Integration of multi-omics and crowdsourcing assessment of placenta-brain axis biomarkers for predicting neurodevelopmental disorders.

Neurodevelopmental disorders (NDDs) are heterogeneous and multifactorial psychiatric disorders with abnormalities in multiple biological domains. It is increasingly recognized that the placenta profoundly influences fetal neurodevelopment due to the finding of the placenta-brain axis. However, few studies have investigated the interplay between placenta dysfunction and NDDs, especially autism spectrum disorder (ASD) symptoms, attention-deficit/hyperactivity disorder (ADHD) symptoms, and intellectual disability (ID) symptoms, by using integrative multi-omics data. Here, we performed an analysis of transcriptomic and non-targeted metabolomic individually and integratively to characterize the placental multi-omics profiles of children with NDDs in Ma'anshan Birth Cohort, and to identify biomarkers associated with the placenta-brain axis. Integrating transcriptome and metabolome perspectives, we further conducted a multi-omics machine learning workflow to discover reliable placental biomarkers for early diagnosis of these NDDs in the participants. Integrative analysis of differentially expressed genes and metabolites revealed a common intrauterine regulation mechanism for ASD symptoms and ADHD symptoms. Combined with machine learning, prediction models were constructed and 99.7 % of ASD symptoms, 99.0 % of ADHD symptoms, and 95.7 % of ID symptoms were correctly classified. This is the first study combining transcriptomics and metabolomics from the perspective of the placental-brain axis in humans, which contributes to a deeper understanding about the pathogenesis of the NDDs and may potentially pave the way toward molecular diagnosis of different disorders.