The Cardiometabolic Risk Profile of Underreporters of Energy Intake Differs from That of Adequate Reporters among Children at Risk of Obesity
Bibliographic record
Abstract
Background: Misreporting of energy intake (EI) in nutritional epidemiology is a concern because of information bias, and tends to occur differentially in obese compared with nonobese subjects. Objective: We examined characteristics of misreporters within a cohort of children with a parental history of obesity and the bias introduced by underreporting. Methods: The QUebec Adipose and Lifestyle InvesTigation in Youth (QUALITY) cohort included 630 Caucasian children aged 8-10 y at recruitment with ≥1 obese parent [body mass index (BMI; in kg/m2) >30 or waist circumference >102 cm (men), >88 cm (women)] and free of diabetes or severe illness. Children on antihypertensive medications or following a restricted diet were excluded. Child and parent characteristics were measured directly or by questionnaire. Three 24-h dietary recalls were administered by phone by a dietitian. Goldberg's cutoff method identified underreporters (URs). Logistic regression identified correlates of URs. We compared coefficients from linear regressions of BMI after 2 y on total EI at baseline 1) in all participants; 2) in adequate reporters (ARs) (excluding URs); 3) in all participants statistically adjusted for underreporting; 4) excluding URs using individual physical activity level (PAL)-specific cutoffs; and 5) in all participants statistically adjusted for underreporting using PAL-specific cutoffs. Results: We identified 175 URs based on a calculated cutoff of 1.11. URs were older, had a higher BMI z score, and had poorer cardiometabolic health indicators. Parents of URs had a lower family income and higher BMI. Child BMI z score (OR: 3.07; 95% CI: 2.38, 3.97) and age (OR: 1.46/y; 95% CI: 1.14, 1.87/y) were the strongest correlates of underreporting. The association between BMI and total EI was null in all participants but became significantly positive after excluding URs (ß = 0.62/1000 kcal; 95% CI: 0.33, 0.92/1000 kcal) and after adjustment for URs (ß = 0.85/1000 kcal; 95% CI: 0.55, 1.06/1000 kcal). Conclusions: URs in 8- to 10-y-old children differed from ARs. Underreporting biases measurement of nutritional exposures and the assessment of exposure-outcome relations. Identifying URs and using an appropriate correction method is essential.
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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 | 0.005 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".