Login / Signup

Predictors of Serum Per- and Polyfluoroalkyl Substances Concentrations among U.S. Couples Attending a Fertility Clinic.

Yu ZhangQi SunVicente MustielesLeah MartinYang SunZainab BibiNicole TorresAyanna Coburn-SandersonOlivia FirstIrene SouterJohn C PetrozzaJulianne C BotelhoAntonia M CalafatYi-Xin WangCarmen Messerlian
Published in: Environmental science & technology (2024)
Previous studies have examined the predictors of PFAS concentrations among pregnant women and children. However, no study has explored the predictors of preconception PFAS concentrations among couples in the United States. This study included 572 females and 279 males (249 couples) who attended a U.S. fertility clinic between 2005 and 2019. Questionnaire information on demographics, reproductive history, and lifestyles and serum samples quantified for PFAS concentrations were collected at study enrollment. We examined the PFAS distribution and correlation within couples. We used Ridge regressions to predict the serum concentration of each PFAS in females and males using data of (1) socio-demographic and reproductive history, (2) diet, (3) behavioral factors, and (4) all factors included in (1) to (3) after accounting for temporal exposure trends. We used general linear models for univariate association of each factor with the PFAS concentration. We found moderate to high correlations for PFAS concentrations within couples. Among all examined factors, diet explained more of the variation in PFAS concentrations (1-48%), while behavioral factors explained the least (0-4%). Individuals reporting White race, with a higher body mass index, and nulliparous women had higher PFAS concentrations than others. Fish and shellfish consumption was positively associated with PFAS concentrations among both females and males, while intake of beans (females), peas (male), kale (females), and tortilla (both) was inversely associated with PFAS concentrations. Our findings provide important data for identifying sources of couples' PFAS exposure and informing interventions to reduce PFAS exposure in the preconception period.
Keyphrases
  • primary care
  • type diabetes
  • healthcare
  • pregnant women
  • body mass index
  • social media
  • electronic health record
  • cross sectional
  • data analysis
  • big data
  • insulin resistance
  • health insurance