Insulin resistance and cancer: the role of insulin and IGFs
Bibliographic record
Abstract
Insulin, IGF1, and IGF2 are the most studied insulin-like peptides (ILPs). These are evolutionary conserved factors well known as key regulators of energy metabolism and growth, with crucial roles in insulin resistance-related metabolic disorders such as obesity, diseases like type 2 diabetes mellitus, as well as associated immune deregulations. A growing body of evidence suggests that insulin and IGF1 receptors mediate their effects on regulating cell proliferation, differentiation, apoptosis, glucose transport, and energy metabolism by signaling downstream through insulin receptor substrate molecules and thus play a pivotal role in cell fate determination. Despite the emerging evidence from epidemiological studies on the possible relationship between insulin resistance and cancer, our understanding on the cellular and molecular mechanisms that might account for this relationship remains incompletely understood. The involvement of IGFs in carcinogenesis is attributed to their role in linking high energy intake, increased cell proliferation, and suppression of apoptosis to cancer risks, which has been proposed as the key mechanism bridging insulin resistance and cancer. The present review summarizes and discusses evidence highlighting recent advances in our understanding on the role of ILPs as the link between insulin resistance and cancer and between immune deregulation and cancer in obesity, as well as those areas where there remains a paucity of data. It is anticipated that issues discussed in this paper will also recover new therapeutic targets that can assist in diagnostic screening and novel approaches to controlling tumor development.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".