Background: Previous studies suggest that specific genes may predispose some to increased risk of dysphagia in the geriatric population, but whether these genes may affect swallowing recovery after a stroke is unknown. This study investigated whether single-nucleotide polymorphisms (SNP) of the brain-derived neurotrophic factor (BDNF), catechol-O-methyl transferase, apolipoprotein E, interleukin-1 receptor antagonist, and dopamine, which have been linked to swallowing, could adversely affect the prognosis of post-stroke dysphagia. Methods: In this study, 218 subjects with confirmed post-stroke dysphagia were enrolled. The primary endpoint was failed recovery from nil per mouth (NPM) status with the first 3 months post-stroke. Key Results: The Val/Val group from the rs6265, BDNF, showed higher score changes on the Functional Oral Intake Scale at 1 month. The proportion of patients with recovery from NPM status within the first 1 month was 60.8% in the Val/Val group, which was statistically higher than those in the Met allele groups (38.1%, P = .017). At 3 months, the BDNF rs6265 showed significant group differences in Modified Barium Swallow Impairment Profile © score changes with the Val/Val allele leading to greater improvement. However, no single SNP was associated with increased risk of poor recovery with persistence of NPM at 3 months post-stroke. Conclusions and Inferences: Those with the dominant Val/Val phenotype of BDNF manifested with faster and greater improvement than the Met-phenotypes. Based on our results, the BDNF Val allele may play a positive role with faster score improvement and rapid recovery from NPM than the Met allele.

No abstract

Abnormal voice may identify those at risk of post-stroke aspiration. This study was aimed to determine whether machine learning algorithms with voice recorded via a mobile device can accurately classify those with dysphagia at risk of tube feeding and post-stroke aspiration pneumonia and be used as digital biomarkers. Voice samples from patients referred for swallowing disturbance in a university-affiliated hospital were collected prospectively using a mobile device. Subjects that required tube feeding were further classified to high risk of respiratory complication, based on the voluntary cough strength and abnormal chest x-ray images. A total of 449 samples were obtained, with 234 requiring tube feeding and 113 showing high risk of respiratory complications. The eXtreme gradient boosting multimodal models that included abnormal acoustic features and clinical variables showed high sensitivity levels of 88.7% (95% CI 82.6–94.7) and 84.5% (95% CI 76.9–92.1) in the classification of those at risk of tube feeding and at high risk of respiratory complications; respectively. In both cases, voice features proved to be the strongest contributing factors in these models. Voice features may be considered as viable digital biomarkers in those at risk of respiratory complications related to post-stroke dysphagia.

No abstract