Errata: Hyperinsulinemia: A unifying theory of chronic disease?
Bibliographic record
Abstract
The original article was published with an error of hyperinsulinemia improves fibrinolysis, instead hyperinsulinemia impairs fibrinolysis.The error has been corrected in this version.Globally, there is an increasing prevalence of non-communicable diseases.The morbidity and mortality from these conditions confer a greater economic societal burden.Epidemiological research associates insulin resistance in the etiology of these diseases, but there is limited evidence for the mechanism of damage.Emerging research suggests that hyperinsulinemia, a symptom of insulin resistance, may cause these pathological changes, and therefore be an independent contributor to these diseases.This review shows that hyperinsulinemia, or excessive insulin secretion, should be considered independently to insulin resistance, defined as glucose uptake rate, even though the two conditions are intertwined and will co-exist under normal conditions.Hyperinsulinemia directly and indirectly contributes to a vast array of metabolic diseases including all inflammatory conditions, all vascular diseases, gestational and type 2 diabetes, non-alcoholic fatty liver disease, obesity and certain cancers and dementias.The mechanisms include increased production of: insulin growth factor-1; reactive oxidative species and advanced glycation end-products; and triglyceride and fatty acids.Hyperinsulinemia also directly and indirectly affects many other hormones and cytokine mechanisms including leptin, adiponectin and estrogen.There is limited research standardizing the hyperinsulinemia diagnostic process.Methodological concerns and lack of standardized reference ranges preclude the use of fasting insulin.Most research has also focused on insulin resistance and it is unknown whether these methods translate to hyperinsulinemia.
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 | 0.006 | 0.048 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".