scholarly journals Effect of pharmacological interventions on lipid profiles and C‐reactive protein in polycystic ovary syndrome: A systematic review and meta‐analysis

2021 ◽  
Author(s):  
Mohammed A. Abdalla ◽  
Najeeb Shah ◽  
Harshal Deshmukh ◽  
Amirhossein Sahebkar ◽  
Linda Östlundh ◽  
...  
Keyword(s):  
Systematic Review ◽  
Polycystic Ovary Syndrome ◽  
Polycystic Ovary ◽  
Meta Analysis ◽  
Lipid Profiles ◽  
C Reactive Protein ◽  
Pharmacological Interventions ◽  
Ovary Syndrome ◽  
Reactive Protein

scholarly journals Impact of metformin on C-reactive protein levels in women with polycystic ovary syndrome: a meta-analysis

Oncotarget ◽  
2017 ◽  
Vol 8 (21) ◽  
pp. 35425-35434
Author(s):  
Yong Chen ◽  
Meng Li ◽  
Hongli Deng ◽  
Sheying Wang ◽  
Lihua Chen ◽  
...  
Keyword(s):  
Polycystic Ovary Syndrome ◽  
Polycystic Ovary ◽  
Meta Analysis ◽  
C Reactive Protein ◽  
Ovary Syndrome ◽  
Protein Levels ◽  
Reactive Protein

scholarly journals Non‐alcoholic fatty liver disease in premenopausal women with polycystic ovary syndrome: A systematic review and meta‐analysis

JGH Open ◽  
2021 ◽  
Vol 5 (4) ◽  
pp. 434-445
Author(s):  
Mohamed Shengir ◽  
Tianyan Chen ◽  
Elena Guadagno ◽  
Agnihotram V Ramanakumar ◽  
Peter Ghali ◽  
...  
Keyword(s):  
Systematic Review ◽  
Liver Disease ◽  
Polycystic Ovary Syndrome ◽  
Fatty Liver Disease ◽  
Polycystic Ovary ◽  
Meta Analysis ◽  
Premenopausal Women ◽  
Ovary Syndrome ◽  
Alcoholic Fatty Liver ◽  
Alcoholic Fatty Liver Disease

scholarly journals Relationships Between Biochemical Markers of Hyperandrogenism and Metabolic Parameters in Women with Polycystic Ovary Syndrome: A Systematic Review and Meta-Analysis

2019 ◽  
Vol 51 (01) ◽  
pp. 22-34 ◽  
Author(s):  
Mina Amiri ◽  
Fahimeh Tehrani ◽  
Razieh Bidhendi-Yarandi ◽  
Samira Behboudi-Gandevani ◽  
Fereidoun Azizi ◽  
...  
Keyword(s):  
Systematic Review ◽  
Polycystic Ovary Syndrome ◽  
Biochemical Markers ◽  
Polycystic Ovary ◽  
Meta Analysis ◽  
Metabolic Parameters ◽  
High Density Lipoproteins ◽  
Total Testosterone ◽  
Ovary Syndrome ◽  
Increased Risk

AbstractWhile several studies have documented an increased risk of metabolic disorders in patients with polycystic ovary syndrome (PCOS), associations between androgenic and metabolic parameters in these patients are unclear. We aimed to investigate the relationships between biochemical markers of hyperandrogenism (HA) and metabolic parameters in women with PCOS. In this systematic review and meta-analysis, a literature search was performed in the PubMed, Scopus, Google Scholar, ScienceDirect, and Web of Science from 2000 to 2018 for assessing androgenic and metabolic parameters in PCOS patients. To assess the relationships between androgenic and metabolic parameters, meta-regression analysis was used. A total number of 33 studies involving 9905 patients with PCOS were included in this analysis. The associations of total testosterone (tT) with metabolic parameters were not significant; after adjustment for age and BMI, we detected associations of this androgen with low-density lipoproteins cholesterol (LDL-C) (β=0.006; 95% CI: 0.002, 0.01), high-density lipoproteins cholesterol (HDL-C) (β=–0.009; 95% CI: –0.02, –0.001), and systolic blood pressure (SBP) (β=–0.01; 95% CI: –0.03, –0.00). We observed a positive significant association between free testosterone (fT) and fasting insulin (β=0.49; 95% CI: 0.05, 0.91); this association remained significant after adjustment for confounders. We also detected a reverse association between fT and HDL-C (β=–0.41; 95% CI: –0.70, –0.12). There was a positive significant association between A4 and TG (β=0.02; 95% CI: 0.00, 0.04) after adjustment for PCOS diagnosis criteria. We also found significant negative associations between A4, TC, and LDL-C. Dehydroepiandrosterone sulfate (DHEAS) had a positive association with LDL-C (β=0.02; 95% CI: 0.001, 0.03) and a reverse significant association with HDL-C (β=–0.03; 95% CI: –0.06, –0.001). This meta-analysis confirmed the associations of some androgenic and metabolic parameters, indicating that measurement of these parameters may be useful for predicting metabolic risk in PCOS patients.

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