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      Proteome‐wide profiling reveals dysregulated molecular features and accelerated aging in osteoporosis: A 9.8‐year prospective study

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          Abstract

          The role of circulatory proteomics in osteoporosis is unclear. Proteome‐wide profiling holds the potential to offer mechanistic insights into osteoporosis. Serum proteome with 413 proteins was profiled by liquid chromatography–tandem mass spectrometry (LC–MS/MS) at baseline, and the 2nd, and 3rd follow‐ups (7704 person‐tests) in the prospective Chinese cohorts with 9.8 follow‐up years: discovery cohort ( n = 1785) and internal validation cohort ( n = 1630). Bone mineral density (BMD) was measured using dual‐energy X‐ray absorptiometry (DXA) at follow‐ups 1 through 3 at lumbar spine (LS) and femoral neck (FN). We used the Light Gradient Boosting Machine (LightGBM) to identify the osteoporosis (OP)‐related proteomic features. The relationships between serum proteins and BMD in the two cohorts were estimated by linear mixed‐effects model (LMM). Meta‐analysis was then performed to explore the combined associations. We identified 53 proteins associated with osteoporosis using LightGBM, and a meta‐analysis showed that 22 of these proteins illuminated a significant correlation with BMD ( p < 0.05). The most common proteins among them were PHLD, SAMP, PEDF, HPTR, APOA1, SHBG, CO6, A2MG, CBPN, RAIN APOD, and THBG. The identified proteins were used to generate the biological age (BA) of bone. Each 1 SD‐year increase in KDM‐Proage was associated with higher risk of LS‐OP (hazard ratio [HR], 1.25; 95% CI, 1.14–1.36, p = 4.96 × 10 −06), and FN‐OP (HR, 1.13; 95% CI, 1.02–1.23, p = 9.71 × 10 −03). The findings uncovered that the apolipoproteins, zymoproteins, complements, and binding proteins presented new mechanistic insights into osteoporosis. Serum proteomics could be a crucial indicator for evaluating bone aging.

          Abstract

          Serum proteomics offers potential insight into accelerated aging in osteoporosis. Osteoporosis is commonly referred to as an aging disorder characterized by decreased bone mineral density (BMD) and an elevated risk of fractures. Using longitudinal serum proteomics analyses with 413 protein species covering various protein classes in a population‐based study, we found that the proteins of apolipoproteins, zymoprotein, coagulation, immunoglobulins, complement, and binding proteins may be the potential therapeutic targets for osteoporosis. Serum proteomics could be a crucial indicator for evaluating bone aging.

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          Genomic atlas of the human plasma proteome

          Although plasma proteins have important roles in biological processes and are the direct targets of many drugs, the genetic factors that control inter-individual variation in plasma protein levels are not well understood. Here we characterize the genetic architecture of the human plasma proteome in healthy blood donors from the INTERVAL study. We identify 1,927 genetic associations with 1,478 proteins, a fourfold increase on existing knowledge, including trans associations for 1,104 proteins. To understand the consequences of perturbations in plasma protein levels, we apply an integrated approach that links genetic variation with biological pathway, disease, and drug databases. We show that protein quantitative trait loci overlap with gene expression quantitative trait loci, as well as with disease-associated loci, and find evidence that protein biomarkers have causal roles in disease using Mendelian randomization analysis. By linking genetic factors to diseases via specific proteins, our analyses highlight potential therapeutic targets, opportunities for matching existing drugs with new disease indications, and potential safety concerns for drugs under development.
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            A reference standard for the description of osteoporosis.

            In 1994, the World Health Organization published diagnostic criteria for osteoporosis. Since then, many new technologies have been developed for the measurement of bone mineral at multiple skeletal sites. The information provided by each assessment will describe the clinical characteristics, fracture risk and epidemiology of osteoporosis differently. Against this background, there is a need for a reference standard for describing osteoporosis. In the absence of a true gold standard, this paper proposes that the reference standard should be based on bone mineral density (BMD) measurement made at the femoral neck with dual-energy X-ray absorptiometry (DXA). This site has been the most extensively validated, and provides a gradient of fracture risk as high as or higher than that of many other techniques. The recommended reference range is the NHANES III reference database for femoral neck measurements in women aged 20-29 years. A similar cut-off value for femoral neck BMD that is used to define osteoporosis in women can be used for the diagnosis of osteoporosis in men - namely, a value for BMD 2.5 SD or more below the average for young adult women. The adoption of DXA as a reference standard provides a platform on which the performance characteristics of less well established and new methodologies can be compared.
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              Connecting genetic risk to disease end points through the human blood plasma proteome

              Genome-wide association studies (GWAS) with intermediate phenotypes, like changes in metabolite and protein levels, provide functional evidence to map disease associations and translate them into clinical applications. However, although hundreds of genetic variants have been associated with complex disorders, the underlying molecular pathways often remain elusive. Associations with intermediate traits are key in establishing functional links between GWAS-identified risk-variants and disease end points. Here we describe a GWAS using a highly multiplexed aptamer-based affinity proteomics platform. We quantify 539 associations between protein levels and gene variants (pQTLs) in a German cohort and replicate over half of them in an Arab and Asian cohort. Fifty-five of the replicated pQTLs are located in trans. Our associations overlap with 57 genetic risk loci for 42 unique disease end points. We integrate this information into a genome-proteome network and provide an interactive web-tool for interrogations. Our results provide a basis for novel approaches to pharmaceutical and diagnostic applications.
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                Author and article information

                Contributors
                zhengjusheng@westlake.edu.cn
                guotiannan@westlake.edu.cn
                chenyum@mail.sysu.edu.cn
                Journal
                Aging Cell
                Aging Cell
                10.1111/(ISSN)1474-9726
                ACEL
                Aging Cell
                John Wiley and Sons Inc. (Hoboken )
                1474-9718
                1474-9726
                16 November 2023
                February 2024
                : 23
                : 2 ( doiID: 10.1111/acel.v23.2 )
                : e14035
                Affiliations
                [ 1 ] Department of Epidemiology, Guangdong Provincial Key Laboratory of Food, Nutrition and Health, School of Public Health Sun Yat‐sen University Guangzhou China
                [ 2 ] School of Life Sciences Westlake University Hangzhou China
                [ 3 ] Westlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine Hangzhou China
                [ 4 ] Shenzhen Bao'an Center for Chronic Diseases Control Shenzhen China
                Author notes
                [*] [* ] Correspondence

                Yu‐ming Chen, Department of Epidemiology, School of Public Health, Sun Yat‐sen University, Guangzhou 510080, China.

                Email: chenyum@ 123456mail.sysu.edu.cn

                Ju‐Sheng Zheng and Tiannan Guo, School of Life Sciences, Westlake University, 18 Shilongshan Rd, Cloud Town, Hangzhou, China.

                Email: zhengjusheng@ 123456westlake.edu.cn and guotiannan@ 123456westlake.edu.cn

                Author information
                https://orcid.org/0009-0002-7625-6187
                https://orcid.org/0000-0002-3955-9376
                https://orcid.org/0000-0001-6560-4890
                https://orcid.org/0000-0003-3869-7651
                https://orcid.org/0000-0003-1658-5528
                Article
                ACEL14035 ACE-23-0549.R1
                10.1111/acel.14035
                10861190
                37970652
                f3999955-e9de-4787-9a5b-2a1c04fc1f64
                © 2023 The Authors. Aging Cell published by the Anatomical Society and John Wiley & Sons Ltd.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 15 October 2023
                : 21 July 2023
                : 23 October 2023
                Page count
                Figures: 6, Tables: 1, Pages: 14, Words: 10247
                Funding
                Funded by: National Natural Science Foundation of China , doi 10.13039/501100001809;
                Award ID: 81773416
                Award ID: 82073529
                Award ID: 82073546
                Funded by: Shenzhen Fundamental Research Program , doi 10.13039/501100017607;
                Award ID: JCYJ20210324125202006
                Funded by: the 5010 Program for Clinical Researches of the Sun Yat‐sen University
                Award ID: 2007032
                Categories
                Research Article
                Research Articles
                Custom metadata
                2.0
                February 2024
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.3.6 mode:remove_FC converted:12.02.2024

                Cell biology
                biological age,longitudinal study,osteoporosis,proteome‐wide study
                Cell biology
                biological age, longitudinal study, osteoporosis, proteome‐wide study

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