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Updated: Jan 17, 2026
Imaging Features of Systemic Sclerosis-Associated Interstitial Lung Disease
Published on: June 16, 2020
Cluster analysis of seasonal KL-6 variations in interstitial lung diseases
Yuki Iijima1, Tsukasa Okamoto1, Shiro Sonoda2
1Department of Respiratory Medicine, Institute of Science Tokyo, 1-5-45, Yushima, Bunkyo-ku, Tokyo, 113-8519, Japan; Center for Personalized Medicine for Healthy Aging, Institute of Science Tokyo, 1-5-45, Yushima, Bunkyo-ku, Tokyo, 113-8519, Japan.
Background:
Serum Krebs von den Lungen-6 (KL-6) is a biomarker that reflects the pathophysiology and activity of interstitial lung disease (ILDs); however, its fluctuation patterns remain understudied.
Methods:
This retrospective cohort study included 910 patients with ILD with at least one year of regular KL-6 measurements. Cluster analysis was performed to identify the distinct annual KL-6 fluctuation patterns. Patient demographics, disease distribution, and prognostic outcomes were compared across clusters.
Results:
Four distinct clusters of KL-6 patterns were identified: minimal change (cluster 1, n = 722), decrease in summer (cluster 2, n = 74), increase in autumn (cluster 3, n = 21), and increase in winter (cluster 4, n = 93). Mean KL-6 value of the first year (p < 0.01), percent predicted forced vital capacity (p = 0.01), and diagnoses of idiopathic pulmonary fibrosis (p < 0.01), nonfibrotic hypersensitivity pneumonitis (p < 0.01), and fibrotic hypersensitivity pneumonitis (p = 0.04) were significantly associated with specific KL-6 fluctuation patterns. When seasonal trends were defined as belonging to the same cluster for two consecutive years, nonfibrotic hypersensitivity pneumonitis showed significant association with seasonal trends (p < 0.01). Multivariate analysis, adjusted for age and etiology, showed a trend for cluster 4 to have a poorer prognosis compared to cluster 1 (hazard ratio: 1.62, 95 % confidence interval: 0.93-2.80, p = 0.09).
Conclusion:
KL-6 fluctuations were categorized into four seasonal patterns, which may provide insights for diagnosing ILD etiology and predicting the prognosis of patients with ILD.
