Association of long-term glycemic variability with longitudinal motor and nonmotor progression in patients with Parkinson’s disease: an 8-year follow-up
Bibliographic record
Abstract
BACKGROUND: Although recent evidence suggests that glycemic variability (GV) has a negative impact on neurodegeneration, its role in Parkinson's disease (PD) remains unclear. OBJECTIVE: To explore the association between long-term GV and longitudinal motor and nonmotor progression in patients with PD and to uncover the disease-specific and nonspecific mechanisms underlying this association. METHODS: We used data obtained from the Parkinson's Progression Markers Initiative cohort. Three hundred seventy-seven patients with early untreated PD underwent annual motor and nonmotor assessments covering neuropsychiatric, sleep-related, and autonomic symptoms for up to 8 years of follow-up. Dopamine transporter (DAT) imaging results and cerebrospinal fluid (CSF) marker levels, including α-synuclein, β-amyloid 1-42, total tau, phosphorylated tau181, and neurofilament light chain (NfL) were collected at baseline and for up to 6 years of follow-up. We defined GV as the intra-individual visit-to-visit variability in annual fasting blood glucose levels. RESULTS: With respect to motor symptoms, a greater GV was associated with a greater increase in postural instability/gait difficulty scores (P = .001) and a greater risk of developing freezing of gait (P = .002). With respect to nonmotor symptoms, higher GV was associated with a steeper decrease in Montreal Cognitive Assessment (P < .001) and semantic fluency test (P = .002) scores and a greater increase in Geriatric Depression Scale scores (P = .001). With respect to DAT imaging and CSF biomarkers, increased GV was associated with a greater increase in CSF NfL levels (P = .001) but not with other biomarker changes. CONCLUSION: Our findings suggest that increased GV is related to unfavourable motor and nonmotor outcomes in patients with PD. However, we did not identify the specific mechanisms underlying these GV-related effects, despite its association with more severe neurodegeneration.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".