Maternal Fiber Intake During Pregnancy and Development of Attention-Deficit/Hyperactivity Disorder Symptoms Across Childhood: The Norwegian Mother, Father, and Child Cohort Study
Bibliographic record
Abstract
BACKGROUND Epidemiological studies suggest that the maternal diet quality during pregnancy may influence the risk of neurodevelopmental disorders in the offspring. Here we investigated the associations between maternal intake of dietary fiber and ADHD symptoms in early childhood. METHODS We used longitudinal data of up to 21,852 mother-father-child trios (49.2% females) from the Norwegian Mother, Father, and Child Cohort Study. The relationships between maternal fiber intake during pregnancy and offspring ADHD symptoms at ages three, five, and eight years were examined using: a) multivariate regression (overall levels of ADHD symptoms), b) latent class analysis (subclasses of ADHD symptoms by sex at each age), and c) latent growth curves (longitudinal change in offspring ADHD symptoms). Covariates were ADHD polygenic scores in child and parents, total energy intake and energy-adjusted sugar intake, parental ages at birth of the child, and socio-demographic factors. RESULTS: a) Higher maternal prenatal fiber intake was associated with lower offspring ADHD symptom scores at all examined ages (β age3 =-0.14(95%CI -0.18, -0.10); β age5 =-0.14(-0.19, -0.09); β age8 =-0.14(-0.20, -0.09)). b) Of the derived low/middle/high subclasses of ADHD symptoms, fiber was associated with lower risk of belonging to middle subclass for boys and girls, and to high subclass for girls only (middle: OR boys 0.91(0.86-0.97)/OR girls 0.86 (0.81-0.91); high OR girls 0.82 (0.72-0.94)). c) Maternal fiber intake and rate of change in child ADHD symptoms across ages were not associated. CONCLUSIONS A low prenatal maternal fiber intake may increase symptom levels of ADHD in childhood, independently of genetic predisposition to ADHD, unhealthy dietary exposures, and socio-demographic factors.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".