Biomarkers of immuno-metabolic dysfunction as risk factors and predictors of psychotic-like experiences: Analysis of the UK Biobank dataset

Introduction: Psychotic disorders are associated with a 15-year shortened life expectancy, largely driven by physical conditions including cardiometabolic disorders. Immune and metabolic changes are detectable in psychotic disorder populations prior to illness onset and may interact to worsen pathology and symptom profiles. Despite growing evidence for immuno-metabolic interactions as a mechanism for the development of psychotic disorders, existing research in schizophrenia populations is limited by predominantly small sample sizes, and has not comprehensively considered both immune and metabolic dysfunction together. Improved understanding of the role of immuno-metabolic dysfunction in psychotic disorders will provide further evidence of its multi-systemic underpinnings and potentially enable the identification of biologically meaningful subgroups for targeted therapeutic interventions. Aims: To use the large, population-level UK Biobank dataset to characterise system-level immuno-metabolic differences in people with psychotic symptoms, and to investigate whether distinct immuno-metabolic patterns meaningfully identify subgroups of individuals with psychotic experiences.

Methods: We used data from UK BioBank participants who completed the Mental Health Questionnaire (N = 157,161), from which we established a cohort who presented with PLEs (N = 7,787) and a control group (N = 149,362). We extracted data on metabolic (lipids, glycaemic biomarkers, apolipoproteins, weight indices) and immune (peripheral blood cell counts, cytokines, proteins) markers, and calculated markers of insulin resistance (triglyceride-glucose index, triglyceride:high-density lipoprotein), and systemic inflammation (systemic inflammation index). Immuno-metabolic biomarkers were quantified for each group, and between-group differences assessed using analysis of covariance, adjusting for demographics, body mass index, physical activity. Principal component analysis was conducted across 23 biomarkers to investigate differences in principal component (PCs), and logistic regression used to examine cross-sectional associations with PLEs. Propensity score weighting and covariate adjustment were used to explore the impact of confounding.

Results: We found that immune-inflammatory biomarkers including the systemic inflammation index, lymphocytes, basophils, eosinophils, neutrophils, platelets, and lymphocyte-monocyte ratio were all significantly elevated in people with PLEs. We derived 6-PCs from the 23 included biomarkers. PC1: ‘Insulin Resistance’ (Standardised Mean Difference (SMD) = 0.14, p <0.01) and PC4: Immune Metabolic Axis (SMD = 0.09, p <0.01) scores were significantly elevated in the PLE-group compared to controls, after adjustment for ethnicity, age, alcohol consumption, and tobacco consumption, and BMI. However, these PC1 (SMD = 0.07, 95% CI: -0.05 to 0.21) and PC4 (SMD scores did not survive correction when BMI was added as a mediator. There was no moderation effect of biological sex on principal component scores.

Conclusions: These findings suggest modest system-level immuno-metabolic differences in people with PLEs. Although associations were largely attenuated after mediation analysis for body mass index, estimate points remained elevated in people with PLEs. Future studies using datasets with larger inventories of biomarkers may be useful to more accurately define principal components, which will help to better understand the contribution of immuno-metabolic differences to PLEs.

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