Dysphagia in primary progressive aphasia: Clinical predictors and neuroanatomical basis

Abstract

Background and purpose: Dysphagia is an important feature of neurodegenerative diseases and potentially life-threatening in primary progressive aphasia (PPA) but remains poorly characterized in these syndromes. We hypothesized that dysphagia would be more prevalent in nonfluent/agrammatic variant (nfv)PPA than other PPA syndromes, predicted by accompanying motor features, and associated with atrophy affecting regions implicated in swallowing control.

Methods: In a retrospective case-control study at our tertiary referral centre, we recruited 56 patients with PPA (21 nfvPPA, 22 semantic variant [sv]PPA, 13 logopenic variant [lv]PPA). Using a pro forma based on caregiver surveys and clinical records, we documented dysphagia (present/absent) and associated, potentially predictive clinical, cognitive, and behavioural features. These were used to train a machine learning model. Patients' brain magnetic resonance imaging scans were assessed using voxel-based morphometry and region-of-interest analyses comparing differential atrophy profiles associated with dysphagia presence/absence.

Results: Dysphagia was significantly more prevalent in nfvPPA (43% vs. 5% svPPA and no lvPPA). The machine learning model revealed a hierarchy of features predicting dysphagia in the nfvPPA group, with excellent classification accuracy (90.5%, 95% confidence interval = 77.9-100); the strongest predictor was orofacial apraxia, followed by older age, parkinsonism, more severe behavioural disturbance, and more severe cognitive impairment. Significant grey matter atrophy correlates of dysphagia in nfvPPA were identified in left middle frontal, right superior frontal, and right supramarginal gyri and right caudate.

Conclusions: Dysphagia is a common feature of nfvPPA, linked to underlying corticosubcortical network dysfunction. Clinicians should anticipate this symptom particularly in the context of other motor features and more severe disease.

Keywords: Alzheimer dementia; dysphagia; primary progressive aphasia; semantic dementia; swallowing.

FIGURE 1

Bar chart showing the relative importance (with 95% confidence intervals) of clinical features associated with dysphagia (at baseline) in patients with nonfluent/agrammatic variant primary progressive aphasia. The features shown were used to train the random forest algorithm used in analysis of dysphagia associations (see text and Appendix S1). CBI‐R, Cambridge Behavioural Inventory–Revised total score; MMSE, Mini‐Mental State Examination score.

FIGURE 2

Kaplan–Meier survival analysis for proportions of patients developing dysphagia during follow‐up in each primary progressive aphasia syndromic group: nfvPPA (n = 12), svPPA (n = 21), and lvPPA (n = 13). For patients who did not develop dysphagia, time to dysphagia indicates follow‐up time. Crosses (+) indicate censored cases (patients who were lost to follow‐up). lvPPA, patient group with logopenic variant primary progressive aphasia; nfvPPA, patient group with nonfluent/agrammatic primary progressive aphasia; svPPA, patient group with semantic variant primary progressive aphasia.

FIGURE 3

Statistical parametric maps showing regional grey matter differences between subgroups of patients with nonfluent/agrammatic variant primary progressive aphasia who did and did not report dysphagia, based on a voxel‐based morphometric analysis of their brain magnetic resonance (MR) images. For display purposes, maps are thresholded at p < 0.001 uncorrected over the whole brain and presented on coronal (left) and sagittal (middle, right) sections of a group (combined patient group) mean T1‐weighted brain MR image; the plane of each section is indicated using Montreal Neurological Institute coordinates, and the left cerebral hemisphere is displayed on the left in the coronal section. The colour bar codes voxelwise T‐scores for the atrophy maps. The grey matter associations shown signify more marked regional grey matter loss in patients with dysphagia, and all had local maxima significant at p < 0.05, after correction for multiple voxelwise comparisons within the anatomical region of interest (see text and Table 3).