Evidence map›Paper›PMID 39490735›Full record

ArticleJournal of advanced research2025

Plasma proteomic profiles reveal proteins and three characteristic patterns associated with osteoporosis: A prospective cohort study.

Yi Zheng, Jincheng Li, Yucan Li, Jiacheng Wang, Chen Suo, Yanfeng Jiang, Li Jin, Kelin Xu, Xingdong Chen

Abstract read
In one paragraph

Article in Journal of advanced research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing 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

18 citing papers in PubMed.

  1. Explainable Plasma Proteomics-Based Machine Learning for Osteoporosis Diagnosis, Prognosis, and Protein Biomarker Discovery in the UK Biobank.FASEB journal : official publication of the Federation of American Societies for Experimental Biology · 2026
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  9. Comparative Proteomic Profiling of ClinicalJournal of extracellular biology · 2026
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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.

Yi ZhengState Key Laboratory of Genetic Engineering, Zhangjiang Fudan International Innovation Center, School of Life Sciences, Human Phenome Institute, Fudan University, Shanghai, China.
Jincheng LiState Key Laboratory of Genetic Engineering, Zhangjiang Fudan International Innovation Center, School of Life Sciences, Human Phenome Institute, Fudan University, Shanghai, China.
Yucan LiState Key Laboratory of Genetic Engineering, Zhangjiang Fudan International Innovation Center, School of Life Sciences, Human Phenome Institute, Fudan University, Shanghai, China.
Jiacheng WangDepartment of Epidemiology, School of Public Health, and the Key Laboratory of Public Health Safety of Ministry of Education, Fudan University, Shanghai, China.
Chen SuoDepartment of Biostatistics, School of Public Health, and the Key Laboratory of Public Health Safety of Ministry of Education, Fudan University, Shanghai, China; Fudan University Taizhou Institute of Health Sciences, Taizhou, Jiangsu, China.
Yanfeng JiangState Key Laboratory of Genetic Engineering, Zhangjiang Fudan International Innovation Center, School of Life Sciences, Human Phenome Institute, Fudan University, Shanghai, China; Fudan University Taizhou Institute of Health Sciences, Taizhou, Jiangsu, China.
Li JinState Key Laboratory of Genetic Engineering, Zhangjiang Fudan International Innovation Center, School of Life Sciences, Human Phenome Institute, Fudan University, Shanghai, China; Fudan University Taizhou Institute of Health Sciences, Taizhou, Jiangsu, China.
Kelin XuDepartment of Biostatistics, School of Public Health, and the Key Laboratory of Public Health Safety of Ministry of Education, Fudan University, Shanghai, China; Fudan University Taizhou Institute of Health Sciences, Taizhou, Jiangsu, China. Electronic address: xukelin@fudan.edu.cn.
Xingdong ChenState Key Laboratory of Genetic Engineering, Zhangjiang Fudan International Innovation Center, School of Life Sciences, Human Phenome Institute, Fudan University, Shanghai, China; Fudan University Taizhou Institute of Health Sciences, Taizhou, Jiangsu, China; National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China; Yiwu Research Institute of Fudan University, Yiwu, Zhejiang, China. Electronic address: xingdongchen@fudan.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionExploration of plasma proteins associated with osteoporosis can offer insights into its pathological development, identify novel biomarkers for screening high-risk populations, and facilitate the discovery of effective therapeutic targets.

objectivesThe present study aimed to identify potential proteins associated with osteoporosis and to explore the underlying mechanisms from a proteomic perspective.

methodsThe study included 42,325 participants without osteoporosis in the UK Biobank (UKB), of whom 1,477 developed osteoporosis during the follow-up. We used Cox regression and Mendelian randomization analysis to examine the association between plasma proteins and osteoporosis. Machine learning was utilized to explore proteins with strong predictive power for osteoporosis risk.

resultsOf 2,919 plasma proteins, we identified 134 significantly associated with osteoporosis, with sclerostin (SOST), adiponectin (ADIPOQ), and creatine kinase B-type (CKB) exhibiting strong associations. Twelve of these proteins showed significant associations with bone mineral density (BMD) T-score at the femoral neck, lumbar spine, and total body. Mendelian randomization further supported causal relationships between 17 plasma proteins and osteoporosis. Moreover, follitropin subunit beta (FSHB), SOST, and ADIPOQ demonstrated high importance in predictive modeling. Utilizing a predictive model built with 10 proteins, we achieved relatively accurate prediction of osteoporosis onset up to 5 years in advance (AUC = 0.803). Finally, we identified three osteoporosis-related protein modules associated with immunity, lipid metabolism, and follicle-stimulating hormone (FSH) regulation from a network perspective, elucidating their mediating roles between various risk factors (smoking, sleep, physical activity, polygenic risk score (PRS), and menopause) and osteoporosis.

conclusionWe identified several proteins associated with osteoporosis and highlighted the role of plasma proteins in influencing its progression through three primary pathways: immunity, lipid metabolism, and FSH regulation. This provides further insights into the distinct molecular patterns and pathogenesis of bone loss and may contribute to strengthening early diagnosis and long-term monitoring of the condition.

Indexed as

Blood ProteinsOsteoporosisProteomicsAdaptor Proteins, Signal TransducingAdiponectinAgedBiomarkersBone DensityFemaleHumansMaleMendelian Randomization AnalysisMiddle AgedProspective StudiesRisk FactorsAdaptor Proteins, Signal TransducingAdiponectinADIPOQ protein, humanBiomarkersBlood ProteinsSOST protein, humanOsteoporosisPlasma proteomicPredictionProtein module

Identifiers

PMID39490735
PMCPMC12536596

What Socratic holds

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LicenceCC BY-NC-ND
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Registered trials

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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 Socratic graph.