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      Serum Proteomic Analysis Reveals Vitamin D-Binding Protein (VDBP) as a Potential Biomarker for Low Bone Mineral Density in Mexican Postmenopausal Women

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          Abstract

          Osteoporosis is a skeletal disease mainly affecting women over 50 years old and it represents a serious public health problem because of the high socioeconomic burden. This disease is characterized by deterioration of bone microarchitecture, low bone mineral density (BMD), and increased risk of fragility fractures. This study aimed to identify serum useful proteins as biomarkers for the diagnosis and/or prognosis of osteoporosis and fracture risk. We collected 446 serum samples from postmenopausal women aged ≥45 years old. Based on the BMD measurement, we classified the participants into three groups: osteoporotic, osteopenic, and normal. In an initial discovery stage, we conducted a proteomic approach using two-dimensional differential gel electrophoresis (2D-DIGE). The peptides into the spots of interest were identified through matrix-assisted laser desorption/ionization-time of flight (MALDI-TOF/TOF). Enzyme-linked immunosorbent assay (ELISA) was performed to validate the proteins of interest. We identified 27 spots of interest when comparing low BMD versus normal BMD postmenopausal women. Based on their relevance in bone metabolism, we analyzed three proteins: ceruloplasmin (CP), gelsolin (GSN), and vitamin D-binding protein (VDBP). Our results demonstrated that low serum VDBP levels correlate with low BMD (osteopenic and osteoporotic). Therefore, VDBP could be considered as a novel, potential, and non-invasive biomarker for the early detection of osteoporosis.

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          Postmenopausal osteoporosis.

          Osteoporosis is a metabolic bone disorder that is characterized by low bone mass and micro-architectural deterioration of bone tissue. Fractures of the proximal femur, the vertebrae and the distal radius are the most frequent osteoporotic fractures, although most fractures in the elderly are probably at least partly related to bone fragility. The incidence of fractures varies greatly by country, but on average up to 50% of women >50 years of age are at risk of fractures. Fractures severely affect the quality of life of an individual and are becoming a major public health problem owing to the ageing population. Postmenopausal osteoporosis, resulting from oestrogen deficiency, is the most common type of osteoporosis. Oestrogen deficiency results in an increase in bone turnover owing to effects on all types of bone cells. The imbalance in bone formation and resorption has effects on trabecular bone (loss of connectivity) and cortical bone (cortical thinning and porosity). Osteoporosis is diagnosed using bone density measurements of the lumbar spine and proximal femur. Preventive strategies to improve bone health include diet, exercise and abstaining from smoking. Fractures may be prevented by reducing falls in high-risk populations. Several drugs are licensed to reduce fracture risk by slowing down bone resorption (such as bisphosphonates and denosumab) or by stimulating bone formation (such as teriparatide). Improved understanding of the cellular basis for osteoporosis has resulted in new drugs targeted to key pathways, which are under development.
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            Osteoporosis

            Osteoporosis is a common systemic skeletal disorder resulting in bone fragility and increased fracture risk. However, management of osteoporosis and fracture prevention strategies are often not addressed by primary care clinicians, even in older patients with recent fractures. Evidence-based screening strategies will improve identification of patients who are most likely to benefit from drug treatment to prevent fracture. In addition, careful consideration of when pharmacotherapy should be started and choice of medication and duration of treatment will maximize the benefits of fracture prevention while minimizing potential harms of long-term drug exposure.
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              Exosomes in bodily fluids are a highly stable resource of disease biomarkers.

              Biomarkers are measurable indicators of a biological state. As our understanding of diseases meliorates, it is generally accepted that early diagnosis renders the best chance to cure a disease. In the context of proteomics, the discovery phase of identifying bonafide biomarkers and the ensuing validation phase involving large cohort of patient samples are impeded by the complexity of bodily fluid samples. High abundant proteins found in blood plasma make it difficult for the detection of low abundant proteins that may be potential biomarkers. Extracellular vesicles (EVs) have reignited interest in the field of biomarker discovery. EVs contain a tissue-type signature wherein a rich cargo of proteins and RNA are selectively packaged. In addition, as EVs are membranous structures, the luminal contents are protected from degradation by extracellular proteases and are highly stable in storage conditions. Interestingly, an appealing feature of EV-based biomarker analysis is the significant reduction in the sample complexity compared to whole bodily fluids. With these prescribed attributes, which are the rate-limiting factors of traditional biomarker analysis, there is immense potential for the use of EVs for biomarker detection in clinical settings. This review will discuss the current issues with biomarker analysis and the potential use of EVs as reservoirs of disease biomarkers.
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                Author and article information

                Journal
                Nutrients
                Nutrients
                nutrients
                Nutrients
                MDPI
                2072-6643
                21 November 2019
                December 2019
                : 11
                : 12
                : 2853
                Affiliations
                [1 ]Laboratorio de Genómica del metabolismo óseo, Instituto Nacional de Medicina Genomica (INMEGEN), Ciudad de México 14610, Mexico; mmaye.sol@ 123456gmail.com (M.M.M.-A.); eramirez@ 123456inmegen.gob.mx (E.G.R.-S.); audelacm@ 123456gmail.com (A.H.D.l.C.-M.)
                [2 ]Subdirección de Aplicaciones Clínicas, Instituto Nacional de Medicina Genómica (INMEGEN), Ciudad de México 14610, Mexico; daparicio@ 123456inmegen.gob.mx (D.I.A.-B.); jreyes@ 123456inmegen.gob.mx (J.P.R.-G.)
                [3 ]Consejo Nacional de Ciencia y Tecnología (CONACYT)-Laboratorio de Genómica del Metabolismo Óseo, Instituto Nacional de Medicina Genómica (INMEGEN), Ciudad de México 14610, Mexico
                [4 ]Laboratorio de Enfermedades Cardiovasculares, Instituto Nacional de Medicina Genómica (INMEGEN), Ciudad de México 14610, Mexico; bapuente@ 123456gmail.com
                [5 ]Laboratorio de Genética, Dirección de Investigación, Instituto Nacional de Rehabilitación, Ciudad de México 14389, Mexico; dr_genetica@ 123456yahoo.com (A.H.-B.); marvaldes@ 123456yahoo.com (M.V.-F.)
                [6 ]Centro de Investigación en Políticas, Población y Salud, Facultad de Medicina, Universidad Nacional Autónoma de México, Ciudad de México 04510, Mexico; bereriveraparedez7@ 123456gmail.com (B.R.-P.); jorge.salmec@ 123456gmail.com (J.S.)
                [7 ]Unidad de Investigación Epidemiológica y en Servicios de Salud, Instituto Mexicano del Seguro Social (IMSS), Cuernavaca Morelos 62000, Mexico; paula_rzps@ 123456hotmail.com
                [8 ]Centro de Investigación en Salud Poblacional, Instituto Nacional de Salud Pública, Cuernavaca, Morelos 62100, Mexico; mquiterio@ 123456insp.mx
                Author notes
                [* ]Correspondence: rvelazquez@ 123456inmegen.gob.mx ; Tel.: +52-(55)-5350-1900 (ext. 1233); Fax: +52-(55)-5350-1999
                Author information
                https://orcid.org/0000-0001-8063-3497
                https://orcid.org/0000-0003-0036-7227
                https://orcid.org/0000-0002-0767-9357
                https://orcid.org/0000-0003-1096-579X
                https://orcid.org/0000-0002-2586-4396
                https://orcid.org/0000-0003-4515-0777
                Article
                nutrients-11-02853
                10.3390/nu11122853
                6950314
                31766436
                1b0eae0d-a45f-429e-b2ff-fdc99d0f5b31
                © 2019 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 14 October 2019
                : 16 November 2019
                Categories
                Article

                Nutrition & Dietetics
                biomarker,bone mineral density,osteoporosis,proteomics,vitamin d-binding protein (vdbp)

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