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Wiener klinische Wochenschrift
Erschienen in: Wiener klinische Wochenschrift 7/2016

01.12.2016 | geriatrics: at crossroads of medicine

P4 medicine and osteoporosis: a systematic review

verfasst von: Klemen Kodrič, Klemen Čamernik, Darko Černe, Radko Komadina, Prof. Dr. Janja Marc, EuSpLM

Erschienen in: Wiener klinische Wochenschrift | Sonderheft 7/2016

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Summary

Background

Osteoporosis is the most frequent bone metabolic disease. In order to improve early detection, prediction, prevention, diagnosis, and treatment of the disease, a new model of P4 medicine (personalized, predictive, preventive, and participatory medicine) could be applied. The aim of this work was to systematically review the publications of four different types of “omics” studies related to osteoporosis, in order to discover novel predictive, preventive, diagnostic, and therapeutic targets for better management of the geriatric population.

Methods

To systematically search the PubMed database, we created specific groups of criteria for four different types of “omics” information on osteoporosis: genomic, transcriptomic, proteomic, and metabolomic. We then analyzed the intersections between them in order to find correlations and common pathways or molecules with important roles in osteoporosis, and with a potential application in disease prediction, prevention, diagnosis, or treatment.

Results

Altogether, 180 publications of “omics” studies in the field of osteoporosis were found and reviewed at first selection. After introducing the inclusion and exclusion criteria (the secondary selection), 46 papers were included in the systematic review.

Conclusions

The intersection of reviewed papers identified five genes (ESR1, IBSP, CTNNB1, SOX4, and IDUA) and processes like the Wnt pathway, JAK/STAT signaling, and ERK/MAPK, which should be further validated for their predictive, diagnostic, or other clinical value in osteoporosis. Such molecular insights will enable us to fit osteoporosis into the P4 strategy and could increase the effectiveness of disease prediction and prevention, with a decrease in morbidity in the geriatric population.
Vorheriger Artikel Psychocardiology in the elderly
Nächster Artikel Outcome parameters in orthogeriatric co‑management – a mini-review
Literatur
1.
Košnik MMF, Štajer D, Koželj M, Černelč P. Interna medicina, 4th ed. Ljubljana: Littera picta; 2011.
2.
Mafi Golchin M, Heidari L, Ghaderian SM, Akhavan-Niaki H. Osteoporosis: a silent disease with complex genetic contribution. J Genet Genomics. 2016;43(2):49–61.CrossRefPubMed
3.
4.
De Laet C, Kanis JA, Oden A, Johanson H, Johnell O, Delmas P, et al. Body mass index as a predictor of fracture risk: a meta-analysis. Osteoporos Int. 2005;16(11):1330–8.CrossRefPubMed
5.
Klotzbuecher CM, Ross PD, Landsman PB, Abbott TA, Berger M. Patients with prior fractures have an increased risk of future fractures: a summary of the literature and statistical synthesis. J Bone Miner Res. 2000;15(4):721–39.CrossRefPubMed
6.
Kanis JA, Johnell O, Oden A, Johansson H, De Laet C, Eisman JA, et al. Smoking and fracture risk: a meta-analysis. Osteoporos Int. 2005;16(2):155–62.CrossRefPubMed
7.
Kanis JA, Johansson H, Johnell O, Oden A, De Laet C, Eisman JA, et al. Alcohol intake as a risk factor for fracture. Osteoporos Int. 2005;16(7):737–42.CrossRefPubMed
8.
Kanis JA, Johansson H, Oden A, Johnell O, de Laet C, Melton IL, et al. A meta-analysis of prior corticosteroid use and fracture risk. J Bone Miner Res. 2004;19(6):893–9.CrossRefPubMed
9.
Ugurlu U, Nayki U, Nayki C, Ulug P, Kulhan M, Yildirim Y. Assessment of smoking for low bone mineral density in postmenopausal Turkish women. Wien Klin Wochenschr. 2016;128(3):114–9.CrossRefPubMed
10.
Hood L, Rowen L, Galas DJ, Aitchison JD. Systems biology at the Institute for Systems Biology. Brief Funct Genomic Proteomic. 2008;7(4):239–48.CrossRefPubMed
11.
Sobradillo P, Pozo F, Agusti A. P4 medicine: the future around the corner. Arch Bronconeumol. 2011;47(1):35–40.CrossRefPubMed
12.
Younesi E, Hofmann-Apitius M. From integrative disease modeling to predictive, preventive, personalized and participatory (P4) medicine. EPMA J. 2013;4(1):23.CrossRefPubMedPubMedCentral
13.
Flores M, Glusman G, Brogaard K, Price ND, Hood L. P4 medicine: how systems medicine will transform the healthcare sector and society. Per Med. 2013;10(6):565–76.CrossRefPubMedPubMedCentral
14.
National Research Council (US) Committee on A Framework for Developing a New Taxonomy of Disease. Toward precision medicine: building a knowledge network for biomedical research and a new taxonomy of disease. Washington DC: National Academies Press; 2011.
15.
Hood L, Flores M. A personal view on systems medicine and the emergence of proactive P4 medicine: predictive, preventive, personalized and participatory. N Biotechnol. 2012;29(6):613–24.CrossRefPubMed
16.
David JG, Leroy H. Systems biology and emerging technologies will catalyze the transition from reactive medicine to predictive, personalized, preventive and participatory (P4) medicine. Interdiscipl Bio Cent. 2009;1:6.CrossRef
17.
18.
19.
Estrada K, Styrkarsdottir U, Evangelou E, Hsu YH, Duncan EL, Ntzani EE, et al. Genome-wide meta-analysis identifies 56 bone mineral density loci and reveals 14 loci associated with risk of fracture. Nat Genet. 2012;44(5):491–501.CrossRefPubMedPubMedCentral
20.
Guo Y, Tan LJ, Lei SF, Yang TL, Chen XD, Zhang F, et al. Genome-wide association study identifies ALDH7A1 as a novel susceptibility gene for osteoporosis. PLOS Genet. 2010;6(1):e1000806.CrossRefPubMedPubMedCentral
21.
Rivadeneira F, Styrkarsdottir U, Estrada K, Halldorsson BV, Hsu YH, Richards JB, et al. Twenty bone-mineral-density loci identified by large-scale meta-analysis of genome-wide association studies. Nat Genet. 2009;41(11):1199–206.CrossRefPubMedPubMedCentral
22.
Duncan EL, Danoy P, Kemp JP, Leo PJ, McCloskey E, Nicholson GC, et al. Genome-wide association study using extreme truncate selection identifies novel genes affecting bone mineral density and fracture risk. PLOS Genet. 2011;7(4):e1001372.CrossRefPubMedPubMedCentral
23.
Zhang L, Choi HJ, Estrada K, Leo PJ, Li J, Pei YF, et al. Multistage genome-wide association meta-analyses identified two new loci for bone mineral density. Hum Mol Genet. 2014;23(7):1923–33.CrossRefPubMed
24.
Styrkarsdottir U, Halldorsson BV, Gretarsdottir S, Gudbjartsson DF, Walters GB, Ingvarsson T, et al. Multiple genetic loci for bone mineral density and fractures. N Engl J Med. 2008;358(22):2355–65.CrossRefPubMed
25.
Guo Y, Zhang LS, Yang TL, Tian Q, Xiong DH, Pei YF, et al. IL21R and PTH may underlie variation of femoral neck bone mineral density as revealed by a genome-wide association study. J Bone Miner Res. 2010;25(5):1042–8.PubMed
26.
Kung AW, Xiao SM, Cherny S, Li GH, Gao Y, Tso G, et al. Association of JAG1 with bone mineral density and osteoporotic fractures: a genome-wide association study and follow-up replication studies. Am J Hum Genet. 2010;86(2):229–39.CrossRefPubMedPubMedCentral
27.
Richards JB, Rivadeneira F, Inouye M, Pastinen TM, Soranzo N, Wilson SG, et al. Bone mineral density, osteoporosis, and osteoporotic fractures: a genome-wide association study. Lancet. 2008;371(9623):1505–12.CrossRefPubMedPubMedCentral
28.
Styrkarsdottir U, Halldorsson BV, Gretarsdottir S, Gudbjartsson DF, Walters GB, Ingvarsson T, et al. New sequence variants associated with bone mineral density. Nat Genet. 2009;41(1):15–7.CrossRefPubMed
29.
Zheng HF, Duncan EL, Yerges-Armstrong LM, Eriksson J, Bergstrom U, Leo PJ, et al. Meta-analysis of genome-wide studies identifies MEF2C SNPs associated with bone mineral density at forearm. J Med Genet. 2013;50(7):473–8.CrossRefPubMedPubMedCentral
30.
Guo Y, Wang JT, Liu H, Li M, Yang TL, Zhang XW, et al. Are bone mineral density loci associated with hip osteoporotic fractures? A validation study on previously reported genome-wide association loci in a Chinese population. Genet Mol Res. 2012;11(1):202–10.CrossRefPubMedPubMedCentral
31.
Xiao SM, Kung AW, Gao Y, Lau KS, Ma A, Zhang ZL, et al. Post-genome wide association studies and functional analyses identify association of MPP7 gene variants with site-specific bone mineral density. Hum Mol Genet. 2012;21(7):1648–57.CrossRefPubMed
32.
Styrkarsdottir U, Thorleifsson G, Gudjonsson SA, Sigurdsson A, Center JR, Lee SH, et al. Sequence variants in the PTCH1 gene associate with spine bone mineral density and osteoporotic fractures. Nat Commun. 2016;7:10129.CrossRefPubMedPubMedCentral
33.
Paternoster L, Lorentzon M, Vandenput L, Karlsson MK, Ljunggren O, Kindmark A, et al. Genome-wide association meta-analysis of cortical bone mineral density unravels allelic heterogeneity at the RANKL locus and potential pleiotropic effects on bone. PLOS Genet. 2010;6(11):e1001217.CrossRefPubMedPubMedCentral
34.
Trost Z, Trebse R, Prezelj J, Komadina R, Logar DB, Marc J. A microarray based identification of osteoporosis-related genes in primary culture of human osteoblasts. Bone. 2010;46(1):72–80.CrossRefPubMed
35.
Mak YT, Hampson G, Beresford JN, Spector TD. Variations in genome-wide gene expression in identical twins – a study of primary osteoblast-like culture from female twins discordant for osteoporosis. BMC Genet. 2004;5:14.CrossRefPubMedPubMedCentral
36.
Hopwood B, Tsykin A, Findlay DM, Fazzalari NL. Gene expression profile of the bone microenvironment in human fragility fracture bone. Bone. 2009;44(1):87–101.CrossRefPubMed
37.
Liu YZ, Dvornyk V, Lu Y, Shen H, Lappe JM, Recker RR, et al. A novel pathophysiological mechanism for osteoporosis suggested by an in vivo gene expression study of circulating monocytes. J Biol Chem. 2005;280(32):29011–6.CrossRefPubMed
38.
Chen XD, Xiao P, Lei SF, Liu YZ, Guo YF, Deng FY, et al. Gene expression profiling in monocytes and SNP association suggest the importance of the STAT1 gene for osteoporosis in both Chinese and Caucasians. J Bone Miner Res. 2010;25(2):339–55.CrossRefPubMed
39.
Liu YZ, Zhou Y, Zhang L, Li J, Tian Q, Zhang JG, et al. Attenuated monocyte apoptosis, a new mechanism for osteoporosis suggested by a transcriptome-wide expression study of monocytes. PLOS ONE. 2015;10(2):e0116792.CrossRefPubMedPubMedCentral
40.
Xiao P, Chen Y, Jiang H, Liu YZ, Pan F, Yang TL, et al. In vivo genome-wide expression study on human circulating B cells suggests a novel ESR1 and MAPK3 network for postmenopausal osteoporosis. J Bone Miner Res. 2008;23(5):644–54.CrossRefPubMedPubMedCentral
41.
Chen Y, Xia RG. Screening and functional microarray analysis of differentially expressed genes related to osteoporosis. Genet Mol Res. 2014;13(2):3228–36.CrossRefPubMed
42.
Yan B, Li J, Zhang L. Identification of B cells participated in the mechanism of postmenopausal women osteoporosis using microarray analysis. Int J Clin Exp Med. 2015;8(1):1027–34.PubMedPubMedCentral
43.
Ma M, Chen X, Lu L, Yuan F, Zeng W, Luo S, et al. Identification of crucial genes related to postmenopausal osteoporosis using gene expression profiling. Aging Clin Exp Res. 2015; doi:10.​1007/​s40520-015-0509-y.
44.
Liu L, Zhu Q, Wang J, Xi Q, Zhu H, Gu M. Gene expression changes in human mesenchymal stem cells from patients with osteoporosis. Mol Med Rep. 2015;12(1):981–7.PubMedPubMedCentral
45.
Zhou Z, Gao M, Liu Q, Tao MD. Comprehensive transcriptome analysis of mesenchymal stem cells in elderly patients with osteoporosis. Aging Clin Exp Res. 2015;27(5):595–601.CrossRefPubMed
46.
Xie W, Ji L, Zhao T, Gao P. Identification of transcriptional factors and key genes in primary osteoporosis by DNA microarray. Med Sci Monit. 2015;21:1333–44.CrossRefPubMedPubMedCentral
47.
Wu X, Guo S, Shen G, Ma X, Tang C, Xie K, et al. Screening of osteoprotegerin-related feature genes in osteoporosis and functional analysis with DNA microarray. Eur J Med Res. 2013;18:15.CrossRefPubMedPubMedCentral
48.
Wen Y, Guo X, Hao J, Xiao X, Wang W, Wu C, et al. Integrative analysis of genome-wide association studies and gene expression profiles identified candidate genes for osteoporosis in Kashin-Beck disease patients. Osteoporos Int. 2016;27(3):1041–6.CrossRefPubMed
49.
Zhang Y, Wang N, Ma J, Chen XC, Li Z, Zhao W. Expression profile analysis of new candidate genes for the therapy of primary osteoporosis. Eur Rev Med Pharmacol Sci. 2016;20(3):433–40.PubMed
50.
Pineda B, Serna E, Laguna-Fernandez A, Noguera I, Panach L, Hermenegildo C, et al. Gene expression profile induced by ovariectomy in bone marrow of mice: a functional approach to identify new candidate genes associated to osteoporosis risk in women. Bone. 2014;65:33–41.CrossRefPubMed
51.
He H, Cao S, Niu T, Zhou Y, Zhang L, Zeng Y, et al. Network-based meta-analyses of associations of multiple gene expression profiles with bone mineral density variations in women. PLOS ONE. 2016;11(1):e0147475.CrossRefPubMedPubMedCentral
52.
Deng FY, Liu YZ, Li LM, Jiang C, Wu S, Chen Y, et al. Proteomic analysis of circulating monocytes in Chinese premenopausal females with extremely discordant bone mineral density. Proteomics. 2008;8(20):4259–72.CrossRefPubMedPubMedCentral
53.
54.
Zhang L, Liu YZ, Zeng Y, Zhu W, Zhao YC, Zhang JG, et al. Network-based proteomic analysis for postmenopausal osteoporosis in Caucasian females. Proteomics. 2016;16(1):12–28.CrossRefPubMed
55.
Zeng Y, Zhang L, Zhu W, Xu C, He H, Zhou Y, et al. Quantitative proteomics and integrative network analysis identified novel genes and pathways related to osteoporosis. J Proteomics. 2016;142:45–52.CrossRefPubMed
56.
Qundos U, Drobin K, Mattsson C, Hong MG, Sjoberg R, Forsstrom B, et al. Affinity proteomics discovers decreased levels of AMFR in plasma from Osteoporosis patients. Proteomics Clin Appl. 2016;10(6):681–90.CrossRefPubMed
57.
Fan Y, Liu J, Wang S, Wang H, Shi F, Xiong L, et al. Functional proteome of bones in rats with osteoporosis following ovariectomy. Life Sci. 2005;76(25):2893–901.CrossRefPubMed
58.
You YS, Lin CY, Liang HJ, Lee SH, Tsai KS, Chiou JM, et al. Association between the metabolome and low bone mineral density in Taiwanese women determined by (1)H NMR spectroscopy. J Bone Miner Res. 2014;29(1):212–22.CrossRefPubMed
59.
Qi H, Bao J, An G, Ouyang G, Zhang P, Wang C, et al. Association between the metabolome and bone mineral density in pre- and post-menopausal Chinese women using GC-MS. Mol Biosyst. 2016;12(7):2265–75. doi:10.​1039/​c6mb00181e.CrossRefPubMed
60.
Ma B, Liu J, Zhang Q, Ying H, Jiye A, Sun J, et al. Metabolomic profiles delineate signature metabolic shifts during estrogen deficiency-induced bone loss in rat by GC-TOF/MS. PLOS ONE. 2013;8(2):e54965.CrossRefPubMedPubMedCentral
61.
Liu X, Liu Y, Cheng M, Zhang X, Xiao H. A metabolomics study of the inhibitory effect of 17-beta-estradiol on osteoclast proliferation and differentiation. Mol Biosyst. 2015;11(2):635–46.CrossRefPubMed
62.
63.
Moore SC, Matthews CE, Sampson JN, Stolzenberg-Solomon RZ, Zheng W, Cai Q, et al. Human metabolic correlates of body mass index. Metabolomics. 2013;10(2):259–69.PubMedCentral
64.
65.
66.
67.
68.
Metadaten
Titel
P4 medicine and osteoporosis: a systematic review
verfasst von
Klemen Kodrič
Klemen Čamernik
Darko Černe
Radko Komadina
Prof. Dr. Janja Marc, EuSpLM
Publikationsdatum
01.12.2016
Verlag
Springer Vienna
Erschienen in
Wiener klinische Wochenschrift / Ausgabe Sonderheft 7/2016
Print ISSN: 0043-5325
Elektronische ISSN: 1613-7671
DOI
https://doi.org/10.1007/s00508-016-1125-3

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