Blood biomarkers as predictors of long-term mortality in COPD

Abstract

Background: Blood biomarkers are easily accessible and might reflect chronic obstructive pulmonary disease (COPD) activity.

Aim: The aim of this study was to determine whether a panel of blood biomarkers [C-reactive protein (CRP), neutrophils, eosinophils, albumin and vitamin D] could predict mortality in COPD.

Methods: We analyzed data from 431 COPD participants to the 2007-2010 National Health and Nutrition Examination Surveys who were followed for a median time of 36 months. COPD was defined as post-bronchodilator forced expiratory volume in 1 second (FEV1) and forced vital capacity ratio <0.70. Weibull survival analysis adjusted for covariates was performed to calculate the risk of mortality associated with the biomarkers, and C-statistics was used to assess their added predictive value.

Results: During follow-up, 38 of the 431 participants died. Participants with high CRP, eosinophil count <2%, hypoalbuminemia and hypovitaminosis D had worse baseline FEV1 and subsequently higher mortality compared to controls. In adjusted analysis, increasing CRP [hazard ratio (HR): 4.45, 95% CI: 1.91-10.37] and neutrophil count (HR: 1.07, 95% CI: 1.03-1.11) as well as decreasing eosinophil count (HR: 7.03, 95% CI: 2.05-24.01) were associated with an increased risk of mortality. The addition of CRP with eosinophil and/or neutrophil count significantly improved a base model for the prediction of mortality which included age, gender, race/ethnicity, body mass index, smoking, poverty income ratio, asthma, diabetes, hypertension and history of stroke or myocardial infarction.

Conclusion: High CRP and neutrophils as well as low eosinophils are predictive of poor COPD prognosis. They also add significant value to prediction models of mortality in COPD.

Keywords: biologic markers; chronic obstructive pulmonary disease; prognosis.

Figure 1

Risk of mortality in COPD associated with a) CRP, b) neutrophils, c) eosinophils, d) albumin, and e) vitamin D using parametric Weibull regression. Models adjusted for age, gender, race/ethnicity, body mass index, smoking, poverty income ratio, asthma, diabetes, hypertension, and history of stroke or myocardial infarction.

Figure 2

Venn diagram for the number of participants and deaths in each category of the biomarkers associated with mortality.

Figure 3

Receiver operating characteristic (ROC) curves showing improvement of the predictive value of the initial model by the addition of CRP, eosinophils, and NLR. The initial model included age, gender, race/ethnicity, PIR, smoking, BMI, and comorbidities (diabetes, hypertension, history of MI/stroke).