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Rivista sulle Malattie del Sistema Endocrino

Indexed/Abstracted in: EMBASE, PubMed/MEDLINE, Science Citation Index Expanded (SciSearch), Scopus
Impact Factor 1,118

Periodicità: Trimestrale

ISSN 0391-1977

Online ISSN 1827-1634


Minerva Endocrinologica 2016 Marzo;41(1):57-69


The effect of dietary carbohydrates in women with polycystic ovary syndrome: a systematic review

Johanna M. FRARY 1, Kamilla P. BJERRE 1, Dorte GLINTBORG 2, Penille RAVN 1

1 Department of Gynecology and Obstetrics, Odense University Hospital, Denmark; 2 Department of Endocrinology, Odense University Hospital, Denmark

Weight loss improves ovulation, testosterone levels and insulin resistance in women with polycystic ovarian syndrome (PCOS), but the optimal diet composition is disputed. A diet low in carbohydrates (LCD) may be superior to a standard diet in terms of improving fertility, endocrine/metabolic parameters, weight loss and satiety in women with PCOS. The aim of the present study was to review the literature on the effects of LCD in PCOS, and to summarize the findings into evidence-based guidelines. A literature review based on publications in PubMed and Cochrane was carried out. The outcomes during LCD were compared to other types of diet interventions and exercise. Studies including insulin-sensitizing agents, such as metformin, were excluded. The outcomes were fertility, endocrine/metabolic parameters, weight loss and satiety. The review resulted in fifteen articles. Fertility parameters, endocrine hormones, metabolic outcomes and satiety hormones were not further improved during LCD compared to a standard diet. LCD had a 1-5% significant additional effect on weight loss compared to a standard diet. Energy restriction and weight loss in PCOS improve ovulation rates, conception, hyperandrogenemia, glucose- and insulin levels, insulin resistance and satiety hormones, whereas diet composition is of less importance. A LCD has an additional effect to caloric restriction in terms of weight loss. Conclusions are summarized as evidence-based recommendations.

lingua: Inglese


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