Triglyceride High-Density Lipoprotein Ratios Predict Glycemia-Lowering in Response to Insulin Sensitizing Drugs in Type 2 Diabetes: A Post Hoc Analysis of the BARI 2D
Bibliographic record
Abstract
Glycemic management is central in prevention of small vessel and cardiovascular complications in type 2 diabetes. With the plethora of newer medications and recommendations for a patient centered approach, more information is necessary to match the proper drug to each patient. We showed that BARI 2D, a five-year trial designed to compare two different glycemic treatment strategies, was suitable for assessing different responses according to different phenotypic characteristics. Treatment with insulin sensitizing medications such as thiazolidinediones and metformin was more effective in improving glycemic control, particularly in the more insulin resistant patient, when compared to the insulin provision strategy using insulin and or sulfonylureas. Triglyceride and high density lipoprotein ratio (TG/HDL-cholesterol ratio) was found to be a readily available and practical biomarker that helps to identify the insulin resistant patient. These results support the concept that not all medications for glycemic control work the same in all patients. Thus, tailored therapy can be done using phenotypic characteristics rather than a "one-size-fits-all approach."
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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.001 | 0.000 |
| 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 | 0.000 |
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".