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Prevalence of Vitamin D Insufficiency and Its Determinants among Women Undergoing In Vitro Fertilization Treatment for Infertility in Sweden.

Paulina Maaherra ArmstrongHanna AugustinLinnea BärebringAmra OsmancevicMaria BullarboAnn Thurin-KjellbergPanagiotis Tsiartas
Published in: Nutrients (2023)
There is a lack of research on women with infertility in the northern latitudes, where vitamin D insufficiency is high. Therefore, this study aimed to assess the prevalence and determinants of vitamin D insufficiency (serum 25(OH)D concentration < 50 nmol/L) among women undergoing in vitro fertilization (IVF) treatment. Thus, 265 women scheduled for IVF/intracytoplasmic sperm injection (ICSI) between September 2020 and August 2021 at Sahlgrenska University Hospital in Gothenburg, Sweden, were included. Data on serum 25(OH)D concentration, vitamin D intake, and sun exposure were collected via questionnaires and blood samples. Approximately 27% of the women had 25(OH)D insufficiency, which was associated with longer infertility duration. The likelihood of insufficiency was higher among women from non-Nordic European countries (OR 2.92, 95% CI 1.03-8.26, adjusted p = 0.043), the Middle East (OR 9.90, 95% CI 3.32-29.41, adjusted p < 0.001), and Asia (OR 5.49, 95% CI 1.30-23.25, adjusted p = 0.020) than among women from Nordic countries. Women who did not use vitamin D supplements were more likely to have insufficiency compared with supplement users (OR 3.32, 95% CI 1.55-7.10, adjusted p = 0.002), and those who avoided sun exposure had higher odds of insufficiency compared to those who stayed "in the sun all the time" (OR 3.24, 95% CI 1.22-8.62, adjusted p = 0.018). Women with infertility in northern latitudes and those from non-Nordic countries who avoid sun exposure and do not take vitamin supplements have a higher prevalence of 25(OH)D insufficiency and longer infertility duration.
Keyphrases
  • polycystic ovary syndrome
  • pregnancy outcomes
  • insulin resistance
  • cervical cancer screening
  • risk factors
  • type diabetes
  • machine learning
  • physical activity
  • weight loss
  • electronic health record