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This paper aims at predicting lung function values based on patientshistorical lung function values and serum biomarkers in Scleroderma patients.The progression of disease is measured by three lung function indexes (FVC,TLC, DLCO). Values of four biomarkers (TIMP1, P3NP, HA, NT-proBNP) areavailable. The data are sparse (6 months intervals) and irregular (many visitsare missed). We consider two modeling approaches to achieve our goal, namely,the mixed effects model which is the standard approach in epidemiologicalstudies and the functional principal component analysis model which istypically used for dense temporal datasets. We find that functional datamethodology was able to recover the trajectories of three lung function indexesand to predict the future values very well.

Prediction and forecasting models based on patient's history and biomarkers with application to Scleroderma disease