Dietary Fat Intake and Insulin Resistance in Black and White Children
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to determine whether dietary fat intake above current Acceptable Macronutrient Distribution Range (AMDR) guidelines was associated with greater insulin resistance in black and white children. RESEARCH METHODS AND PROCEDURES: We studied 142 healthy children (n = 81 whites, n = 61 blacks), 6.5 to 14 years old. Dietary composition was determined by repeated 24-hour dietary recall, body composition by DXA, visceral fat by computed tomography, and insulin sensitivity (SI) and acute insulin response to glucose (AIRg) by frequently sampled intravenous glucose tolerance test. Subjects were categorized by ethnicity (white/black) and dietary fat intake (above-AMDR/within-AMDR guidelines), and differences were analyzed by 2 x 2 analysis of covariance, adjusting for covariates. RESULTS: After adjusting for total body fat, gender, and Tanner stage, subjects consuming dietary fat above AMDR intake guidelines had lower SI and higher AIRg. This effect was specific to black children (32% lower SI and 62% higher AIRg in above-AMDR compared with within-AMDR blacks) and was not seen in whites. DISCUSSION: In black, but not white, children, those with dietary fat intake above current AMDR guidelines had lower SI and higher AIRg than those who met AMDR guidelines. These findings support current AMDR guidelines for dietary fat in black children and adolescents. The mechanism(s) underlying the ethnic differences in the relationship between dietary fat intake and SI in children require further investigation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".