Abstract P247: Under-reporters of Caloric Intake Have Worst Cardiometabolic Risk Profile Among Children at Risk of Obesity
Bibliographic record
Abstract
Introduction: Misreporting of dietary data in nutritional epidemiology is a major concern for information bias, as they rely on subjects’ ability to accurately remember and report the foods they consumed. In children, underreporting is common and tends to occur differentially according to certain characteristics. We sought to describe characteristics of under-reporters (UR) within a cohort of children with a parental history of obesity, and to examine the bias introduced with underreporting in children. Methods: Data stem from the QUALITY cohort of 630 children, 8-10 years at recruitment with at least one obese parent. Three separate 24-hour dietary recalls were administered by a dietitian at baseline. Child and parent characteristics were obtained through direct measurement (blood pressure, blood lipids, anthropometrics) or questionnaires (socio-economic characteristics). Goldberg's cut-off method identified UR, by comparing a ratio of reported energy intake and basal metabolic rate to a calculated cut-off value. We used logistic regression to identify correlates of UR. We examined the bias resulting from underreporting by comparing the coefficients from the linear regression of BMI z-score after 2 years on glycemic load (GL) at baseline in all participants and in the adequate reporters (AR) subset, after excluding UR. Results: We identified 167 UR and 408 AR in the QUALITY cohort based on a calculated Goldberg's cut-off of 1.11. UR had a tendency to be older (9.9 vs. 9.5), had a higher BMI z-score (1.5 vs. 0.4) and had poorer cardiometabolic health indicators including higher SBP, DBP, triglycerides and LDL and lower HDL. UR had a lower family income (38,561 vs. 44,078 $CAN), parents were less educated (47.3% vs. 56.9% with a university education) and had a higher BMI compared to parents of AR. In logistic regression, age per year (OR: 1.48, 95%CI: 1.15-1.91), BMI z-score (OR: 2.04, 95% CI: 1.17-3.54), percent fat mass (OR: 1.06, 95%CI: 1.01-1.12) and family income (OR: 0.86 per 10,000$, 95%CI: 0.76-0.98) were the strongest correlates of underreporting. Linear regressions showed that the association between BMI and GL was null when all participants were included, but became significantly positive (ß=0.06 per 10 units, 95%CI: 0.05-0.07) after exclusion of the UR. Conclusion: In the QUALITY cohort, UR were different from AR. Underreporting is an important source of error in nutritional epidemiology that can bias measurement of nutritional exposures and the assessment of exposure outcome relationships. To prevent this bias, UR must be identified and an appropriate correction method must be used.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".