Erythropoietin single nucleotide polymorphisms are associated with anemia and dyslipidemia in a cardiovascular disease population
Bibliographic record
Abstract
Abstract Human erythropoietin (EPO) is an essential erythropoietic cytokine and recombinant biological therapeutic in anemia in chronic kidney disease. Single nucleotide polymorphisms (SNPs) in EPO have been linked to anemia and diabetic microvascular complications but no other cardiovascular comorbidities like dyslipidemia or hypertension. Here identified a clinical cohort of cardiovascular patients with anemia, dyslipidemia, hypertension, type-2 diabetes, or heart failure with preserved ejection fraction. Sanger sequencing was used for genotyping three EPO SNPs (rs1617640, rs507392 and rs551238), which were compared to clinical outcomes with SNPstat and clinical parameters profiles by student t-test. Nonstandard clinical chemistry measures of plasma EPO, hepcidin, transferrin, and ferritin were determined by ELISA. In the additive model, the C allele of rs1617640 was associated with lower EPO and anemia. In the recessive model, the SNP rs507392 genotype of GG was associated with dyslipidemia, which was associated with lower EPO in plasma and elevated total cholesterol and low-density lipoproteins. Interestingly, the G allele of rs507392 was found to be associated with hypertension, but only in females. Here we show that the SNPs in EPO are a potential risk factor for anemia, dyslipidemia, and hypertension in cardiovascular disease patients.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".