Abstract P053: The Frequency of High Glycemic Load Meals is Predictive of Cardiovascular Risk Factors in Children at Risk for Obesity After 2 Years
Bibliographic record
Abstract
Introduction: Consuming a daily diet of high glycemic load (GL) carbohydrates has potential long-term effects on obesity and cardiovascular health. However, it is possible that the GL of certain meals (breakfast, lunch and supper) have different effects. Few studies have examined the effect of meal-specific GL and frequency of high GL meals on adiposity and cardiovascular health in children. We hypothesized that the number of high GL meals per day, and secondarily, the meal-specific GL, would predict cardiovascular risk factors in children after two years of follow-up. Methods: We used data from the QUALITY cohort which recruited 630 children, ages 8-10 years at baseline with at least one obese parent. Three separate 24-hour dietary recalls were administered by a dietitian at baseline and individual meal-specific GL scores were calculated using the International table of GI. CV risk factors measured at 2 years of follow-up included continuous values of BMI z-score, percent fat mass, triglycerides, LDL and HDL cholesterol, and systolic (SBP) and diastolic (DBP) blood pressure obtained through direct measurement (blood pressure, blood lipids, anthropometrics) or questionnaires (socio-economic characteristics). Linear regressions between meal-specific GL categorized as high ( 2 33) vs. low (<33) and CV risk factors were estimated, adjusting for important confounders, including underreporting, as well as anthropometric, socio-economic and dietary factors. Secondary analysis consisted of linear regression with number of high GL meals as an ordinal exposure variable. Results: Mean age at baseline was 9.6 years, with 33% of children overweight or obese. Breakfast, lunch and supper glycemic load were positively associated with each other. A higher number of high GL meals was positively associated with BMI z-score (β=0.18, 95%CI: 0.06, 0.30), percent fat mass (β=1.74, 95%CI: 0.52, 2.96) and triglycerides (β=0.08, 95%CI: 0.03, 0.14) and negatively associated with HDL (β=-0.04, 95%CI: -0.07, -0.01). Our secondary analysis revealed that High breakfast GL was positively associated with increased triglycerides (β=0.14, 95%CI: 0.05, 0.23) after 2 years but not adiposity, HDL, LDL and blood pressure. High GL lunch (β=0.27, 95%CI: 0.08, 0.46) and supper (β=0.22, 95%CI: 0.02, 0.41) were associated with higher BMI z-score after 2 years. Conclusion: In conclusion, consuming more than one high GL meal per day is associated with greater BMI and unhealthy lipid profile after 2 years.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".