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Relationship Between C-Reactive Protein and Respiratory Diseases in Patients with Type 2 Diabetic Retinopathy

BACKGROUND: The aim of this study was to explore the relationship between C-reactive protein (CRP) and respiratory diseases in patients with diabetic retinopathy. MATERIAL/METHODS: We identified 855 patients with diabetic retinopathy who met the inclusion criteria from the “Diabetes Complications Da...

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Detalles Bibliográficos
Autores principales: Chen, Kejia, Yan, Jiamin, Wu, Ling, Gu, Xingbo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: International Scientific Literature, Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9123838/
https://www.ncbi.nlm.nih.gov/pubmed/35578564
http://dx.doi.org/10.12659/MSM.935807
Descripción
Sumario:BACKGROUND: The aim of this study was to explore the relationship between C-reactive protein (CRP) and respiratory diseases in patients with diabetic retinopathy. MATERIAL/METHODS: We identified 855 patients with diabetic retinopathy who met the inclusion criteria from the “Diabetes Complications Data Set” in the National Population Health Data Center. We divided patients into 3 groups according to CRP tertiles: Q1 (<0.3 mg/dL), Q2 (0.3–0.35 mg/dL), and Q3 (>0.35 mg/dL). A multivariate logistic regression model was used to evaluate the relationship between CRP and respiratory diseases. The area under the receiver operating characteristic (ROC) curve was used to investigate the independent predictive effect of CRP on respiratory diseases. RESULTS: Of the 855 patients with diabetic retinopathy, 137 (16%) had respiratory diseases. Prevalence of respiratory diseases gradually increased with an increase in CRP level (P for trend=0.001). With CRP as a continuous variable in the logistic regression model adjusted for confounding factors (model 3), the odds ratio (OR) per 1 standard deviation increment of CRP was 1.25 (95% CI 1.07–1.45, P=0.004). When the lowest CRP tertile group was used as the reference group, the OR of the highest CRP tertile group was 1.99 (95% CI 1.22–1.3.26, P=0.006). Adding CRP to the risk factor model increased the area under the ROC curve (0.68 vs 0.65, P=0.017). Subgroup analysis showed that the relationship between CRP and respiratory diseases had no potential heterogeneity among subgroups. CONCLUSIONS: CRP can be used as an effective biomarker in predicting risk of respiratory diseases in patients with diabetic retinopathy.