Unifying the hallmarks of major depression through neuroimmune-metabolic-oxidative (NIMETOX) dysregulation: a mechanistic systems framework.

Authors

Michael Maes, Abbas F Almulla, Drozdstoj Stoyanov, Yingqiang Zhang

Published in

Cellular & molecular immunology. Jul 13, 2026. Epub Jul 13, 2026.

Abstract

Current state-of-the-art neuroimmune, metabolic, and oxidative stress (NIMETOX) knowledge that has been developed in clinical major depressive disorder (MDD) research over the past three decades is explored in this review. Between 1990 and 2000, the acute phase of severe MDD was characterized by the activation of T helper (Th)1 cells and M1 macrophages, leading to immune dysregulation that affects nutritional immunity and alters protein, tryptophan, iron, and lipid metabolism. The latter comprises lower high-density lipoprotein cholesterol, reverse cholesterol transport (RCT), ω3 polyunsaturated fatty acids, heightened lipid peroxidation and atherogenicity. Additionally, immune alterations regulate stress-responsive systems and modify the biological basis of depressive symptoms through neurotoxic effects and reduced neuroprotection. The incremental information acquired from 2000 to 2026 revealed that the acute phase of severe MDD is characterized by immune sensitization, imbalances between the compensatory immunoregulatory system (CIRS) and the immune-inflammatory response system (IRS) and that there are multiple interactions between increased atherogenicity, metabolic syndrome, oxidative stress, and lower antioxidant activity and RCT. Additionally, the NIMETOX pathway may be fuelled by increased expression of TLR4 and NF-κB intracellular signaling driven by increased lipopolysaccharides, lipids, and oxidatively modified epitopes. This paper presents evidence that peripheral NIMETOX pathways may lead to neuroinflammation, microglial activation, and neuronal damage and that increased lipid load impairs these central pathways. This paper assesses the field's future advancements by conducting a comprehensive examination of the reviewed knowledge base, deep phenotyping, panomics methodologies, and machine learning techniques, including the nomothetic precision approach.

PMID:
42437805
Bibliographic data and abstract were imported from PubMed on 13 Jul 2026.

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