Impact of type 2 diabetes mellitus on severity of Parkinson's disease: a cross-sectional study
Bibliographic record
Abstract
Background and objectives. Parkinson's disease (PD) is a progressive neurodegenerative disorder characterized by motor and non-motor symptoms that significantly impact quality of life. This study aimed to investigate whether the presence of type 2 diabetes mellitus (T2DM) influences the clinical severity and manifestations of PD across motor and non-motor domains. Materials and methods. This cross-sectional study compared 80 PD patients (40 with T2DM and 40 without) at the Neurology Department of Mansoura University Hospital, Egypt. Demographic and clinical characteristics were documented. The Unified Parkinson's Disease Rating Scale (UPDRS), Montreal Cognitive Assessment (MoCA), Hamilton Anxiety and Depression Scales, and Schwab and England Activities of Daily Living Scale were assessed in all participants. Results. PD patients with T2DM exhibited significantly more severe motor impairments, including substantial gait problems (p = 0.003) and reduced independence (p < 0.001). The T2DM group showed higher scores across UPDRS components: non-motor experiences (p < 0.001), motor experiences (p = 0.001), and motor examination (p = 0.003). While cognitive function was comparable, T2DM patients demonstrated significantly higher anxiety levels (p = 0.014) and more advanced disease with higher Hoehn and Yahr staging (p = 0.004), despite similar disease duration and levodopa equivalent daily doses. Conclusions. T2DM is associated with more severe manifestations of PD across multiple domains, suggesting significant interaction between the two conditions. These findings emphasize the importance of early screening and aggressive management of T2DM in PD patients to optimize clinical outcomes.
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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 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".