scholarly journals Association Between Gestational Weight Gain and Autism Spectrum Disorder in Offspring: A Meta‐Analysis

Obesity ◽  
2020 ◽  
Vol 28 (11) ◽  
pp. 2224-2231
Author(s):  
Le Su ◽  
Cheng Chen ◽  
Liping Lu ◽  
Anny H. Xiang ◽  
Linda Dodds ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Weight Gain ◽  
Gestational Weight Gain ◽  
Meta Analysis ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Gestational Weight

scholarly journals Early life influences on child weight outcomes in the Study to Explore Early Development

Autism ◽  
2018 ◽  
Vol 23 (4) ◽  
pp. 954-962
Author(s):  
Tanja VE Kral ◽  
Jesse Chittams ◽  
Chyrise B Bradley ◽  
Julie L Daniels ◽  
Carolyn G DiGuiseppi ◽  
...  
Keyword(s):  
Body Mass Index ◽  
Autism Spectrum Disorder ◽  
Weight Gain ◽  
Confidence Interval ◽  
Gestational Weight Gain ◽  
Children With Autism ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Rapid Weight Gain ◽  
Gestational Weight

We examined associations between child body mass index at 2–5 years and maternal pre-pregnancy body mass index, gestational weight gain, and rapid weight gain during infancy in children with autism spectrum disorder, developmental delays, or population controls. The Study to Explore Early Development is a multi-site case–control study of children, aged 2–5 years, classified as autism spectrum disorder ( n = 668), developmental delays ( n = 914), or population controls ( n = 884). Maternal gestational weight gain was compared to the Institute of Medicine recommendations. Rapid weight gain was a change in weight-for-age z-scores from birth to 6 months > 0.67 standard deviations. After adjusting for case status, mothers with pre-pregnancy overweight/obesity were 2.38 times (95% confidence interval: 1.96–2.90) more likely, and mothers who exceeded gestational weight gain recommendations were 1.48 times (95% confidence interval: 1.17–1.87) more likely, to have an overweight/obese child than other mothers ( P < 0.001). Children with autism spectrum disorder showed the highest frequency of rapid weight gain (44%) and were 3.47 times (95% confidence interval: 1.85–6.51) more likely to be overweight/obese as children with autism spectrum disorder without rapid weight gain ( P < 0.001). Helping mothers achieve a healthy pre-pregnancy body mass index and gestational weight gain represent important targets for all children. Healthy infant growth patterns carry special importance for children at increased risk for an autism spectrum disorder diagnosis.

scholarly journals Maternal Pre-pregnancy Body Mass Index and Gestational Weight Gain in Relation to Autism Spectrum Disorder and other Developmental Disorders in Offspring

2018 ◽  
Vol 12 (2) ◽  
pp. 316-327 ◽  
Author(s):  
Gayle C. Windham ◽  
Meredith Anderson ◽  
Kristen Lyall ◽  
Julie L. Daniels ◽  
Tanja V.E. Kral ◽  
...  
Keyword(s):  
Body Mass Index ◽  
Autism Spectrum Disorder ◽  
Weight Gain ◽  
Gestational Weight Gain ◽  
Body Mass ◽  
Developmental Disorders ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Gestational Weight

scholarly journals Maternal excessive gestational weight gain as a risk factor for autism spectrum disorder in offspring: a systematic review

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Sorayya Kheirouri ◽  
Mohammad Alizadeh
Keyword(s):  
Systematic Review ◽  
Autism Spectrum Disorder ◽  
Weight Gain ◽  
Gestational Weight Gain ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Cochrane Library ◽  
Gestational Weight ◽  
Excessive Gestational Weight Gain ◽  
Increased Risk

Abstract Background Abnormal gestational weight gain (GWG) is a prenatal complication that may contribute to long-term behavioral and neurodevelopmental differences in offspring. This systematic review summarizes research on the association between maternal GWG and risk of autism spectrum disorder (ASD) in offspring. Methods Google and electronic databases, including PubMed, SCOPUS, Embase, Cochrane Library and Google Scholar, were searched for original human studies published in English through June 2020. Articles that examined the association between GWG and risk of ASD in offspring were included. Duplicate and irrelevant studies were removed; and data were obtained through critical analysis. Results Of 96 articles searched, eight studies were included in the final review. All studies (n = 7) investigating the association of maternal excessive GWG with risk of ASD in offspring indicated that high GWG was independently associated with an increased risk of ASD. Of five studies investigating the association of inadequate GWG with the risk of ASD, four indicated that low GWG was not associated with an increased risk of ASD. Of seven studies examining the association of maternal pre-pregnancy BMI or weight with the risk of ASD, five reported that maternal pre-pregnancy BMI or weight did not appear to be independently associated with risk of ASD. The GWG-ASD association is independent of maternal BMI and child’s intellectual disability, but offspring’s genetic susceptibility connection to the GWG-ASD association remains a topic of debate. Conclusions The findings suggest that maternal excessive GWG may be associated with increased risk of ASD in offspring. However, insufficient GWG does not appear to have such association.

Diagnostic test accuracy for use of machine learning in diagnosis of autism spectrum disorder: A Systematic Review and Meta-Analysis (Preprint)

2019 ◽  
Author(s):  
Sun Jae Moon ◽  
Jin Seub Hwang ◽  
Rajesh Kana ◽  
John Torous ◽  
Jung Won Kim
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Autism Spectrum Disorder ◽  
Meta Analysis ◽  
Learning Algorithms ◽  
Structural Mri ◽  
Autism Spectrum ◽  
Machine Learning Algorithms ◽  
Spectrum Disorder ◽  
Test Accuracy

BACKGROUND Over the recent years, machine learning algorithms have been more widely and increasingly applied in biomedical fields. In particular, its application has been drawing more attention in the field of psychiatry, for instance, as diagnostic tests/tools for autism spectrum disorder. However, given its complexity and potential clinical implications, there is ongoing need for further research on its accuracy. OBJECTIVE The current study aims to summarize the evidence for the accuracy of use of machine learning algorithms in diagnosing autism spectrum disorder (ASD) through systematic review and meta-analysis. METHODS MEDLINE, Embase, CINAHL Complete (with OpenDissertations), PsyINFO and IEEE Xplore Digital Library databases were searched on November 28th, 2018. Studies, which used a machine learning algorithm partially or fully in classifying ASD from controls and provided accuracy measures, were included in our analysis. Bivariate random effects model was applied to the pooled data in meta-analysis. Subgroup analysis was used to investigate and resolve the source of heterogeneity between studies. True-positive, false-positive, false negative and true-negative values from individual studies were used to calculate the pooled sensitivity and specificity values, draw SROC curves, and obtain area under the curve (AUC) and partial AUC. RESULTS A total of 43 studies were included for the final analysis, of which meta-analysis was performed on 40 studies (53 samples with 12,128 participants). A structural MRI subgroup meta-analysis (12 samples with 1,776 participants) showed the sensitivity at 0.83 (95% CI-0.76 to 0.89), specificity at 0.84 (95% CI -0.74 to 0.91), and AUC/pAUC at 0.90/0.83. An fMRI/deep neural network (DNN) subgroup meta-analysis (five samples with 1,345 participants) showed the sensitivity at 0.69 (95% CI- 0.62 to 0.75), the specificity at 0.66 (95% CI -0.61 to 0.70), and AUC/pAUC at 0.71/0.67. CONCLUSIONS Machine learning algorithms that used structural MRI features in diagnosis of ASD were shown to have accuracy that is similar to currently used diagnostic tools.

Autism spectrum disorder and air pollution: a systematic review and meta-analysis

2021 ◽  
pp. 116856
Author(s):  
Frédéric Dutheil ◽  
Aurélie Comptour ◽  
Roxane Morlon ◽  
Martial Mermillod ◽  
Bruno Pereira ◽  
...  
Keyword(s):  
Systematic Review ◽  
Autism Spectrum Disorder ◽  
Air Pollution ◽  
Meta Analysis ◽  
Autism Spectrum ◽  
Spectrum Disorder
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