Defective glycolysis and the use of 2-deoxy-d-glucose in polycystic kidney disease: from animal models to humans
Bibliographic record
Abstract
Autosomal dominant polycystic kidney disease (ADPKD) is an inherited renal disease characterized by bilateral renal cyst formation. ADPKD is one of the most common rare disorders, accounting for ~10% of all patients with end-stage renal disease (ESRD). ADPKD is a chronic disorder in which the gradual expansion of cysts that form in a minority of nephrons eventually causes loss of renal function due to the compression and degeneration of the surrounding normal parenchyma. Numerous deranged pathways have been identified in the cyst-lining epithelia, prompting the design of potential therapies. Several of these potential treatments have proved effective in slowing down disease progression in pre-clinical animal studies, while only one has subsequently been proven to effectively slow down disease progression in patients, and it has recently been approved for therapy in Europe, Canada and Japan. Among the affected cellular functions and pathways, recent investigations have described metabolic derangement in ADPKD as a major trait offering additional opportunities for targeted therapies. In particular, increased aerobic glycolysis (the Warburg effect) has been described as a prominent feature of ADPKD kidneys and its inhibition using the glucose analogue 2-deoxy-D-glucose (2DG) proved effective in slowing down disease progression in preclinical models of the disease. At the same time, previous clinical experiences have been reported with 2DG, showing that this compound is well tolerated in humans with minimal and reversible side effects. In this work, we review the literature and speculate that 2DG could be a good candidate for a clinical trial in humans affected by ADPKD.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".