Glucose Tolerance, Glucose Utilization and Insulin Secretion in Ageing
Bibliographic record
Abstract
Ageing is associated with an increased incidence of hypertension, macrovascular disease and type 2 diabetes (non-insulin-dependent diabetes). It has been suggested that a common mechanism may be responsible for all of these pathological states since all of these conditions often cluster in the same individual. Epidemiological and clinical data have consistently demonstrated an association between insulin resistance and/or hyperinsulinaemia and glucose intolerance, dyslipidaemia and elevated systolic blood pressures. Therefore, insulin resistance and hyperinsulinaemia have been proposed as the causal link among the elements of the clusters. The elderly are more glucose intolerant and insulin resistant, but it remains controversial whether this decrease in function is due to an inevitable consequence of 'biological ageing' or due to environmental or lifestyle variables, noticeably increased adiposity/altered fat distribution and physical inactivity. An increase of these modifiable factors has been shown to result in increases in insulin resistance and hyperinsulinaemia and vice versa. However, insulin secretion appears to decrease with age even after adjustments for differences in adiposity, fat distribution and physical activity. The glucose intolerance of ageing may be due, in part, to decreased insulin sensitivity of pancreatic / cells to insulinotropic gut hormones (GLP1/GIP) and in part to alterations of hepatic glucose production.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".