The psychosis continuum: Systematic review on prodromal markers, symptom progression, and early intervention strategies

Abstract

This systematic review synthesizes evidence from 60 studies (2000–2025) examining prodromal symptoms and psychotic onsets, following PRISMA guidelines. The review included 35 prospective cohort studies (58.3 %), 9 cross-sectional studies (15.0 %), 9 randomized controlled trials (15.0 %), 5 case-control studies (8.3 %), and 2 qualitative studies (3.3 %).

Prodromal presentations show marked heterogeneity, with initial manifestations predominantly non-specific (depression 52 %, worry 41 %, anxiety 38 %) before attenuated psychotic symptoms emerge. Negative symptoms typically precede positive symptoms by 12 months, with three distinct trajectories: persistent (35 %), fluctuating (42 %), and improving (23 %). Neurobiological markers demonstrate stepwise brain alterations, with machine learning approaches achieving clinically meaningful prediction accuracy (77.6 % for eye-tracking; AUC=0.84 for multimodal assessment). Environmental factors significantly influence transition risk: childhood trauma affects 61 % of UHR individuals, while cannabis use confers 4-fold increased risk (10-fold with genetic vulnerability). Notably, premorbid deterioration may reflect environmental factors (particularly high-potency cannabis) rather than elevated genetic liability.

Psychological interventions, particularly cognitive-behavioral approaches, show favorable risk-benefit profiles (NNT=5), with evidence suggesting shared anti-inflammatory mechanisms with biological treatments. Omega-3 supplementation demonstrates promise with reduced transition rates (4.9 % vs. 27.5 % controls) and favorable safety profile. Emerging interventions including low-dose lithium show potential neuroprotective effects warranting further investigation. The evidence supports expanding from transition-focused approaches to broader recovery-oriented frameworks that prioritize psychosis prevention while also addressing functioning, distress, and quality of life in all individuals at risk. We recommend personalized, stage-specific interventions integrating phenomenological, neurobiological, and intervention insights, emphasizing layered assessment, developmentally-contextualized interpretation, trauma-informed care, and accessible neurophysiological markers combined with machine learning for enhanced risk prediction.