Sequential changes of serum KL-6 predict the progression of interstitial lung disease

Background: Interstitial lung disease (ILD) is a slowly progressing fatal fibrotic lung disease with a widely variable clinical course and a poor prognosis. Clinicians and patients would benefit from a highly efficient and accurate predictor for ILD. The purpose of this study was to evaluate whether blood biomarkers can predict ILD progression.

Methods: In this study, 85 patients diagnosed as having ILD at the Guangzhou Institute of Respiratory Health participated, including 20 patients with idiopathic pulmonary fibrosis (IPF). During the mean follow-up time of 12 months, every patient was examined during four or five visits in our center. Serum samples were collected at baseline, and after 1, 2, 6, and 12 months and tested for the Klebs von den Lungen-6 (KL-6) concentration. Dynamic fluctuations in this biomarker concentration were examined using a logistic regression model to see if they reflected the progression of ILD.

Results: The baseline levels of serum KL-6 in the ILD patients were significantly increased compared to healthy controls. Serum KL-6 levels were significantly elevated in patients with progression of disease (1,985.2±1,497.8 vs. 1,387.6±1,313.1 µg/mL; P<0.001). Logistic regression revealed sequential changes of KL-6 was a significant predictor of ILD progression in the next follow-up (OR, 2.569; 95% CI, 2.260-2.880; P=0.001), and that sequential changes of KL-6 were significant predictors for the progression of IPF (OR, 3.611; 95% CI, 1.048-12.442; P<0.01). Baseline concentrations were not predictive for ILD or IPF. Univariate Cox analysis showed that KL-6 was significantly associated with survival [relative risk (RR), 1.901; 95% CI, 1.294-2.793; P<0.001], along with other variables.

Conclusions: Serum levels of KL-6 were elevated in ILD patients with severe respiratory function compared to those without. The rate of poor prognosis and mortality was associated with increased biomarker concentrations. Sequential measurements of biomarkers could be valuable in disease monitoring and evaluations in clinical management.