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Genetic Polymorphisms Associated with the Neutrophil⁻Lymphocyte Ratio and Their Clinical Implications for Metabolic Risk Factors.
Journal of Clinical Medicine 2018 August 9
BACKGROUND: The neutrophil⁻lymphocyte ratio (NLR) is a valuable prognostic or predictive biomarker in various diseases, but the genetic factors that underlie the NLR have not been studied. We attempted to investigate polymorphisms related to NLR phenotype and analyze their ability to predict metabolic risks.
METHODS: A genome-wide association study was performed with log-transformed NLR using an Affymetrix Axiom™ KORV1.1-96 Array. Regression models for metabolic risk status were designed using the identified significant single-nucleotide polymorphisms (SNPs).
RESULTS: We identified four SNPs near the TMEM116 , NAA25 , and PTPN11 genes that were associated with the NLR. The top SNP associated with the log-transformed NLR was rs76181728 in TMEM116 . A case⁻control study was performed to analyze the metabolic risks associated with each SNP after adjusting for age, sex, and body mass index (BMI). Three SNPs displayed significant odds ratios (ORs) for increased blood pressure and increased waist circumference. In the regression model for metabolic syndrome, rs76181728 showed a significant association (OR = 1.465, 95% confidence interval (CI) = 1.091⁻1.969, P = 0.011) after adjustment for the NLR phenotype.
CONCLUSIONS: We identified four novel SNPs that are associated with the NLR in healthy Koreans. SNPs in relevant genes might therefore serve as biomarkers for metabolic risks.
METHODS: A genome-wide association study was performed with log-transformed NLR using an Affymetrix Axiom™ KORV1.1-96 Array. Regression models for metabolic risk status were designed using the identified significant single-nucleotide polymorphisms (SNPs).
RESULTS: We identified four SNPs near the TMEM116 , NAA25 , and PTPN11 genes that were associated with the NLR. The top SNP associated with the log-transformed NLR was rs76181728 in TMEM116 . A case⁻control study was performed to analyze the metabolic risks associated with each SNP after adjusting for age, sex, and body mass index (BMI). Three SNPs displayed significant odds ratios (ORs) for increased blood pressure and increased waist circumference. In the regression model for metabolic syndrome, rs76181728 showed a significant association (OR = 1.465, 95% confidence interval (CI) = 1.091⁻1.969, P = 0.011) after adjustment for the NLR phenotype.
CONCLUSIONS: We identified four novel SNPs that are associated with the NLR in healthy Koreans. SNPs in relevant genes might therefore serve as biomarkers for metabolic risks.
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