Impaired “Glycine”-mia in Type 2 Diabetes and Potential Mechanisms Contributing to Glucose Homeostasis
Bibliographic record
Abstract
The onset and/or progression of type 2 diabetes (T2D) can be prevented if intervention is early enough. As such, much effort has been placed on the search for indicators predictive of prediabetes and disease onset or progression. An increasing body of evidence suggests that changes in plasma glycine may be one such biomarker. Circulating glycine levels are consistently low in patients with T2D. Levels of this nonessential amino acid correlate negatively with obesity and insulin resistance. Plasma glycine correlates positively with glucose disposal, and rises with interventions such as exercise and bariatric surgery that improve glucose homeostasis. A role for glycine in the regulation of glucose, beyond being a potential biomarker, is less clear, however. Dietary glycine supplementation increases insulin, reduces systemic inflammation, and improves glucose tolerance. Emerging evidence suggests that glycine, a neurotransmitter, also acts directly on target tissues that include the endocrine pancreas and the brain via glycine receptors and as a coligand for N-methyl-d-aspartate glutamate receptors to control insulin secretion and liver glucose output, respectively. Here, we review the current evidence supporting a role for glycine in glucose homeostasis via its central and peripheral actions and changes that occur in T2D.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| 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".