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Query: UMLS:C0948265 (metabolic syndrome)
 24,271 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

One of the great strengths of the Framingham Heart Study data, provided for the Genetic Analysis Workshop 13, is the long-term survey of phenotypic data. We used this unique data to create new phenotypes representing the pattern of longitudinal change of the provided phenotypes, especially systolic blood pressure and body weight. We performed a linear regression of body weight and systolic blood pressure on age and took the slopes as new phenotypes for quantitative trait linkage analysis using the SOLAR package. There was no evidence for heritability of systolic blood pressure change. Heritability was estimated as 0.15 for adult life "body weight change", measured as the regression slope, and "body weight gain" (including only individuals with a positive regression slope), and as 0.22 for body weight "change up to 50" (regression slope of weight on age up to an age of 50). With multipoint analysis, two regions on the long arm of chromosome 8 showed the highest LOD scores of 1.6 at 152 cM for "body weight change" and of >1.9 around location 102 cM for "body weight gain" and "change up to 50". The latter two LOD scores almost reach the threshold for suggestive linkage. We conclude that the chromosome 8 region may harbor a gene acting on long-term body weight regulation, thereby contributing to the development of the metabolic syndrome.
BMC Genet 2003 Dec 31
PMID:Quantitative trait linkage analysis of longitudinal change in body weight. 1497 75

There are no well accepted criteria for the diagnosis of the metabolic syndrome. However, the metabolic syndrome is identified clinically by the presence of three or more of these five variables: larger waist circumference, higher triglyceride levels, lower HDL-cholesterol concentrations, hypertension, and impaired fasting glucose. We use sets of two or three variables, which are available in the Framingham Heart Study data set, to localize genes responsible for this syndrome using multivariate quantitative linkage analysis. This analysis demonstrates the applicability of using multivariate linkage analysis and how its use increases the power to detect linkage when genes are involved in the same disease mechanism.
BMC Genet 2003 Dec 31
PMID:Localization of genes involved in the metabolic syndrome using multivariate linkage analysis. 1497 25

To estimate the prevalence of metabolic syndrome (MS) in a population receiving attention in primary care centers (PCC) we selected a random cohort of ostensibly normal subjects from the registers of 5 basic-health area (BHA) PCC. Diagnosis of MS was with the WHO, NCEP and IDF criteria. Variables recorded were: socio-demographic data, CVD risk factors including lipids, obesity, diabetes, blood pressure and smoking habit and a glucose tolerance test outcome. Of the 720 individuals selected (age 60.3 +/- 11.5 years), 431 were female, 352 hypertensive, 142 diabetic, 233 pre-diabetic, 285 obese, 209 dyslipemic and 106 smokers. CVD risk according to the Framingham and REGICOR calculation was 13.8 +/- 10% and 8.8 +/- 9.8%, respectively. Using the WHO, NCEP and IDF criteria, MS was diagnosed in 166, 210 and 252 subjects, respectively and the relative risk of CVD complications in MS subjects was 2.56. Logistic regression analysis indicated that the MS components (WHO set), the MS components (IDF set) and the female gender had an increased odds ratio for CVD of 3.48 (95CI%: 2.26-5.37), 2.28 (95%CI: 1.84-4.90) and 2.26 (95%CI: 1.48-3.47), respectively. We conclude that MS and concomitant CVD risk is high in ostensibly normal population attending primary care clinics, and this would necessarily impinge on resource allocation in primary care.
BMC Public Health 2008 Jul 22
PMID:Metabolic syndrome as a cardiovascular disease risk factor: patients evaluated in primary care. 1864 83

We investigated the association of metabolic syndrome (MetS) with a 500 k and a 50 k single-nucleotide polymorphism (SNP) gene chip in the Framingham Heart Study. We cross-sectionally evaluated the MetS longitudinal trends. Data analyzed were from the Offspring Cohort (four exams: first (n = 2,441), third (n = 2,185), fifth (n = 2,308), and seventh (n = 2,328)) and the Generation 3 Cohort (one exam: the first exam (n = 3,997)). The prevalence of MetS was determined using the National Cholesterol Education Program Adult Treatment Panel III diagnostic criteria, modified with a newly developed correction for medication use. The association test between an SNP and MetS was performed with a generalized estimating equations method under the additive genetic model. Multiple-testing corrections were also performed. The prevalence of MetS in the offspring cohort increased from one visit to the next, and reached the highest point by the seventh exam comparable with the prevalence reported for the general US population. The pattern of the MetS prevalence over time also reflected itself in the association tests, in which the highest significances were seen in the fifth and seventh exams. The association tests showed that SNPs within genes PRDM16, CETP, PTHB1, PAPPA, and FBN3, and also some SNPs not in genes were significant or close to significance at the genome-wide thresholds. These findings are important in terms of eventually identifying with the causal loci for MetS.
BMC Proc 2009 Dec 15
PMID:Longitudinal trends in the association of metabolic syndrome with 550 k single-nucleotide polymorphisms in the Framingham Heart Study. 2001 81

Metabolic syndrome, by definition, is the manifestation of multiple, correlated metabolic impairments. It is known to have both strong environmental and genetic contributions. However, isolating genetic variants predisposing to such a complex trait has limitations. Using pedigree data, when available, may well lead to increased ability to detect variants associated with such complex traits. The ability to incorporate multiple correlated traits into a joint analysis may also allow increased detection of associated genes. Therefore, to demonstrate the utility of both univariate and multivariate family-based association analysis and to identify possible genetic variants associated with metabolic syndrome, we performed a scan of the Affymetrix 50 k Human Gene Panel data using 1) each of the traits comprising metabolic syndrome: triglycerides, high-density lipoprotein, systolic blood pressure, diastolic blood pressure, blood glucose, and body mass index, and 2) a composite trait including all of the above, jointly. Two single-nucleotide polymorphisms within the cholesterol ester transfer protein (CETP) gene remained significant even after correcting for multiple testing in both the univariate (p < 5 x 10-7) and multivariate (p < 5 x 10-9) association analysis. Three genes met significance for multiple traits after correction for multiple testing in the univariate analysis, while five genes remained significant in the multivariate association. We conclude that while both univariate and multivariate family-based association analysis can identify genes of interest, our multivariate approach is less affected by multiple testing correction and yields more significant results.
BMC Proc 2009 Dec 15
PMID:Multivariate association analysis of the components of metabolic syndrome from the Framingham Heart Study. 2001 34

We used data reduction and clustering methods to identify five phenotypically homogeneous groups of study participants with similar profiles for cardiovascular disease risk factors. We constructed both qualitative (binary subgroup membership) and quantitative traits (probability of subgroup membership) for each individual. The Cluster 1 comprised individuals who were generally healthy and had some history of smoking. Cluster 2 was dropped from the analyses due to the preponderance of missing data. Cluster 3 was used as the control group, healthy non-smokers. Members of Cluster 4 had features of the metabolic syndrome and were generally not as obese as Cluster 5. Obesity was the hallmark of Cluster 5, the members of which also had some features of the metabolic syndrome.We then examined the genetic associations with both qualitative and quantitative representations of these empirically derived traits. Genetic analyses of the qualitative traits were conducted, comparing each of the affected groups with the unaffected cluster alone and, to increase statistical power, the unaffected group and healthy smokers combined. One single-nucleotide polymorphism on chromosome 4 met a conservative genome-wide significance level, but the effect was muted when we accounted for population stratification. The results for the quantitative traits were similar, with a small number of genome-wide significant findings muted by control for admixture. The directional findings will provide the basis for hypothesis generation for syndromes such as the metabolic syndrome and obesity.
BMC Proc 2009 Dec 15
PMID:Genome-wide association study for empirically derived metabolic phenotypes in the Framingham Heart Study offspring cohort. 2001 46

Metabolic syndrome (MetS) is a complex disorder defined by a cluster of interconnected factors that increase the risk of cardiovascular atherosclerotic diseases and diabetes mellitus type 2. Currently, several different definitions of MetS exist, causing substantial confusion as to whether they identify the same individuals or represent a surrogate of risk factors. Recently, a number of other factors besides those traditionally used to define MetS that are also linked to the syndrome have been identified. In this review, we critically consider existing definitions and evolving information, and conclude that there is still a need to develop uniform criteria to define MetS, so as to enable comparisons between different studies and to better identify patients at risk. As the application of the MetS model has not been fully validated in children and adolescents as yet, and because of its alarmingly increasing prevalence in this population, we suggest that diagnosis, prevention and treatment in this age group should better focus on established risk factors rather than the diagnosis of MetS.
BMC Med 2011 May 05
PMID:Metabolic syndrome: definitions and controversies. 2154 44

The prevalence of obesity worldwide has dramatically increased during the last three decades. With obesity comes a variety of adverse health outcomes which are grouped under the umbrella of metabolic syndrome. The liver in particular seems to be significantly impacted by fat deposition in the presence of obesity. In this article we discuss several liver conditions which are directly affected by overweight and obese status, including non-alcoholic fatty liver disease, chronic infection with hepatitis C virus and post-liver transplant status. The deleterious effects of obesity on liver disease and overall health can be significantly impacted by a culture that fosters sustained nutritional improvement and regular physical activity. Here we summarize the current evidence supporting non-pharmacological, lifestyle interventions that lead to weight reduction, improved physical activity and better nutrition as part of the management and treatment of these liver conditions.
BMC Med 2011 Jun 06
PMID:The role of lifestyle changes in the management of chronic liver disease. 2164 44

Insulin resistance is one of the major aggravating factors for metabolic syndrome. There are many methods available for estimation of insulin resistance which range from complex techniques down to simple indices. For all methods of assessing insulin resistance it is essential that their validity and reliability is established before using them as investigations. The reference techniques of hyperinsulinaemic euglycaemic clamp and its alternative the frequently sampled intravenous glucose tolerance test are the most reliable methods available for estimating insulin resistance. However, many simple methods, from which indices can be derived, have been assessed and validated e.g. homeostasis model assessment (HOMA), quantitative insulin sensitivity check index (QUICKI). Given the increasing number of simple indices of IR it may be difficult for clinicians and researchers to select the most appropriate index for their studies. This review therefore provides guidelines and advices which must be considered before proceeding with a study.
BMC Med Res Methodol 2011 Nov 23
PMID:Selection of the appropriate method for the assessment of insulin resistance. 2211 29

In patients with metabolic syndrome, body iron overload exacerbates insulin resistance, impairment of glucose metabolism, endothelium dysfunction and coronary artery responses. Conversely, iron depletion is effective to ameliorate glucose metabolism and dysfunctional endothelium. Most of its effectiveness seems to occur through the amelioration of systemic and hepatic insulin resistance. In a study published by BMC Medicine, Michalsen et al. demonstrated a dramatic improvement of blood pressure, serum glucose and lipids after removing 550 to 800 ml of blood in subjects with metabolic syndrome. This effect was apparently independent of changes in insulin resistance, in contrast to previous cross-sectional and cohort studies investigating the association between iron overload, insulin resistance and cardiovascular disease. Despite drawbacks in the study design, its findings may lead the way to investigations aimed at exploring iron-dependent regulatory mechanisms of vascular tone in healthy individuals and patients with metabolic disease, thus providing a rationale for novel preventive and therapeutic strategies to counteract hypertension. Please see related article: http://www.biomedcentral.com/1741-7015/10/54.
BMC Med 2012 May 30
PMID:Back to past leeches: repeated phlebotomies and cardiovascular risk. 2264 17


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