Obesity Associates with Impaired Quality of Life in Parkinson Disease (P2.056)
Bibliographic record
Abstract
Objective/Background: Medical comorbidities are increasingly recognized as relevant contributors to meaningful outcomes in Parkinson disease (PD) including poor quality of life (QoL). Obesity is a common health condition that is usually not considered in QoL assessments in PD. It may impact PD-QoL, however, by several potential pathways including a) worsening of overall motor burden b) worsening of non-motor features including mood, fatigue, or sleep disorders, or c) contributing to cognitive dysfunction given the increasingly noted interaction between metabolic syndrome and neurodegeneration. Design/Methods: Using multivariable linear regression, we explored the relative influence of obesity, as assessed using body mass index (BMI), in a cross-sectional cohort study of 120 subjects with PD. The PDQ-39 scale was used to assess QoL. The overall model included other relevant mediators of quality of life including age, disease duration, Movement Disorders Society-Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) motor score, Montreal Cognitive Assessment (MoCA) score, Geriatric Depression scale (GDS), the Fatigue Severity Scale, and the Parkinson Disease Sleep Severity scale (PDSS) scores. Results: The overall model R2 value was 0.476 (F=12.59, p<0.0001). Significant predictors of quality of life included BMI (t=2.56, p=0.012), MDS-UPDRS motor score (t=3.19, p=0.0018), GDS score (t=3.61, p=0.0005), PDSS score (t=-2.80, p=0.006), and FSS score (t=3.54, p=0.0006). Conclusions: Obesity may contribute to poor QoL in PD through mechanisms independent of overall motor burden, depression, fatigue, sleep dysfunction, or cognitive decline. Additional research is needed to determine whether obesity is a trait marker for other unmeasured QoL-influencing features in PD or whether targeted weight loss amongst overweight patients with PD may merit further clinical exploration. Supported by NIH grants PO1 NS015655, RO1 NS070856, Department of Veterans Affairs and the Michael J. Fox Foundation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".