Towards a psychosis risk blood diagnostic for persons experiencing high-risk symptoms: preliminary results from the NAPLS project

Abstract

Introduction: A barrier to preventative treatments for psychosis is the absence of accurate identification of persons at highest risk. A blood test that could substantially increase diagnostic accuracy would enhance development of psychosis prevention interventions.

Methods: The North American Prodrome Longitudinal Study project is a multisite endeavor that aims to better understand predictors and mechanisms for the development of psychosis. In this study, we measured expression of plasma analytes reflecting inflammation, oxidative stress, hormones, and metabolism. A "greedy algorithm" selected analytes that best distinguished persons with clinical high-risk symptoms who developed psychosis (CHR-P; n = 32) from unaffected comparison (UC) subjects (n = 35) and from those who did not develop psychosis during a 2-year follow-up (CHR-NP; n = 40).

Results: The classifier included 15 analytes (selected from 117), with an area under the receiver operating curve for CHR-P vs UC of 0.91 and CHR-P vs CHR-NP of 0.88. Randomly scrambled group membership followed by reconstructions of the entire classifier method yielded consistently weak classifiers, indicating that the true classifier is highly unlikely to be a chance occurrence. Such randomization methods robustly imply the assays contain consistent information distinguishing the groups which was not obscured by the data normalization method and was revealed by classifier construction. These results support the hypothesis that inflammation, oxidative stress, and dysregulation of hypothalamic-pituitary axes may be prominent in the earliest stages of psychosis.

Conclusion: If confirmed in other groups of persons at elevated risk of psychosis, a multiplex blood assay has the potential for high clinical utility.

Keywords: clinical high risk; immune; inflammation; malondialdehyde-modified low-density lipoprotein (MDA-LDL); multiplex; oxidative stress; prodrome; psychosis; risk prediction.

Fig. 1.

Shown analytes were the most frequently appearing in 20 5-by-5-by-5 cross-validation trials, each trial testing 125 partitions to generate ~80% subsets of UC, CHR-NP, and CHR-P samples.

Fig. 2.

Fifteen-analyte receiver operating curves and 95% confidence intervals: (A) for CHR-P vs UC and (B) CHR-P vs CHR-NP.

Fig. 3.

Distribution of AUCs for 100 classifiers built with random data. Shown is a beta distribution fit and P value (A) for UC vs CHR-P and (B) CHR-NP vs CHR-P. For UC vs P: the alpha values were >.2 for both Kolmogorov-Smirnov and Anderson-Darling tests of fit with beta distributions. Using the top 15, the area under the receiver operating curve (AUC) for true classifier on true data was 0.91. For the beta fit, this value is out of range and has a P value = 0. For NP vs P: the alpha values were >.2 for both Kolmogorov-Smirnov and Anderson-Darling tests of fit with beta distributions. Using the top 15, the AUC for true classifier on true data was 0.88. For the beta fit, this value has a P value = 6.51E-05.