Sensory Dysfunction As a Marker of Clinical Phenotype Across the Lewy Body Disease Spectrum
Bibliographic record
Abstract
Abstract Background Lewy Body Disease (LBD) presents with a complex phenotype encompassing neuropsychiatric, motor, and autonomic symptoms, with substantial clinical variability across individuals. Sensory impairments - particularly in vision, hearing, and olfaction - are emerging as early and non-invasive markers of neurodegeneration, yet their role in LBD remains underexplored. This study investigates the association between sensory dysfunction and core non-cognitive clinical features of LBD across its spectrum, from Parkinson’s Disease with Mild Cognitive Impairment (PD-MCI) to probable Dementia with Lewy Bodies (DLB), aiming to identify sensory profiles linked to more severe clinical phenotypes. Methods In this observational cross-sectional study, participants with PD-MCI and probable DLB underwent a comprehensive assessment across sensory, neuropsychiatric, motor, and autonomic domains. Sensory function was assessed using objective measures of olfaction, vision, and hearing. Associations between sensory impairments and core LBD symptoms were explored to identify potential phenotype patterns. Results To date, 46 participants have been recruited (37 LBD, 9 PD-MCI; mean age = 74.1, SD = 5.6; 30% female). The overall sample showed widespread sensory dysfunction: 100% exhibited moderate-to-severe olfactory loss, 98% auditory deficits, and 24.4% visual impairment. Neuropsychiatric symptoms were highly prevalent, particularly hallucinations (62.2%), apathy (73%) and REM sleep disturbances (64.9%). Clinically significant anxiety and depression were present in 33% and 18.7% of participants, respectively. Motor and autonomic abnormalities were also common. Conclusion These findings highlight the potential of sensory profiling as a feasible and informative approach to capture clinical heterogeneity in LBD. Assessing sensory function may aid early identification of more severe non-cognitive phenotypes and inform tailored clinical strategies across the disease spectrum.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".