Body fat indices as effective predictors of insulin resistance in obese/non-obese polycystic ovary syndrome women in the Southwest of China View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-03-28

AUTHORS

Xin Huang, Qiuyi Wang, Tingting Liu, Tianjiao Pei, Dong Liu, Huili Zhu, Wei Huang

ABSTRACT

PURPOSE: Insulin resistance (IR) is a common feature of polycystic ovary syndrome (PCOS). Body fat indices can be predictive markers of IR. This study is aimed to predict IR in Chinese women with PCOS of different body types based on body fat indices. METHODS: A total of 723 women diagnosed with PCOS according to Rotterdam criteria were recruited in this study and were further divided into two groups based on their BMI. All participants underwent physical examinations and ultrasound; and blood was collected from them on the days 3-5 of the menstrual cycle. Their BMI, waist-to-hip ratio (WHR), waist-to-height ratio (WHtR), lipid accumulation product (LAP) index, visceral adiposity index (VAI), and the homeostasis model assessment index of insulin resistance (HOMA-IR) were calculated. The correlations between body fat indices and HOMA-IR and receiver operating characteristic (ROC) curves were evaluated. RESULTS: In normal weight group (BMI < 24, n = 333), VAI (best cut-off value: 1.681, area under curve (AUC) = 0.754, P < 0.01) and LAP index (best cut-off value: 18.53, AUC = 0.734, P < 0.001) were the reliable indicators of IR based on HOMA-IR ≥ 2.77, while in overweight/obese group (BMI ≥ 24, n = 390), the BMI, WC, WHtR and LAP index had a significant correlation with HOMA-IR. The representative markers to assess IR were BMI (best cut-off value: 26.43, AUC = 0.644, P = 0.001) and WHtR (best cut-off value: 0.544, AUC = 0.604, P = 0.021). CONCLUSIONS: Body fat indices are predictive markers of IR in Chinese PCOS women, especially in those with normal weight. More... »

PAGES

1-5

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s12020-019-01912-1

DOI

http://dx.doi.org/10.1007/s12020-019-01912-1

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1113050052

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/30924083


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