Biomarkers of insulin sensitivity and insulin resistance: Past, present and future
Bibliographic record
Abstract
Insulin resistance in insulin target tissues including liver, skeletal muscle and adipose tissue is an early step in the progression towards type 2 diabetes. Accurate diagnostic parameters reflective of insulin resistance are essential. Longstanding tests for fasting blood glucose and HbA1c are useful and although the hyperinsulinemic euglycemic clamp remains a "gold standard" for accurately determining insulin resistance, it cannot be implemented on a routine basis. The study of adipokines, and more recently myokines and hepatokines, as potential biomarkers for insulin sensitivity is now an attractive and relatively straightforward approach. This review discusses potential biomarkers including adiponectin, RBP4, chemerin, A-FABP, FGF21, fetuin-A, myostatin, IL-6, and irisin, all of which may play significant roles in determining insulin sensitivity. We also review potential future directions of new biological markers for measuring insulin resistance, including metabolomics and gut microbiome. Collectively, these approaches will provide clinicians with the tools for more accurate, and perhaps personalized, diagnosis of insulin resistance.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".