Again, the Association for the Advancement of Science (AAAS) spreads demagoguery! Actually, the AAAS has perverted the findings by claiming the opposite and limiting the link to only obesity and depression! What a case of disinformation!
"... Genetics is a strong familial confounder (plausible explanation) for autism, the researchers say. Certain genes that increase the risk of someone having depression are also more closely tied to them having autism. If a woman suffers a bout of depression during pregnancy and her child is autistic, it is much more likely that mother and child share genes that cause both conditions, rather than that the chemical effects of depression somehow affected the fetus to cause autism during development. ..."
"... But a study of more than 1 million Danish children and their families, published today in Nature Medicine, pushes back against this view. Researchers analyzed more than 200 health conditions that occurred in these children’s mothers before or during pregnancy. They conclude that many of the supposed links to a child’s autism diagnosis may not be causal, and instead reflect inherited genetic variants or environmental factors shared within families. ..."
From the abstract:
"Evidence suggests that maternal health in pregnancy is associated with autism in the offspring. However, most diagnoses in pregnant women have not been examined, and the role of familial confounding remains unknown. Our cohort included all children born in Denmark between 1998 and 2015 (n = 1,131,899) and their parents. We fitted Cox proportional hazard regression models to estimate the likelihood of autism associated with each maternal prenatal ICD-10 diagnosis, accounting for disease chronicity and comorbidity, familial correlations and sociodemographic factors.
We examined the evidence for familial confounding using discordant sibling and paternal negative control designs.
Among the 1,131,899 individuals in our sample, 18,374 (1.6%) were diagnosed with autism by the end of follow-up. Across 236 maternal diagnoses we tested (prevalence ≥0.1%), 30 were significantly associated with autism after accounting for sociodemographic factors, disorder chronicity and comorbidity, and correction for multiple testing. This included obstetric, cardiometabolic and psychiatric disorders (for example, diabetes in pregnancy (hazard ratio (HR) 1.19, 95% confidence interval (CI) 1.08–1.31) and depression (HR 1.49, 95% CI 1.27–1.75)), previously shown to be associated with autism.
Family-based analyses provided strong evidence for familial confounding in most of the observed associations.
Our findings indicate pervasive associations between maternal health in pregnancy and offspring autism and underscore that these associations are largely attributable to familial confounding."
No Evidence That Maternal Sickness During Pregnancy Causes Autism (original press release)
Fig. 1: Associations between ICD-10 level 3 maternal diagnoses and offspring autism in fully adjusted single-diagnosis models.
Point estimates of each association derived from the two-sided Cox proportional hazard model for each diagnosis are illustrated on the x axis, with their P value (−log10(P)) on the y axis.
Dots representing each statistically significant association are colored according to the ICD-10 category of the respective diagnosis; nonsignificant associations after correction for multiple testing are shown in gray. The horizontal dashed line represents the P-value cutoff for nominal significance (P = 0.05).
Fig. 2: Associations between ICD-10 level 3 maternal diagnoses and offspring autism in fully adjusted single-diagnosis models and the multidiagnosis model.
Point estimates are HRs adjusted for maternal age at childbirth, child’s sex and year of birth, maternal income and education, and maternal healthcare utilization in the 12 months preceding childbirth. Estimates from the multidiagnosis model, in addition to the covariates above, are concurrently adjusted for all significant diagnoses (nonchronic and chronic) in fully adjusted single-diagnosis models (presented in this figure). The error bars represent 95% CIs calculated using point estimates and robust standard errors from the respective regression model.
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