Does HbA1cc Play a Role in the Development of Cardiovascular Diseases?
Bibliographic record
Abstract
Cardiovascular diseases (CVD) may be mediated through increases in the cardiovascular risk factors. Hemoglobin A1c (HbA1c) also called glycated hemoglobin is presently used for the diagnosis and management of diabetes. It has adverse effects on cardiovascular system. This review deals with its synthesis and effects on the cardiovascular system. The serum levels of HbA1c have been reported to be affected by various factors including, the lifespan of erythrocytes, factors affecting erythropoiesis, agents interfering glycation of Hb, destruction of erythrocytes, drugs that shift the formation of Hb, statins, and drugs interfering the HbA1c assay. Levels of HbA1c are positively correlated with serum glucose and advanced glycation end products ( AGE), but no correlation between AGE and serum glucose. AGE cannot replace HbA1c for the diagnosis and management of diabetes because there is no correlation of AGE with serum glucose, and because the half-life of protein with which glucose combines is only 14-20 days as compared to erythrocytes which have a half-life of 90-120 days. HbA1c is positively associated with CVD such as the carotid and coronary artery atherosclerosis, ischemic heart disease, ischemic stroke and hypertension.HbA1c induces dyslipidemia, hyperhomocysteinemia, and hypertension, and increases C-reactive protein, oxidative stress and blood viscosity that would contribute to the development of cardiovascular diseases. In conclusion, HbA1c serves as a useful marker for the diagnosis and management of diabetes. AGE cannot replace HbA1c in the diagnosis and management of diabetes. There is an association of HbA1c with CVD which be mediated through modulation of CVD risk factors.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".