Prevalence of autism spectrum disorder diagnosis by birth weight, gestational age, and size for gestational age: a systematic review, meta-analysis, and meta-regression

Maternal dietary patterns during pregnancy and birth weight: a prospective cohort study

BACKGROUND

Existing data on maternal dietary patterns and birth weight remains limited and inconsistent, especially in non-Western populations. We aimed to examine the relationship between maternal dietary patterns and birth weight among a cohort of Chinese.

METHODS

In this study, 4,184 mother-child pairs were included from the Iodine Status in Pregnancy and Offspring Health Cohort. Maternal diet during pregnancy was evaluated using a self-administered food frequency questionnaire with 69 food items. Principal component analysis was used to identify dietary patterns. Information on birth weight and gestational age was obtained through medical records. Adverse outcomes of birth weight were defined according to standard clinical cutoffs, including low birth weight, macrosomia, small for gestational age, and large for gestational age.

RESULTS

Three maternal dietary patterns were identified: plant-based, animal-based, and processed food and beverage dietary patterns, which explained 23.7% variance in the diet. In the multivariate-adjusted model, women with higher adherence to the plant-based dietary patten had a significantly higher risk of macrosomia (middle tertile vs. low tertile: odds ratio (OR) 1.45, 95% CI 1.00-2.10; high tertile vs. low tertile: OR 1.55, 95% CI 1.03-2.34; P-trend = 0.039). For individual food groups, potato intake showed positive association with macrosomia (high tertile vs. low tertile: OR 1.72, 95% CI 1.20-2.47; P-trend = 0.002). Excluding potatoes from the plant-based dietary pattern attenuated its association with macrosomia risk. No significant associations was observed for the animal-based or processed food and beverage dietary pattern with birth weight outcomes.

CONCLUSIONS

Adherence to a plant-based diet high in carbohydrate intake was associated with higher macrosomia risk among Chinese women. Future studies are required to replicate these findings and explore the potential mechanisms involved.

Cortical thickness abnormalities in autism spectrum disorder

The pathological mechanism of autism spectrum disorder (ASD) remains unclear. Nowadays, surface-based morphometry (SBM) based on structural magnetic resonance imaging (sMRI) techniques have reported cortical thickness (CT) variations in ASD. However, the findings were inconsistent and heterogeneous. This current meta-analysis conducted a whole-brain vertex-wise coordinate-based meta-analysis (CBMA) on CT studies to explore the most noticeable and robust CT changes in ASD individuals by applying the seed-based d mapping (SDM) program. A total of 26 investigations comprised 27 datasets were included, containing 1,635 subjects with ASD and 1470 HC, along with 94 coordinates. Individuals with ASD exhibited significantly altered CT in several regions compared to HC, including four clusters with thicker CT in the right superior temporal gyrus (STG.R), the left middle temporal gyrus (MTG.L), the left anterior cingulate/paracingulate gyri, the right superior frontal gyrus (SFG.R, medial orbital parts), as well as three clusters with cortical thinning including the left parahippocampal gyrus (PHG.L), the right precentral gyrus (PCG.R) and the left middle frontal gyrus (MFG.L). Adults with ASD only demonstrated CT thinning in the right parahippocampal gyrus (PHG.R), revealed by subgroup meta-analyses. Meta-regression analyses found that CT in STG.R was positively correlated with age. Meanwhile, CT in MFG.L and PHG.L had negative correlations with the age of ASD individuals. These results suggested a complicated and atypical cortical development trajectory in ASD, and would provide a deeper understanding of the neural mechanism underlying the cortical morphology in ASD.

The association between post-term births and autism spectrum disorders: an updated systematic review and meta-analysis

BACKGROUND

This study aimed to conduct a meta-analysis to determine whether post-term birth has an increased risk of ASD.

MATERIALS AND METHODS

To retrieve eligible studies regarding the effect of post-term and ASD in children, major databases including PubMed, Scopus, and Web of Science were searched. A random effect model was used for meta-analysis. For assessing the quality of included studies, the GRADE checklist was used.

RESULTS

In total, 18 records were included with 1,412,667 sample populations from 12 countries. The pooled estimates of RR and OR showed a significant association between post-term birth and ASD among children, respectively (RR = 1.34, 95% CI 1.10 to 1.58) and (OR = 1.47, 95% CI 1.03 to 1.91). There was no heterogeneity among the studies that reported the risk of ASD among children based on RR (I2 = 6.6%, P = 0.301). There was high heterogeneity in the studies reported risk of ASD based on OR (I2 = 94.1%, P = 0.000).

CONCLUSION

Post-term births still occur relatively frequently (up to 5-10%) even in developed countries. Our results showed that post-term birth is an increased risk of ASD, although high heterogeneity was found among the studies reported based on adjusted and crude forms, however, after subgroup analysis by gender, this heterogeneity disappeared among males.

A Machine Learning Approach to the Diagnosis of Autism Spectrum Disorder and Multi-Systemic Developmental Disorder Based on Retrospective Data and ADOS-2 Score

Early and accurate diagnosis of autism spectrum disorders (ASD) and tailored therapeutic interventions can improve prognosis. ADOS-2 is a standardized test for ASD diagnosis. However, owing to ASD heterogeneity, the presence of false positives remains a challenge for clinicians. In this study, retrospective data from patients with ASD and multi-systemic developmental disorder (MSDD), a term used to describe children under the age of 3 with impaired communication but with strong emotional attachments, were tested by machine learning (ML) models to assess the best predictors of disease development as well as the items that best describe these two autism spectrum disorder presentations. Maternal and infant data as well as ADOS-2 score were included in different ML testing models. Depending on the outcome to be estimated, a best-performing model was selected. RIDGE regression model showed that the best predictors for ADOS social affect score were gut disturbances, EEG retrievals, and sleep problems. Linear Regression Model showed that term pregnancy, psychomotor development status, and gut disturbances were predicting at best for the ADOS Repetitive and Restricted Behavior score. The LASSO regression model showed that EEG retrievals, sleep disturbances, age at diagnosis, term pregnancy, weight at birth, gut disturbances, and neurological findings were the best predictors for the overall ADOS score. The CART classification and regression model showed that age at diagnosis and weight at birth best discriminate between ASD and MSDD.