The ongoing evolution of basal insulin therapy over 100 years and its promise for the future
Bibliographic record
Abstract
The evolution of basal insulin therapy over the past 100 years since the discovery of insulin is a testimony to the biomedical bench-to-bedside process, wherein incremental advances in the basic sciences are progressively translated over time into a series of enhancements in clinical care, each building upon the success of its predecessors. The emergence of recombinant DNA technology and the resultant biosynthesis of human insulin in the 1980s provided the critical capacity to bioengineer designer insulin analogues with pharmacokinetic and pharmacodynamic properties that can better mimic, although not fully replicate, the effects of endogenous insulin secretion. Through these efforts, basal insulin therapy has progressed over this time from first-generation analogues (glargine U-100, detemir) to second-generation analogues (glargine U-300, degludec) to ultra-long-acting formulations that are suitable for administration once weekly (icodec). Each iteration in this progression has represented a step closer towards the goal of replicating the continuous secretion of insulin that normally comprises the basal output of the pancreatic beta-cells between meals, during episodes of fasting and overnight. However, it may be that we may have reached the achievable limit in the context of an "open-loop" approach, such that only with the addition of closed loop control will we be able to achieve physiologic basal insulin replacement. In this review, we will examine the evolution of basal insulin therapy over the past 100 years and its implications for patient care and outcomes in current practice and the future.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".