Movement disorders : official journal of the Movement Disorder Society · 2023
A Novel Metric for Predicting Severity of Disease Features in Friedreich's Ataxia.
СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Rodden Layne N, Rummey Christian, Kessler Sudha, Wilson Robert B, Lynch David R
Аннотация
BACKGROUND: Friedreich's ataxia (FRDA), most commonly caused by a GAA triplet repeat (GAA-TR) expansion in intron 1 of the FXN gene, is characterized by deficiency of frataxin protein and clinical features such as progressive ataxia, dysarthria, impaired proprioception and vibration, abolished deep tendon reflexes, Babinski sign, and vision loss in association with non-neurological features such as skeletal anomalies, hearing loss, cardiomyopathy, and diabetes. Pathogenic GAA-TRs range in size from 60 to 1500 triplets and negatively correlate with age of onset. Clinical severity is predicted by a combination of GAA-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.
OBJECTIVE: We aimed to develop a single metric, which we call "disease burden" (DB), that encompasses both GAA-TR length and DD for predicting disease features of FRDA in small sample sizes.
METHODS: Linear regression and multivariable regression analysis was used to determine correlation coefficients between different disease features of FRDA.
RESULTS: Using large datasets for validation, we found that DB predicts measures of neurological dysfunction in FRDA better than GAA-TR length or DD. Analogous results were found using small datasets.
CONCLUSIONS: 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. © 2023 International Parkinson and Movement Disorder Society.
OBJECTIVE: We aimed to develop a single metric, which we call "disease burden" (DB), that encompasses both GAA-TR length and DD for predicting disease features of FRDA in small sample sizes.
METHODS: Linear regression and multivariable regression analysis was used to determine correlation coefficients between different disease features of FRDA.
RESULTS: Using large datasets for validation, we found that DB predicts measures of neurological dysfunction in FRDA better than GAA-TR length or DD. Analogous results were found using small datasets.
CONCLUSIONS: 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. © 2023 International Parkinson and Movement Disorder Society.