| Literature DB >> 35936067 |
Li Deng1, Fangling Yao1, Feng Tian1, Xiaowen Luo1, Shenyi Yu1, Zhenhua Wen1.
Abstract
Background: Influence of iguratimod on bone mineral density (BMD) and biomarkers of bone metabolism in patients with rheumatoid arthritis (RA) remains not determined. Accordingly, a meta-analysis was performed for systematical evaluation.Entities:
Mesh:
Substances:
Year: 2022 PMID: 35936067 PMCID: PMC9334038 DOI: 10.1155/2022/5684293
Source DB: PubMed Journal: Int J Clin Pract ISSN: 1368-5031 Impact factor: 3.149
Figure 1Flowchart of database search and literature identification.
Characteristics of the included RCTs, BMD, OPG, RANKL, N-MID, T-P1NP, and β-CTX.
| Author, year | Country | Study design | Diagnosis | Number of patients | Male | Age | Duration of RA | Background treatment | Intervention | Control | Follow-up duration | Outcomes reported |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| % | years | years | Weeks | |||||||||
| Tian, 2017 | China | R, OL | RA | 116 | 45.7 | 51.1 | 9.1 | MTX | IGU 25 mg bid | No treatment | 24 | 456 |
| Yan, 2018 | China | R, OL | RA | 70 | 32.9 | 56 | 11.4 | MTX | IGU 25 mg bid | No treatment | 24 | 456 |
| Du, 2018 | China | R, OL | RA | 98 | 47.9 | 48.1 | 4 | MTX | IGU 25 mg bid | No treatment | 16 | 456 |
| Cao, 2018 | China | R, OL | RA | 60 | 65 | 68 | NR | MTX | IGU 25 mg bid | No treatment | 24 | 23 |
| Chen, 2018 | China | R, OL | RA | 120 | 35 | 45.8 | 7.1 | MTX | IGU 25 mg bid | No treatment | 24 | 456 |
| Wang, 2019 | China | R, OL | RA | 100 | 41 | 61.4 | 9.3 | MTX | IGU 25 mg bid | No treatment | 24 | 456 |
| Gao, 2019 | China | R, OL | RA | 108 | 42.6 | 47.9 | 3.1 | MTX | IGU 25 mg bid | No treatment | 24 | 456 |
| Ding, 2019 | China | R, OL | RA | 76 | 42.1 | 72.7 | 8.7 | MTX | IGU 25 mg bid | No treatment | 16 | 456 |
| Zeng, 2019 | China | R, OL | RA | 80 | 61.3 | 46.6 | 7.6 | MTX | IGU 25 mg bid | No treatment | 24 | 456 |
| Xu, 2019 | China | R, OL | RA and osteoporosis | 115 | NR | NR | NR | MTX | IGU 25 mg bid | No treatment | 12 | 1 |
| Huan, 2019 | China | R, OL | RA | 128 | 32.8 | 45.6 | 2.3 | MTX | IGU 25 mg bid | No treatment | 24 | 456 |
| Yang, 2020 | China | R, OL | RA and osteoporosis | 81 | 21.1 | 45.9 | 5.5 | MTX and HCQ | IGU 25 mg bid | No treatment | 24 | 12356 |
| Chen, 2020 | China | R, OL | RA | 60 | 55 | 70.3 | NR | MTX | IGU 25 mg Bid | No treatment | 12 | 23 |
| Liang, 2020 | China | R, OL | RA | 114 | 43.9 | 53.9 | 7.3 | MTX | IGU 25 mg bid | No treatment | 24 | 456 |
| Li, 2020a | China | R, OL | RA | 120 | 55 | 46.2 | 4.1 | MTX | IGU 25 mg bid | No treatment | 53 | 12356 |
| Lun, 2020 | China | R, OL | RA and osteopenia | 60 | 11.7 | 57.5 | 5.7 | MTX | IGU 25 mg bid | No treatment | 12 | 2356 |
| Li, 2020b | China | R, OL | RA | 138 | 41.3 | 65.5 | 5 | MTX | IGU 25 mg bid | No treatment | 12 | 456 |
| Li, 2021a | China | R, OL | RA | 115 | 53.9 | 46.3 | 5.5 | MTX | IGU 25 mg bid | No treatment | 24 | 12356 |
| Fang, 2021 | China | R, OL | RA and osteoporosis | 100 | NR | 61.8 | NR | MTX | IGU 25 mg bid | No treatment | 12 | 1 |
| Li, 2021b | China | R, OL | RA | 120 | 65 | 52.9 | 5.3 | MTX | IGU 25 mg bid | No treatment | 24 | 45 |
| Jiang, 2021 | China | R, OL | RA | 120 | 51.7 | 45.5 | 5.8 | MTX | IGU 25 mg bid | No treatment | 24 | 456 |
| Hu, 2021 | China | R, OL | RA | 134 | 71.6 | 54.9 | 8.6 | MTX or etanercept | IGU 25 mg bid | No treatment | 12 | 16 |
| Zhang, 2021 | China | R, OL | RA and osteopenia | 120 | 40 | 45.7 | 0.5 | MTX | IGU 25 mg bid | No treatment | 24 | 56 |
| Sun, 2022 | China | R, OL | RA | 86 | 58.1 | 49 | 5.9 | MTX | IGU 25 mg bid | No treatment | 24 | 456 |
BMD: bone mineral density; OPG: osteoprotegerin; RANKL: receptor activator for nuclear factor kappa-B ligand; N-MID: the N-terminal middle molecular fragment of osteocalcin; T-P1NP: total procollagen type I amino-terminal propeptide; β-CTX: β-C terminal cross-linking telopeptide of type 1 collagen; RCT: randomized controlled trials; RA: rheumatoid arthritis; R: randomized; OL: open label; NR: not reported; MTX: methotrexate; HCQ: hydroxychloroquine; IGU: iguratimod; bid: twice daily.
Quality evaluation via the Cochrane's Risk of Bias Tool.
| Random sequence generation | Allocation concealment | Blinding in performance | Blinding in outcome detection | Incomplete outcome data | Reporting bias | Other bias | Total | |
|---|---|---|---|---|---|---|---|---|
| Tian, 2017 | Low risk | Unclear | High risk | High risk | Low risk | Low risk | Low risk | 4 |
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| Yan, 2018 | Unclear | Unclear | High risk | High risk | Low risk | Low risk | Low risk | 3 |
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| Du, 2018 | Low risk | Unclear | High risk | High risk | Low risk | Low risk | Low risk | 4 |
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| Cao, 2018 | Low risk | Unclear | High risk | High risk | Low risk | Low risk | Low risk | 4 |
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| Chen, 2018 | Low risk | Unclear | High risk | High risk | Low risk | Low risk | Low risk | 4 |
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| Wang, 2019 | Unclear | Unclear | High risk | High risk | Low risk | Low risk | Low risk | 3 |
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| Gao, 2019 | High risk | Unclear | High risk | High risk | Low risk | Low risk | Low risk | 3 |
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| Ding, 2019 | Unclear | Unclear | High risk | High risk | Low risk | Low risk | Low risk | 3 |
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| Zeng, 2019 | Low risk | Unclear | High risk | High risk | Low risk | Low risk | Low risk | 4 |
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| Xu, 2019 | Unclear | Unclear | High risk | High risk | Low risk | Low risk | Low risk | 3 |
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| Huan, 2019 | Unclear | Unclear | High risk | High risk | Low risk | Low risk | Low risk | 3 |
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| Yang, 2020 | Low risk | Unclear | High risk | High risk | Low risk | Low risk | Low risk | 4 |
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| Chen, 2020 | Low risk | Unclear | High risk | High risk | Low risk | Low risk | Low risk | 4 |
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| Liang, 2020 | Low risk | Unclear | High risk | High risk | Low risk | Low risk | Low risk | 4 |
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| Li, 2020a | Unclear | Unclear | High risk | High risk | Low risk | Low risk | Low risk | 3 |
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| Lun, 2020 | Low risk | Unclear | High risk | High risk | Low risk | Low risk | Low risk | 4 |
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| Li, 2020b | Unclear | Unclear | High risk | High risk | Low risk | Low risk | Low risk | 3 |
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| Li, 2021a | Low risk | Unclear | High risk | High risk | Low risk | Low risk | Low risk | 4 |
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| Fang, 2021 | Unclear | Unclear | High risk | High risk | Low risk | Low risk | Low risk | 3 |
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| Li, 2021b | Unclear | Unclear | High risk | High risk | Low risk | Low risk | Low risk | 3 |
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| Jiang, 2021 | Low risk | Unclear | High risk | High risk | Low risk | Low risk | Low risk | 4 |
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| Hu, 2021 | Low risk | Unclear | High risk | High risk | Low risk | Low risk | Low risk | 4 |
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| Zhang, 2021 | Unclear | Unclear | High risk | High risk | Low risk | Low risk | Low risk | 3 |
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| Sun, 2022 | Unclear | Unclear | High risk | High risk | Low risk | Low risk | Low risk | 3 |
Figure 2Forest plots for the meta-analyses evaluating the influences of IGU on BMD, serum OPG, and RANKL in patients with RA; (a) meta-analysis for BMD stratified by follow-up durations; (b) meta-analysis for serum OPG stratified by follow-up durations; (c) meta-analysis for serum RANKL stratified by follow-up durations.
Figure 3Forest plots for the meta-analyses evaluating the influences of IGU on serum N-MID, T-P1NP, and β-CTX in patients with RA; (a) meta-analysis for serum N-MID stratified by follow-up durations; (b) meta-analysis for serum T-P1NP stratified by follow-up durations; (c) meta-analysis for serumβ-CTX stratified by follow-up durations.
Figure 4Funnel plot for the evaluation of publication biases of the meta-analyses; (a) changes of BMD; (b) changes of serum OPG; (c) changes of serum RANKL; (d) changes of serum N-MID; (e) changes of serum T-P1NP; (f) changes of serum β-CTX.