Early-life inflammatory subgroups in youth with psychotic experiences: A semi-supervised machine learning approach.

Shalini Bhattacharya¹, Kate Merritt², Golam M Khandaker³, Paris Alexandros Lalousis⁴, Anthony S David⁵

¹Division of Psychiatry, University College London, 4th Floor, Maple House, 149 Tottenham Court Road, London W1T 7BN, UK; Department of Clinical, Educational and Health Psychology, University College London, 4(th) Floor, 26 Bedford Way, London WC1H 0AP, UK.

²Division of Psychiatry, University College London, 4th Floor, Maple House, 149 Tottenham Court Road, London W1T 7BN, UK.

³MRC Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK; Centre for Academic Mental Health, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK; NIHR Bristol Biomedical Research Centre, University Hospitals Bristol and Weston NHS Foundation Trust, Bristol, UK; Avon and Wiltshire Mental Health Partnership NHS Trust, Bristol, UK.

⁴Department of Psychosis Studies, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, 16 De Crespigny Park, London SE5 8AF, UK.

⁵Division of Psychiatry, University College London, 4th Floor, Maple House, 149 Tottenham Court Road, London W1T 7BN, UK. Electronic address: anthony.s.david@ucl.ac.uk.

PubMed DOI

Abstract

AIMS: Psychotic experiences (PEs) are relatively common in youth and are associated with increased risk for later psychotic disorders. Although inflammation is implicated in the pathophysiology of psychosis, it remains unclear whether specific immune signatures are present before the emergence of PEs. This study aims to identify inflammatory subgroups among individuals with PEs in early adulthood,…

Create a free account to keep reading

Free members get 10 full research views every month across publications, clinical trials, FDA clearances, adverse events, and NIH grants. No credit card required.

Create Free Account Sign In

Want unlimited research access? See Pro plans

Data Accuracy Notice: Research intelligence on Health AI Central is aggregated from public sources (PubMed, ClinicalTrials.gov, FDA, NIH, CMS, and others) and refreshed nightly. Classifications and derived metrics are produced by automated methods described in our Methodology. We recommend verifying critical data points against the primary sources before making decisions.