Poor Quality Diets Characterized by Low-Nutrient Density Foods Observed in One-Quarter of 2-Year-Olds in a High Resource Setting
Bibliographic record
Abstract
BACKGROUND: Young children have high nutritional requirements relative to their body size, making healthy diets critical for normal growth and development. OBJECTIVE: We aimed to integrate analysis of dietary patterns among 2-y-old children with indicators of dietary quality, micronutrient status, and body weight status. METHODS: Data from the 2-y follow-up of the Cork BASELINE Birth Cohort included dietary assessment using a 2-d weighed food diary, vitamin D and iron status biomarkers, and anthropometry (n = 468). K-means cluster analysis identified predominant dietary patterns based on energy contributions and associations with nutrient intakes and status and body weight were investigated. RESULTS: Four dietary patterns emerged: "Cows' milk" (unmodified cows' milk: 32% of total energy (TE)); "Traditional" (wholemeal breads, butter, fresh meat, fruit); "Low Nutrient Density (LND) foods" (confectionary, processed meat, convenience foods) and "Formula" (young child formula: 23%TE). The LND pattern was associated with excessive free sugar intake (14%TE) and salt intake (153% of daily limit). No differences in patterns of overweight were observed between the 4 groups; however, the LND group had 3-fold higher odds of being underweight [aOR (95% CI): 3.2 (1.2, 8.5)]. Children consuming >400ml/d of cows' milk or formula exhibited lower dietary variety, fewer family-type meals, and continued use of feeding bottles (75% and 81%, respectively, vs. 35-37% in the other groups). CONCLUSIONS: Unhealthy eating habits are common among young children. Dietary guidance to support families to provide healthy diets needs to maintain currency with eating habits and focus on food choices for meals, snacks, and beverages.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".