| Literature DB >> 29629043 |
Mahmoud A Alfaqih1, Yousef S Khader1, Ahmed N Al-Dwairi1, Abdallah Alzoubi1, Othman Al-Shboul1, Amanie Hatim1.
Abstract
BACKGROUND: Polycystic ovary syndrome (PCOS) is a common reproductive disorder. Obesity, which is linked with lower adiponectin levels, increases a woman's risk of developing PCOS; however, the association between adiponectin and PCOS is controversial. Adiponectin levels could be affected by single nucleotide polymorphisms (SNPs) in the ADIPOQ gene. This study aimed to test the relationship between serum adiponectin and PCOS in Jordan and the association between the rs2241766, rs1501299, and rs266729 SNPs in the ADIPOQ gene and PCOS.Entities:
Keywords: Adiponectin; Insulin Resistance; Polycystic Ovarian Syndrome; Single Nucleotide Polymorphism
Year: 2018 PMID: 29629043 PMCID: PMC5876045 DOI: 10.4082/kjfm.2018.39.2.108
Source DB: PubMed Journal: Korean J Fam Med ISSN: 2005-6443
Location and genotyping strategy of ADIPOQ SNP
SNP, single nucleotide polymorphism.
Participants' demographic characteristics
Values are presented as mean±standard deviation or %. P-values were calculated by Student t-test.
Figure 1Adiponectin levels in normally menstruating or PCOS women. Values are presented as mean±standard error of the mean. Adiponectin levels were significantly lower in PCOS compared to normally menstruating women. PCOS, polycystic ovarian syndrome. **Indicates significant differences between the two groups (P<0.01).
Figure 2Schematic representation of the ADIPOQ gene structure with the relative locations of the rs266729, rs1501299, and rs2241766 single nucleotide polymorphisms.
Genotype frequencies of rs266729, rs1501299, and rs2241766 SNPs in normally menstruating and PCOS women
Values are presented as % (number). P-values were calculated by Pearson's chisquare test.
SNP, single nucleotide polymorphism; PCOS, polycystic ovarian syndrome.
Multivariate analysis of study participants with BMI, age, adiponectin, rs266729, rs1501299, and rs2241766 as variables
P-values were calculated by multinomial logistic regression.
BMI, body mass index.