Publications by authors named "J P Clavel"

Article Synopsis
  • The study investigates the link between pregnancy-related factors and the risk of childhood lymphoma, focusing on non-Hodgkin lymphoma (NHL) and Hodgkin lymphoma (HL) in a large French case-control analysis.
  • Results indicate that maternal coffee and alcohol consumption during pregnancy, as well as paternal smoking, are associated with an increased risk of childhood NHL, with a dose-response relation seen in coffee intake.
  • The research suggests that understanding these associations can improve knowledge of childhood lymphoma aetiology, although further studies are needed to confirm these findings.
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Background: Prenatal folate supplementation has been consistently associated with a reduced risk of childhood acute lymphoblastic leukemia (ALL). Previous germline genetic studies examining the one carbon (folate) metabolism pathway were limited in sample size, scope, and population diversity and led to inconclusive results.

Methods: We evaluated whether ∼2,900 single-nucleotide polymorphisms (SNP) within 46 candidate genes involved in the folate metabolism pathway influence the risk of childhood ALL, using genome-wide data from nine case-control studies in the Childhood Cancer and Leukemia International Consortium (n = 9,058 cases including 4,510 children of European ancestry, 3,018 Latinx, and 1,406 Asians, and 92,364 controls).

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Importance: Cancer is a leading cause of death among children worldwide. Treatments used for medically assisted reproduction (MAR) are suspected risk factors because of their potential for epigenetic disturbance and associated congenital malformations.

Objective: To assess the risk of cancer, overall and by cancer type, among children born after MAR compared with children conceived naturally.

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This ecological time series study aimed to examine the temporal trends in the completeness of epidemiological variables from a hospital-based cancer registry (HbCR) of a reference center for pediatric oncology in Brazil from 2010 to 2016. Completeness categories were based on the percentage of missing data, with the categories excellent (<5%), good (5-10%), regular (11-20%), poor (21-50%), and very poor (>50%). Descriptive and bivariate analyses were performed using R.

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