Role of the Kidney in Regulating the Metabolism of HDL in Rabbits: Evidence That Iodination Alters the Catabolism of Apolipoprotein A-I by the Kidney
Bibliographic record
Abstract
To evaluate the factors that regulate HDL catabolism in vivo, we have measured the clearance of human apoA-I from rabbit plasma by following the isotopic decay of (125)I-apoA-I and the clearance of unlabeled apoA-I using a radioimmunometric assay (RIA). We show that the clearance of unlabeled apoA-I is 3-fold slower than that of (125)I-apoA-I. The mass clearance of iodinated apoA-I, as determined by RIA, is superimposable with the isotopic clearance of (125)I-apoA-I. The data demonstrate that iodination of tyrosine residues alters the apoA-I molecule in a manner that promotes an accelerated catabolism. The clearance from rabbit plasma of unmodified apoA-I on HDL(3) and a reconstituted HDL particle (LpA-I) were very similar and about 3-4-fold slower than that for (125)I-apoA-I on the lipoproteins. Therefore, HDL turnover in the rabbit is much slower than that estimated from tracer kinetic studies. To determine the role of the kidney in HDL metabolism, the kinetics of unmodified apoA-I and LpA-I were reevaluated in animals after a unilateral nephrectomy. Removal of one kidney was associated with a 40-50% reduction in creatinine clearance rates and a 34% decrease in the clearance rate of unlabeled apoA-I and LpA-I particles. In contrast, the clearance of (125)I-labeled molecules was much less affected by the removal of a kidney; FCR for (125)I-LpA-I was reduced by <10%. The data show that the kidneys are responsible for most (70%) of the catabolism of apoA-I and HDL in vivo, while (125)I-labeled apoA-I and HDL are rapidly catabolized by different tissues. Thus, the kidney is the major site for HDL catabolism in vivo. Modification of tyrosine residues on apoA-I may increase its plasma clearance rate by enhancing extra-renal degradation pathways.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".