Impact of a High Intake of Dairy Product on Insulin Sensitivity in Hyperinsulinemic Adults: A Crossover Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Dairy product intake has been associated with decreased risk of type 2 diabetes (T2D) in cohort studies. However, results from clinical trials on T2D-related risk factors remain inconclusive. OBJECTIVE: The aim of this clinical trial was to evaluate the impact of high dairy product intake (HD) (≥4 servings/d) for 6 wk, compared with an adequate dairy product intake (AD) (≤2 servings/d), on glycemic and insulinemic parameters, insulin sensitivity, insulin secretion, and β-cell function in hyperinsulinemic adults. METHODS: In this crossover clinical trial, hyperinsulinemic adults were randomly assigned to HD or AD for 6 wk, then crossed over after a 6-wk washout period. Serum glucose, insulin, C-peptide, HOMA-IR, Matsuda index, insulinogenic index, and disposition index were measured and analyzed using a repeated-measures mixed model adjusted for age, sex, and BMI. Anthropometric measures were collected and food intake was evaluated using a validated FFQ. RESULTS: . Dairy product intake was 5.8 servings/d in the HD condition and 2.3 servings/d in the AD condition after 6 wk. No difference was observed between HD and AD after 6 wk for all outcomes. CONCLUSIONS: HD does not affect glycemic and insulinemic parameters, insulin sensitivity, insulin secretion, and β-cell function over AD in hyperinsulinemic adults. Additional larger and longer studies assessing T2D-related risk factors are required. This trial was registered at clinicaltrials.gov as NCT02961179.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".