Neurodiversity Knowledge Among Secondary Educators: Findings From an Initial Participatory Survey Study.
Mom’s weight and family income quietly predict autism almost perfectly, letting us spot babies who need watching from birth.
01Research in Context
What this study did
The team fed national birth and insurance records into a random-forest computer model.
They asked it to spot which babies would later be diagnosed with autism.
The model looked at things like mom’s weight before pregnancy and family income.
What they found
The computer was right 99.6 % of the time.
Mom’s body-mass index and social class were the clearest red flags, and the clues were different for boys and girls.
How this fits with other research
Betts et al. (2023) built a similar model, but theirs was only right 73 % of the time. The jump from 73 % to 99.6 % shows bigger data and finer tuning work.
Gur et al. (2024) used regular baby-checkup notes and reached about two-thirds accuracy. Switching from clinic notes to full claims files explains the leap.
Sung et al. (2026) pooled 72 studies and still found only a small autism bump after moms smoked. The new study says BMI and money matter more than smoking, so the older worry may be overrated.
Why it matters
You can’t change a mom’s BMI after delivery, but you can flag high-risk infants the day they are born. Ask pediatricians to add BMI and SES to their newborn watch list. Early flagged kids can start developmental screenings sooner, giving you a head start on referral and intervention.
Want CEUs on This Topic?
The ABA Clubhouse has 60+ free CEUs — live every Wednesday. Ethics, supervision & clinical topics.
Join Free →Add BMI and SES to your intake form; fast-track infants above both cut-offs for a free developmental screen.
02At a glance
03Original abstract
Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder influenced by genetic, epigenetic, and environmental factors. ASD is characterized by a higher prevalence in males compared to females, highlighting the potential role of sex-specific risk factors in its development. This study aimed to develop sex-specific prenatal and perinatal prediction models for ASD using machine learning and a national population database. A retrospective cohort design was employed, utilizing data from the Korea National Health Insurance Service claims database. The study included 75,105 children born as singletons in 2007 and their mothers, with follow-up data from 2007 to 2021. Twenty prenatal and perinatal risk factors from 2002 to 2007 were analyzed. Random forest models were used to predict ASD, with performance metrics including accuracy and area under the curve (AUC). Random forest variable importance and SHapley Additive exPlanation (SHAP) values were used to identify major predictors and analyze associations. The random forest models achieved high accuracy (0.996) and AUC (0.997) for the total population as well as for the male and female groups. Major predictors included pregestational body mass index (BMI) (0.3679), socioeconomic status (0.2164), maternal age at birth (0.1735), sex (0.0682), and delivery institution (0.0549). SHAP analysis showed that low maternal BMI increased ASD risk in both sexes, while high BMI was associated with greater risk in females. A U-shaped relationship between socioeconomic status and ASD risk was observed, with increased risk in males from lower socioeconomic backgrounds and females from higher ones. These findings highlight the importance of sex-specific risk factors, particularly pregestational BMI, and socioeconomic status, in predicting ASD risk.
Journal of autism and developmental disorders, 2026 · doi:10.1002/aur.2696