Diet Quality As It Relates to ADHD and Emotional Dysregulation Symptoms in a Pediatric Population
Bibliographic record
Abstract
Attention Deficit Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder with a US pediatric prevalence of 10%. It presents with inattention and hyperactivity/impulsivity as well as emotional dysregulation (ED) symptoms such as irritability and defiant behavior, typical of Oppositional Defiance Disorder (ODD) and Disruptive Mood Dysregulation Disorder (DMDD). The etiology of ADHD is multi-factorial with suggested effects related to diet. Building on prior studies, this study examines the association of diet quality with ADHD and ED symptoms among a pediatric population. This cross-sectional study utilized baseline data for 134 children aged 6–12 years with symptoms of ADHD and ED enrolled in an RCT of multinutrient supplementation. Diet quality was based on Healthy Eating Index-2015 (HEI) determined from the Vioscreen FFQ. ADHD, ODD, and DMDD symptoms were assessed using the Child and Adolescent Symptom Inventory-5. Other ED symptoms were assessed using the Strengths and Difficulties Questionnaire. Analysis for association was conducted using linear regression models, adjusting for covariates when necessary. Family income level was significantly associated with severity of inattention (P = 0.04), emotional problems (P = 0.01), conduct problems (P = 0.002), along with ODD (P = 0.004) and DMDD (P = 0.005) symptoms. Mean HEI score was 63.4 (SD = 8.8) and it was not significantly associated with any of the outcome symptoms. However, scores of HEI components vegetables (β = −0.118, P = 0.016) and refined grains (β = 0.052, P = 0.017) were both associated with inattention even after adjusting for covariates. Similarly, total fruit (β = −0.423, P = 0.037) was associated with conduct problems after adjusting for covariates. While better vegetable and total fruit scores were associated with better symptoms in aspects of ADHD and emotional dysregulation, overall diet quality was not associated with inattention, hyperactivity/impulsivity, and ED symptoms severity among this cohort of children. Our findings could be explained by the fact that our study sample had a good diet quality and were only mildly impaired in their ADHD and ED symptoms. This study was funded by the Foundation for Excellence in Mental Health Care, the Wells Fargo/Gratis Foundation, and OSU CTSA award # UL1TR002733.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 |
| 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".