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1.
Korean Journal of Family Medicine ; : 108-113, 2018.
Article in English | WPRIM | ID: wpr-713400

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. METHODS: One hundred and fifty-four women with PCOS and 149 age- and body mass index–matched normally menstruating controls were recruited. Serum adiponectin levels were measured using enzyme-linked immunosorbent assay. Genotyping was performed using polymerase chain reaction–restriction fragment length polymorphism analysis. RESULTS: Serum adiponectin levels were significantly lower (P=0.0064) in PCOS women and rs1501299 (+276 G/T) genotype distributions were significantly different (P=0.01) between them and normally menstruating women. Multivariate analysis revealed that adiponectin levels remained significantly lower in PCOS women (P=0.001; odds ratio [OR], 0.9; 95% confidence interval [CI], 0.84–0.96). The GT genotype of rs1501299 increased the risk of PCOS (P < 0.001; OR, 5.46; 95% CI, 2.42–12.33) and increased the risk of PCOS by three-fold (P < 0.001; OR, 3.00; 95% CI, 1.36–6.60) relative to the TT genotype. The GG genotype increased the risk of PCOS as well (P < 0.001; OR, 3:00; 95% CI, 1.36–6.60). CONCLUSION: PCOS is associated with lower serum adiponectin levels independent of age and body mass index. The T allele of the rs1501299 (+276 G/T) SNP of the ADIPOQ gene protects against PCOS.


Subject(s)
Female , Humans , Adiponectin , Alleles , Body Mass Index , Enzyme-Linked Immunosorbent Assay , Genotype , Insulin Resistance , Jordan , Multivariate Analysis , Obesity , Odds Ratio , Polycystic Ovary Syndrome , Polymorphism, Single Nucleotide
2.
Korean Journal of Family Medicine ; : 137-146, 2018.
Article in English | WPRIM | ID: wpr-714514

ABSTRACT

Diabetes is a major public health problem worldwide. Depression is a serious mental condition that decreases mental and physical functioning and reduces the quality of life. Several lines of evidence suggest a bidirectional relationship between diabetes and depression: diabetes patients are twice as likely to experience depression than nondiabetic individuals. In contrast, depression increases the risk of diabetes and interferes with its daily self-management. Diabetes patients with depression have poor glycemic control, reduced quality of life, and an increased risk of diabetes complications, consequently having an increased mortality rate. Conflicting evidence exists on the potential role of factors that may account for or modulate the relationship between diabetes and depression. Therefore, this review aims to highlight the most notable body of literature that dissects the various facets of the bidirectional relationship between diabetes and depression. A focused discussion of the proposed mechanisms underlying this relationship is also provided. We systematically reviewed the relevant literature in the PubMed database, using the keywords “Diabetes AND Depression”. After exclusion of duplicate and irrelevant material, literature eligible for inclusion in this review was based on meta-analysis studies, clinical trials with large sample sizes (n ≥1,000), randomized clinical trials, and comprehensive national and cross-country clinical studies. The evidence we present in this review supports the pressing need for long, outcome-oriented, randomized clinical trials to determine whether the identification and treatment of patients with these comorbid conditions will improve their medical outcomes and quality of life.


Subject(s)
Humans , Depression , Diabetes Complications , Diabetes Mellitus , Mortality , Public Health , Quality of Life , Sample Size , Self Care
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