Plasma proteomics reveal heterogeneous subtypes of depression linked to inflammation and aging

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Authors: Zhengxu Lian, Lena K. Palaniyappan, Yuanli Guo, Zhaowen Liu, Nanyu Kuang, Yu Liu, Yuchao Jiang, Barbara Jacquelyn Sahakian, Trevor William Robbins, Jianfeng Feng, Benjamin Becker, Wei Cheng
+3 more Xinran Wu, Edward Thomas Bullmore, Jie Zhang
Cited by: 0
References: 42

Abstract

Abstract The clinical heterogeneity of depression has defied biological classification, limiting personalized treatment. Previous neuroimaging- or symptom-based subtyping of depression failed to clarify the underlying pathoetiology, while plasma proteins which integrates signals from multiple organ systems, offers a promising way to define biologically grounded subtypes. Using plasma proteomics from 2,127 incident depression cases in a cohort of 53000 individuals, we identified three biologically distinct subtypes differing in inflammation, aging, and metabolic profiles. The most prevalent subtype (‘inflammation/ageing’) was characterized by aging-related inflammation, poorest prognosis with hippocampal atrophy and highest suicide risk, mediated by age-related amygdala atrophy; this subtype had highest anhedonia burden. A distinct ‘inflammation/energy dysregulation’ group had metabolic pathway enrichment with high inflammation and lifestyle risk factors (smoking) but no ageing trend, predominantly physical/psychomotor symptoms and decreased thalamic volume. In contrast, the ‘inflammation-resilient’ group had the lowest inflammatory proteomic loading, lowest depression severity, more resilient lifestyle and increased hippocampal volume. These proteomic signatures, detectable years before symptom onset, enable risk stratification and suggest subtype-specific targeted physical and lifestyle interventions.

Fields of Study & Subjects

Tryptophan and brain disorders Treatment of Major Depression Stress Responses and Cortisol

Publication details

Journal / source
bioRxiv (Cold Spring Harbor Laboratory)
Publication date
15 January 2026
Document type
Preprint