Background: Lymphangioleiomyomatosis (LAM) is a rare multisystem disease with variable manifestations and differing rates of progression among individuals. Classification of its phenotypes is an issue for consideration. We hypothesized that clinical manifestations associated with LAM cluster together and identifying these associations would be useful for identifying phenotypes.
Methods: Using cross-sectional data from the National Database of Designated Intractable Diseases of Japan, we performed a hierarchical cluster analysis based on disease-associated manifestations.
Results: Four clusters were identified from 404 patients (50.4% of 801 LAM patients registered in 2016). Patients in cluster 1 had only dyspnea on exertion, relatively low lung function, the earliest onset age, and the lowest prevalence of tuberous sclerosis complex (TSC). Those in cluster 2 had various manifestations with the highest prevalence of TSC. Patients in cluster 3 had major respiratory symptoms (cough, sputum, or dyspnea on exertion) or fatigue and the lowest lung function. Those in cluster 4 were asymptomatic and had the latest onset age, shortest disease duration, and relatively high prevalence of TSC. Patients in cluster 1 had the highest rate of receiving mechanistic target of rapamycin (mTOR) inhibitor treatment, suggesting that cluster 1 included those with declining lung function for which mTOR inhibitor treatment was required.
Conclusions: Hierarchical cluster analysis based on manifestations data identified four clusters. The characteristics of cluster 1 are noteworthy in relation to the indication for mTOR inhibitor treatment. A cluster analysis of accumulated and longitudinal data that allows valid clustering and outcome comparisons is required in the future.
Keywords: Cluster analysis; Database; Lymphangioleiomyomatosis; Manifestation; Treatment.
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