血浆蛋白质组图谱揭示了与骨质疏松症相关的蛋白质和三种特征模式:前瞻性队列研究

IF 11.4 1区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Yi Zheng, Jincheng Li, Yucan Li, Jiacheng Wang, Chen Suo, Yanfeng Jiang, Li Jin, Kelin Xu, Xingdong Chen
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引用次数: 0

摘要

导言:研究与骨质疏松症相关的血浆蛋白可以深入了解骨质疏松症的病理发展过程,为筛查高危人群确定新的生物标记物,并促进发现有效的治疗靶点。研究目的:本研究旨在确定与骨质疏松症相关的潜在蛋白,并从蛋白质组学的角度探讨其潜在机制。我们使用 Cox 回归和孟德尔随机分析法研究了血浆蛋白与骨质疏松症之间的关系。结果 在 2,919 种血浆蛋白中,我们发现 134 种与骨质疏松症显著相关,其中硬骨蛋白 (SOST)、脂肪连蛋白 (ADIPOQ) 和 B 型肌酸激酶 (CKB) 表现出很强的相关性。其中 12 种蛋白质与股骨颈、腰椎和全身的骨矿物质密度 (BMD) T 值有显著关联。孟德尔随机分析进一步证实了 17 种血浆蛋白与骨质疏松症之间的因果关系。此外,促甲状腺激素亚单位 beta(FSHB)、SOST 和 ADIPOQ 在预测模型中表现出高度重要性。通过利用 10 种蛋白质建立的预测模型,我们实现了对骨质疏松症提前 5 年发病的相对准确预测(AUC = 0.803)。最后,我们从网络角度确定了与免疫、脂质代谢和促卵泡激素(FSH)调节相关的三个骨质疏松症相关蛋白模块,阐明了它们在各种风险因素(吸烟、睡眠、体力活动、多基因风险评分(PRS)和绝经)与骨质疏松症之间的中介作用。这进一步揭示了骨质流失的独特分子模式和发病机制,可能有助于加强对该疾病的早期诊断和长期监测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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

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

Introduction

Exploration 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.

Objectives

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

Methods

The 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.

Results

Of 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.

Conclusion

We 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.
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来源期刊
Journal of Advanced Research
Journal of Advanced Research Multidisciplinary-Multidisciplinary
CiteScore
21.60
自引率
0.90%
发文量
280
审稿时长
12 weeks
期刊介绍: Journal of Advanced Research (J. Adv. Res.) is an applied/natural sciences, peer-reviewed journal that focuses on interdisciplinary research. The journal aims to contribute to applied research and knowledge worldwide through the publication of original and high-quality research articles in the fields of Medicine, Pharmaceutical Sciences, Dentistry, Physical Therapy, Veterinary Medicine, and Basic and Biological Sciences. The following abstracting and indexing services cover the Journal of Advanced Research: PubMed/Medline, Essential Science Indicators, Web of Science, Scopus, PubMed Central, PubMed, Science Citation Index Expanded, Directory of Open Access Journals (DOAJ), and INSPEC.
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