A fat‐based supplement containing essential fatty acids increased plasma α‐linolenic acid and linear growth of Ghanaian infants
Bibliographic record
Abstract
There is interest in long chain PUFAs due to reports suggesting their positive role in growth and neural development. We randomized 313 Ghanaian infants to receive either “Sprinkles” (SP) or crushable tablets, Nutritabs (NT) or a fortified fat‐based spread, Nutributter (NB) providing 6, 16 and 19 vitamins and minerals, respectively, daily from 6 to 12 mo of age. NB (20 g/d) contained linoleic, LA (1.29 g/d) and α‐linolenic, ALA (0.29 g/d) acids. We measured plasma fatty acids (FA) and growth at 6 and 12 mo, and recorded food intake weekly. Infants not randomly selected for intervention (Non‐Intervention, NI: n=96) were assessed at 12 mo. The NB group had greater weight and length gain 6–12 mo, and, adjusting for sex, breastfeeding frequency and baseline values (where possible), also had greater ALA (7.3 ± 5.0 mg/L) at 12 mo than the other 3 groups (SP 5.5 ± 4.9, p = 0.03; NT 5.2 ± 4.9, p = 0.01; NI 6.2 ± 5.5, p = 0.07), and a lower percentage of saturated FA (% SFA). ALA and % SFA were significantly correlated with length gain, but not weight gain. In path analyses, the differences in fatty acid profile accounted for a significant part of the greater length gain in the NB group. Energy intake from complementary foods was greater in the NB group and accounted for part of the difference in weight gain but not length gain. We conclude that providing micronutrients in a fat‐based spread containing essential FA altered plasma FA levels, and that these changes were associated with greater linear growth. Supported by Nestle Foundation and USAID.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".