N-acetylcysteine for polycystic ovary syndrome: a systematic review and meta-analysis of randomized controlled clinical trials.
Divyesh ThakkerAmit RavalIsha PatelRama WaliaPublished in: Obstetrics and gynecology international (2015)
Objective. To review the benefits and harms of N-acetylcysteine (NAC) in women with polycystic ovary syndrome (PCOS). Method. Literature search was conducted using the bibliographic databases, MEDLINE (Ovid), CINAHL, EMBASE, Scopus, PsyInfo, and PROQUEST (from inception to September 2013) for the studies on women with PCOS receiving NAC. Results. Eight studies with a total of 910 women with PCOS were randomized to NAC or other treatments/placebo. There were high risk of selection, performance, and attrition bias in two studies and high risk of reporting bias in four studies. Women with NAC had higher odds of having a live birth, getting pregnant, and ovulation as compared to placebo. However, women with NAC were less likely to have pregnancy or ovulation as compared to metformin. There was no significant difference in rates of the miscarriage, menstrual regulation, acne, hirsutism, and adverse events, or change in body mass index, testosterone, and insulin levels with NAC as compared to placebo. Conclusions. NAC showed significant improvement in pregnancy and ovulation rate as compared to placebo. The findings need further confirmation in well-designed randomized controlled trials to examine clinical outcomes such as live birth rate in longer follow-up periods. Systematic review registration number is CRD42012001902.
Keyphrases
- polycystic ovary syndrome
- transcription factor
- double blind
- phase iii
- insulin resistance
- systematic review
- clinical trial
- placebo controlled
- genome wide analysis
- body mass index
- open label
- case control
- randomized controlled trial
- pregnancy outcomes
- phase ii
- type diabetes
- meta analyses
- pregnant women
- physical activity
- skeletal muscle
- preterm birth
- emergency department
- study protocol
- adipose tissue
- deep learning
- machine learning
- adverse drug