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

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.