Mild Caloric Restriction Decreases Insulin Requirements in Patients With Type 2 Diabetes and Severe Insulin Resistance
Bibliographic record
Abstract
Type 2 diabetes (T2D) affects ~10% of the US population, a subset of whom have severe insulin resistance (SIR) (>200 units/d). Treatment of these patients with high-dose insulin presents logistical and compliance challenges. We hypothesized that mild caloric restriction would reduce insulin requirements in patients with T2D and SIR.This was a retrospective study at the National Institutes of Health Clinical Center. Inclusion criteria were as follows: T2D, and insulin dose >200 units/d or >2 units/kg/d. The intervention consisted of mild caloric restriction during a 3 to 6-day hospitalization. The major outcomes were change in insulin dose and blood glucose from admission to discharge.Ten patients met inclusion criteria. Baseline glycated hemoglobin A1c was 10.0 ± 1.6% and body mass index 38.8 ± 9.0 kg/m. Food intake was restricted from 2210 ± 371 kcal/d preadmission to 1810 ± 202 during the hospital stay (16.5% reduction). Insulin dose decreased from 486 ± 291 units/d preadmission to 223 ± 127 at discharge (44% reduction, P = 0.0025). Blood sugars decreased nonsignificantly in the fasting state (from 184 ± 85 to 141 ± 42, P = 0.20), before lunch (239 ± 68 to 180 ± 76, P = 0.057), and at bedtime (212 ± 95 to 176 ± 48, P = 0.19), and significantly decreased before dinner (222 ± 92 to 162 ± 70, P = 0.016).Mild caloric restriction, an accessible and affordable intervention, substantially reduced insulin doses in patients with T2D and SIR. Further studies are needed to determine if the intervention and results are sustainable outside of a hospital setting.
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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 | 0.002 |
| 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) | 0.001 | 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".