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Int J Gynaecol Obstet ; 166(1): 250-257, 2024 Jul.
Article in English | MEDLINE | ID: mdl-38268339

ABSTRACT

OBJECTIVE: To investigate the impacts of fat distribution on assisted reproductive outcomes in infertile women. METHODS: The study randomly recruited 576 infertile women who underwent assisted reproductive technology treatment at the Reproductive Medicine Center of the First Affiliated Hospital of Anhui Medical University between July and October 2022. Questionnaires and body composition measurements were administered to assess baseline information and fat distribution. The numbers of oocytes, zygotes presenting with two pronuclei (2PN), and available embryos were tracked at the end of the cycle. Multifactorial logistic regression models and restricted cubic spline (RCS) curve models were used to explore the relationships between fat distribution and reproductive outcomes while controlling for confounding factors. RESULTS: The study found that the participants had a mean age of 30.82 years. The analysis showed that there was a significant difference between the amount of leg body fat mass (LBFM) and the distribution of reproductive outcomes. However, there was no significant correlation between the level of visceral fat and reproductive outcomes. After taking confounding factors into account, the multifactorial regression analysis showed that the total body fat mass and the number of oocytes (odds ratio (OR) 0.92, 95% confidence interval (CI) 0.84-0.99), 2PN (OR 0.92, 95% CI 0.84-0.99), and embryos available for transfer (OR 0.90, 95% CI 0.82-0.99) were negatively correlated. RCS modeling revealed a linear dose-response relationship between LBFM and assisted reproductive outcomes. CONCLUSION: Fat distribution varies among infertile women, and higher amounts of fat are associated with poorer assisted reproductive outcomes.


Subject(s)
Body Fat Distribution , Infertility, Female , Reproductive Techniques, Assisted , Humans , Female , Adult , Infertility, Female/therapy , Prospective Studies , Pregnancy , Pregnancy Rate , Logistic Models , China
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