Protein-protein interactions and novel biomarkers of prediabets
Bibliographic record
Abstract
Prediabetes is a state of hyperglycemia which does not meet the diagnostic levels of diabetes. More than half of the patients with prediabetes go on to develop diabetes and its’ complications. Understanding clearly prediabetes could aid in earlier detection of disease, earlier treatment and better prevention strategies. Worldwide, the incidence and prevalence of diabetes is exponentially rising and could be considered a silent pandemic. Similarly, in Canada, the population and healthcare burden are increasing. This review aims to identify potential novel biomarkers of prediabetes by analyzing protein-protein interactions. Prediabetes is defined as impaired fasting glucose (IFG), impaired glucose tolerance (IGT) or a glycated haemoglobin A1C of 6% to 6.4%[1]. Each of these places individuals at risk for developing diabetes and its’ complications. In Canada, there are 11.2 million people living with diabetes or prediabetes ( 30% of the population), reflecting a steady and continued increase of the disease within our population and a projected rise in diabetes by 27 % in the next ten years. Those diagnosed with prediabetes have a 50 % chance of developing diabetes. A condition with a myriad of complications, the burden of diabetes results in a reduction of an individuals’ lifespan by 5 to 15 years and a three-fold increase in diabetes related hospitalisations for cardiovascular disease, twelve times more likely for end-stage renal disease and twenty times more likely for non-traumatic lower limb amputations. Additionally, in 2022 diabetes is responsible for 3.8 billion dollars health care costs. Given the disease burden, understanding novel pathways and potential targets for therapy is of paramount importance, beginning with the precursor prediabetes.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".