Bibliographic record
Abstract
Sugar-sweetened beverages (SSB), which contain both glucose and fructose, have been linked to an increased incidence of colorectal cancer. Their effects on colorectal cancer progression, however, are unclear. In their recent work, Feng and colleagues investigated how exposure to SSBs affects colorectal cancer metastasis. They discovered that several colorectal cancer cell lines showed enhanced migration when exposed to glucose and fructose together, compared with cells exposed to glucose or fructose alone. Similarly, in mouse models of colorectal cancer liver metastasis, mice fed both glucose and fructose developed more liver metastases, suggesting that SSBs promote colorectal cancer spread. Leveraging metabolomic analyses, they discovered that in the presence of both glucose and fructose, the enzyme sorbitol dehydrogenase (SORD) converts fructose to sorbitol, regenerating NAD+ from NADH. Deleting SORD reduced the NAD+/NADH ratio and colorectal cancer cell migration and metastasis. Restoring the NAD+/NADH ratio rescued migration, suggesting that SORD-driven NAD+ regeneration promotes metastatic behavior. Furthermore, they demonstrated that increased NAD+/NADH levels have a profound effect on cell metabolism, supporting glycolysis, the TCA cycle, and the mevalonate pathway. Interestingly, pharmacologic inhibition of the mevalonate pathway with statins reduced cell migration and liver metastasis in mice consuming SSBs. Together, these findings demonstrate that SSBs enhance colorectal cancer metastasis through SORD-dependent metabolic reprogramming. By regenerating NAD+ and glycolysis and supporting the mevalonate pathway, SORD links SSB consumption to increased tumor cell migration and metastatic potential.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".