Fig 1.
Principal Component Analysis (PCA) of study cohort.
(A) Scatter plot for individuals based on metabolite contributions and grouped as per autism diagnosis status. (B) Scree plot of eigenvalues of principal components from A. (C) Loading plot of metabolites contributing to principal component 1 (PC1). (D) Loading plot of metabolites contributing to principal component 2 (PC2).
Table 1.
Demographic characteristics of study cohort.
Fig 2.
Heatmap of generalised estimating equations (GEE) model estimates.
(A) Autism diagnosis across full cohort (N = 96), monozygotic twins (n = 52) and dizygotic twins (n = 44) tested using GEE Models A and B. (B) Autistic traits across full cohort, monozygotic twins and dizygotic twins tested using GEE Models A and B. (* p<0.05, ** p<0.01, *** p<0.001).
Fig 3.
Pathway enrichment of significant metabolites based on autism diagnosis.
(A) Enrichment dot-plot for top 10 enriched pathways from GEE Model A. (B) Interaction network for enriched pathways from GEE Model A. (C) Enrichment dot-plot for top 10 enriched pathways from GEE Model B. (D) Interaction network for enriched pathways from GEE Model B.
Fig 4.
Pathway enrichment of significant metabolites based on autistic traits.
(A) Enrichment dot-plot for top 10 enriched pathways from GEE Model A. (B) Interaction network for enriched pathways from GEE Model A. (C) Enrichment dot-plot for top 10 enriched pathways from GEE Model B. (D) Interaction network for enriched pathways from GEE Model B.