Type 2 Diabetes as a Determinant of Parkinson's Disease Risk and Progression
Bibliographic record
Abstract
BACKGROUND: Type 2 diabetes (T2DM) and Parkinson's disease (PD) are prevalent diseases that affect an aging population. Previous systematic reviews and meta-analyses have explored the relationship between diabetes and the risk of PD, but the results have been conflicting. OBJECTIVE: The objective was to investigate T2DM as a determinant of PD through a meta-analysis of observational and genetic summary data. METHODS: A systematic review and meta-analysis of observational studies was undertaken by searching 6 databases. We selected the highest-quality studies investigating the association of T2DM with PD risk and progression. We then used Mendelian randomization (MR) to investigate the causal effects of genetic liability toward T2DM on PD risk and progression, using summary data derived from genome-wide association studies. RESULTS: In the observational part of the study, pooled effect estimates showed that T2DM was associated with an increased risk of PD (odds ratio [OR] 1.21, 95% confidence interval [CI] 1.07-1.36), and there was some evidence that T2DM was associated with faster progression of motor symptoms (standardized mean difference [SMD] 0.55, 95% CI 0.39-0.72) and cognitive decline (SMD -0.92, 95% CI -1.50 to -0.34). Using MR, we found supportive evidence for a causal effect of diabetes on PD risk (inverse-variance weighted method [IVW] OR 1.08, 95% CI 1.02-1.14; P = 0.010) and some evidence of an effect on motor progression (IVW OR 1.10, 95% CI 1.01-1.20; P = 0.032) but not on cognitive progression. CONCLUSIONS: Using meta-analyses of traditional observational studies and genetic data, we observed convincing evidence for an effect of T2DM on PD risk and new evidence to support a role in PD progression. © 2021 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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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 | 0.014 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.015 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".