4 Urinary Fluoride Levels and Metal Co-Exposures Among Pregnant Women in Los Angeles, California
Notice bibliographique
Résumé
Objective: Approximately 73% of the United States (US) population on public water systems receives fluoridated water for tooth decay prevention. In Los Angeles (LA) County, 89% of cities are at least partially fluoridated. Drinking water is the primary source of fluoride exposure in the US. Studies conducted in Mexico and Canada suggest that prenatal fluoride exposure, at levels relevant to the US, may contribute to poorer neurodevelopment in offspring. However, data on biomarkers and patterns of fluoride exposure among US pregnant women are scarce. This study examined urinary fluoride levels according to sociodemographic factors and metal co-exposures among pregnant women in the US. Participants and Methods: Participants were from the Maternal and Developmental Risks from Environmental and Social Stressors (MADRES) cohort based in Los Angeles, California. There were 293 and 490 women with urine fluoride measured during the first and third trimesters of pregnancy, respectively. An intra-class correlation coefficient examined consistency of specific gravity-adjusted maternal urinary fluoride (MUFsg) between trimesters. Kruskal-Wallis and Mann-Whitney U tests examined associations of MUFsg with sociodemographic variables. Spearman correlations examined associations of MUFsg with blood and urine metals within and between trimesters. A False Discovery Rate (FDR) correction accounted for multiple comparisons. The criterion for statistical significance was an alpha of 0.05. Results: Participants were approximately 29 years old on average, and 80% were Hispanic or Latina. Median (IQR) MUFsg during trimesters one and three was 0.65 (0.5) mg/L and 0.8 (0.59) mg/L, respectively. MUFsg levels were moderately consistent between trimesters (N=292, ICC = 0.46, 95%CI: 0.32,0.57). Maternal age was positively associated with MUFsg during first (p = 0.16, p = 0.006) and third (p = 0.18, p < 0.001) trimesters. MUFsg differed by race/ethnicity during first and third trimesters (N = 293, H (3) = 7.99, p = 0.046; N = 486, H (3) = 25.31, p < 0.001, respectively). Specifically, MUFsg was higher for White, Non-Hispanic participants (first trimester Median (IQR) =1.03 (1.31) mg/L; third trimester Median (IQR) = 1.32 (1.24) mg/L) than for Hispanic participants in both trimesters (first trimester Median (IQR) =0.64 (0.48) mg/L; third trimester Median (IQR) = 0.76 (0.55) mg/L). Additionally, during trimester three, MUFsg was higher for White, Non-Hispanic participants than for Black Non-Hispanic participants (Median (IQR) = 0.82 (0.49) mg/L). MUFsg also differed by education during trimester one (N = 293, H (4) = 10.61, p = 0.031), and was higher for participants with some graduate training than for those with high school or some college/technical school education (ps = 0.03 and 0.04, respectively). After FDR correction, MUFsg was associated with blood lead (N =91, p = 0.29, p = 0.024) and urinary cadmium (N =279, p = 0.19, p = 0.042), copper (N=279, p = 0.16, p = 0.042), and tungsten (N=279, p = 0.16, p = 0.049) during trimester three. Conclusions: Consistent with studies conducted in Canada and Mexico, MUFsg increased across pregnancy. Lower MUFsg among Hispanic and Non-Hispanic Black participants may reflect lower tap water consumption. Metal co-exposures are important to consider when examining potential neurodevelopmental impacts of fluoride.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».