Effect of renal impairment on atherosclerosis: only partially mediated by homocysteine
Bibliographic record
Abstract
BACKGROUND: Cardiovascular risk and plasma total homocysteine (tHcy) are high in patients with renal failure. High tHcy may account for a substantial part of the increased risk. We assessed mediation by tHcy of the association of estimated glomerular filtration rate (eGFR CKD/EPI) with carotid total plaque area (TPA) and carotid stenosis. METHODS: TPA and carotid stenosis were measured by ultrasound. Multiple linear regression was used to assess the effects of eGFR and/or tHcy after adjustment for age, sex, systolic blood pressure (SBP), smoking, LDL, HDL and weight. RESULTS: Complete data were available for 1967 patients. eGFR decreased, and TPA and total stenosis increased linearly with age. After adjustment [age, sex, SBP, smoking (in pack years), low-density lipoprotein (LDL), high-density lipoprotein (HDL) and weight], eGFR and tHcy were independently associated with TPA (P < 0.01), but when both were added to the model, their significance was attenuated (P = 0.06 for eGFR, 0.03 for tHcy). Mediation analysis showed that tHcy seems to contribute to a significant mediation of the association of eGFR with TPA but not stenosis; after adjustment for the set of risk factors listed above, tHcy still demonstrated significant mediation on TPA (P = 0.03), but not on stenosis (P = 0.16). CONCLUSIONS: tHcy accounts for a significant part, but not all of the effect of renal impairment on atherosclerosis. Other uremic toxins including metabolic products of the intestinal microbiome may explain residual effects of renal failure on atherosclerosis. Therapeutic approaches arising from that hypothesis are discussed.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".