Evidence for an Important Role of CIDEA in Human Cancer Cachexia
Bibliographic record
Abstract
Loss of fat mass in cancer cachexia is linked to increased adipocyte lipolysis; however, the fate of the excess fatty acids (FA) generated by lipolysis is not known. We investigated if the adipocyte-specific gene cell death-inducing DNA fragmentation factor-alpha-like effector A (CIDEA) could be involved. CIDEA mRNA expression was assessed in s.c. white adipose tissue from 23 cancer cachexia patients, 17 weight-stable cancer patients, and 8 noncancer patients. CIDEA was also overexpressed in adipocytes in vitro. CIDEA expression was increased in cancer cachexia (P < 0.05) and correlated with elevated levels of FAs and reported weight loss (P < 0.001). CIDEA overexpression in vitro increased FA oxidation 2- to 4-fold (P < 0.01), decreased glucose oxidation by 40% (P < 0.01), increased the expression of pyruvate dehydrogenase kinase (PDK) 1 and PDK4 (P < 0.01), and enhanced the phosphorylation (inactivation) of the pyruvate dehydrogenase complex (PDC). Inactivation of PDC facilitates FA oxidation by favoring the metabolism of FAs over glucose to acetyl-CoA. In accordance with the in vitro data, PDK1 and PDK4 expression correlated strongly with CIDEA expression in white adipose tissue (P < 0.001). We conclude that CIDEA is involved in adipose tissue loss in cancer cachexia and this may, at least in part, be due to its ability to inactivate PDC, thereby switching substrate oxidation in human fat cells from glucose to FAs.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".