Evidence map›Paper›PMID 36303230›Full record

ArticleAdvances in rheumatology (London, England)2022

Serum MMP-9, SP-D, and VEGF levels reflect the severity of connective tissue disease-associated interstitial lung diseases.

Chengna Lv, Qipan Zhang, Pan Tang, Lun Guo, Qunli Ding

Open access · diamondAbstract read
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In one paragraph

Article in Advances in rheumatology (London, England), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 1 pooled it
2.4field-weighted citation impact, top 10% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed, 1 synthesis or guideline pooled it, 17 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Review
  4. Observational
  5. Article
  6. Treating connective tissue disease-associated interstitial lung disease - think outside the box: a perspective.European respiratory review : an official journal of the European Respiratory Society · 2025
    Review
  7. Article
  8. Article
  9. Article
  10. Review
  11. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 1 institution in 1 country.

Chengna LvDepartment of Pulmonary and Critical Care Medicine, The Affiliated Hospital of Medical School, Ningbo University, Ningbo, 315020, China.ORCID 0000-0003-2867-5213
Qipan ZhangDepartment of Pulmonary and Critical Care Medicine, The Affiliated Hospital of Medical School, Ningbo University, Ningbo, 315020, China.ORCID 0000-0002-9342-2119
Pan TangDepartment of Pulmonary and Critical Care Medicine, The Affiliated Hospital of Medical School, Ningbo University, Ningbo, 315020, China.ORCID 0000-0001-8088-2725
Lun GuoDepartment of Pulmonary and Critical Care Medicine, The Affiliated Hospital of Medical School, Ningbo University, Ningbo, 315020, China.ORCID 0000-0001-7497-9790
Qunli DingDepartment of Pulmonary and Critical Care Medicine, The Affiliated Hospital of Medical School, Ningbo University, Ningbo, 315020, China. dingqunli@nbu.edu.cn.ORCID 0000-0002-6181-4548
Ningbo University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundInterstitial lung disease (ILD) is a common pulmonary complication of connective tissue disease (CTD). This study aims to evaluate the clinical diagnostic value of matrix metalloproteinase-9 (MMP-9), surfactant protein-D (SP-D), and vascular endothelial growth factor (VEGF) as potential biomarkers for CTD-ILD.

methodsThis research included 33 CTD-ILD patients, 31 CTD patients without ILD, and 24 healthy control subjects. Then, the value of biomarkers for the diagnosis and evaluation of CTD-ILD was assessed through high-resolution computed tomography (HRCT) findings and pulmonary function test (PFT) parameters.

resultsThe serum MMP-9, SP-D, and VEGF levels in the CTD-ILD group were higher than those in the CTD-NILD group and healthy group. The ROC curve indicates that VEGF has good to excellent diagnostic performance in diagnosing CTD-ILD, the cut-off that best optimizes sensitivity and specificity in diagnosing CTD-ILD is 277.60 pg/ml (sensitivity, 87.9%; specificity, 83.6%), with an area under the curve (AUC) of 0.905 (95% confidence interval (CI) 0.842-0.968); The ROC curve for MMP-9 suggests this biomarker is fair for diagnosis of CTD-ILD(sensitivity, 81.8%; specificity, 81.8%), with an AUC of 0.867 (95% CI 0.784-0.950), but SP-D only provided lower specificity with higher sensitivity in diagnosing CTD-ILD(sensitivity, 90.9%; specificity, 40.0%). The different serum biomarkers are more specific and sensitive when combined to diagnose ILD. The semiquantitative score for the degree of ILD severity on HRCT was positively correlated with SP-D and VEGF levels (r = 0.461, P = 0.007; r = 0.362, P = 0.039), and serum MMP-9 levels were elevated in the UIP subgroup compared to the non-UIP subgroup. The percentage of diffusing capacity of the lung for carbon monoxide (DLco) (% predicted) had a negative correlation with the SP-D level (r = - 0.407, P = 0.044) and a statistically negative correlation between MMP-9 and the forced vital capacity (FVC) (r = - 0.451, P = 0.024).

conclusionsSerum MMP-9, SP-D, and VEGF levels may have clinical value in screening and evaluating the severity of CTD-ILD.

Indexed as

Connective Tissue DiseasesLung Diseases, InterstitialBiomarkersHumansMatrix Metalloproteinase 9Pulmonary Surfactant-Associated Protein DVascular Endothelial Growth Factor ABiomarkersMatrix Metalloproteinase 9Pulmonary Surfactant-Associated Protein DVascular Endothelial Growth Factor AConnective tissue disease-associated interstitial lung diseaseMatrix metalloproteinase-9Surfactant protein-DVascular endothelial growth factor

Identifiers

PMID36303230
OpenAlexW4307638406

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.