Lipidomics analysis in cerebrovascular disease and peripheral arterial disease : a systematic review
Bibliographic record
Abstract
Introduction: Cardiovascular diseases are the leading cause of death worldwide and one of contemporary medicine's most critical problems. Several classes of lipids are thought to contribute to the development of this disease. Aim: This systematic review aims to summarize the current evidence on how lipid dysregulation affects these diseases, particularly, cerebrovascular disease and peripheral arterial disease. Methods: This review was performed according to the PRISMA guidelines. A search was achieved at PubMed combining the word "Lipidomics" with keywords related to the diseases, yielding a total of 305 articles. Inclusion criteria were if there was an association between cerebrovascular disease or peripheral arterial disease and lipid markers and if the articles presented an analysis of the lipidomic profile of patients with the diseases under study. Exclusion criteria were if the articles studied another disease other than those under study and if the articles were a review or an editorial. A total of 15 articles were included in the qualitative analysis, which were divided into four groups with those studying atherosclerosis in general (n=6), those studying cerebrovascular disease (n=3), those studying peripheral arterial disease (n=2), and those who study both diseases (n=4). The Newcastle – Ottawa tool was used to assess the quality of each article. Results: Among the 11 articles retained on human studies, 7 are cross-sectional studies and 4 case-control studies, and the other 4 are experimental animal studies. The diseases studied were analyzed together and separately, using different methodologies. For lipid extraction, two methods were used: the Liquid-Liquid Extraction, and the Solid-Phase Extraction, and ten distinct methods for the lipidomic analysis. There was greater lipid dysregulation in fatty acids, glycerolipids, glycerophospholipids, and sphingolipids. Conclusion: Lipidomics is a fundamental tool in the identification of lipids that might play a relevant role in both cerebrovascular disease and peripheral arterial disease, and these biomarkers might be used to improve the individual cardiovascular risk stratification and to provide adequate clinical management. However, they need to be studied in larger settings, and further research is needed.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 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; both teacher heads agree on what is shown here.
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".