital, Reykjavik, Iceland
| | - Dena G. Hernandez
- Laboratory of Neurogenetics, National Institute on Aging, Bethesda, Maryland, United States of America
| | - Ranil DeSilva
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
| | - Paul Elliott
- Department of Epidemiology and Biostatistics, Medical Research Council–Health Protection Agency Centre for Environment and Health, Imperial College London, London, United Kingdom
| | | | - Jack M. Guralnik
- Laboratory of Epidemiology, Demography, and Biometry, National Institute on Aging, Bethesda, Maryland, United States of America
| | - James Scott
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
| | - Unnur Thorsteinsdottir
- deCODE Genetics, Reykjavik, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | | | - John Chambers
- Department of Epidemiology and Public Health, Imperial College London, London, United Kingdom
| | - Kari Stefansson
- deCODE Genetics, Reykjavik, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Gérard Waeber
- Department of Medicine, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
| | - Luigi Ferrucci
- Clinical Research Branch, National Institute on Aging, Baltimore, Maryland, United States of America
| | - Jaspal S. Kooner
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
| | - Vincent Mooser
- Division of Genetics, GlaxoSmithKline, King of Prussia, Pennsylvania, United States of America
| | - Peter Vollenweider
- Department of Medicine, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
| | - Jacques S. Beckmann
- Department of Medical Genetics, University of Lausanne, Lausanne, Switzerland
- Service of Medical Genetics, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
| | - Murielle Bochud
- Institute of Social and Preventive Medicine, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland
| | - Sven Bergmann
- Department of Medical Genetics, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
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Velho S, Paccaud F, Waeber G, Vollenweider P, Marques-Vidal P. Metabolically healthy obesity: different prevalences using different criteria. Eur J Clin Nutr 2010; 64:1043-51. [PMID: 20628408 DOI: 10.1038/ejcn.2010.114] [Citation(s) in RCA: 182] [Impact Index Per Article: 12.1] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
OBJECTIVE To estimate the prevalence of metabolically healthy obesity (MHO) according to different definitions. METHODS Population-based sample of 2803 women and 2557 men participated in the study. Metabolic abnormalities were defined using six sets of criteria, which included different combinations of the following: waist; blood pressure; total, high-density lipoprotein or low-density lipoprotein-cholesterol; triglycerides; fasting glucose; homeostasis model assessment; high-sensitivity C-reactive protein; personal history of cardiovascular, respiratory or metabolic diseases. For each set, prevalence of MHO was assessed for body mass index (BMI); waist or percent body fat. RESULTS Among obese (BMI 30 kg/m(2)) participants, prevalence of MHO ranged between 3.3 and 32.1% in men and between 11.4 and 43.3% in women according to the criteria used. Using abdominal obesity, prevalence of MHO ranged between 5.7 and 36.7% (men) and 12.2 and 57.5% (women). Using percent body fat led to a prevalence of MHO ranging between 6.4 and 43.1% (men) and 12.0 and 55.5% (women). MHO participants had a lower odd of presenting a family history of type 2 diabetes. After multivariate adjustment, the odds of presenting with MHO decreased with increasing age, whereas no relationship was found with gender, alcohol consumption or tobacco smoking using most sets of criteria. Physical activity was positively related, whereas increased waist was negatively related with BMI-defined MHO. CONCLUSION MHO prevalence varies considerably according to the criteria used, underscoring the need for a standard definition of this metabolic entity. Physical activity increases the likelihood of presenting with MHO, and MHO is associated with a lower prevalence of family history of type 2 diabetes.
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Affiliation(s)
- S Velho
- Department of Nutrition and Dietetics, Portuguese Institute of Oncology Francisco Gentil, Lisbon, Portugal
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Calboli FCF, Tozzi F, Galwey NW, Antoniades A, Mooser V, Preisig M, Vollenweider P, Waterworth D, Waeber G, Johnson MR, Muglia P, Balding DJ. A genome-wide association study of neuroticism in a population-based sample. PLoS One 2010; 5:e11504. [PMID: 20634892 PMCID: PMC2901337 DOI: 10.1371/journal.pone.0011504] [Citation(s) in RCA: 61] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2009] [Accepted: 05/17/2010] [Indexed: 11/22/2022] Open
Abstract
Neuroticism is a moderately heritable personality trait considered to be a risk factor for developing major depression, anxiety disorders and dementia. We performed a genome-wide association study in 2,235 participants drawn from a population-based study of neuroticism, making this the largest association study for neuroticism to date. Neuroticism was measured by the Eysenck Personality Questionnaire. After Quality Control, we analysed 430,000 autosomal SNPs together with an additional 1.2 million SNPs imputed with high quality from the Hap Map CEU samples. We found a very small effect of population stratification, corrected using one principal component, and some cryptic kinship that required no correction. NKAIN2 showed suggestive evidence of association with neuroticism as a main effect (p<10−6) and GPC6 showed suggestive evidence for interaction with age (p≈10−7). We found support for one previously-reported association (PDE4D), but failed to replicate other recent reports. These results suggest common SNP variation does not strongly influence neuroticism. Our study was powered to detect almost all SNPs explaining at least 2% of heritability, and so our results effectively exclude the existence of loci having a major effect on neuroticism.
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Affiliation(s)
- Federico C F Calboli
- Department of Epidemiology and Biostatistics, Imperial College London, London, United Kingdom.
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530
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Sanders AR, Levinson DF, Duan J, Dennis JM, Li R, Kendler KS, Rice JP, Shi J, Mowry BJ, Amin F, Silverman JM, Buccola NG, Byerley WF, Black DW, Freedman R, Cloninger CR, Gejman PV. The Internet-based MGS2 control sample: self report of mental illness. Am J Psychiatry 2010; 167:854-65. [PMID: 20516154 PMCID: PMC6385597 DOI: 10.1176/appi.ajp.2010.09071050] [Citation(s) in RCA: 40] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
OBJECTIVE The Molecular Genetics of Schizophrenia (MGS2) project recruited an adult control sample of non-Hispanic European-ancestry (N=3,364) and African American (N=1,301) subjects. METHOD Subjects gave consent to deposit phenotypic data and blood samples into a repository for general research use, with full anonymization of the sample. The authors compared the control sample with population census data for demographic data and with previous population surveys for anthropometrics and prevalences of psychiatric disorders as estimated by an Internet-administered questionnaire. RESULTS The full MGS2 control sample includes 4,665 subjects (European-ancestry: N=3,364; African American: N=1,301), of whom 3,626 were included in the MGS2 genome-wide association study (GWAS). The sample is generally demographically representative of the U.S. population, except for being older and more female, educated, and affluent, although all strata are represented. Self-reported ancestry was consistent with genotypic and census data. Lifetime prevalences for depressive, anxiety, and substance use diagnoses were higher than in previous population-based surveys, probably due to use of an abbreviated self-report instrument. However, patterns such as sex ratios, comorbidity, and demographic associations were consistent with previous reports. DNA quality for the Internet collected/evaluated control sample was comparable to that of the face-to-face case sample. CONCLUSIONS The Internet-based methods facilitated the rapid collection of large and anonymized non-Hispanic European-ancestry and African American control samples that have been validated as being generally representative for many aspects of demography, ancestry, and morbidity. Utilization of clinical screening data shared with the scientific community may permit investigators to select appropriate controls for some studies.
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Affiliation(s)
- Alan R Sanders
- Center for Psychiatric Genetics, Department of Psychiatry and Behavioral Sciences, NorthShore University HealthSystem, Evanston, Ill 60201-3137, USA.
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Kutalik Z, Johnson T, Bochud M, Mooser V, Vollenweider P, Waeber G, Waterworth D, Beckmann JS, Bergmann S. Methods for testing association between uncertain genotypes and quantitative traits. Biostatistics 2010; 12:1-17. [DOI: 10.1093/biostatistics/kxq039] [Citation(s) in RCA: 33] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
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532
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Muglia P, Tozzi F, Galwey NW, Francks C, Upmanyu R, Kong XQ, Antoniades A, Domenici E, Perry J, Rothen S, Vandeleur CL, Mooser V, Waeber G, Vollenweider P, Preisig M, Lucae S, Müller-Myhsok B, Holsboer F, Middleton LT, Roses AD. Genome-wide association study of recurrent major depressive disorder in two European case-control cohorts. Mol Psychiatry 2010; 15:589-601. [PMID: 19107115 DOI: 10.1038/mp.2008.131] [Citation(s) in RCA: 196] [Impact Index Per Article: 13.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Major depressive disorder (MDD) is a highly prevalent disorder with substantial heritability. Heritability has been shown to be substantial and higher in the variant of MDD characterized by recurrent episodes of depression. Genetic studies have thus far failed to identify clear and consistent evidence of genetic risk factors for MDD. We conducted a genome-wide association study (GWAS) in two independent datasets. The first GWAS was performed on 1022 recurrent MDD patients and 1000 controls genotyped on the Illumina 550 platform. The second was conducted on 492 recurrent MDD patients and 1052 controls selected from a population-based collection, genotyped on the Affymetrix 5.0 platform. Neither GWAS identified any SNP that achieved GWAS significance. We obtained imputed genotypes at the Illumina loci for the individuals genotyped on the Affymetrix platform, and performed a meta-analysis of the two GWASs for this common set of approximately half a million SNPs. The meta-analysis did not yield genome-wide significant results either. The results from our study suggest that SNPs with substantial odds ratio are unlikely to exist for MDD, at least in our datasets and among the relatively common SNPs genotyped or tagged by the half-million-loci arrays. Meta-analysis of larger datasets is warranted to identify SNPs with smaller effects or with rarer allele frequencies that contribute to the risk of MDD.
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Affiliation(s)
- P Muglia
- Genetics Division, Drug Discovery, GlaxoSmithKline R&D, Verona, Italy.
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533
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Liu JZ, Tozzi F, Waterworth DM, Pillai SG, Muglia P, Middleton L, Berrettini W, Knouff CW, Yuan X, Waeber G, Vollenweider P, Preisig M, Wareham NJ, Zhao JH, Loos RJF, Barroso I, Khaw KT, Grundy S, Barter P, Mahley R, Kesaniemi A, McPherson R, Vincent JB, Strauss J, Kennedy JL, Farmer A, McGuffin P, Day R, Matthews K, Bakke P, Gulsvik A, Lucae S, Ising M, Brueckl T, Horstmann S, Wichmann HE, Rawal R, Dahmen N, Lamina C, Polasek O, Zgaga L, Huffman J, Campbell S, Kooner J, Chambers JC, Burnett MS, Devaney JM, Pichard AD, Kent KM, Satler L, Lindsay JM, Waksman R, Epstein S, Wilson JF, Wild SH, Campbell H, Vitart V, Reilly MP, Li M, Qu L, Wilensky R, Matthai W, Hakonarson HH, Rader DJ, Franke A, Wittig M, Schäfer A, Uda M, Terracciano A, Xiao X, Busonero F, Scheet P, Schlessinger D, St Clair D, Rujescu D, Abecasis GR, Grabe HJ, Teumer A, Völzke H, Petersmann A, John U, Rudan I, Hayward C, Wright AF, Kolcic I, Wright BJ, Thompson JR, Balmforth AJ, Hall AS, Samani NJ, Anderson CA, Ahmad T, Mathew CG, Parkes M, Satsangi J, Caulfield M, Munroe PB, Farrall M, Dominiczak A, Worthington J, et alLiu JZ, Tozzi F, Waterworth DM, Pillai SG, Muglia P, Middleton L, Berrettini W, Knouff CW, Yuan X, Waeber G, Vollenweider P, Preisig M, Wareham NJ, Zhao JH, Loos RJF, Barroso I, Khaw KT, Grundy S, Barter P, Mahley R, Kesaniemi A, McPherson R, Vincent JB, Strauss J, Kennedy JL, Farmer A, McGuffin P, Day R, Matthews K, Bakke P, Gulsvik A, Lucae S, Ising M, Brueckl T, Horstmann S, Wichmann HE, Rawal R, Dahmen N, Lamina C, Polasek O, Zgaga L, Huffman J, Campbell S, Kooner J, Chambers JC, Burnett MS, Devaney JM, Pichard AD, Kent KM, Satler L, Lindsay JM, Waksman R, Epstein S, Wilson JF, Wild SH, Campbell H, Vitart V, Reilly MP, Li M, Qu L, Wilensky R, Matthai W, Hakonarson HH, Rader DJ, Franke A, Wittig M, Schäfer A, Uda M, Terracciano A, Xiao X, Busonero F, Scheet P, Schlessinger D, St Clair D, Rujescu D, Abecasis GR, Grabe HJ, Teumer A, Völzke H, Petersmann A, John U, Rudan I, Hayward C, Wright AF, Kolcic I, Wright BJ, Thompson JR, Balmforth AJ, Hall AS, Samani NJ, Anderson CA, Ahmad T, Mathew CG, Parkes M, Satsangi J, Caulfield M, Munroe PB, Farrall M, Dominiczak A, Worthington J, Thomson W, Eyre S, Barton A, Wellcome Trust Case Control Consortium, Mooser V, Francks C, Marchini J. Meta-analysis and imputation refines the association of 15q25 with smoking quantity. Nat Genet 2010; 42:436-40. [PMID: 20418889 PMCID: PMC3612983 DOI: 10.1038/ng.572] [Show More Authors] [Citation(s) in RCA: 503] [Impact Index Per Article: 33.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2009] [Accepted: 03/18/2010] [Indexed: 12/14/2022]
Abstract
Smoking is a leading global cause of disease and mortality. We established the Oxford-GlaxoSmithKline study (Ox-GSK) to perform a genome-wide meta-analysis of SNP association with smoking-related behavioral traits. Our final data set included 41,150 individuals drawn from 20 disease, population and control cohorts. Our analysis confirmed an effect on smoking quantity at a locus on 15q25 (P = 9.45 x 10(-19)) that includes CHRNA5, CHRNA3 and CHRNB4, three genes encoding neuronal nicotinic acetylcholine receptor subunits. We used data from the 1000 Genomes project to investigate the region using imputation, which allowed for analysis of virtually all common SNPs in the region and offered a fivefold increase in marker density over HapMap2 (ref. 2) as an imputation reference panel. Our fine-mapping approach identified a SNP showing the highest significance, rs55853698, located within the promoter region of CHRNA5. Conditional analysis also identified a secondary locus (rs6495308) in CHRNA3.
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Affiliation(s)
- Jason Z Liu
- Department of Statistics, University of Oxford, Oxford, UK
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534
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Marques-Vidal P, Bochud M, Paccaud F, Waterworth D, Bergmann S, Preisig M, Waeber G, Vollenweider P. No interaction between alcohol consumption and HDL-related genes on HDL cholesterol levels. Atherosclerosis 2010; 211:551-7. [PMID: 20430392 DOI: 10.1016/j.atherosclerosis.2010.04.001] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/06/2009] [Revised: 03/12/2010] [Accepted: 04/02/2010] [Indexed: 10/19/2022]
Abstract
OBJECTIVE To assess the relationships and possible interactions between polymorphisms related to HDL levels and alcohol consumption. METHODS Cross-sectional population-based study including 2863 women and 2546 men aged 35-75 years (CoLaus study). Alcohol intake was assessed by the reported alcohol consumption of the last 7 days. Nineteen candidate genes known to influence HDL levels were studied. RESULTS Alcohol consumption increased HDL cholesterol levels in both genders. After multivariate adjustment for gender, age, body mass index, smoking, hypolipidaemic drug treatment, physical activity and alcohol consumption, APOA5, CETP, LIPC and LPL gene polymorphisms were significantly (10(-5) threshold) related with HDL cholesterol levels, while no genexalcohol intake interaction was found for all SNPs studied. ABCA1 polymorphisms were related to HDL cholesterol levels on bivariate analysis but the relationship was no longer significant after multivariate analysis. CONCLUSION Our data confirm the association of alcohol consumption and of APOA5, CETP, LIPC and LPL gene polymorphisms with HDL cholesterol levels. Conversely, no genexalcohol consumption interactions were found, suggesting that the effect of alcohol consumption on HDL cholesterol levels is not mediated via a modulation of HDL related genes.
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535
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Rauch A, Kutalik Z, Descombes P, Cai T, Di Iulio J, Mueller T, Bochud M, Battegay M, Bernasconi E, Borovicka J, Colombo S, Cerny A, Dufour JF, Furrer H, Günthard HF, Heim M, Hirschel B, Malinverni R, Moradpour D, Müllhaupt B, Witteck A, Beckmann JS, Berg T, Bergmann S, Negro F, Telenti A, Bochud PY. Genetic variation in IL28B is associated with chronic hepatitis C and treatment failure: a genome-wide association study. Gastroenterology 2010; 138:1338-45, 1345.e1-7. [PMID: 20060832 DOI: 10.1053/j.gastro.2009.12.056] [Citation(s) in RCA: 867] [Impact Index Per Article: 57.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/08/2009] [Revised: 11/25/2009] [Accepted: 12/29/2009] [Indexed: 12/12/2022]
Abstract
BACKGROUND & AIMS Hepatitis C virus (HCV) induces chronic infection in 50% to 80% of infected persons; approximately 50% of these do not respond to therapy. We performed a genome-wide association study to screen for host genetic determinants of HCV persistence and response to therapy. METHODS The analysis included 1362 individuals: 1015 with chronic hepatitis C and 347 who spontaneously cleared the virus (448 were coinfected with human immunodeficiency virus [HIV]). Responses to pegylated interferon alfa and ribavirin were assessed in 465 individuals. Associations between more than 500,000 single nucleotide polymorphisms (SNPs) and outcomes were assessed by multivariate logistic regression. RESULTS Chronic hepatitis C was associated with SNPs in the IL28B locus, which encodes the antiviral cytokine interferon lambda. The rs8099917 minor allele was associated with progression to chronic HCV infection (odds ratio [OR], 2.31; 95% confidence interval [CI], 1.74-3.06; P = 6.07 x 10(-9)). The association was observed in HCV mono-infected (OR, 2.49; 95% CI, 1.64-3.79; P = 1.96 x 10(-5)) and HCV/HIV coinfected individuals (OR, 2.16; 95% CI, 1.47-3.18; P = 8.24 x 10(-5)). rs8099917 was also associated with failure to respond to therapy (OR, 5.19; 95% CI, 2.90-9.30; P = 3.11 x 10(-8)), with the strongest effects in patients with HCV genotype 1 or 4. This risk allele was identified in 24% of individuals with spontaneous HCV clearance, 32% of chronically infected patients who responded to therapy, and 58% who did not respond (P = 3.2 x 10(-10)). Resequencing of IL28B identified distinct haplotypes that were associated with the clinical phenotype. CONCLUSIONS The association of the IL28B locus with natural and treatment-associated control of HCV indicates the importance of innate immunity and interferon lambda in the pathogenesis of HCV infection.
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Affiliation(s)
- Andri Rauch
- University Clinic of Infectious Diseases, University Hospital Bern and University of Bern, Bern, Switzerland.
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536
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Bochud M, Rousson V. Usefulness of Mendelian randomization in observational epidemiology. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2010; 7:711-28. [PMID: 20616999 PMCID: PMC2872313 DOI: 10.3390/ijerph7030711] [Citation(s) in RCA: 60] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/29/2009] [Accepted: 02/16/2010] [Indexed: 11/16/2022]
Abstract
Mendelian randomization refers to the random allocation of alleles at the time of gamete formation. In observational epidemiology, this refers to the use of genetic variants to estimate a causal effect between a modifiable risk factor and an outcome of interest. In this review, we recall the principles of a "Mendelian randomization" approach in observational epidemiology, which is based on the technique of instrumental variables; we provide simulations and an example based on real data to demonstrate its implications; we present the results of a systematic search on original articles having used this approach; and we discuss some limitations of this approach in view of what has been found so far.
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Affiliation(s)
- Murielle Bochud
- University Institute of Social and Preventive Medicine, Rue du Bugnon 17, Lausanne, Switzerland.
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537
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Walters RG, Jacquemont S, Valsesia A, de Smith AJ, Martinet D, Andersson J, Falchi M, Chen F, Andrieux J, Lobbens S, Delobel B, Stutzmann F, Moustafa JSES, Chèvre JC, Lecoeur C, Vatin V, Bouquillon S, Buxton JL, Boute O, Holder-Espinasse M, Cuisset JM, Lemaitre MP, Ambresin AE, Brioshi A, Gaillard M, Giusti V, Fellmann F, Ferrarini A, Hadjikhani N, Campion D, Guilmatre A, Goldenberg A, Calmels N, Mandel JL, Le Caignec C, David A, Isidor B, Cordier MP, Dupuis-Girod S, Labalme A, Sanlaville D, Béri-Deixheimer M, Jonveaux P, Leheup B, Õunap K, Bochukova EG, Henning E, Keogh J, Ellis RJ, MacDermot KD, Vincent-Delorme C, Plessis G, Touraine R, Philippe A, Malan V, Mathieu-Dramard M, Chiesa J, Blaumeiser B, Kooy RF, Caiazzo R, Pigeyre M, Balkau B, Sladek R, Bergmann S, Mooser V, Waterworth D, Reymond A, Vollenweider P, Waeber G, Kurg A, Palta P, Esko T, Metspalu A, Nelis M, Elliott P, Hartikainen AL, McCarthy MI, Peltonen L, Carlsson L, Jacobson P, Sjöström L, Huang N, Hurles ME, O’Rahilly S, Farooqi IS, Männik K, Jarvelin MR, Pattou F, Meyre D, Walley AJ, Coin LJM, Blakemore AIF, Froguel P, Beckmann JS. A new highly penetrant form of obesity due to deletions on chromosome 16p11.2. Nature 2010; 463:671-5. [PMID: 20130649 PMCID: PMC2880448 DOI: 10.1038/nature08727] [Citation(s) in RCA: 367] [Impact Index Per Article: 24.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2009] [Accepted: 12/01/2009] [Indexed: 01/04/2023]
Abstract
Obesity has become a major worldwide challenge to public health, owing to an interaction between the Western 'obesogenic' environment and a strong genetic contribution. Recent extensive genome-wide association studies (GWASs) have identified numerous single nucleotide polymorphisms associated with obesity, but these loci together account for only a small fraction of the known heritable component. Thus, the 'common disease, common variant' hypothesis is increasingly coming under challenge. Here we report a highly penetrant form of obesity, initially observed in 31 subjects who were heterozygous for deletions of at least 593 kilobases at 16p11.2 and whose ascertainment included cognitive deficits. Nineteen similar deletions were identified from GWAS data in 16,053 individuals from eight European cohorts. These deletions were absent from healthy non-obese controls and accounted for 0.7% of our morbid obesity cases (body mass index (BMI) >or= 40 kg m(-2) or BMI standard deviation score >or= 4; P = 6.4 x 10(-8), odds ratio 43.0), demonstrating the potential importance in common disease of rare variants with strong effects. This highlights a promising strategy for identifying missing heritability in obesity and other complex traits: cohorts with extreme phenotypes are likely to be enriched for rare variants, thereby improving power for their discovery. Subsequent analysis of the loci so identified may well reveal additional rare variants that further contribute to the missing heritability, as recently reported for SIM1 (ref. 3). The most productive approach may therefore be to combine the 'power of the extreme' in small, well-phenotyped cohorts, with targeted follow-up in case-control and population cohorts.
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Affiliation(s)
- R. G. Walters
- Section of Genomic Medicine, Imperial College London, London, UK
- Department of Epidemiology and Public Health, Imperial College London, London, UK
| | - S. Jacquemont
- Service de Génétique Médicale, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
| | - A. Valsesia
- Departement de Génétique Médicale, Université de Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - A. J. de Smith
- Section of Genomic Medicine, Imperial College London, London, UK
| | - D. Martinet
- Service de Génétique Médicale, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
| | - J. Andersson
- Section of Genomic Medicine, Imperial College London, London, UK
| | - M. Falchi
- Section of Genomic Medicine, Imperial College London, London, UK
| | - F. Chen
- Brain Mind Institute, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - J. Andrieux
- Laboratoire de Génétique Médicale, Centre Hospitalier Régional Universitaire, Lille, France
| | - S. Lobbens
- CNRS 8090-Institute of Biology, Pasteur Institute, Lille, France
| | - B. Delobel
- Centre de Génétique Chromosomique, Hôpital Saint-Vincent de Paul, GHICL, Lille, France
| | - F. Stutzmann
- CNRS 8090-Institute of Biology, Pasteur Institute, Lille, France
| | | | - J.-C. Chèvre
- CNRS 8090-Institute of Biology, Pasteur Institute, Lille, France
| | - C. Lecoeur
- CNRS 8090-Institute of Biology, Pasteur Institute, Lille, France
| | - V. Vatin
- CNRS 8090-Institute of Biology, Pasteur Institute, Lille, France
| | - S. Bouquillon
- Laboratoire de Génétique Médicale, Centre Hospitalier Régional Universitaire, Lille, France
| | - J. L. Buxton
- Section of Genomic Medicine, Imperial College London, London, UK
| | - O. Boute
- Service de Génétique Clinique, Hôpital Jeanne de Flandre, Centre Hospitalier Universitaire de Lille, Lille, France
| | - M. Holder-Espinasse
- Service de Génétique Clinique, Hôpital Jeanne de Flandre, Centre Hospitalier Universitaire de Lille, Lille, France
| | - J.-M. Cuisset
- Service de Neuropédiatrie, Centre Hospitalier Régional Universitaire, Lille, France
| | - M.-P. Lemaitre
- Service de Neuropédiatrie, Centre Hospitalier Régional Universitaire, Lille, France
| | - A.-E. Ambresin
- Unité Multidisciplinaire de Santé des Adolescents, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
| | - A. Brioshi
- Service de Neuropsychologie et de Neuroréhabilitation, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
| | - M. Gaillard
- Service de Génétique Médicale, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
| | - V. Giusti
- Service d’Endocrinologie, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
| | - F. Fellmann
- Service de Génétique Médicale, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
| | - A. Ferrarini
- Service de Génétique Médicale, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
| | - N. Hadjikhani
- Brain Mind Institute, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Athinoula A Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown MA, USA
| | - D. Campion
- INSERM, U614, Faculté de Médecine, Rouen, France
| | - A. Guilmatre
- INSERM, U614, Faculté de Médecine, Rouen, France
| | - A. Goldenberg
- Service de Génétique, Centre Hospitalier Universitaire de Rouen, Rouen, France
| | - N. Calmels
- Laboratoire de Diagnostic Génétique, Nouvel hôpital civil, Strasbourg, France
| | - J.-L. Mandel
- Laboratoire de Diagnostic Génétique, Nouvel hôpital civil, Strasbourg, France
| | - C. Le Caignec
- Centre Hospitalier Universitaire Nantes, Service de Génétique Médicale, Nantes, France
- INSERM, UMR915, L’Institut du Thorax, Nantes, France
| | - A. David
- Centre Hospitalier Universitaire Nantes, Service de Génétique Médicale, Nantes, France
| | - B. Isidor
- Centre Hospitalier Universitaire Nantes, Service de Génétique Médicale, Nantes, France
| | - M.-P. Cordier
- Service de Génétique, Hospices Civils de Lyon, Hôpital de l’Hotel Dieu, Lyon, France
| | - S. Dupuis-Girod
- Service de Génétique, Hospices Civils de Lyon, Hôpital de l’Hotel Dieu, Lyon, France
| | - A. Labalme
- Service de Génétique, Hospices Civils de Lyon, Hôpital de l’Hotel Dieu, Lyon, France
| | - D. Sanlaville
- Service de Génétique, Hospices Civils de Lyon, Hôpital de l’Hotel Dieu, Lyon, France
- EA 4171, Université Claude Bernard, Lyon, France
| | - M. Béri-Deixheimer
- Laboratoire de Génétique, Centre Hospitalier Universitaire, Nancy University, Nancy, France
| | - P. Jonveaux
- Laboratoire de Génétique, Centre Hospitalier Universitaire, Nancy University, Nancy, France
| | - B. Leheup
- Laboratoire de Génétique, Centre Hospitalier Universitaire, Nancy University, Nancy, France
- EA4368 Medical School Nancy, Université Henri Poincaré, Nancy, France
| | - K. Õunap
- Department of Genetics, United Laboratories,Tartu University Children’s Hospital, Tartu, Estonia
| | - E. G. Bochukova
- University of Cambridge Metabolic Research Laboratories, Institute of Metabolic Science, Addenbrooke’s Hospital, Cambridge, UK
| | - E. Henning
- University of Cambridge Metabolic Research Laboratories, Institute of Metabolic Science, Addenbrooke’s Hospital, Cambridge, UK
| | - J. Keogh
- University of Cambridge Metabolic Research Laboratories, Institute of Metabolic Science, Addenbrooke’s Hospital, Cambridge, UK
| | - R. J. Ellis
- North West Thames Regional Genetics Service, Northwick Park & St Marks Hospital, Harrow, UK
| | - K. D. MacDermot
- North West Thames Regional Genetics Service, Northwick Park & St Marks Hospital, Harrow, UK
| | | | - G. Plessis
- Service de Génétique Médicale, Centre Hospitalier Universitaire Clemenceau, Caen, France
| | - R. Touraine
- Centre Hospitalier Universitaire–Hôpital Nord, Service de Génétique, Saint Etienne, France
| | - A. Philippe
- Département de Génétique et INSERM U781, Université Paris Descartes, Hôpital Necker-Enfants Malades, Paris, France
| | - V. Malan
- Département de Génétique et INSERM U781, Université Paris Descartes, Hôpital Necker-Enfants Malades, Paris, France
| | - M. Mathieu-Dramard
- Service de Génétique Clinique, Centre Hospitalier Universitaire, Amiens, France
| | - J. Chiesa
- Laboratoire de Cytogénétique, Centre Hospitalier Universitaire Caremeau, Nîmes, France
| | - B. Blaumeiser
- Department of Medical Genetics, University Hospital & University of Antwerp, Antwerp, Belgium
| | - R. F. Kooy
- Department of Medical Genetics, University Hospital & University of Antwerp, Antwerp, Belgium
| | - R. Caiazzo
- INSERM U859, Biotherapies for Diabetes, Lille, France
- University Lille Nord de France, Centre Hospitalier Universitaire Lille, France
| | - M. Pigeyre
- University Lille Nord de France, Centre Hospitalier Universitaire Lille, France
| | - B. Balkau
- INSERM U780-IFR69, Villejuif, France
| | - R. Sladek
- Genome Quebec Innovation Centre, Montreal, Canada
- Department of Medicine and Human Genetics, McGill University, Montreal, Canada
| | - S. Bergmann
- Departement de Génétique Médicale, Université de Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - V. Mooser
- Division of Genetics, GlaxoSmithKline, Philadelphia PA, USA
| | - D. Waterworth
- Division of Genetics, GlaxoSmithKline, Philadelphia PA, USA
| | - A. Reymond
- The Center for Integrated Genomics, University of Lausanne, Lausanne, Switzerland
| | - P. Vollenweider
- Department of Medicine, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
| | - G. Waeber
- Department of Medicine, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
| | - A. Kurg
- Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia
| | - P. Palta
- Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia
| | - T. Esko
- Estonian Genome Project, University of Tartu, Tartu, Estonia
- Estonian Biocentre, Tartu, Estonia
| | - A. Metspalu
- Estonian Genome Project, University of Tartu, Tartu, Estonia
- Estonian Biocentre, Tartu, Estonia
| | - M. Nelis
- Estonian Genome Project, University of Tartu, Tartu, Estonia
- Estonian Biocentre, Tartu, Estonia
| | - P. Elliott
- Department of Epidemiology and Public Health, Imperial College London, London, UK
| | - A.-L. Hartikainen
- Department of Obstetrics and Gynaecology, University of Oulu, Oulu, Finland
| | - M. I. McCarthy
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
| | - L. Peltonen
- Wellcome Trust Sanger Institute, Hinxton, Cambridgeshire, UK
- Massachusetts Institute of Technology, The Broad Institute, Cambridge MA, USA
| | - L. Carlsson
- Department of Molecular and Clinical Medicine and Center for Cardiovascular and Metabolic Research, The Sahlgrenska Academy, Göteborg, Sweden
| | - P. Jacobson
- Department of Molecular and Clinical Medicine and Center for Cardiovascular and Metabolic Research, The Sahlgrenska Academy, Göteborg, Sweden
| | - L. Sjöström
- Department of Molecular and Clinical Medicine and Center for Cardiovascular and Metabolic Research, The Sahlgrenska Academy, Göteborg, Sweden
| | - N. Huang
- Wellcome Trust Sanger Institute, Hinxton, Cambridgeshire, UK
| | - M. E. Hurles
- Wellcome Trust Sanger Institute, Hinxton, Cambridgeshire, UK
| | - S. O’Rahilly
- University of Cambridge Metabolic Research Laboratories, Institute of Metabolic Science, Addenbrooke’s Hospital, Cambridge, UK
| | - I. S. Farooqi
- University of Cambridge Metabolic Research Laboratories, Institute of Metabolic Science, Addenbrooke’s Hospital, Cambridge, UK
| | - K. Männik
- Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia
| | - M.-R. Jarvelin
- Department of Epidemiology and Public Health, Imperial College London, London, UK
- Department of Child and Adolescent Health, National Public Health Institute, Oulu, Finland
- Institute of Health Sciences and Biocenter Oulu, University of Oulu, Oulu Finland
| | - F. Pattou
- INSERM U859, Biotherapies for Diabetes, Lille, France
- University Lille Nord de France, Centre Hospitalier Universitaire Lille, France
| | - D. Meyre
- CNRS 8090-Institute of Biology, Pasteur Institute, Lille, France
| | - A. J. Walley
- Section of Genomic Medicine, Imperial College London, London, UK
| | - L. J. M. Coin
- Department of Epidemiology and Public Health, Imperial College London, London, UK
| | | | - P. Froguel
- Section of Genomic Medicine, Imperial College London, London, UK
- CNRS 8090-Institute of Biology, Pasteur Institute, Lille, France
| | - J. S. Beckmann
- Service de Génétique Médicale, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
- Departement de Génétique Médicale, Université de Lausanne, Lausanne, Switzerland
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538
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Marques-Vidal P, Bochud M, Paccaud F, Mooser V, Waeber G, Vollenweider P. Distribution of plasma levels of adiponectin and leptin in an adult Caucasian population. Clin Endocrinol (Oxf) 2010; 72:38-46. [PMID: 19473178 DOI: 10.1111/j.1365-2265.2009.03628.x] [Citation(s) in RCA: 37] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
OBJECTIVES Little is known regarding the distribution and the determinants of leptin and adiponectin levels in the general population. DESIGN Cross-sectional study. PATIENTS Women (3004) and men (2552) aged 35-74 living in Lausanne, Switzerland. MEASUREMENTS Plasma levels of leptin and adiponectin (ELISA measurement). RESULTS Women had higher leptin and adiponectin levels than men. In both genders, leptin and adiponectin levels increased with age. After adjusting for fat mass, leptin levels were significantly and negatively associated with age in women: 18.1 +/- 0.3, 17.1 +/- 0.3, 16.7 +/- 0.3 and 15.5 +/- 0.4 ng/ml (adjusted mean +/- SE) for age groups [35-44], [45-54], [55-64] and [65-75], respectively, P < 0.001. A similar but nonsignificant trend was also found in men. Conversely, the age-related increase of adiponectin was unrelated to body fat in both genders. Post-menopausal women had higher leptin and adiponectin levels than premenopausal women, independently of hormone replacement therapy. Although body fat mass was associated with leptin and adiponectin, the associations were stronger with body mass index (BMI), waist and hip in both genders. Finally, after adjusting for age and anthropometry, no relationships were found between leptin or adiponectin levels with alcohol, caffeine consumption and physical activity, whereas smoking and diabetes decreased leptin and adiponectin levels in women only. CONCLUSIONS The age-related increase in leptin levels is attributable to changes in fat mass in women and probably also in men. Leptin and adiponectin levels are more related to BMI than to body fat mass. The effects of smoking and diabetes appear to be gender-specific.
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Affiliation(s)
- Pedro Marques-Vidal
- Centre for Cardiovascular and Metabolic Research (Cardiomet), GlaxoSmithKline, Philadelphia, PA, USA.
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539
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Richards JB, Waterworth D, O'Rahilly S, Hivert MF, Loos RJF, Perry JRB, Tanaka T, Timpson NJ, Semple RK, Soranzo N, Song K, Rocha N, Grundberg E, Dupuis J, Florez JC, Langenberg C, Prokopenko I, Saxena R, Sladek R, Aulchenko Y, Evans D, Waeber G, Erdmann J, Burnett MS, Sattar N, Devaney J, Willenborg C, Hingorani A, Witteman JCM, Vollenweider P, Glaser B, Hengstenberg C, Ferrucci L, Melzer D, Stark K, Deanfield J, Winogradow J, Grassl M, Hall AS, Egan JM, Thompson JR, Ricketts SL, König IR, Reinhard W, Grundy S, Wichmann HE, Barter P, Mahley R, Kesaniemi YA, Rader DJ, Reilly MP, Epstein SE, Stewart AFR, Van Duijn CM, Schunkert H, Burling K, Deloukas P, Pastinen T, Samani NJ, McPherson R, Davey Smith G, Frayling TM, Wareham NJ, Meigs JB, Mooser V, Spector TD. A genome-wide association study reveals variants in ARL15 that influence adiponectin levels. PLoS Genet 2009; 5:e1000768. [PMID: 20011104 PMCID: PMC2781107 DOI: 10.1371/journal.pgen.1000768] [Citation(s) in RCA: 139] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2009] [Accepted: 11/12/2009] [Indexed: 12/22/2022] Open
Abstract
The adipocyte-derived protein adiponectin is highly heritable and inversely associated with risk of type 2 diabetes mellitus (T2D) and coronary heart disease (CHD). We meta-analyzed 3 genome-wide association studies for circulating adiponectin levels (n = 8,531) and sought validation of the lead single nucleotide polymorphisms (SNPs) in 5 additional cohorts (n = 6,202). Five SNPs were genome-wide significant in their relationship with adiponectin (P< or =5x10(-8)). We then tested whether these 5 SNPs were associated with risk of T2D and CHD using a Bonferroni-corrected threshold of P< or =0.011 to declare statistical significance for these disease associations. SNPs at the adiponectin-encoding ADIPOQ locus demonstrated the strongest associations with adiponectin levels (P-combined = 9.2x10(-19) for lead SNP, rs266717, n = 14,733). A novel variant in the ARL15 (ADP-ribosylation factor-like 15) gene was associated with lower circulating levels of adiponectin (rs4311394-G, P-combined = 2.9x10(-8), n = 14,733). This same risk allele at ARL15 was also associated with a higher risk of CHD (odds ratio [OR] = 1.12, P = 8.5x10(-6), n = 22,421) more nominally, an increased risk of T2D (OR = 1.11, P = 3.2x10(-3), n = 10,128), and several metabolic traits. Expression studies in humans indicated that ARL15 is well-expressed in skeletal muscle. These findings identify a novel protein, ARL15, which influences circulating adiponectin levels and may impact upon CHD risk.
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Affiliation(s)
- J Brent Richards
- Departments of Medicine, Human Genetics, and Epidemiology and Biostatistics, Jewish General Hospital, McGill University, Montréal, Québec, Canada.
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Bochud M, Marquant F, Marques-Vidal PM, Vollenweider P, Beckmann JS, Mooser V, Paccaud F, Rousson V. Association between C-reactive protein and adiposity in women. J Clin Endocrinol Metab 2009; 94:3969-77. [PMID: 19584180 DOI: 10.1210/jc.2008-2428] [Citation(s) in RCA: 55] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
CONTEXT The link between C-reactive protein (CRP) and adiposity deserves to be further explored, considering the controversial diabetogenic role of CRP. OBJECTIVE We explored the potential causal role of CRP on measures of adiposity. DESIGN We used a Mendelian randomization approach with the CRP and LEPR genes as instrumental variables in a cross-sectional Caucasian population-based study comprising 2526 men and 2836 women. Adiposity was measured using body mass index (BMI), fat and lean mass estimated by bioelectrical impedance, and waist circumference. RESULTS Log-transformed CRP explained by the rs7553007 single-nucleotide polymorphism tagging the CRP gene was significantly associated with BMI [regression coefficient: 1.22 (0.18; 2.25), P = 0.02] and fat mass [2.67 (0.65; 4.68), P = 0.01] but not with lean mass in women, whereas no association was found in men. Log-transformed CRP explained by the rs1805096 LEPR single-nucleotide polymorphism was also positively associated, although not significantly, with BMI or fat mass. The combined CRP-LEPR instrument explained 2.24 and 0.77% of CRP variance in women and men, respectively. Log-transformed CRP explained by this combined instrument was significantly associated with BMI [0.98 (0.32; 1.63), P = 0.004], fat mass [2.07 (0.79; 3.34), P = 0.001], and waist [2.09 (0.39; 3.78), P = 0.01] in women but not men. CONCLUSION Our data suggest that CRP is causally and positively related to BMI in women and that this is mainly due to fat mass. Results on the combined CRP-LEPR instrument suggest that leptin may play a role in the causal association between CRP and adiposity in women. Results in men were not significant.
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Affiliation(s)
- Murielle Bochud
- Department of Medicine, University Institute of Social and Preventive Medicine, Centre Hospitalier Universitaire Vaudois and University of Lausanne, 1005 Lausanne, Switzerland.
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541
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Association between white-coat effect and blunted dipping of nocturnal blood pressure. Am J Hypertens 2009; 22:1054-61. [PMID: 19629048 DOI: 10.1038/ajh.2009.133] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND In this study, we assessed whether the white-coat effect (difference between office and daytime blood pressure (BP)) is associated with nondipping (absence of BP decrease at night). METHODS Data were available in 371 individuals of African descent from 74 families selected from a population-based hypertension register in the Seychelles Islands and in 295 Caucasian individuals randomly selected from a population-based study in Switzerland. We used standard multiple linear regression in the Swiss data and generalized estimating equations to account for familial correlations in the Seychelles data. RESULTS The prevalence of systolic and diastolic nondipping (<10% nocturnal BP decrease) and white-coat hypertension (WCH) was respectively 51, 46, and 4% in blacks and 33, 37, and 7% in whites. When white coat effect and nocturnal dipping were taken as continuous variables (mm Hg), systolic (SBP) and diastolic BP (DBP) dipping were associated inversely and independently with white-coat effect (P < 0.05) in both populations. Analogously, the difference between office and daytime heart rate was inversely associated with the difference between daytime and night-time heart rate in the two populations. These results did not change after adjustment for potential confounders. CONCLUSIONS The white-coat effect is associated with BP nondipping. The similar associations between office-daytime values and daytime-night-time values for both BP and heart rate suggest that the sympathetic nervous system might play a role. Our findings also further stress the interest, for clinicians, of assessing the presence of a white-coat effect as a means to further identify patients at increased cardiovascular risk and guide treatment accordingly.
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542
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Kollerits B, Coassin S, Beckmann ND, Teumer A, Kiechl S, Döring A, Kavousi M, Hunt SC, Lamina C, Paulweber B, Kutalik Z, Nauck M, van Duijn CM, Heid IM, Willeit J, Brandstätter A, Adams TD, Mooser V, Aulchenko YS, Völzke H, Kronenberg F. Genetic evidence for a role of adiponutrin in the metabolism of apolipoprotein B-containing lipoproteins. Hum Mol Genet 2009; 18:4669-76. [PMID: 19729411 DOI: 10.1093/hmg/ddp424] [Citation(s) in RCA: 45] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
Adiponutrin (PNPLA3) is a predominantly liver-expressed transmembrane protein with phospholipase activity that is regulated by fasting and feeding. Recent genome-wide association studies identified PNPLA3 to be associated with hepatic fat content and liver function, thus pointing to a possible involvement in the hepatic lipoprotein metabolism. The aim of this study was to examine the association between two common variants in the adiponutrin gene and parameters of lipoprotein metabolism in 23,274 participants from eight independent West-Eurasian study populations including six population-based studies [Bruneck (n = 800), KORA S3/F3 (n = 1644), KORA S4/F4 (n = 1814), CoLaus (n = 5435), SHIP (n = 4012), Rotterdam (n = 5967)], the SAPHIR Study as a healthy working population (n = 1738) and the Utah Obesity Case-Control Study including a group of 1037 severely obese individuals (average BMI 46 kg/m2) and 827 controls from the same geographical region of Utah. We observed a strong additive association of a common non-synonymous variant within adiponutrin (rs738409) with age-, gender-, and alanine-aminotransferase-adjusted lipoprotein concentrations: each copy of the minor allele decreased levels of total cholesterol on average by 2.43 mg/dl (P = 8.87 x 10(-7)), non-HDL cholesterol levels by 2.35 mg/dl (P = 2.27 x 10(-6)) and LDL cholesterol levels by 1.48 mg/dl (P = 7.99 x 10(-4)). These associations remained significant after correction for multiple testing. We did not observe clear evidence for associations with HDL cholesterol or triglyceride concentrations. In conclusion, our study suggests that adiponutrin is involved in the metabolism of apoB-containing lipoproteins.
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Affiliation(s)
- Barbara Kollerits
- Division of Genetic Epidemiology, Department of Medical Genetics, Molecular and Clinical Pharmacology, Innsbruck Medical University, Innsbruck, Austria
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543
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544
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Marques-Vidal P, Bochud M, Mooser V, Paccaud F, Waeber G, Vollenweider P. Obesity markers and estimated 10-year fatal cardiovascular risk in Switzerland. Nutr Metab Cardiovasc Dis 2009; 19:462-468. [PMID: 19185476 DOI: 10.1016/j.numecd.2008.10.001] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/18/2008] [Revised: 10/03/2008] [Accepted: 10/06/2008] [Indexed: 01/10/2023]
Abstract
BACKGROUND AND AIM There is an ongoing debate on which obesity marker better predicts cardiovascular disease (CVD). In this study, the relationships between obesity markers and high (>5%) 10-year risk of fatal CVD were assessed. METHODS AND RESULTS A cross-sectional study was conducted including 3047 women and 2689 men aged 35-75 years. Body fat percentage was assessed by tetrapolar bioimpedance. CVD risk was assessed using the SCORE risk function and gender- and age-specific cut points for body fat were derived. The diagnostic accuracy of each obesity marker was evaluated through receiver operating characteristics (ROC) analysis. In men, body fat presented a higher correlation (r=0.31) with 10-year CVD risk than waist/hip ratio (WHR, r=0.22), waist (r=0.22) or BMI (r=0.19); the corresponding values in women were 0.18, 0.15, 0.11 and 0.05, respectively (all p<0.05). In both genders, body fat showed the highest area under the ROC curve (AUC): in men, the AUC (95% confidence interval) were 76.0 (73.8-78.2), 67.3 (64.6-69.9), 65.8 (63.1-68.5) and 60.6 (57.9-63.5) for body fat, WHR, waist and BMI, respectively. In women, the corresponding values were 72.3 (69.2-75.3), 66.6 (63.1-70.2), 64.1 (60.6-67.6) and 58.8 (55.2-62.4). The use of the body fat percentage criterion enabled the capture of three times more subjects with high CVD risk than the BMI criterion, and almost twice as much as the WHR criterion. CONCLUSION Obesity defined by body fat percentage is more related with 10-year risk of fatal CVD than obesity markers based on WHR, waist or BMI.
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545
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Ong KK, Elks CE, Li S, Zhao JH, Luan J, Andersen LB, Bingham SA, Brage S, Smith GD, Ekelund U, Gillson CJ, Glaser B, Golding J, Hardy R, Khaw KT, Kuh D, Luben R, Marcus M, McGeehin MA, Ness AR, Northstone K, Ring SM, Rubin C, Sims MA, Song K, Strachan DP, Vollenweider P, Waeber G, Waterworth DM, Wong A, Deloukas P, Barroso I, Mooser V, Loos RJ, Wareham NJ. Genetic variation in LIN28B is associated with the timing of puberty. Nat Genet 2009; 41:729-33. [PMID: 19448623 PMCID: PMC3000552 DOI: 10.1038/ng.382] [Citation(s) in RCA: 263] [Impact Index Per Article: 16.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2008] [Accepted: 04/21/2009] [Indexed: 11/09/2022]
Abstract
The timing of puberty is highly variable. We carried out a genome-wide association study for age at menarche in 4,714 women and report an association in LIN28B on chromosome 6 (rs314276, minor allele frequency (MAF) = 0.33, P = 1.5 × 10(-8)). In independent replication studies in 16,373 women, each major allele was associated with 0.12 years earlier menarche (95% CI = 0.08-0.16; P = 2.8 × 10(-10); combined P = 3.6 × 10(-16)). This allele was also associated with earlier breast development in girls (P = 0.001; N = 4,271); earlier voice breaking (P = 0.006, N = 1,026) and more advanced pubic hair development in boys (P = 0.01; N = 4,588); a faster tempo of height growth in girls (P = 0.00008; N = 4,271) and boys (P = 0.03; N = 4,588); and shorter adult height in women (P = 3.6 × 10(-7); N = 17,274) and men (P = 0.006; N = 9,840) in keeping with earlier growth cessation. These studies identify variation in LIN28B, a potent and specific regulator of microRNA processing, as the first genetic determinant regulating the timing of human pubertal growth and development.
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Affiliation(s)
- Ken K Ong
- Medical Research Council (MRC) Epidemiology Unit, Addenbrooke's Hospital, Cambridge, UK.
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546
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Ong KK, Elks CE, Li S, Zhao JH, Luan J, Andersen LB, Bingham SA, Brage S, Smith GD, Ekelund U, Gillson CJ, Glaser B, Golding J, Hardy R, Khaw KT, Kuh D, Luben R, Marcus M, McGeehin MA, Ness AR, Northstone K, Ring SM, Rubin C, Sims MA, Song K, Strachan DP, Vollenweider P, Waeber G, Waterworth DM, Wong A, Deloukas P, Barroso I, Mooser V, Loos RJ, Wareham NJ. Genetic variation in LIN28B is associated with the timing of puberty. Nat Genet 2009. [PMID: 19448623 DOI: 10.1038/ng.382.genetic] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/22/2023]
Abstract
The timing of puberty is highly variable. We carried out a genome-wide association study for age at menarche in 4,714 women and report an association in LIN28B on chromosome 6 (rs314276, minor allele frequency (MAF) = 0.33, P = 1.5 × 10(-8)). In independent replication studies in 16,373 women, each major allele was associated with 0.12 years earlier menarche (95% CI = 0.08-0.16; P = 2.8 × 10(-10); combined P = 3.6 × 10(-16)). This allele was also associated with earlier breast development in girls (P = 0.001; N = 4,271); earlier voice breaking (P = 0.006, N = 1,026) and more advanced pubic hair development in boys (P = 0.01; N = 4,588); a faster tempo of height growth in girls (P = 0.00008; N = 4,271) and boys (P = 0.03; N = 4,588); and shorter adult height in women (P = 3.6 × 10(-7); N = 17,274) and men (P = 0.006; N = 9,840) in keeping with earlier growth cessation. These studies identify variation in LIN28B, a potent and specific regulator of microRNA processing, as the first genetic determinant regulating the timing of human pubertal growth and development.
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Affiliation(s)
- Ken K Ong
- Medical Research Council (MRC) Epidemiology Unit, Addenbrooke's Hospital, Cambridge, UK.
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547
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Esteghamati A, Meysamie A, Khalilzadeh O, Rashidi A, Haghazali M, Asgari F, Kamgar M, Gouya MM, Abbasi M. Third national Surveillance of Risk Factors of Non-Communicable Diseases (SuRFNCD-2007) in Iran: methods and results on prevalence of diabetes, hypertension, obesity, central obesity, and dyslipidemia. BMC Public Health 2009; 9:167. [PMID: 19480675 PMCID: PMC2697989 DOI: 10.1186/1471-2458-9-167] [Citation(s) in RCA: 224] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2008] [Accepted: 05/29/2009] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND The burden of non-communicable diseases is rising globally. This trend seems to be faster in developing countries of the Middle East. In this study, we presented the latest prevalence rates of a number of important non-communicable diseases and their risk factors in the Iranian population. METHODS The results of this study are extracted from the third national Surveillance of Risk Factors of Non-Communicable Diseases (SuRFNCD-2007), conducted in 2007. A total of 5,287 Iranian citizens, aged 15-64 years, were included in this survey. Interviewer-administered questionnaires were applied to collect the data of participants including the demographics, diet, physical activity, smoking, history of hypertension, and history of diabetes. Anthropometric characteristics were measured and serum biochemistry profiles were determined on venous blood samples. Diabetes (fasting plasma glucose >or= 126 mg/dl), hypertension (systolic blood pressure >or= 140 mmHg, diastolic blood pressure >or= 90 mmHg, or use of anti-hypertensive drugs), dyslipidemia (hypertriglyceridemia: triglycerides >or= 150 mg/dl, hypercholesterolemia: total cholesterol >or= 200 mg/dl), obesity (body mass index >or= 30 kg/m2), and central obesity (waist circumference >or= 80 cm in females and >or= 94 cm in males) were identified and the national prevalence rates were estimated. RESULTS The prevalence of diabetes, hypertension, obesity, and central obesity was 8.7% (95%CI = 7.4-10.2%), 26.6% (95%CI = 24.4-28.9%), 22.3% (95%CI = 20.2-24.5%), and 53.6% (95%CI = 50.4-56.8%), respectively. The prevalence of hypertriglyceridemia and hypercholesterolemia was 36.4% (95%CI = 34.1-38.9%) and 42.9% (95%CI = 40.4-45.4%), respectively. All of the mentioned prevalence rates were higher among females (except hypertriglyceridemia) and urban residents. CONCLUSION We documented a strikingly high prevalence of a number of chronic non-communicable diseases and their risk factors among Iranian adults. Urgent preventive interventions should be implemented to combat the growing public health problems in Iran.
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Affiliation(s)
- Alireza Esteghamati
- Endocrinology and Metabolism Research Center (EMRC), Vali-Asr Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Alipasha Meysamie
- Department of Community Medicine, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Omid Khalilzadeh
- Endocrinology and Metabolism Research Center (EMRC), Vali-Asr Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Armin Rashidi
- Endocrinology and Metabolism Research Center (EMRC), Vali-Asr Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Mehrdad Haghazali
- Center for Disease Control, Ministry of Health and Medical Education, Tehran, Iran
| | - Fereshteh Asgari
- Center for Disease Control, Ministry of Health and Medical Education, Tehran, Iran
| | - Mandana Kamgar
- Endocrinology and Metabolism Research Center (EMRC), Vali-Asr Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Mohammad Mehdi Gouya
- Center for Disease Control, Ministry of Health and Medical Education, Tehran, Iran
| | - Mehrshad Abbasi
- Endocrinology and Metabolism Research Center (EMRC), Vali-Asr Hospital, Tehran University of Medical Sciences, Tehran, Iran
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548
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Abstract
Hypertension is the first single modifiable cause of disease burden worldwide. Genes encoding proteins that are involved in the metabolism (CYP3A5) and transport (ABCB1) of drugs and hormones might contribute to blood pressure control in humans. Indeed, recent data have suggested that CYP3A5 and ABCB1 gene polymorphisms are associated with blood pressure in the rat as well as in humans. Interestingly, the effects of these genes on blood pressure appear to be modified by dietary salt intake. This review summarizes what is known regarding the relationships of the ABCB1 and CYP3A5 genes with blood pressure, and discusses the potential underlying mechanisms of the association. If the role of these genes in blood pressure control is confirmed in other populations and other ethnic groups, these findings would point toward a new pathway for blood pressure control in humans.
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Affiliation(s)
- Murielle Bochud
- Institute of Social and Preventive Medicine, Centre Hospitalier Universitaire Vaudois (CHUV) et Université de Lausanne, Rue du Bugnon 17, CH-1005 Lausanne, Switzerland.
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549
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Nanchen D, Chiolero A, Cornuz J, Marques-Vidal PM, Firmann M, Mooser V, Paccaud F, Waeber G, Vollenweider P, Rodondi N. Cardiovascular risk estimation and eligibility for statins in primary prevention comparing different strategies. Am J Cardiol 2009; 103:1089-95. [PMID: 19361595 DOI: 10.1016/j.amjcard.2008.12.042] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/09/2008] [Revised: 12/21/2008] [Accepted: 12/21/2008] [Indexed: 10/21/2022]
Abstract
Recommendations for statin use for primary prevention of coronary heart disease (CHD) are based on estimation of the 10-year CHD risk. It is unclear which risk algorithm and guidelines should be used in European populations. Using data from a population-based study in Switzerland, we first assessed 10-year CHD risk and eligibility for statins in 5,683 women and men 35 to 75 years of age without cardiovascular disease by comparing recommendations by the European Society of Cardiology without and with extrapolation of risk to age 60 years, the International Atherosclerosis Society, and the US Adult Treatment Panel III. The proportions of participants classified as high-risk for CHD were 12.5% (15.4% with extrapolation), 3.0%, and 5.8%, respectively. Proportions of participants eligible for statins were 9.2% (11.6% with extrapolation), 13.7%, and 16.7%, respectively. Assuming full compliance to each guideline, expected relative decreases in CHD deaths in Switzerland over a 10-year period would be 16.4% (17.5% with extrapolation), 18.7%, and 19.3%, respectively; the corresponding numbers needed to treat to prevent 1 CHD death would be 285 (340 with extrapolation), 380, and 440, respectively. In conclusion, the proportion of subjects classified as high risk for CHD varied over a fivefold range across recommendations. Following the International Atherosclerosis Society and the Adult Treatment Panel III recommendations might prevent more CHD deaths at the cost of higher numbers needed to treat compared with European Society of Cardiology guidelines.
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550
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Lin X, Song K, Lim N, Yuan X, Johnson T, Abderrahmani A, Vollenweider P, Stirnadel H, Sundseth SS, Lai E, Burns DK, Middleton LT, Roses AD, Matthews PM, Waeber G, Cardon L, Waterworth DM, Mooser V. Risk prediction of prevalent diabetes in a Swiss population using a weighted genetic score--the CoLaus Study. Diabetologia 2009; 52:600-8. [PMID: 19139842 DOI: 10.1007/s00125-008-1254-y] [Citation(s) in RCA: 98] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/17/2008] [Accepted: 12/03/2008] [Indexed: 02/07/2023]
Abstract
AIMS/HYPOTHESIS Several susceptibility genes for type 2 diabetes have been discovered recently. Individually, these genes increase the disease risk only minimally. The goals of the present study were to determine, at the population level, the risk of diabetes in individuals who carry risk alleles within several susceptibility genes for the disease and the added value of this genetic information over the clinical predictors. METHODS We constructed an additive genetic score using the most replicated single-nucleotide polymorphisms (SNPs) within 15 type 2 diabetes-susceptibility genes, weighting each SNP with its reported effect. We tested this score in the extensively phenotyped population-based cross-sectional CoLaus Study in Lausanne, Switzerland (n = 5,360), involving 356 diabetic individuals. RESULTS The clinical predictors of prevalent diabetes were age, BMI, family history of diabetes, WHR, and triacylglycerol/HDL-cholesterol ratio. After adjustment for these variables, the risk of diabetes was 2.7 (95% CI 1.8-4.0, p = 0.000006) for individuals with a genetic score within the top quintile, compared with the bottom quintile. Adding the genetic score to the clinical covariates improved the area under the receiver operating characteristic curve slightly (from 0.86 to 0.87), yet significantly (p = 0.002). BMI was similar in these two extreme quintiles. CONCLUSIONS/INTERPRETATION In this population, a simple weighted 15 SNP-based genetic score provides additional information over clinical predictors of prevalent diabetes. At this stage, however, the clinical benefit of this genetic information is limited.
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Affiliation(s)
- X Lin
- Discovery Analytics, GlaxoSmithKline, Collegeville, PA, USA
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