Peptídeo C e mortalidade cardiovascular: revisão sistemática e metanálise
Bibliographic record
Abstract
OBJECTIVE: To analyze the available evidence regarding the association between C-peptide and cardiovascular and overall mortality. METHODS: A systematic review of MEDLINE and EMBASE was performed. Articles published in English, Portuguese, or Spanish, reporting observational studies investigating the association between C-peptide and cardiovascular or overall mortality were included. The association between C-peptide and cardiometabolic risk factors, hemodynamic factors, and anthropometric measures was also investigated. The methodological quality of studies was assessed using the Newcastle-Ottawa Scale. RESULTS: The literature search revealed 107 articles on the topic of interest. Following the screening step, 18 articles presenting data on the association between C-peptide and cardiovascular risk were included. Five studies provided data on the relationship between C-peptide and cardiovascular or overall mortality. C-peptide was positively associated with body mass index in Chinese individuals, and inversely associated with HDL cholesterol in population samples from Asia, Middle East, and the United Stated. Nevertheless, meta-analysis of cardiovascular risk components was not possible. In the meantime, C-peptide was associated with cardiovascular mortality (RR = 1.62; 95%CI: 0.99-2.66) and overall mortality (RR = 1.39; 95%CI: 1.04-1.84). CONCLUSIONS: The present systematic review and meta-analysis showed that serum levels of C-peptide were positively associated with overall mortality in all individuals and with cardiovascular mortality in individuals without comorbidities. Based on these results, it is possible to recommend the use of C-peptide in clinical practice as a proxy of insulin resistance associated with cardiovascular mortality.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.020 | 0.050 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.030 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".