The vicious circle of chronic kidney disease and hypertriglyceridemia: What is first, the hen or the egg?
Bibliographic record
Abstract
Chronic kidney disease (CKD) is documented to cause alterations in lipid metabolism, and this was considered a potent driver of increased cardiovascular risk. Among the diverse alteration of lipid traits in CKD, research endeavours have predominantly concentrated on low-density lipoproteins (LDL) in view of the potent pro-atherogenic role of these lipoprotein particles and the demonstration of protective cardiovascular effect of reducing LDL. However, few studies have focused on the metabolism of triglyceride-rich lipoproteins and even fewer on their role in causing kidney damage. Therefore, the comprehensive description of the impact of hypertriglyceridemia (HTG) in CKD pathophysiology remains largely undetermined. This reflects the difficulty of disentangling the independent role of triglycerides (TG) in the complex, bidirectional relationship between TG and kidney disease. Abnormal neutral lipid accumulation in the intrarenal vasculature and renal cells eventually due to HTG may also promote glomerular injury, throughout mechanisms including oxidative stress, mitochondrial dysfunction and proinflammatory responses. While epidemiological and experimental evidence suggests a potential role of TG in kidney damage, the causal mechanisms and their clinical relevance remain unclear, representing a significant area for future investigation. This review aims to highlight the intricate interplay between TG metabolism and kidney disease, shedding light on the mechanisms through which HTG may influence kidney functionality.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".