Evidence for the use of glomerulomegaly as a surrogate marker of glomerular damage and for alpha-linolenic acid-rich oils in the treatment of early obesity-related glomerulopathy in a diet-induced rodent model of obesity
Bibliographic record
Abstract
Obesity-related glomerulopathy (ORG) is a unique and emerging condition that can lead to renal failure. Early detection, aided by an earlier diagnostic marker, would improve patient outcomes; this could be facilitated by an accurate model. Such a model would be useful to examine interventions like dietary fatty acids, which are known to influence renal diseases in later stages. In this study, obese-prone rats were provided high-fat (55% of energy) diets for 12 weeks to generate a model of diet-induced obesity. The rats were subsequently provided dietary oils with various levels of alpha-linolenic acid (ALA) and linoleic acid (LA) for 8 weeks, as follows: (g ALA:LA per 100 g oil): canola/flax (20:18), canola (8:18), soy (9:53), high-oleic canola/canola (5:16), high-oleic canola (2:15), lard/soy (1:8), and safflower (0.2:73). The model developed obesity, glomerulomegaly, proteinuria, and scarce glomerular damage with an indolent course. Morphometry and histology revealed glomerulomegaly as the first renal structural alteration. The utility of this marker as a predictor for the presence of ORG and renal injury was evidenced by its correlation to visceral adiposity (p < 0.0001, r = 0.44), proteinuria (p < 0.0001, ρ = 0.55), change in proteinuria (p = 0.0092, ρ = 0.42), and glomerular damage (p < 0.0001, ρ = 0.48). Renal triglyceride ALA:LA was strongly correlated with dietary ALA:LA (p < 0.0005, ρ = 0.96), and inversely associated with mean glomerular volume (p = 0.02, ρ = -0.82). The diet-induced obese model accurately represents early ORG, and implicates glomerulomegaly as an early surrogate diagnostic marker. Early intervention with ALA-rich dietary oils slowed glomerular enlargement; these findings warrant further clinical investigation to promote optimal patient outcomes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".