Fig 1.
Set 1 biomarker data collected from 35 autistic patients and 38 healthy control participants.
PE: phosphatidylethanolamine, PS: phosphatidylserine, PC: phosphatidylcholine, MAP2K1: mitogen-activated protein kinase kinase 1, IL-10: interleukin 10, IL-12: interleukin 12, NF-κB: nuclear factor kappa B.
Fig 2.
Set 2 biomarker data collected from 29 autistic patients and 16 healthy control participants.
PGE2: prostaglandin E2, PGE2-EP2: prostaglandin E2 receptor 2, mPGES-1: membrane-bound prostaglandin E synthase 1, COX-2: cyclooxygenase 2, cPLA2: cytosolic phospholipase A2.
Fig 3.
Separation of autistic and healthy control participants based on two sets of biomarkers.
Principal component analysis (a and b) and multidimensional scaling (c and d) scatter plots based on set 1 (a and c) and set 2 biomarkers (b and d) show complete separation of autistic and healthy control groups. Hierarchical clustering shows efficient separation of autistic and healthy control groups based on set 1 (e) or 2 (f) biomarkers. Dendrograms were constructed from Canberra distances data using Neighbor joining algorithm. Heat maps depict marker values with darker grey indicative of higher values. Heat map variables from left to right are (e) PE, PS, PC, MAP2K1, IL-10, IL-12, and NFκB, and (f) PGE2, PGE2-EP2, PGES, cPLA2, 8-isoprostane, and COX-2. Autistic subjects with mild to moderate disease, those with severe disease, and control subjects are indicated with magenta, purple, and green squares, respectively.
Fig 4.
Determination of statistical significance of components in principal component analysis using Monte Carlo simulation.
Set 1 (a) and 2 (b) data obtained from autistic and healthy control participants were analyzed with PCA. Screen plots show Eigen values of raw data (blue), as well as the 50th (green) and 95th percentile (yellow) simulated data. A principal component was considered statistically significant (circled in red) whenever its raw data Eigen value lay above the corresponding 95th percentile simulated data Eigen value.
Table 1.
Contributions of variables (markers) to the first and second principal components (PC1 and PC2) in principal component analysis of 35 autistic and 38 healthy control subjects.
Data were collected for a set of 7 markers; PE, PS, PC, MAP2K1, IL-10, IL-12, and NF-κB (set 1). Separation between autistic and control groups was mostly on PC2 coordinate. The most discriminatory variables are shown in boldface. The portion of variance explained by each principal component is shown between parentheses. * indicates a statistically significant principal component.
Table 2.
Variable (marker) contributions to the first and second principal components (PC1 and PC2) in principal component analysis of 29 autistic and 16 healthy control subjects.
Data were collected for a set of 6 markers; PGE2, PGE2-EP2, PGES, COX-2, cPLA2, and 8-isoprostane (set 2). In this analysis, autistic and control groups were mostly separated on PC1 coordinate. The most discriminatory variables are shown in boldface. The portion of variance explained by each principal component is shown between parentheses. * indicates a statistically significant principal component.
Fig 5.
Separation of autistic patience based on the severity of sensory profile impairment.
Autistic patients were classified into two groups, one with severe impairment of sensory profiles (purple squares) and the other with mild or moderate impairment (magenta squares). Principal component analysis (a&b) and multidimensional scaling (c&d) scatter plots based on set 1 (a&c) do not show separation between groups, while set 2 biomarkers (b&d) show visually discernible—although not complete—separation. Hierarchical clustering failed to show separation of autistic patient groups based on either set 1 (e) or 2 (f) biomarkers. Dendrograms were constructed from Canberra distances data using Neighbor joining algorithm. Heat maps depict marker values with darker grey indicative of higher values. Heat map variables from left to right are (e) PE, PS, PC, MAP2K1, IL-10, IL-12, and NFκB, and (f) PGE2, PGE2-EP2, PGES, cPLA2, 8-isoprostane, and COX-2.
Fig 6.
Determination of statistical significance of components in principal component analysis using Monte Carlo simulation.
Set 1 (a) and 2 (b) data obtained from mild/moderate and severe autism groups were analyzed with PCA. Scree plots show eigenvalues of raw data (blue), as well as the 50th (green) and 95th percentile (yellow) simulated data. A principal component was considered statistically significant (circled in red) whenever its raw data eigenvalue lay above the corresponding 95th percentile simulated data eigenvalue.
Table 3.
Variable (marker) contributions to the first and second principal components (PC1 and PC2) in principal component analysis of six markers tat corelated with sensory profiles of autistic patients.
Fig 7.
Correlation between sensory profiles of autistic patients and six markers.
Correlation between sensory profile scores and each of the variables was calculated using Pearson and Spearman Correlation Coefficients to evaluate linear and monotonic nonlinear correlations, respectively. P values (in parenthesis) indicate the likelihood of obtaining the corresponding correlation value or a higher value due to random sampling alone. A linear regression line is shown with 95% confidence intervals (dotted lines).
Table 4.
Multiple regression analysis identifies a combination of 3 biomarkers as the best predictors of the degree of sensory profile impairment.
Data of 29 autistic participants, including 17 with mild-to-moderate impairment and 12 with severe impairment.
Fig 8.
Rates of correct assignment in library-based identification.
(a) Rates of correct assignment of autistic and control groups. Control and autistic library units were composed, respectively, of 39 and 35 participants when using set 1 biomarkers; and 16 and 29 participants when using set 2 biomarkers. (b) Rates of correct assignment in autstic patients with mild-to-moderate sensory profile impairment and those with high impairment. Severe and mild-to-moderate library units wre composed of 12 and 17 participants, respectively. Biomarkers used in the identification process were set 1: PE, PS, PC, MAP2K1, IL-10, IL-12, and NFκB; set 2: PGE2, PGE2-EP2, PGES, cPLA2, 8-isoprostane, and COX-2; set 3: PGE2, PGES, cPLA2, 8-isoprostane, COX-2, and PE; and set 4: PGE2, PGES, and PE.