Table 1.
The characteristics and NOS of the included studies.
| References | Country | Study Design | No. of OP or ON/Control | Age of OP or ON/Control (years) | BMI of OP or ON/Control (m/kg2) | Technique | Biological Sample | Key Findings | NOS | |
|---|---|---|---|---|---|---|---|---|---|---|
|
Yin et al., 2021 [48] |
China | case-control | 30 OP vs. 30 control | 66.1 ± 7.5/64.3 ± 6.3 | 24.34 ± 2.99/24.11 ± 2.87 | UPLC/MS | blood | 15 different metabolites in OP with Yin deficiency syndrome: Glycocholic Acid, Bilirubin, Diloxanide, etc. |
7 | |
| Zhu 2020 [49] | China | case-control | 30 OP(A) vs. 30 OP(I) 30 control | 65.47 ± 7.54/66.1 ± 7.47 /55.97 ± 9.47 |
22.93 ± 3.42/24.34 ± 2.99 /24.11 ± 2.87 |
UPLC/MS | serum | 15 different metabolites | 10 (↑) *: Inosine, Lucidenic acid G, etc. | 7 |
| 5 (↓) *: Dodecanoic acid, Cohibin B, etc. | ||||||||||
|
Li 2020 [50] |
China | case-control | 120 OP vs. 18 control | 46–87 | 14.69–33.33 | HNMR | serum | 20 different metabolites: Glutamine, Leucine, etc. | 6 | |
|
Guo et al., 2022 [51] |
China | case-control | 20 OP vs. 12 control | 62.7 ± 2.2/47.5 ± 5.4 | NA/NA * | UPLC/MS/MS | serum | 157 different metabolites | 93 (↑): L-isoleucine, γ-Aminobutyric acid, etc. 64 (↓): Alanine, Glutamate, etc. |
7 |
|
Yin et al., 2022 [38] |
China | case-control | 30 OP vs. 30 control | 65.47 ± 7.54/55.97 ± 9.47 | 22.93 ± 3.42/24.11 ± 2.87 | UPLC/MS | serum | 11 potential metabolite biomarkers of KYADS: Indole, Lotusine, etc. | 6 | |
|
Poor et al., 2003 [39] |
Hungary | case-control | 11 OP vs. 13 control | 53.8 ± 4.9/56.6 ± 5.7 | NA/NA | capillary gas chromatography | urine | 8 Urinary steroid different metabolites: Tetrahydro-corticosterone, 11-O-androsterone, etc. | 6 | |
|
Wang et al., 2019 [33] |
China | case-control | Male: 40 OP vs. 46 ON vs. 46 control Female: 60 OP vs. 61 ON vs. 61 control |
Male: 66.9 ± 2.9/67.2 ± 1.3/67.4 ± 1.4 Female: 60.7 ± 3.9/60.8 ± 4.0/60.1 ± 4.2 |
Male: 23.3 ± 2.5/23.4 ± 2.5/23.4 ± 2.4 Female: 26.8 ± 3.5/26.7 ± 3.5/26.7 ± 3.5 |
LC-MS/MS | blood | Male: 8 metabolites in males showed significant differences between the three groups Female: 12 metabolites showed significant differences between the three groups |
8 | |
|
Miyamoto et al., 2017 [40] |
Japan | case-control | 5 OP vs. 42 control | 55.83 ± 3.6/56.34 ± 3.5 | 23.09 ± 1.8/22.25 ± 2.53 | LC/MS | serum | protein metabolism | (↓) Gly-Gly, cystine (↑) hydroxyproline |
6 |
|
Aleidi et al., 2021 [34] |
Jordan | case-control | 25 OP vs. 22 ON vs. 22 control | 66.16 ± 1.78/64.64 ± 1.72 /54.82 ± 1.03 |
30.70 ± 1.4/30.38 ± 1.84 /32.21 ± 1.1 |
UPLC/MS | serum | 94 dysregulated metabolites: | 52 (↑) 42 (↓) |
8 |
|
Deng et al., 2021 [41] |
China | case-control | 32 OP vs. 32 control | 60.47 ± 12.39/60.59 ± 14.14 | NA/NA | UHPLC-HRMS | serum | The differential metabolites | (↑) PE, TG(18:0/18:0/18:0), cyclic Melatonin, etc. (↓): LPC, 4-Hydroxyproline, etc. |
9 |
|
Cao et al., 2021 [42] |
China | case-control | 36 OP vs. 55 control | 57.51 ± 4.59 | NA/NA | LC-MS | blood | 10 different lipid metabolites: | 6 (↑): PC (18:0/20:4), TG (16:0/10:0/20:4), CL (19:0/18:2/20:0/22:6), CL (75:4), PC (36:5), Tand G (54:4) 4 (↓): PC (36:2), CL (22:3/18:0/18:0/20:4), LPC (18:1), SM (d16:0/18:1) |
7 |
|
Kou et al., 2022 [43] |
China | case-control | 50 OP vs. 50 control | 69.3 ± 9.3/66.3 ± 10 | 23.8 ± 3.2/23.5 ± 4.4 | GC/LC-MS | serum | 18 different metabolites | 8 | |
|
Pontes et al., 2019 [35] |
Brazil | case-control | 24 OP vs. 26 ON vs. 28 control | 60. 8 ± 6.0/61.88 ± 7.9/ 60.38 ± 6.2 |
25.58 ± 4.8/27.20 ± 5.2/ 25.35 ± 3.4 |
H NMR | serum | 9 different metabolites OP | 6 (↑): Cholesterol, Leucine, isoleucine, Lactate, Unsaturated lipids, Allantoin 3 (↓): Tyrosine, Choline, Taurine |
7 |
|
Zhang et al., 2022 [44] |
China | case-control | 120 OP vs. 80 control | 71/70 | NA/NA | LC-MS/MS | serum | (↑) NEOs and their metabolites |
7 | |
|
LIM et al., 1997 [32] |
Korea | case-control | 34 ON vs. 25 control | 56.8 ± 0.4/57.2 ± 0.4 | 23.15 ± 0.36/24.38 ± 0.36 | GC-MS | urinary | 18 estrogen metabolites: | 7 | |
|
Qi et al., 2016 [36] |
China | case-control | 67 OP vs. 114 ON vs. 79 control | 58.37 ± 4.78/57.03 ± 4.53/ 54.43 ± 4.9 |
23.52 ± 3.39/23.56 ± 3.05/ 24.75 ± 3.21 |
GC-MS | serum | 12 different metabolites between low BMD and control 5 free fatty acids (LA, Oleic acid, AA and 11, 14-Eicosadienoic acid) correlations with BMD |
8 | |
|
Zhao et al., 2018 [45] |
USA | case-control | 65 OP vs. 71 control | 31.2 ± 4.9/31.8 ± 55.3 | 21.9 ± 2.5/29.7 ± 8.6 | LC-MS | serum | 14 metabolites, 7 amino acids and amino acid derivatives, 5 lipids (including three bile acids), and 2 organic acids were significantly associated with the risk for low BMD |
7 | |
|
Yu et al., 2018 [37] |
China | case-control | 77 OP vs. 92 ON vs. 71 control | 57.97 ± 4.07/56.72 ± 4.79/ 54.71 ± 4.81 |
23.12 ± 3.08/23.01 ± 2.98/ 24.73 ± 3.14 |
GC–MS | Urine | 17 different metabolites | 8 | |
|
You et al., 2014 [46] |
China | cross-sectional study | Premenopausal: 134 OP vs. 349 control Postmenopausal: 77 OP vs. 41 control |
Premenopausal: 44.7 ± 0.29/44.9 ± 0.19 Postmenopausal: 52.5 ± 0.29/50.7 ± 0.47 |
Premenopausal: 21.2 ± 0.27/22.5 ± 0.17 Postmenopausal: 21.8 ± 0.56/24.3 ± 0.60 |
GC–MS | blood | 7 different metabolites | 2 (↑): Acetate, Glutamine 5 (↓): Lactate, Acetone, Lipids, VLDLs, Glucose |
9 |
|
Mei et al., 2020 [27] |
China | case-control | Discovery set: 83 OP vs. 205 ON vs. 413 control Replication set: 107 OP vs. 68 ON vs. 103 control |
Discovery set: 63.0 ± 9.1/59.0 ± 10.8/ 52.9 ± 12 Replication set: 70.3 ± 9.5/66.5 ± 13.9/ 62.6 ± 12.7 |
Discovery set: 22.8 ± 2.9/24.2 ± 3.3/ 24.7 ± 3.2 Replication set: 22.4 ± 3.7/23.2 ± 3.2/ 24.3 ± 3.7 |
LC-MS | blood | 47 different metabolites (13 amino acids, 2 carboxylic acids, 14 glycerophospholipids, 3 purines and purine derivatives, 7 sphingolipids, and 8 others) |
9 | |
|
Miyamoto et al., 2018 [47] |
Japan | case-control | 33 OP vs. 46 control | 39–61 | NA/NA | LC/MS | serum | 24 different metabolites | 8 | |
* (↑): Increased expression; (↓): Decreased expression; NA/NA: Not available.