Remembrance of things past: The consequences of recurrent hypoglycaemia in diabetes
Bibliographic record
Abstract
Abstract Aims People with type 1 and type 2 diabetes still frequently experience hypoglycaemia, which can be severe, leading to loss of consciousness. This review will examine the cellular consequences of recurrent hypoglycaemia. Methods This review, based on the Dorothy Hodgkin Lecture given at the Diabetes UK 2022 annual symposium by the author, will discuss our current understanding of the mechanisms by which hypoglycaemia is detected and the consequences of recurrent exposure to hypoglycaemia. Results Glucose‐responsive cells found in the periphery as well as multiple areas of the brain are organised in a classical sensori‐motor integrative network encompassing peripheral, hindbrain and hypothalamic components. The mechanism used by glucose‐responsive neurons to detect hypoglycaemia parallel those of the classical glucose sensor the pancreatic ß‐cell, namely in their use of glucokinase, KATP channels and AMP‐activated protein kinase. Recurrent exposure to hypoglycaemia results in a series of cellular adaptations that may be designed to increase the resilience of cells to future hypoglycaemia. This review also highlights how hypoglycaemia, as an oxidative stressor, may also exacerbate chronic hyperglycaemia‐induced increases in oxidative stress and inflammation, leading to damage to vulnerable brain regions. Conclusions Impaired awareness of hypoglycaemia follows the adaptation of central glucose‐responsive neurons to repeated hypoglycaemia and may represent a form of memory called habituation. In diabetes, recurrent hypoglycaemia may have tissue consequences as a result of a profound disruption in the cellular response to a hypoglycaemic challenge that increases vulnerability to oxidative damage.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".