Abstract 17134: Cardiorenal Syndrome Arising From Chronic Diabetes Causes Maladaptive Carbohydrate Utilisation in the Heart and Kidneys
Bibliographic record
Abstract
Clinical and epidemiological data have identified a cardiorenal syndrome (CRS), in which heart and/or kidney failure accelerates dysfunction in the other organ. There is a need for new therapeutics targeting the mechanisms that cause CRS, in order to treat the whole patient. Here, we tested the hypotheses that 1) chronic diabetes (aged Goto-Kakizaki [GK] rats) would yield a model of CRS, and 2) assessing in vivo cardiorenal metabolism using hyperpolarized 13 C MR spectroscopy (MRS) would identify targets for CRS therapy. Statistical significance between GK rats and Wistar controls was considered at P<0.05. Glycated hemoglobin confirmed that GK rats were diabetic at 20 weeks. Forty-week-old GK rats (n=6) developed proteinuria, LV hypertrophy, and pulmonary congestion. Invasive pressure-volume loops (n=5) demonstrated preserved systolic, yet impaired diastolic, function. Histology demonstrated myocyte and glomerular hypertrophy, interstitial fibrosis and glomerulosclerosis. Intravenous infusion of hyperpolarized [1- 13 C]pyruvate (n=4), followed by MRS data acquisition that alternated between heart and kidney, indicated that carbohydrate metabolism was reprogrammed to promote lactate production over oxidation. In the heart, this was evidenced by reduced pyruvate dehydrogenase flux to form 13 C-bicarbonate, increased 13 C-lactate production, and reduced 13 C-alanine production. In the kidney, 13 C-lactate was increased at the expense of 13 C-alanine. Metabolic reprogramming to produce cardiorenal lactate was likely mediated by inflammation: in both organs, macrophage infiltration and expression of toll like receptor 4 protein were increased. Increased expression of renal Pck1 and G6pc mRNA indicated involvement of maladaptive systemic gluconeogenesis in CRS pathogenesis. Normalizing whole-body carbohydrate utilization represents a novel target for therapy of diabetes-induced CRS.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".