Insulin Resistance as a Physiological Defense Against Metabolic Stress: Implications for the Management of Subsets of Type 2 Diabetes
Bibliographic record
Abstract
Stratifying the management of type 2 diabetes (T2D) has to take into account marked variability in patient phenotype due to heterogeneity in its pathophysiology, different stages of the disease process, and multiple other patient factors including comorbidities. The focus here is on the very challenging subgroup of patients with T2D who are overweight or obese with insulin resistance (IR) and the most refractory hyperglycemia due to an inability to change lifestyle to reverse positive energy balance. For this subgroup of patients with T2D, we question the dogma that IR is primarily harmful to the body and should be counteracted at any cost. Instead we propose that IR, particularly in this high-risk subgroup, is a defense mechanism that protects critical tissues of the cardiovascular system from nutrient-induced injury. Overriding IR in an effort to lower plasma glucose levels, particularly with intensive insulin therapy, could therefore be harmful. Treatments that nutrient off-load to lower glucose are more likely to be beneficial. The concepts of "IR as an adaptive defense mechanism" and "insulin-induced metabolic stress" may provide explanation for some of the unexpected outcomes of recent major clinical trials in T2D. Potential molecular mechanisms underlying these concepts; their clinical implications for stratification of T2D management, particularly in overweight and obese patients with difficult glycemic control; and future research requirements are discussed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".