The kidney disease wasting: Inflammation, oxidative stress, and diet‐gene interaction
Bibliographic record
Abstract
The 350,000 maintenance hemodialysis (MHD) patients in the United States have an unacceptably high mortality rate of >20%/year. Almost half of all deaths are assumed to be cardiovascular. Markers of kidney disease wasting (KDW) such as hypoalbuminemia, anorexia, body weight and fat loss, rather than traditional cardiovascular risk factors, appear to be the strongest predictors of early death in these patients. The KDW is closely related to oxidative stress (SOX). Such SOX markers as serum myeloperoxidase are associated with pro-inflammatory cytokines and poor survival in MHD patients. Identifying the conditions that modulate the KDW/SOX-axis may be the key to improving outcomes in MHD patients. Dysfunctional lipoproteins such as a higher ratio of the high-density lipoprotein inflammatory index (HII) may engender or aggravate the KDW, whereas functionally intact or larger lipoprotein pools, as in hypercholesterolemia and obesity, may mitigate the KDW in MHD patients. Hence, a reverse epidemiology or "bad-gone-good" phenomenon may be observed. Diet and gene and their complex interaction may lead to higher proportions of pro-inflammatory or oxidative lipoproteins such as HII, resulting in the aggravation of the SOX and inflammatory processes, endothelial dysfunction, and subsequent atherosclerotic cardiovascular disease and death in MHD patients. Understanding the factors that modulate the KDW/SOX complex and their associations with genetic polymorphism, nutrition, and outcomes in MHD patients may lead to developing more effective strategies to improve outcomes in this and the 20 to 30 million Americans with chronic disease states such as individuals with chronic heart failure, advanced age, malignancies, AIDS, or cachexia.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.000 | 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".