Gene-environment contributions to energy and macronutrient intakes in 9-year-old children: Results from the Quebec Newborn Twin Study
Bibliographic record
Abstract
Abstract Background Few twin studies have examined nutrition-related phenotypes among children, and none has investigated energy and macronutrient intakes. Objective The objective was to quantify genetic and environmental influences on variations in energy and macronutrient intakes among children aged 9 years. Design We conducted a nutrition study among children participating in the Quebec Newborn Twin Study, a population-based birth cohort of twins. We derived dietary data from two multiple-pass 24-hour dietary recalls with a parent and his or her child. The analysis employed a classic twin study design and used data from 379 twin pairs. Results Univariate analyses indicate that heritability for mean daily energy (kcal) and macronutrient (g) intakes was moderate, ranging from 0.34 (95% CI: 0.22, 0.46) to 0.42 (0.31, 0.53). Genetic effects also accounted for 0.28 (0.16, 0.40) of the variance in percent of energy from lipids, while only environmental (shared and unique) effects accounted for the variance in percent of energy from proteins and carbohydrates. The shared environment did not contribute to variations in daily intakes for most of the nutritional variables under study. Multivariate analyses suggest the presence of macronutrient-specific genetic influences for lipids and carbohydrates, estimated at 0.12 (0.04, 0.19) and 0.20 (0.11, 0.29) respectively. Conclusions The unique environment (i.e., not shared by family members) has the largest influence on variances in daily energy and macronutrient intakes in 9-year-old children. This finding underscores the need to take obesogenic environments into account when planning dietary interventions for younger populations.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".