Exploring Early Childhood Diet, Stress, Trophic Position and Dietary Protein Quality Using Amino Acid Nitrogen Isotope Compositions of Fingernail Keratin
Bibliographic record
Abstract
ABSTRACT Objectives Evaluate the effectiveness of compound‐specific nitrogen isotope analysis of amino acids (CSIA‐AA) in reconstructing early childhood diets and detecting episodes of stress. Examine (1) proline's potential for identifying breastfeeding and weaning; (2) the influence of physiological and pathological stress on AA δ 15 N; (3) the reliability of trophic position (TP) estimates from phenylalanine (Phe) and glutamate (Glx) δ 15 N during dietary transitions; and (4) mother‐infant trophic enrichment factors (TEF Glx‐Phe ) as indicators of infant dietary protein quality. Materials and Methods Three mother‐infant dyads provided fingernail clippings ( n = 43) for CSIA‐AA analysis pre‐ and post‐birth, alongside dietary and health surveys. Results Proline δ 15 N was elevated by 2.4‰–3.5‰ in exclusively breastfed infants compared to their mothers and decreased by 2.2‰–4.1‰ during weaning. Phenylalanine δ 15 N showed large positive shifts (e.g., by 6.7‰) during maternal stress, despite being a source AA expected to remain stable. TP differences between mother‐infant pairs were minimal (−0.2 to 0.1), except for one pair with higher infant TP (by 0.6–1.5). The calculated TEF Glx‐Phe for infants ranged from −1.4‰ to 11.3‰. Discussion Proline δ 15 N reliably tracks nutritional transitions, likely due to its role in arginine synthesis during infancy. The unexpected variability in δ 15 N Phe complicates its use in TP and dietary protein quality assessments. This variability may result from phenylalanine's slow turnover and delayed dietary incorporation during endogenous catabolism. TP is an unreliable marker of breastfeeding or weaning. TEF Glx‐Phe for infants seems indicative of high dietary protein quality, but interpretations must consider the influence of non‐dietary factors on δ 15 N Phe .
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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 | 0.000 |
| 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".