The involvement of the wnt signaling pathway and TCF7L2 in diabetes mellitus: The current understanding, dispute, and perspective
Bibliographic record
Abstract
The Wnt signaling pathway was initially discovered for its role in tumorigenesis and the development of Drosophila and other eukaryotic organisms. The key effector of this pathway, the bipartite transcription factor β-cat/TCF, is formed by free β-catenin (β-cat) and a TCF protein, including TCF7L2. Extensive recent investigations have highlighted the role of the Wnt signaling pathway in metabolic homeostasis and its implication in diabetes and other metabolic diseases. Genome-wide association studies have shown that several key components of the Wnt signaling pathway are implicated in metabolic homeostasis and the development of type 2 diabetes (T2D). Despite controversial observations regarding the role of Wnt signaling in the development and function of pancreatic islets, the discovery of the association between certain single nucleotide polymorphisms of TCF7L2 and T2D susceptibility has fueled great efforts to explore the role of Wnt signaling in the function of pancreatic β-cells and glucose homeostasis. Here we have introduced our basic understanding of the canonical Wnt signaling pathway, summarized our current knowledge on its implication in metabolic homeostasis and T2D, discussed the work on TCF7L2 as a T2D susceptibility gene, and presented the controversial role of Wnt signaling and TCF7L2 in pancreatic islets as well as their potential metabolic function in other organs. We then expanded our view into the crosstalk among Wnt, insulin and FOXO signaling cascades, which further illustrates the complexity of the Wnt signaling pathway in metabolic homeostasis. Finally, we have presented our perspectives.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".