Understanding the Links Between Cardiovascular Disease and Parkinson's Disease
Bibliographic record
Abstract
Studies investigating the associations between genetic or environmental factors and Parkinson's disease (PD) have uncovered a number of factors shared with cardiovascular disease, either as risk factors or manifestations of cardiovascular disease itself. Older age, male sex, and possibly type 2 diabetes are examples. On the other hand, coffee consumption and physical activity are each associated with a lower risk of both PD and cardiovascular disease. This observation raises questions about the underlying pathophysiological links between cardiovascular disease and PD. There is evidence for common mechanisms in the areas of glucose metabolism, cellular stress, lipid metabolism, and inflammation. On the other hand, smoking and total/low-density lipoprotein cholesterol appear to have opposite associations with cardiovascular disease and PD. Thus, it is uncertain whether the treatment of cardiovascular risk factors will impact on the onset or progression of PD. The available data suggest that a nuanced approach is necessary to manage risk factors such as cholesterol levels once the associations are better understood. Ultimately, the choice of therapy may be tailored to a patient's comorbidity profile. This review presents the epidemiological evidence for both concordant and discordant associations between cardiovascular disease and PD, discusses the cellular and metabolic processes that may underlie these links, and explores the implications this has for patient care and future research. © 2019 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".