Associations among High-Quality Protein and Energy Intake, Serum Transthyretin, Serum Amino Acids and Linear Growth of Children in Ethiopia
Bibliographic record
Abstract
Limited evidence is available on the associations of high-quality protein and energy intake, serum transthyretin (TTR), serum amino acids and serum insulin-like growth factor-1 (IGF-1) with linear growth of young children. Data collected during the baseline of a randomized control trial involving rural Ethiopian children aged 6–35 months (n = 873) were analyzed to evaluate the associations among height/length-for-age z-scores, dietary intakes, and these biomarkers (i.e., serum level of TTR, IGF-1, tryptophan and lysine, and inflammation). The prevalence of stunting was higher for children >23 months (38%) than ≤23 months (25%). The prevalence of inflammation was 35% and of intestinal parasites 48%. Three-quarters of the children were energy deficient, and stunted children had lower daily energy intake that non-stunted children (p < 0.05). Intakes of tryptophan, protein, and energy, and serum levels of tryptophan and IGF-1 were positively correlated with the linear growth of children. Controlling for inflammation, intestinal parasites, and sociodemographic characteristics, daily tryptophan (b = 0.01, p = 0.001), protein (b = 0.01, p = 0.01) and energy (b = 0.0003, p = 0.04) intakes and serum TTR (b = 2.58, p = 0.04) and IGF-1 (b = 0.01, p = 0.003) were positively associated with linear growth of children. Linear growth failure in Ethiopian children is likely associated with low quality protein intake and inadequate energy intake. Nutrition programs that emphasize improved protein quantity and quality and energy intake may enhance the linear growth of young children and need to be further investigated in longitudinal and interventional studies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".