Dataset Information

Genes and lifestyle factors in obesity: results from 12,462 subjects from MONICA/KORA.

ABSTRACT: Data from meta-analyses of genome-wide association studies provided evidence for an association of polymorphisms with body mass index (BMI), and gene expression results indicated a role of these variants in the hypothalamus. It was consecutively hypothesized that these associations might be evoked by a modulation of nutritional intake or energy expenditure.It was our aim to investigate the association of these genetic factors with BMI in a large homogenous population-based sample to explore the association of these polymorphisms with lifestyle factors related to nutritional intake or energy expenditure, and whether such lifestyle factors could be mediators of the detected single-nucleotide polymorphism (SNP)-association with BMI. It was a further aim to compare the proportion of BMI explained by genetic factors with the one explained by lifestyle factors.The association of seven polymorphisms in or near the genes NEGR1, TMEM18, MTCH2, FTO, MC4R, SH2B1 and KCTD15 was analyzed in 12,462 subjects from the population-based MONICA/KORA Augsburg study. Information on lifestyle factors was based on standardized questionnaires. For statistical analysis, regression-based models were used.The minor allele of polymorphism rs6548238 C>T (TMEM18) was associated with lower BMI (-0.418?kg?m(-2), P=1.22 × 10(-8)), and of polymorphisms rs9935401 G>A (FTO) and rs7498665 A>G (SH2B1) with increased BMI (0.290?kg?m(-2), P=2.85 × 10(-7) and 0.145?kg?m(-2), P=9.83 × 10(-3)). The other polymorphisms were not significantly associated. Lifestyle factors were correlated with BMI and explained 0.037% of the BMI variance as compared with 0.006% of explained variance by the associated genetic factors. The genetic variants associated with BMI were not significantly associated with lifestyle factors and there was no evidence of lifestyle factors mediating the SNP-BMI association.Our data first confirm the findings for TMEM18 with BMI in a single study on adults and also confirm the findings for FTO and SH2B1. There was no evidence for a direct SNP-lifestyle association.

SUBMITTER: Holzapfel C

PROVIDER: S-EPMC3251754 | biostudies-literature | 2010 Oct

REPOSITORIES: biostudies-literature

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Genes and lifestyle factors in obesity: results from 12,462 subjects from MONICA/KORA.

Holzapfel C C Grallert H H Huth C C Wahl S S Fischer B B Döring A A Rückert I M IM Hinney A A Hebebrand J J Wichmann H-E HE Hauner H H Illig T T Heid I M IM

International journal of obesity (2005) 20100413 10

<h4>Background</h4>Data from meta-analyses of genome-wide association studies provided evidence for an association of polymorphisms with body mass index (BMI), and gene expression results indicated a role of these variants in the hypothalamus. It was consecutively hypothesized that these associations might be evoked by a modulation of nutritional intake or energy expenditure.<h4>Objective</h4>It was our aim to investigate the association of these genetic factors with BMI in a large homogenous po ...[more]

PMID: 20386550

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Project description:OBJECTIVES:To test the effects of body weight maintenance, gain, and loss on health care utilisation in terms of outpatient visits to different kinds of physicians in the general adult population. METHODS:Self-reported utilisation data were collected within two population-based cohorts (baseline surveys: MONICA-S3 1994/95 and KORA-S4 1999/2001; follow-ups: KORA-F3 2004/05 and KORA-F4 2006/08) in the region of Augsburg, Germany, and were pooled for present purposes. N=5,147 adults (complete cases) aged 25 to 64 years at baseline participated. Number of visits to general practitioners (GPs), internists, and other specialists as well as the total number of physician visits at follow-up were compared across 10 groups defined by body mass index (BMI) category maintenance or change. Body weight and height were measured anthropometrically. Hierarchical generalized linear regression analyses with negative binomial distribution adjusted for sex, age, socioeconomic status (SES), survey, and the need factors incident diabetes and first cancer between baseline and follow-up were conducted. RESULTS:In fully adjusted models, compared to the group of participants that maintained normal weight from baseline to follow-up, the following groups had significantly higher GP utilisation rates: weight gain from normal weight (+36%), weight loss from preobesity (+39%), maintained preobesity (+34%), weight gain after preobesity (+43%), maintained moderate obesity (+48%), weight gain from moderate obesity (+107%), weight loss from severe obesity (+114%), and maintained severe obesity (+83%). Regarding internists, those maintaining moderate obesity reported +107% more visits; those with weight gain from moderate obesity reported +91%. The latter group also had +41% more consultations with other physicians. Across all physicians, mean number of visits were estimated at 7.8 per year for maintained normal weight, 9 for maintained preobesity, 11 for maintained moderate obesity, and 12 for maintained severe obesity. Among those with weight loss, the mean number of visits were 8.7, 10.6 and 10.8 for baseline preobesity, moderate obesity, and severe obesity, respectively. Finally, those with weight gain from normal weight and preobesity reported 9.4 and 9.3 visits, respectively, and those with baseline moderate and follow-up severe obesity reported 13.1 visits (the most overall). Women reported higher GP and other physician utilisation. While all utilisation rates increased with age, GP utilisation was lower in middle to high SES groups. CONCLUSION:Compared to maintained normal weight over a 7- to 10-year period, maintained overweight, weight gain and weight loss are associated with higher outpatient physician utilisation in adults, especially after baseline obesity. These effects only partly became insignificant after inclusion of incident diabetes or first cancer into the model. Future research should further elucidate the associations between weight development and health care utilisation by BMI status and the mechanisms underlying these associations.

Project description:BACKGROUND:The chemokine RANTES (regulated on activation, normal T-cell expressed and secreted)/CCL5 is involved in the pathogenesis of cardiovascular disease in mice, whereas less is known in humans. We hypothesised that its relevance for atherosclerosis should be reflected by associations between CCL5 gene variants, RANTES serum concentrations and protein levels in atherosclerotic plaques and risk for coronary events. METHODS AND FINDINGS:We conducted a case-cohort study within the population-based MONICA/KORA Augsburg studies. Baseline RANTES serum levels were measured in 363 individuals with incident coronary events and 1,908 non-cases (mean follow-up: 10.2±4.8 years). Cox proportional hazard models adjusting for age, sex, body mass index, metabolic factors and lifestyle factors revealed no significant association between RANTES and incident coronary events (HR [95% CI] for increasing RANTES tertiles 1.0, 1.03 [0.75-1.42] and 1.11 [0.81-1.54]). None of six CCL5 single nucleotide polymorphisms and no common haplotype showed significant associations with coronary events. Also in the CARDIoGRAM study (>22,000 cases, >60,000 controls), none of these CCL5 SNPs was significantly associated with coronary artery disease. In the prospective Athero-Express biobank study, RANTES plaque levels were measured in 606 atherosclerotic lesions from patients who underwent carotid endarterectomy. RANTES content in atherosclerotic plaques was positively associated with macrophage infiltration and inversely associated with plaque calcification. However, there was no significant association between RANTES content in plaques and risk for coronary events (mean follow-up 2.8±0.8 years). CONCLUSIONS:High RANTES plaque levels were associated with an unstable plaque phenotype. However, the absence of associations between (i) RANTES serum levels, (ii) CCL5 genotypes and (iii) RANTES content in carotid plaques and either coronary artery disease or incident coronary events in our cohorts suggests that RANTES may not be a novel coronary risk biomarker. However, the potential relevance of RANTES levels in platelet-poor plasma needs to be investigated in further studies.

Project description:BackgroundThis study compares inflammation-related biomarkers with established cardiometabolic risk factors in the prediction of incident type 2 diabetes and incident coronary events in a prospective case-cohort study within the population-based MONICA/KORA Augsburg cohort.Methods and findingsAnalyses for type 2 diabetes are based on 436 individuals with and 1410 individuals without incident diabetes. Analyses for coronary events are based on 314 individuals with and 1659 individuals without incident coronary events. Mean follow-up times were almost 11 years. Areas under the receiver-operating characteristic curve (AUC), changes in Akaike's information criterion (ΔAIC), integrated discrimination improvement (IDI) and net reclassification index (NRI) were calculated for different models. A basic model consisting of age, sex and survey predicted type 2 diabetes with an AUC of 0.690. Addition of 13 inflammation-related biomarkers (CRP, IL-6, IL-18, MIF, MCP-1/CCL2, IL-8/CXCL8, IP-10/CXCL10, adiponectin, leptin, RANTES/CCL5, TGF-β1, sE-selectin, sICAM-1; all measured in nonfasting serum) increased the AUC to 0.801, whereas addition of cardiometabolic risk factors (BMI, systolic blood pressure, ratio total/HDL-cholesterol, smoking, alcohol, physical activity, parental diabetes) increased the AUC to 0.803 (ΔAUC [95% CI] 0.111 [0.092-0.149] and 0.113 [0.093-0.149], respectively, compared to the basic model). The combination of all inflammation-related biomarkers and cardiometabolic risk factors yielded a further increase in AUC to 0.847 (ΔAUC [95% CI] 0.044 [0.028-0.066] compared to the cardiometabolic risk model). Corresponding AUCs for incident coronary events were 0.807, 0.825 (ΔAUC [95% CI] 0.018 [0.013-0.038] compared to the basic model), 0.845 (ΔAUC [95% CI] 0.038 [0.028-0.059] compared to the basic model) and 0.851 (ΔAUC [95% CI] 0.006 [0.003-0.021] compared to the cardiometabolic risk model), respectively.ConclusionsInclusion of multiple inflammation-related biomarkers into a basic model and into a model including cardiometabolic risk factors significantly improved the prediction of type 2 diabetes and coronary events, although the improvement was less pronounced for the latter endpoint.

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Genes and lifestyle factors in obesity: results from 12,462 subjects from MONICA/KORA.

Publications

Genes and lifestyle factors in obesity: results from 12,462 subjects from MONICA/KORA.

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