Cited by 238Schizophrenia research and treatmentTryptophan and brain disordersPsychopathy, Forensic Psychiatry, Sexual Offending

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Towards a Psychosis Risk Blood Diagnostic for Persons Experiencing High-Risk Symptoms: Preliminary Results From the NAPLS Project

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Perkins, Diana O., Jeffries, Clark D., Addington, Jean, Bearden, Carrie E., Cadenhead, Kristin S., Cannon, Tyrone D., Cornblatt, Barbara A., Mathalon, Daniel H., McGlashan, Thomas H., Seidman, Larry J., Tsuang, Ming T., Walker, Elaine F., Woods, Scott W., Heinssen, Robert·2015·Schizophrenia Bulletin

DOI 10.1093/schbul/sbu099PMC PMC4332942

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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.

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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.CC BY-NC · PubMed Central

Fig. 2. Fifteen-analyte receiver operating curves and 95% confidence intervals: (A) for CHR-P vs UC and (B) CHR-P vs CHR-NP.CC BY-NC · PubMed Central

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.CC BY-NC · PubMed Central

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Toward Generalizable and Transdiagnostic Tools for Psychosis Prediction: An Independent Validation and Improvement of the NAPLS-2 Risk Calculator in the Multisite PRONIA Cohort
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Complement pathway changes at age 12 are associated with psychotic experiences at age 18 in a longitudinal population-based study: evidence for a role of stress
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Differential expression of the inflammation marker IL12p40 in the at-risk mental state for psychosis: a predictor of transition to psychotic disorder?
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“While inflammatory biomarkers are clearly important in psychosis, few previous studies have examined whether blood markers of inflammation distinguish those who convert to psychotic disorder from those who do not [6, 20].”
BackgroundResultInfluential
Inflammatory subgroups of schizophrenia and their association with brain structure: A semi-supervised machine learning examination of heterogeneity
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“9 significant main effect of group in the following domains: premorbid functioning (F(4,446) = 318 2.”
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Peripheral complement is increased in schizophrenia and inversely related to cortical thickness
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Serum immune markers and transition to psychosis in individuals at clinical high risk
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“Previous studies involved CHR samples of n = 39 (Labad et al., 2015), n = 39 (Föcking et al., 2016), and n = 72 (Perkins et al., 2015).”
BackgroundMethodResultInfluential
Markers of Psychosis Risk in the General Population
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“Furthermore, Perkins and colleagues [90] described the use of clinical high-risk patients who converted to psychosis within 2 years in comparison to those that did not convert (results not included [46]).”
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Counterpoint. Early intervention for psychosis risk syndromes: Minimizing risk and maximizing benefit
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“Indeed, increased proinflammatory cytokines were present in ultra-high risk individuals (Zeni-Graiff et al., 2016) and with the risk of conversion to psychosis (Perkins et al., 2015; Khoury and Nasrallah, 2018).”
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Brain Biomarkers of Vulnerability and Progression to Psychosis
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Is Prophylactic Psychiatry around the Corner? Combating Adolescent Oxidative Stress for Adult Psychosis and Schizophrenia
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Inflammation, Stress Response, and Redox Dysregulation Biomarkers: Clinical Outcomes and Pharmacological Implications for Psychosis
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Immuno-inflammatory changes across phases of early psychosis: The impact of antipsychotic medication and stage of illness
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Self-citation
Unbiased Metabolite Profiling of Schizophrenia Fibroblasts under Stressful Perturbations Reveals Dysregulation of Plasmalogens and Phosphatidylcholines
Huang, J., Park, H., Iaconelli, J., Berkovitch, S. S., Watmuff, B., McPhie, D. L., Öngür, D., Cohen, B. M., Clish, C. B., & Karmacharya, R. (2016) · Journal of Proteome Research · doi:10.1021/acs.jproteome.6b00628 · Cited by 28
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Insights into psychosis risk from leukocyte microRNA expression
Jeffries, C. D., Perkins, D. O., Chandler, S. D., Stark, T., Yeo, E., Addington, J., Bearden, C. E., Cadenhead, K. S., Cannon, T. D., Cornblatt, B. A., et al. (2016) · Translational Psychiatry · doi:10.1038/tp.2016.148 · Cited by 29
“24,25 Thus, peripheral immune cells may be of value for schizophrenia biomarker discovery because their dysregulation may be directly linked to emerging psychosis.”
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Galantamine-Memantine Combination as an Antioxidant Treatment for Schizophrenia
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“Increased peripheral cytokines have been associated with both symptom severity and degree of grey matter loss in CHR subjects (24), as well as predicting conversion to psychosis (78), and peripheral TNF-alpha levels have been shown to predict negative symptom severity (79).”
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Effects of omega-3 PUFA on immune markers in adolescent individuals at ultra-high risk for psychosis – Results of the randomized controlled Vienna omega-3 study
Smesny, S., Milleit, B., Schaefer, M. R., Hesse, J., Schlögelhofer, M., Langbein, K., Hipler, U., Berger, M., Cotter, D., Sauer, H., et al. (2017) · Schizophrenia Research · doi:10.1016/j.schres.2017.01.026 · Cited by 26
“, 2016), and were identified in the search for a particular pattern of biomarkers in UHR individuals that might be predictive in terms of outcome (Perkins et al., 2015).”
Elevated serum chemokine CCL22 levels in first-episode psychosis: associations with symptoms, peripheral immune state and in vivo brain glial cell function
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“An additional limitation of the current meta -analysis is the requirement of mean age less than or equal to 18 for inclusion, which excluded the large biomarker study NAPLS, which found that baseline markers of inflammation and oxidative stress were associated with transition from CHR-P to TPD (Perkins et al., 2015).”
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“Nongenomic biomarkers (e.g., blood analytes, gray matter volume) can be as predictive and, in some cases, even more predictive of disease than genomic biomarkers (Koutsouleris et al., 2015; Lu et al., 2017; Perkins et al., 2015; Sullivan et al., 2017).”
BackgroundInfluential
Networks of blood proteins in the neuroimmunology of schizophrenia
Jeffries, C. D., Perkins, D. O., Fournier, M., Kim, Q., Cuénod, M., Khadimallah, I., Domenici, E., Addington, J., Bearden, C. E., Cadenhead, K. S., et al. (2018) · Translational Psychiatry · doi:10.1038/s41398-018-0158-y · Cited by 23
Self-citation

Authors: Perkins, D. O., Jeffries, C. D., Addington, J., Bearden, C. E., Cadenhead, K. S., Cannon, T. D., Cornblatt, B. A., Mathalon, D. H., McGlashan, T. H., Seidman, L. J., Tsuang, M. T., Walker, E. F., Woods, S. W., & Heinssen, R.
Affiliations: University of North Carolina at Chapel Hill (US)
University of Calgary (CA)
University of California, Los Angeles (US)
University of California San Diego (US)
Yale University (US)
Zucker Hillside Hospital (US)
University of California, San Francisco (US)
Beth Israel Deaconess Medical Center (US)
Emory University (US)
National Institute of Mental Health (US)
Corresponding author: Perkins, Diana O.
Jeffries, Clark D.
ORCID: Perkins, Diana O.
Jeffries, Clark D.
Addington, Jean
Bearden, Carrie E.
Cadenhead, Kristin S.
Cannon, Tyrone D.
Cornblatt, Barbara A.
Mathalon, Daniel H.
McGlashan, Thomas H.
Seidman, Larry J.
Tsuang, Ming T.
Walker, Elaine F.
Woods, Scott W.
Venue: Schizophrenia Bulletin
Publisher: Oxford University Press (OUP)
Published: 2014-08-06
Volume: 41
Issue: 2
Pages: 419-428
ISSN: 0586-7614 (ISSN-L)
1745-1701
DOI: 10.1093/schbul/sbu099
PMC: PMC4332942
OpenAlex: W2144107861
Cited by: 238
References: 60
Citations by year: 2026: 8 · 2025: 15 · 2024: 16 · 2023: 28 · 2022: 14 · 2021: 15 · 2020: 25 · 2019: 22 · 2018: 17 · 2017: 20 · 2016: 32 · 2015: 19 · 2014: 7
FWCI: 19.85
Type: journal article
Language: en
Open access: hybrid
doi.org
Licence: cc-by-nc
Locations: Schizophrenia Bulletin (publishedVersion, cc-by-nc)
Europe PMC (PubMed Central) (publishedVersion, cc-by-nc)
PubMed Central (publishedVersion, cc-by-nc)
PubMed (publishedVersion)
Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
Carolina Digital Repository (University of North Carolina at Chapel Hill) (submittedVersion)
eScholarship (California Digital Library) (submittedVersion)
eScholarship (California Digital Library) (submittedVersion, cc-by-nc)
UNC Libraries
Funders: National Institute of Mental Health (U01MH081928, U01MH066160, U01MH082022, U01MH076989, U01MH081984, U01MH0818902, U01MH081857, U01MH082004, U01MH081944, P50MH066286, U01MH081988)
Topics: Health Sciences › Medicine › Psychiatry and Mental health › Schizophrenia research and treatment
Life Sciences › Neuroscience › Biological Psychiatry › Tryptophan and brain disorders
Social Sciences › Psychology › Clinical Psychology › Psychopathy, Forensic Psychiatry, Sexual Offending
Keywords: psychosis risk, clinical high risk for psychosis
MeSH terms: Adult, Female, Follow-Up Studies, Humans, Hypothalamo-Hypophyseal System, Inflammation, Male, Pituitary-Adrenal System, Psychotic Disorders, Oxidative Stress, Risk Assessment, Young Adult, Prodromal Symptoms

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Towards a Psychosis Risk Blood Diagnostic for Persons Experiencing High-Risk Symptoms: Preliminary Results From the NAPLS Project

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