The importance of serum proteins in the interpretation of total homocysteine
Bibliographic record
Abstract
"Objective: Increased circulating total homocysteine (tHcy) has been implicated as an independent risk factor for atherosclerosis and thromboembolic disease. Since 70% of tHcy is bound to proteins, we wished to determine whether tHcy assays are influenced by altered serum protein profiles, such as those typical of multiple myeloma (MM). Method: We therefore assayed tHcy, albumin, globulins, and creatinine in 46 MM patients with IgG paraproteinemia to test this hypothesis and determine the dependence of the tHcy concentration on the protein profile in this disease. Results: Mean tHcy in MM subjects was 15.9 ± 9.9 ¿mol/L, significantly higher than in our reference population (10.1 ± 6.6 ¿mol/L, n = 711). As expected, mean serum IgG was increased (22.5 ± 16.6 g/L), and serum albumin modestly decreased (39 ± 6 g/L). A significant correlation was observed (r = 0.33, p < 0.05), such that a 1 g/L decrease in albumin was associated with a 5.5% decrease in tHcy. Serum creatinine is normally a significant covariate of tHcy, since homocysteine metabolism is dependent on normal renal function. However, no correlation was seen in our MM cohort. In fact, some MM patients with low serum albumin had tHcy levels below the lower reference limit (5 ¿mol/L), despite high serum creatinines. Conclusion: Our observations emphasize that tHcy levels should be interpreted with caution in any patients with any disorder affecting the serum protein profile."
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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 | 0.014 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".