Genetic architecture of the APM1 gene and its influence on adiponectin plasma levels and parameters of the metabolic syndrome in 1,727 healthy Caucasians

Copyright: Copyright 2008 Elsevier B.V., All rights reserved.The associations of the adiponectin (APM1) gene with parameters of the metabolic syndrome are inconsistent. We performed a systematic investigation based on fine-mapped single nucleotide polymorphisms (SNPs) highlighting the genetic architecture and their role in modulating adiponectin plasma concentrations in a particularly healthy population of 1,727 Caucasians avoiding secondary effects from disease processes. Genotyping 53 SNPs (average spacing of 0.7 kb) in the APM1 gene region in 81 Caucasians revealed a two-block linkage disequilibrium (LD) structure and enabled comprehensive tag SNP selection. We found particularly strong associations with adiponectin concentrations for 11 of the 15 tag SNPs in the 1,727 subjects (five P values <0.0001). Haplotype analysis provided a thorough differentiation of adiponectin concentrations with 9 of 17 haplotypes showing significant associations (three P values <0.0001). No significant association was found for any SNP with the parameters of the metabolic syndrome. We observed a two-block LD structure of APM1 pointing toward at least two independent association signals, one including the promoter SNPs and a second spanning the relevant exons. Our data on a large number of healthy subjects suggest a clear modulation of adiponectin concentrations by variants of APM1, which are not merely a concomitant effect in the course of type 2 diabetes or coronary artery disease.publishersversionPeer reviewe

Fetuin-A Induces Cytokine Expression and Suppresses Adiponectin Production

BACKGROUND: The secreted liver protein fetuin-A (AHSG) is up-regulated in hepatic steatosis and the metabolic syndrome. These states are strongly associated with low-grade inflammation and hypoadiponectinemia. We, therefore, hypothesized that fetuin-A may play a role in the regulation of cytokine expression, the modulation of adipose tissue expression and plasma concentration of the insulin-sensitizing and atheroprotective adipokine adiponectin. METHODOLOGY AND PRINCIPAL FINDINGS: Human monocytic THP1 cells and human in vitro differenttiated adipocytes as well as C57BL/6 mice were treated with fetuin-A. mRNA expression of the genes encoding inflammatory cytokines and the adipokine adiponectin (ADIPOQ) was assessed by real-time RT-PCR. In 122 subjects, plasma levels of fetuin-A, adiponectin and, in a subgroup, the multimeric forms of adiponectin were determined. Fetuin-A treatment induced TNF and IL1B mRNA expression in THP1 cells (p<0.05). Treatment of mice with fetuin-A, analogously, resulted in a marked increase in adipose tissue Tnf mRNA as well as Il6 expression (27- and 174-fold, respectively). These effects were accompanied by a decrease in adipose tissue Adipoq mRNA expression and lower circulating adiponectin levels (p<0.05, both). Furthermore, fetuin-A repressed ADIPOQ mRNA expression of human in vitro differentiated adipocytes (p<0.02) and induced inflammatory cytokine expression. In humans in plasma, fetuin-A correlated positively with high-sensitivity C-reactive protein, a marker of subclinical inflammation (r = 0.26, p = 0.01), and negatively with total- (r = -0.28, p = 0.02) and, particularly, high molecular weight adiponectin (r = -0.36, p = 0.01). CONCLUSIONS AND SIGNIFICANCE: We provide novel evidence that the secreted liver protein fetuin-A induces low-grade inflammation and represses adiponectin production in animals and in humans. These data suggest an important role of fatty liver in the pathophysiology of insulin resistance and atherosclerosis

Mitochondrial DNA haplogroup T is associated with coronary artery disease and diabetic retinopathy: a case control study

<p>Abstract</p> <p>Background</p> <p>There is strong and consistent evidence that oxidative stress is crucially involved in the development of atherosclerotic vascular disease. Overproduction of reactive oxygen species (ROS) in mitochondria is an unifying mechanism that underlies micro- and macrovascular atherosclerotic disease. Given the central role of mitochondria in energy and ROS production, mitochondrial DNA (mtDNA) is an obvious candidate for genetic susceptibility studies on atherosclerotic processes. We therefore examined the association between mtDNA haplogroups and coronary artery disease (CAD) as well as diabetic retinopathy.</p> <p>Methods</p> <p>This study of Middle European Caucasians included patients with angiographically documented CAD (n = 487), subjects with type 2 diabetes mellitus with (n = 149) or without (n = 78) diabetic retinopathy and control subjects without clinical manifestations of atherosclerotic disease (n = 1527). MtDNA haplotyping was performed using multiplex PCR and subsequent multiplex primer extension analysis for determination of the major European haplogroups. Haplogroup frequencies of patients were compared to those of control subjects without clinical manifestations of atherosclerotic disease.</p> <p>Results</p> <p>Haplogroup T was significantly more prevalent among patients with CAD than among control subjects (14.8% vs 8.3%; p = 0.002). In patients with type 2 diabetes, the presence of diabetic retinopathy was also significantly associated with a higher prevalence of haplogroup T (12.1% vs 5.1%; p = 0.046).</p> <p>Conclusion</p> <p>Our data indicate that the mtDNA haplogroup T is associated with CAD and diabetic retinopathy in Middle European Caucasian populations.</p

Automatic identification of variables in epidemiological datasets using logic regression

textabstractBackground: For an individual participant data (IPD) meta-analysis, multiple datasets must be transformed in a consistent format, e.g. using uniform variable names. When large numbers of datasets have to be processed, this can be a time-consuming and error-prone task. Automated or semi-automated identification of variables can help to reduce the workload and improve the data quality. For semi-automation high sensitivity in the recognition of matching variables is particularly important, because it allows creating software which for a target variable presents a choice of source variables, from which a user can choose the matching one, with only low risk of having missed a correct source variable. Methods: For each variable in a set of target variables, a number of simple rules were manually created. With logic regression, an optimal Boolean combination of these rules was searched for every target variable, using a random subset of a large database of epidemiological and clinical cohort data (construction subset). In a second subset of this database (validation subset), this optimal combination rules were validated. Results: In the construction sample, 41 target variables were allocated on average with a positive predictive value (PPV) of 34%, and a negative predictive value (NPV) of 95%. In the validation sample, PPV was 33%, whereas NPV remained at 94%. In the construction sample, PPV was 50% or less in 63% of all variables, in the validation sample in 71% of all variables. Conclusions: We demonstrated that the application of logic regression in a complex data management task in large epidemiological IPD meta-analyses is feasible. However, the performance of the algorithm is poor, which may require backup strategies