Abstract:
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Metabolic syndrome is a complex, polygenic condition comprised of a cluster of risk factors that can result in chronic diseases including cardiovascular diseases, cancer, arthritis, and Type II diabetes. Risk factors for metabolic syndrome include abnormal lipid levels, hypertension, insulin resistance, abdominal obesity, and genetic susceptibility. Genes that may predispose individuals to metabolic syndrome are not fully identified. Osteopontin (OPN) is an inflammatory cytokine, ubiquitous in body tissues, that regulates tissue repair and energy metabolism.
In previous studies, OPN has been associated with similar chronic diseases as metabolic syndrome as well as insulin resistance. However, the specific role of OPN as a biomarker of metabolic syndrome in the inflammatory pathway is unknown.
Thus, a population-based case-control genetic association study will be conducted to evaluate OPN as a biomarker of genetic susceptibility for metabolic syndrome. Study population data will be collected from genotyped subjects (n = 155,318); the Foundation Jean Dausset-Centre d'Etude du Polymorphisme Humain (CEPH) database (n = 928) in addition to the National Heart, Lung, and Blood Institute (NHLBI) database (n = 154,390). Cases (those with genotypes for obesity and insulin resistance) and controls (those without those genotypes) will be chosen by random selection from the same population. Additional data will be obtained on single-nucleotide polymorphisms (SNP) and minor allele frequencies (MAF).
To address missing data concerns, a simulation procedure will be implemented to resample data without replacement. A case-control test of association will evaluate differences between biomarkers and disease locus.
Structural equations modeling (SEM) will analyze interactions between OPN and other biomarkers of inflammation and obesity to identify common pathways, genes, and genotypes for obesity and insulin resistance. SEM will be used to perform all association studies using SAS version 13.2 PROC CALIS will estimate model parameters, variable interactions, model fit, multicollinearity, and maximum likelihood estimators (MLE) to account for missing values for the model fit analyses. PROC FACTOR will estimate the latent factors and the observed variable relationships.
These analyses of gene-genotype studies and genotype-phenotype studies of OPN, biomarkers, and environmental variables will confirm OPN as a genetic susceptibility marker in metabolic syndrome. Further research is needed to assess OPN so that therapeutic interventions can be developed.
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