Dynamic interaction between fetal adversity and a genetic score reflecting dopamine function on developmental outcomes at 36 months
Bibliographic record
Abstract
BACKGROUND: Fetal adversity, evidenced by poor fetal growth for instance, is associated with increased risk for several diseases later in life. Classical cut-offs to characterize small (SGA) and large for gestational age (LGA) newborns are used to define long term vulnerability. We aimed at exploring the possible dynamism of different birth weight cut-offs in defining vulnerability in developmental outcomes (through the Bayley Scales of Infant and Toddler Development), using the example of a gene vs. fetal adversity interaction considering gene choices based on functional relevance to the studied outcome. METHODS: 36-month-old children from an established prospective birth cohort (Maternal Adversity, Vulnerability, and Neurodevelopment) were classified according to birth weight ratio (BWR) (SGA ≤0.85, LGA >1.15, exploring a wide range of other cut-offs) and genotyped for polymorphisms associated with dopamine signaling (TaqIA-A1 allele, DRD2-141C Ins/Ins, DRD4 7-repeat, DAT1-10- repeat, Met/Met-COMT), composing a score based on the described function, in which hypofunctional variants received lower scores. RESULTS: There were 251 children (123 girls and 128 boys). Using the classic cut-offs (0.85 and 1.15), there were no statistically significant interactions between the neonatal groups and the dopamine genetic score. However, when changing the cut-offs, it is possible to see ranges of BWR that could be associated with vulnerability to poorer development according to the variation in the dopamine function. CONCLUSION: The classic birth weight cut-offs to define SGA and LGA newborns should be seen with caution, as depending on the outcome in question, the protocols for long-term follow up could be either too inclusive-therefore most costly, or unable to screen true vulnerabilities-and therefore ineffective to establish early interventions and primary prevention.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 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".