GLP-1 and GIP receptor agonists in the treatment of Parkinson’s disease: Translational systematic review and meta-analysis protocol of clinical and preclinical studies
Bibliographic record
Abstract
BACKGROUND: Parkinson's disease (PD) is a progressive multifactorial neurodegenerative condition. Epidemiological studies have shown that patients with type 2 diabetes mellitus (T2DM2) are at increased risk for developing PD, indicating a possible insulin-modulating role in this latter condition. We hypothesized that drugs similar to glucagon-like peptide-1 (GLP-1) and gastric inhibitory polypeptide (GIP), used in the treatment of T2DM2, may play a role in PD. OBJECTIVES: The purpose of this study is to systematically review and meta-analyze data of preclinical and clinical studies evaluating the efficacy and safety of GLP-1 and GIP drugs in the treatment of PD. METHODS: Two reviewers will independently evaluate the studies available in the Ovid Medline, Ovid Embase, Web of Science, Cochrane Central Register of Controlled Trials, Cinahl, and Lilacs databases. Preclinical rodent or non-human primate studies and randomized controlled human clinical trials will be included, without language or publication period restrictions. Outcomes of interest in preclinical studies will be primarily locomotor improvements and adverse effects in animal models of PD. For clinical trials, we will evaluate clinical improvements rated by the Movement Disorders Society Unified Parkinson's Disease Rating Scale-parts I, II, III, and IV, and adverse effects. The risk of bias of preclinical studies will be assessed by the SYRCLE tool and CAMARADES checklist and the clinical studies by the Cochrane tool; the certainty of the evidence will be rated by GRADE. DISCUSSION AND CONCLUSION: There is an urge for new PD treatments that may slow the progression of the disease rather than just restoring dopamine levels. This study will comprehensively review and update the state of the art of what is known about incretin hormones and PD and highlight the strengths and limitations of translating preclinical data to the clinic whenever possible. SYSTEMATIC REVIEW REGISTRATION: PROSPERO registration number CRD42020223435.
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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 | 0.070 | 0.092 |
| Meta-epidemiology (narrow) | 0.006 | 0.004 |
| Meta-epidemiology (broad) | 0.031 | 0.031 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.035 | 0.004 |
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".