Neuroinflammation and insulin resistance in major depression and bipolar disorder: Implications for clinical trials evaluating immunometabolic targeted therapies

TL;DR

This paper explores how inflammation and insulin resistance relate to depression and bipolar disorder, aiming to improve clinical trials through better patient selection using biological markers.

Contribution

The paper proposes multimodal biosignatures and machine learning to identify subgroups likely to benefit from immunometabolic therapies.

Findings

Neuroinflammation and insulin resistance are linked to the severity and progression of mood disorders.

Current clinical trials lack consistent outcomes due to poor participant stratification based on biology.

Combining genetic, epigenetic, and proteomic markers may improve trial design and precision medicine.

Abstract

Bipolar disorder (BD) and major depressive disorder (MDD) are highly prevalent, disabling psychiatric illnesses marked by substantial heterogeneity and frequent metabolic and inflammatory comorbidities. Growing evidence implicates low-grade inflammation, immune dysregulation, and insulin resistance (IR) in the pathophysiology, progression, and treatment response of mood disorders. While numerous clinical trials have investigated immunometabolic targeted interventions, outcomes have been inconsistent, due to limited stratification of participants based on underlying biology. This perspective paper aims to identify practical biomarkers and biosignatures to guide patient selection and optimize immunometabolic trial design. We summarize evidence linking neuroinflammation and IR to illness burden, discuss clinical trials targeting these mechanisms, and highlight emerging markers, including extracellular vesicles, monocyte gene expression profiles, and neuron-derived vesicle signatures of IR. No single validated biomarker for identification of immunometabolic phenotype currently exists, but multimodal biosignatures combining genetic, epigenetic, proteomic, and clinical features offer a pragmatic empirical path forward. Integrating these markers with advanced analytic approaches, such as machine learning, holds promise for identifying biologically coherent subgroups most likely to benefit from targeted immunometabolic interventions, accelerating precision medicine for BD and MDD.