Evidence map›Paper›PMID 41862570›Full record

ArticleMolecular psychiatry2026

Large-scale proteomic analyses before depression diagnosis reveal novel pathophysiological insights.

Bolun Cheng, Shiqiang Cheng, Chuyu Pan, Wenming Wei, Jin Feng, Xin Qi, Boyue Zhao, Yan Wen, Feng Zhang

Abstract read
PubMed Publisher
In one paragraph

Article in Molecular psychiatry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Bolun ChengNHC Key Laboratory of Environment and Endemic Diseases, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, 710061, China.ORCID http://orcid.org/0000-0003-2915-5535
Shiqiang ChengNHC Key Laboratory of Environment and Endemic Diseases, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, 710061, China.ORCID http://orcid.org/0000-0001-8427-0312
Chuyu PanNHC Key Laboratory of Environment and Endemic Diseases, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, 710061, China.
Wenming WeiNHC Key Laboratory of Environment and Endemic Diseases, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, 710061, China.
Jin FengNHC Key Laboratory of Environment and Endemic Diseases, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, 710061, China.
Xin QiNHC Key Laboratory of Environment and Endemic Diseases, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, 710061, China.
Boyue ZhaoNHC Key Laboratory of Environment and Endemic Diseases, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, 710061, China.
Yan WenNHC Key Laboratory of Environment and Endemic Diseases, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, 710061, China.
Feng ZhangNHC Key Laboratory of Environment and Endemic Diseases, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, 710061, China. fzhxjtu@mail.xjtu.edu.cn.ORCID http://orcid.org/0000-0001-5254-2264

Funding

National Natural Science Foundation of China (National Science Foundation of China) 2021JCW-08
6 · The paper itself

Abstract

backgroundThe early pathophysiology of depression is poorly understood. We elucidated the decadal temporal evolution of plasma proteomic alterations before depression diagnosis and evaluated their associations with comorbid conditions and neuroanatomical changes.

methodsThis study analyzed 31,114 depression-free participants and identified 1555 incident depression cases after a median follow-up of 7.8 years. Cox regression was used to identify depression-associated proteins, adjusting for sociodemographic, lifestyle, and genetic factors. Subsequent analyses of depression-related proteins included exome-wide association analysis (EWAS), temporal change modeling of pre-diagnostic protein dynamics via LOESS regression, association analyses with eight comorbid conditions and 58 regional brain volumes, and LightGBM-based predictive modeling.

resultWe found 64 depression-related proteins, with PIGR, HAVCR2, and IL4R validated in EWAS. For example, PIGR exhibited risk effects for depression (HR = 1.26, 95%CI: 1.13-1.40) and comorbid conditions, particularly diabetes (HR = 2.34, 95%CI: 2.12-2.58). Temporal profiling identified three protein clusters: one (cell-matrix adhesion) characterized by initial stability and subsequent decline, another (including PIGR and HAVCR2) characterized by MAPK cascade activation, and the third characterized by increased apoptosis and immune response. Neuroimaging correlations confirmed that elevated PIGR levels were associated with reduced volume in the bilateral ventral diencephalon (β = -0.062--0.061). A predictive model combined proteins and clinical features, achieving superior accuracy in depression prediction after 15 years (area under the curve = 0.74). Our findings reveal early peripheral pathophysiological changes in depression, suggesting a progression that may involve early apoptotic processes, an intermediate inflammatory phase, and later proteolytic dysregulation.

conclusionThese insights hold significant potential for developing early biomarkers and precision therapies.

Indexed as

DepressionAdultBiomarkersBrainFemaleGenome-Wide Association StudyHumansMaleMiddle AgedProteomicsBiomarkers

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.