| Literature DB >> 32131900 |
Olga Egorova1, Robin Myte2, Jörn Schneede3, Bruno Hägglöf4, Sven Bölte5,6,7, Erik Domellöf8, Barbro Ivars A'roch4, Fredrik Elgh9, Per Magne Ueland10,11, Sven-Arne Silfverdal12.
Abstract
BACKGROUND: Autism spectrum disorder (ASD) evolves from an interplay between genetic and environmental factors during prenatal development. Since identifying maternal biomarkers associated with ASD risk in offspring during early pregnancy might result in new strategies for intervention, we investigated maternal metabolic biomarkers in relation to occurrence of ASD in offspring using both univariate logistic regression and multivariate network analysis.Entities:
Keywords: Autism; Folate; Inflammation; One-carbon metabolism; Pregnancy; Vitamin A; Vitamin B; Vitamin D
Mesh:
Substances:
Year: 2020 PMID: 32131900 PMCID: PMC6964211 DOI: 10.1186/s13229-020-0315-z
Source DB: PubMed Journal: Mol Autism Impact factor: 7.509
Fig. 1Study design
Baseline characteristics
| Cases ( | Controls ( | ||||
|---|---|---|---|---|---|
| Median (IQRa) or % | n | Median (IQR) or % | |||
| Mother’s age (years) | 100 | 31 (28–34) | 100 | 30 (26–33) | 0.02 |
| Year of blood serum sampling | 100 | 2003 (2001–2005) | 100 | 2002 (1999–2005) | 0.07 |
| Month of birth, child | 100 | 7 (4–10) | 100 | 8 (4–10) | 0.50 |
| Child sex, boy | 76 | 76% | 78 | 78% | 0.87 |
| ASD diagnosis | |||||
| Infantile autism (F84.0) | 74 | 74% | |||
| Asperger’s syndrome (F84.5) | 26 | 26% | |||
aInterquartile range (IQR), 25–75th percentile
bTest for difference with Mann-Whitney U test (continuous variables) or Chi-square test (categorical variables)
Fig. 4a Bayesian network of serum biomarkers and background information variables estimated by a Hill-climbing algorithm and averaged over 1000 bootstrap samples. A line between two variables indicates an association independent of all other variables in the network. Line thickness corresponds to association strength measured as proportion of times an association was present in 1000 bootstrap sample networks (a thicker line indicates a stronger association). Node size corresponds to the number of connections. The network was estimated using discrete data (with biomarkers divided into low/high groups, with cut-off defined by the median biomarker concentrations in the controls). b Association strengths to ASD risk for each biomarker measured as proportion of times an association was present in 1000 bootstrap sample networks
Fig. 2Spearman’s correlations between analyzed serum biomarkers with a hierarchical cluster analysis based on the correlations
Fig. 3Odds ratios (ORs) for ASD risk by 1 standard deviation (SD) increase in biomarkers levels. ORs were calculated using logistic regression adjusted for mother’s age at sampling, year of sampling, sex of child, and serum cotinine and mTHF levels. Vitamin D3 was further adjusted for month of sampling (light/dark months). Median serum concentration levels in cases and controls are presented in nmol/L for biomarker kynurenic acid; 25-hydroxy vitamin D3; xanthurenic acid; trigonelline; cystathionine; 4-pyridoxic acid; quinolinic acid; pyridoxal; neopterin; nicotinic acid; para-amino benzoylglutamate; acetamidobenzoglutamate; hmTHF; mTHF;and in mg/l for CRP and all remaining biomarkers