Chinese Medical Sciences Journal ›› 2021, Vol. 36 ›› Issue (2): 135-149.doi: 10.24920/003729
• Review • Previous Articles Next Articles
Jieyu Liu1, 2, 3, Jiaxiang Wang1, 2, 3, Li Xu1, 2, Feiyan Deng1, 2, *()
Received:
2020-09-13
Published:
2021-06-30
Contact:
Feiyan Deng
E-mail:fdeng@suda.edu.cn
Jieyu Liu, Jiaxiang Wang, Li Xu, Feiyan Deng. Potential Biomolecules Capable of Assessing Risk of Osteoporotic Fracture: A Scoping Review[J].Chinese Medical Sciences Journal, 2021, 36(2): 135-149.
Add to citation manager EndNote|Reference Manager|ProCite|BibTeX|RefWorks
Table 1
Potential protein and peptide molecules in serum associated with OFs"
Molecules | References | Sample size | Gender | Ethnicity | Age (yrs) (mean ±SD or range) | Follow-up (yrs) | Phenotype | Concentration | Effect size or difference | |
---|---|---|---|---|---|---|---|---|---|---|
Male | Female | |||||||||
IGFBP-I | Lundin et al.[ | 351 | Female | Sweden | 72.8±2.31 | 10 | OF | NA | NA | HF: Adj. HR=1.46, 95%CI(1.08-1.99) per SD increase; MOF: Adj.HR=1.33, 95%CI(1.05-1.69, P=0.017) |
Periostin | Rousseauet al.[ | 607 (women with incident fracture: 75 vs. women without incident fracture: 532) | Female | French | 66.6±8.4 | 7 | VF | 1249±340vs. 1162±298 ng/ml | NA | RR=2.81, 95%CI(1.1-7.3), P=0.03; Adj. RR=1.88, 95%CI(1.1-3.2) |
K-Postn | Bonnetet al.[ | 695 (fracture: 66 vs. no fracture: 629) | Female | Geneva | 65.0±1.5 | (mean±SD) 4.7±1.9 | Low-trauma clinical fracture | 57.5±36.6vs. 42.5±23.4 ng/ml | NA | HR=2.14, 95%CI(1.54-2.97), P<0.001 |
Adiponectin | Johansson et al.[ | 999 (fracture: 150 vs. no fracture: 849) | Male | Swedish | NA | (mean) 5.2 | OF | 14.2±8.9vs. 11.5±5.8 µg/ml | HR/SD=1.46, 95%CI (1.23-1.72) | NA |
Adiponectin | Johansson et al.[ | 989 (fracture: 124 vs. no fracture: 865) | Male | Swedish | 70-81 | (mean) 5.3 | OF | 14.4±9.3vs. 11.5±5.8 µg/ml | Adj. for age and time since baseline HR=1.47, 95%CI(1.23-1.77) per SD; Adj. for FN BMD and age HR=1.40, 95%CI(1.16-1.68) per SD | NA |
CRP | Oei et al.[ | 6386 | 59% female | Rotterdam | 69.1±8.9 | (mean) 11.6 | OF | NA | OF: HR=1.06, 95%CI(1.02-1.11) HF: HR=1.09, 95%CI(1.02-1.17) VF: OR=1.34, 95%CI(1.14-1.58) | |
BAP | Tamaki et al.[ | 522 | Female | Japanese | 54.7 (YSM<5 years); 66.6 (YSM≥5 years) | (median) 10 | VF | NA | NA | YSM<5 years:RR=4.38, 95%CI(1.45-13.21); YSM≥5 years:RR=1.39, 95%CI (1.12-1.74) |
FGF23 | Mirza et al.[ | 2868 | Male | Swedish | 75.4±3.2 | (median) 3.35 | OF/VF | NA | OF: HR=1.20, 95%CI(1.03-1.40); VF: HR=1.33, 95%CI(1.02-1.75); Above 55.7 pg/ml: HF: HR=2.30, 95%CI(1.16-4.58); non-VF: HR=1.63, 95%CI(1.01-2.63) | NA |
FGF23 | Lane et al.[ | 1680 (*Q1: 346; *Q2: 346; *Q3: 345; *Q4: 347) | Male | Swedish | Q1:73.40±5.8; Q2:73.63±5.91; Q3:74.40±6.0; Q4:73.70±5.8 | 5.3 | Non-spine OF | Q1-Q3: 4.2-22.4 pg/ml; vs. Q4: 22.4-111.1 pg/ml | RH=2.02, 95%CI(1.07-3.79) in men with eGFRCrCys <60 ml/min·1.73 m 2 | NA |
Sclerostin | Szulc et al.[ | 710 (fracture: 75 vs. no fracture: 635) | Male | French | 65±7 | 10 | OF | 0.54 (0.47, 0.64) vs. 0.61 (0.49, 0.77) ng/ml | All fracture: HR=0.55, 95%CI(0.31-0.96) per SD,P<0.05 | NA |
PEN | Tamaki et al.[ | 1285 (fracture: 25 vs. no fracture: 1260) | Male | Japanese | 73.1±5.1 | 5 | OF | 0.057 vs. 0.049 µg/ml | FN:HR=1.48, 95%CI(1.00-2.18); LS:HR=1.51, 95%CI(1.03-2.21) | NA |
esRAGE-to-PEN ratio | Tamaki et al.[ | 1285 (fracture: 25 vs. no fracture: 1260) | Male | Japanese | 73.1±5.1 | 5 | OF | (geometric mean ± SD) 3.6±1.8 vs. 4.9±0.9 | FN: HR=0.67, 95%CI(0.45-0.98); LS: HR=0.64, 95%CI(0.43-0.95) | NA |
CTX | Garnero et al.[ | 7598 (hip fracture: 109 vs. controls: 292) | Female | French | HF: 82.6±4.6; Controls: 82.5±4.5 | (mean) 22 months | HF | 305±163vs. 266± 150 µg/mmol Cr | NA | OR=2.2, 95%CI(1.3-3.6) |
CTX | Garneroet al.[ | 408 (fracture: 65 vs. controls: 343) | Female | French | Fracture: 67±9; Controls: 64±9 | (median) 6.8 | VF and peripheral fractures | 22.8±8.0vs. 21.2±8.5 µmmol/mmol Cr | NA | RR=4.5, 95%CI(2.0-10.1) |
beta-CTX | Yoshimura et al.[ | 400 (male: 199 vs. female: 200) | Male/female =1/1 | Japanese | Male: 59.4±11.4; Female: 59.8±11.6 | 10 | Spinal OF | Male: 0.187 ± 0.121 ng/ml; Female: 0.234 ± 0.145 ng/ml | NS | HR=1.80, P<0.001 |
NTX | Yoshimura et al.[ | 400 (male: 199 vs. female: 200) | Male/female =1/1 | Japanese | Male: 59.4±11.4; Female: 59.8±11.6 | 10 | Spinal OF | Male: 13.6±4.2 nmolBCE/L; Female:15.2±4.0 nmolBCE/L | NS | HR=1.96, P<0.01 |
PINP | Yoshimura et al.[ | 400 (male: 199 vs. female: 200) | Male/female =1/1 | Japanese | Male:59.4±11.4; Female: 59.8±11.6 | 10 | Spinal OF | Male: 38.2±19.5 ng/ml; Female: 50.1±21.9 ng/ml | HR=2.80, P<0.05 | HR=1.65, P<0.05 |
Table 2
Potential metabolite molecules capable of predicting OFs"
Molecules | References | Study design | Sample | Sample size | Gender | Ethnicity | Age (yrs) | Follow-up (yrs) | Phenotype | Concentration | Effect size or difference | |
---|---|---|---|---|---|---|---|---|---|---|---|---|
Male | Female | |||||||||||
Free deoxypyridinoline | Garnero et al.[ | Prospective | Urine | 7598 (HF: 109; Controls: 292) | Female | French | (mean±SD) HF: 82.6±4.6; Controls: 82.5±4.5 | (mean) 22 months | OF | 6.41±2.20vs. 5.80±1.94 nmol/mmol Cr | NA | OR=1.9, 95%CI(1.1-3.2) |
Deoxypyridinoline | Yoshimura et al.[ | Prospective | Urine | 400 (Male: 199; Female: 200) | Male/female =1/1 | Japanese | (mean±SD) Male:59.4±11.4; Female: 59.8±11.6 | 10 | Spinal OF | Male:3.06±1.53 pmol/lmol Cr; Female: 4.76±1.91 pmol/lmol Cr | NS | HR=1.40, P<0.05 |
Homocysteine | Gjesdalet al.[ | Prospective | Plasma | 4766 | Male/female=2127/2639 | Norway | 65-67 at enrollment | (mean) 12.6 | HF | 7-20 µmol/L | NS | HR=2.42, 95%CI (1.43-4.09) |
Homocysteine | Enneman et al.[ | Prospective | Plasma | 503 | Female | Netherlands | (median) 68.5 | (mean) 7 | OF | 9.4-11.6 μmol/L | NA | Adj. for age, BMI, FN-BMD, HR=1.82, 95%CI (1.02-3.22) |
DMG | Øyen et al.[ | Longitudinal | Plasma | 3310 | Male/female =1017/1204 | Norway | 71-74 | (mean) 10.8 | HF | 4.4±1.5 μmol/L | Adj. for BMD, HR=1.70, 95%CI (1.28-2.26) | |
S1P | Ardawi et al.[ | Prospective longitudinal | Plasma | 707 (Fracture: 138; No fracture: 569) | Female | Saudi Arabia | (mean) 61 | (median) 5.2±1.3 | OF | 7.23±0.79vs. 5.02±0.51 µmol/L | NA | Adj. HR=6.12, 95%CI(4.92-7.66) per SD increase. |
24, 25(OH)2D | Ginsberget al.[ | Longitudinal | Serum | 890 | Male/female =267/178 | 16% African-American | (mean) 78 | (mean) 8.4 | HF | 1.7±1.0 ng/ml | HR=0.73, 95%CI (0.61-0.87) per SD increase | |
VMR | Ginsberget al.[ | Longitudinal | Serum | 890 | Male/female =267/178 | 16% African-American | (mean) 78 | (mean) 8.4 | HF | 6.84±2.23 ng/ml | HR=0.74, 95%CI (0.61-0.88) per SD increase | |
25(OH)D | Swansonet al.[ | Prospective | Serum | 1000 (Q1: 144; Q2: 171; Q3: 113; Q4: 109) | Male | USA | 74.6±6.2 | (mean) 5.1 | Non-VF | Q1: 3.13-20.90; Q2: 20.91-25.90; Q3: 26-31; Q4: 31.10-55.80 ng/ml | Q1 (Q2,Q3,Q4 as ref.): HR=2.03, 95%CI (1.25-3.29) | NA |
1, 25(OH)2D | Swanson et al.[ | Prospective | Serum | 1000 (Q1: 144; Q2: 171; Q3: 113; Q4: 109) | Male | USA | 74.6±6.2 | (mean) 5.1 | Non-VF | Q1: 8.70-51.60; Q2: 51.70-62.00; Q3: 62.10-75.10; Q4: 75.20-142.00 ng/ml | Q1 (Q2,Q3,Q4 as ref.): HR=2.01, 95%CI (1.20-3.36) | NA |
PUFAs | Harris et al.[ | Longitudinal | Plasma | Fracture: 540; No fracture: 898 | OF: male/female=19/35; NF: male/female=429/469 | NA | OF:78.5±5.69; NF:76.5±5.48 | (median) 7 | OF | NA | HR=0.60, 95%CI(0.41-0.89) | Inverse association, Ptrend=0.06 |
Table 3
Potential proteins having ability to diagnose OFs"
Molecules | References | Study design | Sample | Sample size (n) | Gender | Ethnicity | Age (yrs) | Phenotype | Concentration | Effect size or difference |
---|---|---|---|---|---|---|---|---|---|---|
IGF-I | Sugimotoet al.[ | Cross-sectional | Serum | 165 | Female | Japanese | (mean) 62 | Spinal OF | NA | Lower in OF, P<0.01; cutoff 110 ng/ml provided a specificity of 81% with a sensitivity of 76% |
IGF-I | Nakaokaet al.[ | Cross-sectional | Serum | 205 | Female | Japanese | (mean) 64 | Spinal OF | NA | OR=0.19, 95%CI (0.07-0.59), P=0.003 |
IGFBP-3 | Sugimoto et al.[ | Cross-sectional | Serum | 165 | Female | Japanese | (mean) 62 | Spinal OF | NA | Lower in OF, P<0.01; cutoff 2.1 µg/ml provided a specificity of 81% with a sensitivity of 81% |
IGFBP-3 | Yamaguchi et al.[ | Cross-sectional | Serum | 193 (VF: 61; No fracture: 132) | Female | Japanese | (mean±SD) 62.5±8.0 | VF | 2.18±1.02vs. 3.23±1.07 μg/ml | OR=0.31, 95%CI (0.16-0.61), P=0.0007 |
Periostin | Kim et al.[ | Case-control | Plasma | 266 (VF: 133; Non-VF: 133) | Female | Korean | VFs: 62.7±6.4; Non-VFs: 63.0±6.3 | OF | Per SD increment | Any fracture: OR=1.50, 95%CI (1.14-1.97), P=0.003; non-VF: OR=1.59, 95%CI (1.12-2.24), P=0.009 |
Periostin | Yan et al.[ | Case-control | Serum | 367 (HF: 261; Controls: 106) | Female | Chinese | [median (IQR)]: HF: 80 (76-84); Controls: 78 (72-83) | HF | 45.51 (28.77-79.70) vs. 32.87 (19.71-53.58) ng/ml | FN-BMD: β=-0.390, P=0.004; LS-BMD:β=-0.158, P=0.190 |
Table 4
Potential microRNAs biomarkers to identify OFs"
microRNAs | References | Study design | Sample size (n) | Sample | Expression trend in Casevs. Control | Fracture diagnostic value or difference | Phenotype |
---|---|---|---|---|---|---|---|
miR-21 | Seeligeret al.[ | Case-control | Serum: 30+30*; Bone: 20+20* | Serum, bone tissue | Up-regulated | AUC: 0.63, 95%CI (0.53-0.73), P = 0.013 | OF |
miR-23a | Seeligeret al.[ | Case-control | Serum: 30+30*; Bone: 20+20* | Serum, bone tissue | Up-regulated | AUC: 0.63, 95%CI (0.53-0.73), P=0.015 | OF |
miR-24 | Seeligeret al.[ | Case-control | Serum: 30+30*; Bone: 20+20* | Serum, bone tissue | Up-regulated | AUC: 0.63, 95%CI (0.53-0.74, P=0.013 | OF |
miR-93 | Seeligeret al.[ | Case-control | Serum: 30+30*; Bone: 20+20* | Serum, bone tissue | Up-regulated | AUC: 0.68, 95%CI (0.58-0.78), P=0.001 | OF |
miR-100 | Seeligeret al.[ | Case-control | Serum: 30+30*; Bone: 20+20* | Serum, bone tissue | Up-regulated | AUC: 0.69, 95%CI (0.60-0.78), P=0.0003 | OF |
miR122a | Seeligeret al.[ | Case-control | Serum: 30+30*; Bone: 20+20* | Serum, bone tissue | Up-regulated | AUC: 0.77, 95%CI (0.69-0.86), P<0.0001 | OF |
miR124a | Seeligeret al.[ | Case-control | Serum: 30+30*; Bone: 20+20* | Serum, bone tissue | Up-regulated | AUC: 0.69, 95%CI (0.59-0.78), P=0.0005 | OF |
miR125b | Seeligeret al.[ | Case-control | Serum: 30+30*; Bone: 20+20* | Serum, bone tissue | Up-regulated | AUC: 0.76, 95%CI (0.67-0.85), P<0.0001 | OF |
miR148a | Seeligeret al.[ | Case-control | Serum: 30+30*; Bone: 20+20* | Serum, bone tissue | Up-regulated | AUC: 0.61, 95%CI (0.51-0.72), P=0.038 | OF |
miR-21-5p | Panach et al.[ | Case-control | 15+12* | Serum | Up-regulated | Fold change=1.57, P=0.002 | OF |
miR-122-5p | Panach et al.[ | Case-control | 15+12* | Serum | Up-regulated | Fold change=5.48, P=0.00008 | OF |
miR-125b-5p | Panach et al.[ | Case-control | 15+12* | Serum | Up-regulated | Fold change=4.62, P=0.0003 | OF |
miR-328-3p | Weilneret al.[ | Cross-sectional | 37 | Serum | Down-regulated | Fold change=-1.54, -2.04, P<0.05 | OF |
let-7g-5p | Weilneret al.[ | Cross-sectional | 37 | Serum | Down-regulated | Fold change=-1.24, -1.89, P<0.05 | OF |
miR-19a-3p | Kocijan et al.[ | Case-control | 36+39* | Serum | Down-regulated | AUC: 0.929, 95%CI (0.856-0.983) | Low traumatic fractures |
miR-19b-3p | Kocijan et al.[ | Case-control | 36+39* | Serum | Down-regulated | AUC: 0.944, 95%CI (0.879-0.997) | Low traumatic fractures |
miR-30e-5p | Kocijan et al.[ | Case-control | 36+39* | Serum | Down-regulated | AUC: 0.959, 95%CI (0.901-0.997) | Low traumatic fractures |
miR-140-5p | Kocijan et al.[ | Case-control | 36+39* | Serum | Down-regulated | AUC: 0.947, 95%CI (0.900-0.983) | Low traumatic fractures |
miR-152-3p | Kocijan et al.[ | Case-control | 36+39* | Serum | Down-regulated | AUC: 0.962, 95%CI (0.918-0.993) | Low traumatic fractures |
miR-324-3p | Kocijan et al.[ | Case-control | 36+39* | Serum | Down-regulated | AUC: 0.950, 95%CI (0.885-0.994) | Low traumatic fractures |
miR-335-5p | Kocijan et al.[ | Case-control | 36+39* | Serum | Down-regulated | AUC: 0.939, 95%CI (0.872-0.986) | Low traumatic fractures |
miR-550a-3p | Kocijan et al.[ | Case-control | 36+39* | Serum | Down-regulated | AUC: 0.909, 95%CI (0.837-0.970) | Low traumatic fractures |
hsa-miR-4516 | Mandourah et al.[ | Case-control | 139 | Serum, Plasma | Up-regulated | P=0.00014 | OF |
miR-140-3p | Ramírez-Salazaret al.[ | Case-control | 20+20* | Serum | Up-regulated | AUC: 0.92, P<0.0001 | OF |
miR-23b-3p | Ramírez-Salazaret al.[ | Case-control | 20+20* | Serum | Up-regulated | AUC: 0.88, P<0.0001 | OF |
Table 5
Potential metabolite molecules that diagnose OFs"
Molecules | References | Study design | Sample | Sample size (n) | Gender | Ethnicity | Age§ (yrs) | Phenotype | Concentration | Effect size or difference in concentration or percentage | |
---|---|---|---|---|---|---|---|---|---|---|---|
Male | Female | ||||||||||
Urinary calcium | Rull et al.[ | Cross- sectional | Urine | OFs:55; Non-OFs:32 | Female | Spanish | OF: 70.1±13.8, Non-OF: 56.7±6.4 | OF | 8.95±0.73vs.9.19±0.38 mg/dl | NA | 40% vs. 18.8% hypercalciuria, P=0.04 |
Homocysteine | Kuroda et al.[ | Cross- sectional | Plasma | Grade 0: 1052; Grade 1: 137; Grade 2: 124; Grade 3: 162 | Female | Japanese | 66.6±9.0 | VF | Grade 0: 8.9±2.9, Grade 1: 9.2±2.8, Grade 2: 9.8±3.7, Grade 3: 11.3±5.3 µmol/L | NA | OR (95%CI): 1.22(1.03, 1.46)-1.27(1.04-1.58), P<0.05 |
Homocysteine | Zhu et al.[ | Cross- sectional | Plasma | OF: 39; HEF: 22; NF: 21 | Male/female =14/27 | Chinese | 80.61±5.61 (OF: 81.82±5.49, HEF: 78.88±5.75, Non-OF: 79.75±5.47) | OF | OF: 17.21±5.99, HEF: 10.82±3.60, Non-OF: 12.91±4.32 µmol/L | F=9.483, P= 0.000 | |
Homocysteine | Bahtiri et al.[ | Cross- sectional | Serum | Osteoporosis: 49; Normal: 33 | Female | NA | 56.53±5.84 | OF | 16.84±11.71vs. 13.09±2.53 µmol/L | NA | LS-BMD (r=-0.163, P<0.05), FN-BMD (r=-0.164, P<0.05) |
S1P | Kim et al.[ | Case-control | Plasma | VF: 69; Non-VF: 69 | Female | Korean | VF: 65.1±6.7; Non-VF: 64.9±6.5 | VF | 7.49±3.44vs. 5.58±2.01 µmol/L | NA | OR=9.33, 95%CI (2.68-32.49) |
S1P | Bae et al.[ | Longitudinal | Plasma | Treated with antiosteoporotic:172; Untreated: 76 | Female | Korean | Treated: 58.4±6.3; Untreated: 59.1±7.7, | OF | 4.90±0.2vs. 5.27±0.4 µmol/L | Highest tertile: 5.90-16.51 μmol/L | NA |
Retinol | Navarro-Valverdeet al.[ | Cross- sectional | Serum | 154 | Female | Spanish | >65 | OF | NA | NA | LS-BMD (r=-0.210, P<0.01), FN-BMD (r=-0.324, P<0.001) |
[1.] |
Briggs AM, Cross MJ, Hoy DG. et al. Musculoskeletal health conditions represent a global threat to healthy aging: a report for the 2015 World Health Organization World Report on Ageing and Health. Gerontologist 2016; 56 Suppl 2:S243-55. doi: 10.1093/geront/gnw002.
doi: 10.1093/geront/gnw002 |
[2.] |
Hinz L, Freiheit E, Kline G. How good is our best guess? Clinical application of the WHO FRAX tool in osteoporotic fracture risk determination and treatment decisions. Calcif Tissue Int 2016; 99(2):114-20. doi: 10.1007/s00223-016-0134-6.
doi: 10.1007/s00223-016-0134-6 |
[3.] |
Liu SY, Huang M, Chen R, et al. Comparison of strategies for setting intervention thresholds for Chinese postmenopausal women using the FRAX model. Endocrine 2019; 65(1):200-6. doi: 10.1007/s12020-019-01951-8.
doi: 10.1007/s12020-019-01951-8 |
[4.] |
Su Y, Leung J, Kwok T. The role of previous falls in major osteoporotic fracture prediction in conjunction with FRAX in older Chinese men and women: the Mr. OS and Ms. OS cohort study in Hong Kong. Osteoporos Int 2018; 29(2):355-63. doi: 10.1007/s00198-017-4373-9.
doi: 10.1007/s00198-017-4277-8 pmid: 29067485 |
[5.] |
Estrada K, Styrkarsdottir U, Evangelou E, 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. doi: 10.1038/ng.2249.
doi: 10.1038/ng.2249 |
[6.] |
Kemp JP, Morris JA, Medina-Gomez C. et al. Identification of 153 new loci associated with heel bone mineral density and functional involvement of GPC6 in osteoporosis. Nat Genet 2017; 49(10):1468-75. doi: 10.1038/ng.3949.
doi: 10.1038/ng.3949 |
[7.] |
Cauley JA. Osteoporosis: fracture epidemiology update 2016. Curr Opin Rheumatol 2017; 29(2):150-6. doi: 10.1097/BOR.0000000000000365.
doi: 10.1097/BOR.0000000000000365 pmid: 28072591 |
[8.] |
Liu X, Xu X. MicroRNA-137 dysregulation predisposes to osteoporotic fracture by impeding ALP activity and expression via suppression of leucine-rich repeat-containing G-protein-coupled receptor 4 expression . Int J Mol Med 2018; 42(2):1026-33. doi: 10.3892/ijmm.2018.3690.
doi: 10.3892/ijmm.2018.3690 |
[9.] |
Lundin H, Sääf M, Strender LE, et al. High serum insulin-like growth factor-binding protein 1 (IGFBP-1) is associated with high fracture risk independent of insulin-like growth factor 1 (IGF-I). Calcif Tissue Int 2016; 99(4):333-9. doi: 10.1007/s00223-016-0152-4.
doi: 10.1007/s00223-016-0152-4 |
[10.] |
Rousseau JC, Sornay-Rendu E, Bertholon C, et al. Serum periostin is associated with fracture risk in postmenopausal women: a 7-year prospective analysis of the OFELY study. J Clin Endocrinol Metab 2014; 99(7):2533-9. doi: 10.1210/jc.2013-3893.
doi: 10.1210/jc.2013-3893 pmid: 24628551 |
[11.] |
Bonnet N, Biver E, Chevalley T, et al. Serum Levels of a cathepsin-K generated periostin fragment predict incident low-trauma fractures in postmenopausal women independently of BMD and FRAX. J Bone Miner Res 2017; 32(11):2232-8. doi: 10.1002/jbmr.3203.
doi: 10.1002/jbmr.3203 |
[12.] |
Johansson H, Oden A, Lerner UH. et al. High serum adiponectin predicts incident fractures in elderly men: osteoporotic fractures in men (MrOS) Sweden. J Bone Miner Res 2012; 27(6):1390-6. doi: 10.1002/jbmr.1591.
doi: 10.1002/jbmr.1591 pmid: 22407876 |
[13.] |
Johansson H, Oden A, Karlsson MK. et al. Waning predictive value of serum adiponectin for fracture risk in elderly men: MrOS Sweden. Osteoporos Int 2014; 25(7):1831-6. doi: 10.1007/s00198-014-2654-0.
doi: 10.1007/s00198-014-2654-0 pmid: 24809807 |
[14.] |
Oei L, Campos-Obando N, Dehghan A. et al. Dissecting the relationship between high-sensitivity serum C-reactive protein and increased fracture risk: the Rotterdam Study. Osteoporos Int 2014; 25(4):1247-54. doi: 10.1007/s00198-013-2578-0.
doi: 10.1007/s00198-013-2578-0 pmid: 24337661 |
[15.] |
Tamaki J, Iki M, Kadowaki E. et al. Biochemical markers for bone turnover predict risk of vertebral fractures in postmenopausal women over 10 years: the Japanese Population-Based Osteoporosis (JPOS) Cohort Study. Osteoporos Int 2013; 24(3):887-97. doi: 10.1007/s00198-012-2106-7.
doi: 10.1007/s00198-012-2106-7 pmid: 22885773 |
[16.] |
Mirza MA, Karlsson MK, Mellstrom D, et al. Serum fibroblast growth factor-23 (FGF-23) and fracture risk in elderly men. J Bone Miner Res 2011; 26(4):857-64. doi: 10.1002/jbmr.263.
doi: 10.1002/jbmr.263 pmid: 20928885 |
[17.] |
Lane NE, Parimi N, Corr M, et al. Association of serum fibroblast growth factor 23 (FGF23) and incident fractures in older men: the Osteoporotic Fractures in Men (MrOS) study. J Bone Miner Res 2013; 28(11):2325-32. doi: 10.1002/jbmr.1985.
doi: 10.1002/jbmr.1985 |
[18.] |
Szulc P, Bertholon C, Borel O, et al. Lower fracture risk in older men with higher sclerostin concentration: a prospective analysis from the MINOS study. J Bone Miner Res 2013; 28(4):855-64. doi: 10.1002/jbmr.1823.
doi: 10.1002/jbmr.1823 |
[19.] |
Tamaki J, Kouda K, Fujita Y, et al. Ratio of endogenous secretory receptor for advanced glycation end products to pentosidine predicts fractures in men. J Clin Endocrinol Metab 2018; 103(1):85-94. doi: 10.1210/jc.2017-00929.
doi: 10.1210/jc.2017-00929 pmid: 29040721 |
[20.] |
Garnero P, Hausherr E, Chapuy MC, et al. Markers of bone resorption predict hip fracture in elderly women: the EPIDOS Prospective Study. J Bone Miner Res 1996; 11(10):1531-8. doi: 10.1002/jbmr.5650111021.
doi: 10.1002/jbmr.5650111021 pmid: 8889854 |
[21.] |
Garnero P, Cloos P, Sornay-Rendu E, et al. Type I collagen racemization and isomerization and the risk of fracture in postmenopausal women: the OFELY prospective study. J Bone Miner Res 2002; 17(5):826-33. doi: 10.1359/jbmr.2002.17.5.826.
doi: 10.1359/jbmr.2002.17.5.826 |
[22.] |
Yoshimura N, Muraki S, Oka H, et al. Biochemical markers of bone turnover as predictors of osteoporosis and osteoporotic fractures in men and women: 10-year follow-up of the Taiji cohort. Mod Rheumatol 2011; 21(6):608-20. doi: 10.1007/s10165-011-0455-2.
doi: 10.1007/s10165-011-0455-2 pmid: 21512822 |
[23.] |
Gossiel F, Scott JR, Paggiosi MA, et al. Effect of teriparatide treatment on circulating periostin and its relationship to regulators of bone formation and BMD in postmenopausal women with osteoporosis. J Clin Endocrinol Metab 2018; 103(4):1302-9. doi: 10.1210/jc.2017-00283.
doi: 10.1210/jc.2017-00283 pmid: 29365099 |
[24.] |
Meesters DM, Wijnands KAP, Brink PRG, et al. Malnutrition and fracture healing: are specific deficiencies in amino acids important in nonunion development? Nutrients 2018; 10(11):1597. doi: 10.3390/nu10111597.
doi: 10.3390/nu10111597 |
[25.] |
Gjesdal CG, Vollset SE, Ueland PM, et al. Plasma homocysteine, folate, and vitamin B 12 and the risk of hip fracture: the hordaland homocysteine study. J Bone Miner Res 2007; 22(5):747-56. doi: 10.1359/jbmr.070210.
doi: 10.1359/jbmr.070210 pmid: 17295607 |
[26.] |
Enneman AW, van der Velde N, de Jonge R, et al. The association between plasma homocysteine levels, methylation capacity and incident osteoporotic fractures. Bone 2012; 50(6):1401-5. doi: 10.1016/j.bone.2012.03.013.
doi: 10.1016/j.bone.2012.03.013 pmid: 22465697 |
[27.] |
Øyen J, Svingen GF, Gjesdal CG, et al. Plasma dimethylglycine, nicotine exposure and risk of low bone mineral density and hip fracture: the Hordaland Health Study. Osteoporos Int 2015; 26(5):1573-83. doi: 10.1007/s00198-015-3030-4.
doi: 10.1007/s00198-015-3030-4 pmid: 25616506 |
[28.] |
Cabrera D, Kruger M, Wolber FM, et al. Association of plasma lipids and polar metabolites with low bone mineral density in Singaporean-Chinese menopausal women: a pilot study. Int J Environ Res Public Health 2018; 15(5):1045. doi: 10.3390/ijerph15051045.
doi: 10.3390/ijerph15051045 |
[29.] |
Lecka-Czernik B, Baroi S, Stechschulte LA, et al. Marrow fat-a new target to treat bone diseases? Curr Osteoporos Rep 2018; 16(2):123-9. doi: 10.1007/s11914-018-0426-z.
doi: 10.1007/s11914-018-0426-z pmid: 29460176 |
[30.] |
Ahn SH, Koh JM, Gong EJ, et al. Association of bone marrow sphingosine 1-phosphate levels with osteoporotic hip fractures. J Bone Metab 2013; 20(2):61-5. doi: 10.11005/jbm.2013.20.2.61.
doi: 10.11005/jbm.2013.20.2.61 |
[31.] |
Ardawi MM, Rouzi AA, Al-Senani NS, et al. High plasma sphingosine 1-phosphate levels predict osteoporotic fractures in postmenopausal women: the center of excellence for osteoporosis research study. J Bone Metab 2018; 25(2):87-98. doi: 10.11005/jbm.2018.25.2.87.
doi: 10.11005/jbm.2018.25.2.87 |
[32.] |
Ginsberg C, Katz R, de Boer IH, et al. The 24, 25 to 25-hydroxyvitamin D ratio and fracture risk in older adults: the cardiovascular health study. Bone 2018; 107:124-30. doi: 10.1016/j.bone.2017.11.011.
doi: S8756-3282(17)30430-1 pmid: 29155243 |
[33.] |
Swanson CM., Srikanth P, Lee CG, et al. Associations of 25-hydroxyvitamin D and 1, 25-dihydroxyvitamin D with bone mineral density, bone mineral density change, and incident nonvertebral fracture. J Bone Miner Res 2015; 30(8):1403-13. doi: 10.1002/jbmr.2487.
doi: 10.1002/jbmr.2487 |
[34.] |
Cauley JA, Parimi N, Ensrud KE, et al. Serum 25-hydroxyvitamin D and the risk of hip and nonspine fractures in older men. J Bone Miner Res 2010; 25(3):545-53. doi: 10.1359/jbmr.090826.
doi: 10.1359/jbmr.090826 pmid: 19775201 |
[35.] |
Robinson-Cohen C, Katz R, Hoofnagle AN, et al. Mineral metabolism markers and the long-term risk of hip fracture: the cardiovascular health study. J Clin Endocrinol Metab 2011; 96(7):2186-93. doi: 10.1210/jc.2010-2878.
doi: 10.1210/jc.2010-2878 pmid: 21508146 |
[36.] |
Harris TB, Song X, Reinders I, et al. Plasma phospholipid fatty acids and fish-oil consumption in relation to osteoporotic fracture risk in older adults: the age, gene/environment susceptibility study. Am J Clin Nutr 2015; 101(5):947-55. doi: 10.3945/ajcn.114.087502.
doi: 10.3945/ajcn.114.087502 |
[37.] |
Sugimoto TNK, Kuribayashi F, Chihara K, et al. Serum levels of insulin-like growth factor (IGF) I, IGF-binding protein (IGFBP)-2, and IGFBP-3 in osteoporotic patients with and without spinal fractures. J Bone Miner Res 1997; 12(8):1272-9. doi: 10.1359/jbmr.1997.12.8.1272.
doi: 10.1359/jbmr.1997.12.8.1272 pmid: 9258758 |
[38.] |
Nakaoka D, Sugimoto T, Kaji H, et al. Determinants of bone mineral density and spinal fracture risk in postmenopausal Japanese women. Osteoporos Int 2001; 12(7):548-54. doi: 10.1007/s001980170075.
doi: 10.1007/s001980170075 pmid: 11527051 |
[39.] |
Yamaguchi T, Kanatani M, Yamauchi M, et al. Serum levels of insulin-like growth factor (IGF); IGF-binding proteins-3, -4, and -5; and their relationships to bone mineral density and the risk of vertebral fractures in postmenopausal women. Calcif Tissue Int 2006; 78(1):18-24. doi: 10.1007/s00223-005-0163-z.
doi: 10.1007/s00223-005-0163-z |
[40.] |
Kim BJ, Rhee Y, Kim CH, et al. Plasma periostin associates significantly with non-vertebral but not vertebral fractures in postmenopausal women: clinical evidence for the different effects of periostin depending on the skeletal site. Bone 2015; 81:435-41. doi: 10.1016/j.bone.2015.08.014.
doi: 10.1016/j.bone.2015.08.014 |
[41.] |
Yan J, Liu HJ, Li H, et al. Circulating periostin levels increase in association with bone density loss and healing progression during the early phase of hip fracture in Chinese older women. Osteoporos Int 2017; 28(8):2335-41. doi: 10.1007/s00198-017-4034-z.
doi: 10.1007/s00198-017-4034-z pmid: 28382553 |
[42.] |
Seeliger C, Karpinski K, Haug AT, et al. Five freely circulating miRNAs and bone tissue miRNAs are associated with osteoporotic fractures. J Bone Miner Res 2014; 29(8):1718-28. doi: 10.1002/jbmr.2175.
doi: 10.1002/jbmr.2175 |
[43.] |
Panach L, Mifsut D, Tarin JJ, et al. Serum circulating microRNAs as biomarkers of osteoporotic fracture. Calcif Tissue Int 2015; 97(5):495-505. doi: 10.1007/s00223-015-0036-z.
doi: 10.1007/s00223-015-0036-z |
[44.] |
Lian JB, Stein GS, van Wijnen AJ, et al. MicroRNA control of bone formation and homeostasis. Nat Rev Endocrinol 2012; 8(4):212-27. doi: 10.1038/nrendo.2011.234.
doi: 10.1038/nrendo.2011.234 |
[45.] |
Weilner S, Skalicky S, Salzer B, et al. Differentially circulating miRNAs after recent osteoporotic fractures can influence osteogenic differentiation. Bone 2015; 79:43-51. doi: 10.1016/j.bone.2015.05.027.
doi: 10.1016/j.bone.2015.05.027 pmid: 26026730 |
[46.] |
Kocijan R, Muschitz C, Geiger E, et al. Circulating microRNA signatures in patients with idiopathic and postmenopausal osteoporosis and fragility fractures. J Clin Endocrinol Metab 2016; 101(11):4125-34. doi: 10.1210/jc.2016-2365.
doi: 10.1210/jc.2016-2365 pmid: 27552543 |
[47.] |
Li Z, Hassan MQ, Jafferji M, et al. Biological functions of miR-29b contribute to positive regulation of osteoblast differentiation. J Biol Chem 2009; 284(23):15676-84. doi: 10.1074/jbc.M809787200.
doi: 10.1074/jbc.M809787200 |
[48.] |
Franceschetti T, Dole NS, Kessler CB, et al. Pathway analysis of microRNA expression profile during murine osteoclastogenesis. PLoS One 2014; 9(9):e107262. doi: 10.1371/journal.pone.0107262.
doi: 10.1371/journal.pone.0107262 |
[49.] |
Mandourah AY, Ranganath L, Barraclough R, et al. Circulating microRNAs as potential diagnostic biomarkers for osteoporosis. Sci Rep 2018; 8(1):8421. doi: 10.1038/s41598-018-26525-y.
doi: 10.1038/s41598-018-26525-y pmid: 29849050 |
[50.] |
Ramirez-Salazar EG, Carrillo-Patino S, Hidalgo-Bravo A, et al. Serum miRNAs miR-140-3p and miR-23b-3p as potential biomarkers for osteoporosis and osteoporotic fracture in postmenopausal Mexican-Mestizo women. Gene 2018; 679:19-27. doi: 10.1016/j.gene.2018.08.074.
doi: 10.1016/j.gene.2018.08.074 |
[51.] |
Chen Z, Bemben MG, Bemben DA. Bone and muscle specific circulating microRNAs in postmenopausal women based on osteoporosis and sarcopenia status. Bone 2019; 120:271-8. doi: 10.1016/j.bone.2018.11.001.
doi: 10.1016/j.bone.2018.11.001 |
[52.] |
Rull MA, Cano-Garcia Mdel C, Arrabal-Martin M, et al. The importance of urinary calcium in postmenopausal women with osteoporotic fracture. Can Urol Assoc J 2015; 9(3-4):E183-6. doi: 10.5489/cuaj.2695.
doi: 10.5489/cuaj.2695 |
[53.] |
Wu VC, Chang CH, Wang CY, et al. Risk of fracture in primary aldosteronism: a population-based cohort study. J Bone Miner Res 2017; 32(4):743-52. doi: 10.1002/jbmr.3033.
doi: 10.1002/jbmr.3033 |
[54.] |
Kuroda T, Tanaka S, Saito M, et al. Plasma level of homocysteine associated with severe vertebral fracture in postmenopausal women. Calcif Tissue Int 2013; 93(3):269-75. doi: 10.1007/s00223-013-9754-2.
doi: 10.1007/s00223-013-9754-2 |
[55.] |
Zhu Y, Shen J, Cheng Q, et al. Plasma homocysteine level is a risk factor for osteoporotic fractures in elderly patients. Clin Interv Aging 2016; 11:1117-21. doi: 10.2147/CIA.S107868.
doi: 10.2147/CIA.S107868 |
[56.] |
Bahtiri E, Islami H, Rexhepi S, et al. Relationship of homocysteine levels with lumbar spine and femur neck BMD in postmenopausal women. Acta Reumatol Port 2015; 40(4):355-62.
pmid: 26922199 |
[57.] |
Kim BJ, Koh JM, Lee SY, et al. Plasma sphingosine 1-phosphate levels and the risk of vertebral fracture in postmenopausal women. J Clin Endocrinol Metab 2012; 97(10):3807-14. doi: 10.1210/jc.2012-2346.
doi: 10.1210/jc.2012-2346 |
[58.] |
Bae SJ, Lee SH, Ahn SH, et al. The circulating sphingosine-1-phosphate level predicts incident fracture in postmenopausal women: a 3.5-year follow-up observation study. Osteoporos Int 2016; 27(8):2533-41. doi: 10.1007/s00198-016-3565-z.
doi: 10.1007/s00198-016-3565-z pmid: 26984570 |
[59.] |
Navarro-Valverde C, Caballero-Villarraso J, Mata-Granados JM, et al. High serum retinol as a relevant contributor to low bone mineral density in postmenopausal osteoporotic women. Calcif Tissue Int 2018; 102(6):651-6. doi: 10.1007/s00223-017-0379-8.
doi: 10.1007/s00223-017-0379-8 |
[1] | Siwen Ouyang, Weiming Kang. Research Advances in the Role of Keratins in Gastrointestinal Cancer [J]. Chinese Medical Sciences Journal, 2022, 37(1): 73-78. |
[2] | Liu Lianye, Shi Bingyin, Zhao Fengyi, Hou Peng, Liu Shu, Liu Xiaomei, Wu Liping. Effect of Dihydrotestosterone on CostimulatoryMolecules in a Mouse Model of Graves’ Disease [J]. Chinese Medical Sciences Journal, 2020, 35(3): 215-225. |
[3] | Xinchao Liu, Susu Ye, Wenze Wang, Yueqiu Zhang, Lifan Zhang, Xiaocheng Pan, Ziyue Zhou, Miaoyan Zhang, Jianghao Liu, Zhiyong Liang, Xiaoqing Liu. Diagnostic Utility of Interferon-Gamma Release Assay in Tuberculous Lymphadenitis [J]. Chinese Medical Sciences Journal, 2019, 34(4): 233-240. |
[4] | Kai Sun, Rui-juan Han, Li-fang Cui, Rui-ping Zhao, Li-jun Ma, Li-jun Wang, Li-gang Li, Chang-yong Li. Feasibility and Diagnostic Accuracy for Assessment of Coronary Artery Stenosis of Prospectively Electrocardiogram-gated High-pitch Spiral Acquisition Mode Dual-source CT Coronary Angiography in Patients with Relatively Higher Heart Rates: in Comparison with Catheter Coronary Angiography△ [J]. Chinese Medical Sciences Journal, 2012, 27(4): 213-219. |
[5] | Shun-hua Zhang, Fang-tian Dong, Jin Mao, Ai-ling Bian. Factors Related to Prognosis of Refractory Glaucoma with Diode Laser Transscleral Cyclophotocoagulation Treatment [J]. Chinese Medical Sciences Journal, 2011, 26(3): 137-140. |
Viewed | ||||||
Full text |
|
|||||
Abstract |
|
|||||
|
Supervised by National Health Commission of the People's Republic of China
9 Dongdan Santiao, Dongcheng district, Beijing, 100730 China
Tel: 86-10-65105897 Fax:86-10-65133074
E-mail: cmsj@cams.cn www.cmsj.cams.cn
Copyright © 2018 Chinese Academy of Medical Sciences
All right reserved.
京公安备110402430088 京ICP备06002729号-1