A negative effect of poorly controlled diabetes mellitus on the treatment of Parkinson’s disease
Bibliographic record
Abstract
Objective: Converging evidence ranging from basic pathophysiology to epidemiological and clinical data has described a potential relationship between Parkinson’s disease (PD) and diabetes mellitus (DM). This study aimed to explore the effect of sugar control on the treatment of motor manifestations in PD. Methods: A total of 124 de novo PD patients were included in the study. The participants were classified into three groups, non-DM (n = 45), well-controlled (n = 41) and less-controlled DM (n = 38), based on the glycated hemoglobin (HbA1c) cut-off value of 7.0. Clinical parameters including Unified Parkinson’s Disease Rating Scale (UPDRS), Mini-Mental State Examination (MMSE), and Montreal Cognitive Assessment (MoCA) were evaluated serial twice apart from 1 year. Results: The less-controlled DM group revealed significantly lesser improvement in UPDRS III (baseline to follow-up, 38.1 ± 9.6 to 29.9 ± 7.8; p < 0.001) than those of either non-DM (36.6 ± 8.9 to 22.9 ± 5.7) and well-controlled group (37.0 ± 8.8 to 24.0 ± 6.0) after standard PD treatment for 1 year. A higher HbA1c was correlated with a lesser decline of UPDRS III (b, 0.47; p < 0.001) among PD with DM population. Less-controlled DM effectively predicted a lesser decline of UPDRS III (b = + 5.426; p < 0.001). Conclusions: Our results demonstrated a potential negative effect of sub-optimally controlled blood sugar on treating motor manifestations of PD. Careful glucose management might be a beneficial strategy to improve the burden of motor symptoms in PD who have coexisting DM.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".