# Prognostic Biomarkers and Precision Psychiatry: A Review of the Available Evidence

## TL;DR

This review explores how biomarkers can help predict outcomes and treatment responses in psychiatric disorders, aiming to improve personalized care.

## Contribution

The paper synthesizes current evidence on various prognostic biomarkers in psychiatry and evaluates their clinical utility.

## Key findings

- Inflammatory biomarkers may act as clinical modulators in psychiatric disorders.
- Metabolomic and neuroendocrine factors are linked to treatment response and relapse risk.
- Neuroimaging and genetic biomarkers aid in predicting therapeutic outcomes and patient stratification.

## Abstract

Precision psychiatry aims to overcome clinical heterogeneity by means of biomarkers that allow predicting the clinical evolution and therapeutic response in psychiatric disorders. This literature review addresses its prognostic role and its potential integration into healthcare practice. The main objective was to compile and synthesize current evidence on prognostic biomarkers in psychiatry, evaluating their usefulness in anticipating clinical evolution, therapeutic response, and risk of relapse. A strategic search was carried out on PubMed, selecting original studies that evaluated blood, genetic, epigenetic, neuroimaging, or electrophysiological biomarkers with prognostic value. We included 30 final studies that met the established inclusion and exclusion criteria and were evaluated according to standardized scales (RoB 2, NOS, AXIS). Inflammatory biomarkers showed potential as clinical modulators. Metabolomic, neuroendocrine, and neurotrophic factors reflected specific biological profiles associated with response to treatment or risk of relapse. Functional connectivity and brain morphometry were useful in the therapeutic prediction and stratification of patients. Finally, genetics and epigenetics are consolidated as tools of sensitivity and pharmacological response. Taken together, the findings reveal specific prognostic utility based on the type of biomarker and the patient’s clinical context. Despite the current methodological limitations and scarce replication of studies, prognostic biomarkers represent a step towards a more personalized psychiatry based on biological mechanisms. The future integration of multimodal models will improve clinical decision-making.