A Novel Metric for Predicting Severity of Disease Features in Friedreich's Ataxia

Clinical severity in Friedreich's ataxia (FRDA) is predicted by a combination of GAA-Triplet Repeat (TR) length and disease duration (DD) via multivariable regressions, which cannot typically be used for the small sample sizes in most studies on this rare disease. The authors aimed to develop a single metric, which they call "disease burden" (DB), that encompasses both GAA-TR length and DD for predicting disease features of FRDA in small sample sizes. Linear regression and multivariable regression analysis was used to determine correlation coefficients between different disease features of FRDA. Using large datasets for validation, it was found that DB predicts measures of neurological dysfunction in FRDA better than GAA-TR length or DD. Analogous results were found using small datasets. FRDA DB is a novel metric of disease severity that has utility in small datasets to demonstrate correlations that would not otherwise be evident with either GAA-TR or DD alone. This is important for discovering new biomarkers, as well as improving the prediction of severity of disease features in FRDA.

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