ADHD Research News

Oct 16, 2026

PubMed

ADHD Medication Use in Pregnancy Was Not Linked to More Major Birth Defects

Once other differences between mothers were accounted for, taking a stimulant or atomoxetine during pregnancy was not tied to more major birth defects, miscarriage, preterm birth, or autism and ADHD in children. Two small signals remained: a modest rise in heart defects with methylphenidate, and more low newborn health scores at five minutes after birth. The studies can't show these are caused by the medication.

What you can do

If you're pregnant or planning to be, bring these numbers, including the Apgar and methylphenidate findings, to your prescriber and decide together whether to continue, switch, or stop.

Evidence

Study design: meta-analysis
Population: Pregnancies and offspring exposed prenatally to stimulants (methylphenidate- or amphetamine-based) or atomoxetine for ADHD, versus unexposed comparators; 25 studies included, 23 in quantitative analyses
Sample size: 25 studies included; 23 contributed to quantitative analyses. Participant numbers not reported in the abstract.
Intervention / exposure: Prenatal exposure to stimulant medications (methylphenidate, amphetamine-based) or atomoxetine; exposure windows included periconceptional, first-trimester, early-, late-, or continued-pregnancy exposure
Comparator: Unexposed pregnancies/offspring; for neurodevelopmental outcomes, comparators restricted to ADHD populations were prioritized when available
Key results: Major congenital malformations: Associated in unadjusted analyses (RR 1.25) but not after adjustment (aRR 1.04)
Cardiovascular malformations: No adjusted association overall (aRR 1.20); methylphenidate showed a modest increase (aRR 1.33)
Miscarriage, preterm birth, low birth weight, small for gestational age, NICU admission: Not associated with exposure in adjusted analyses
Neurodevelopmental disorders, autism spectrum disorder, offspring ADHD: Not associated with exposure in adjusted analyses; lower adjusted ASD risk with stimulants was not replicated in methylphenidate-specific analyses
Low 5-minute Apgar score: Remained associated with exposure after adjustment (aRR 1.21)
Effect estimates: risk ratio: 1.25 · 95% CI 1.14-1.37 · Major congenital malformations, prenatal stimulant/atomoxetine exposure vs unexposed, unadjusted
risk ratio: 1.04 (adjusted) · 95% CI 0.97-1.12 · Major congenital malformations, exposed vs unexposed, adjusted
risk ratio: 1.20 (adjusted) · 95% CI 0.88-1.63 · Cardiovascular malformations, exposed vs unexposed, adjusted
risk ratio: 1.33 (adjusted) · 95% CI 1.01-1.74 · Cardiovascular malformations, methylphenidate exposure vs unexposed, adjusted
risk ratio: 1.21 (adjusted) · 95% CI 1.03-1.42 · Low 5-minute Apgar score, exposed vs unexposed, adjusted
Limitations: Authors state attenuation of crude associations after adjustment suggests confounding by indication, residual confounding, and maternal characteristics
Authors state the methylphenidate-cardiovascular malformation and low 5-minute Apgar associations 'warrant continued investigation but should not be interpreted as evidence of causal harm'
Lower adjusted ASD risk with stimulants was not replicated in methylphenidate-specific analyses
Abstract-only; heterogeneity, risk-of-bias ratings, and participant numbers are not reported
Exposure windows varied across studies (periconceptional to continued pregnancy)
Source reviewed: Abstract

References

Komargodski R, Tillawe A, Berlin M et al. Pregnancy and Offspring Outcomes After Prenatal Stimulant or Atomoxetine Exposure: A Systematic Review and Meta-analysis. American journal of obstetrics & gynecology MFM. 2026-10-16

PubMed ↗ DOI / publisher ↗

Oct 16, 2026

PubMed

Genetic Tests Can't Tell You Which ADHD Medication Will Work Best for Your Child

A review of genetic testing for children's mental health medication says these tests may help flag side-effect risk in some children. They can't predict which medication will work best for a particular child. The authors also warn that some commercial reports use simple color-coding that can mislead both families and doctors.

What you can do

If you're considering a genetic test for a child's ADHD medication, treat it as a possible tool for anticipating side-effect risk, not for choosing the most effective drug, and go over any color-coded report with a clinician who understands its limits.

Evidence

Study design: narrative review
Population: Children and adolescents with depression, anxiety, or ADHD (pediatric populations; no specific sample described)
Sample size: Abstract-only; no sample size reported. Appears to be a narrative/expository review, so this is not a primary-data study.
Intervention / exposure: Pharmacogenomic testing to inform psychotropic medication selection (no dose or duration specified)
Comparator: Not reported
Key results: Utility of pharmacogenomic testing for identifying psychotropic medication tolerability or adverse effects: Authors state testing 'can be useful in certain populations to identify a psychotropic medication's tolerability or adverse effects.' No quantitative data in abstract.
Ability of pharmacogenomic testing to predict clinical response: Authors state testing 'cannot predict which medication will provide the best clinical response in a specific patient with a specific mental illness.'
Limitations: Abstract-only extraction; no methods, search strategy, or data are available, so the design is inferred as narrative review from the content.
Authors note testing 'require[s] nuanced understanding and interpretation' in pediatric depression, anxiety, and ADHD.
Authors state testing cannot predict which medication will give the best clinical response.
Authors caution that some commercial kits give clinical guidance based on genetic tests that 'may not have clinical relevance' in an 'over-simplified color-coded format which can be misleading for the patient and for the clinician.'
Usefulness is described as limited to 'certain populations' for tolerability/adverse effects; the populations are not specified in the abstract.
Source reviewed: Abstract

References

Rahmani M. Utility of pharmacogenomic tests in treating pediatric depression, anxiety and ADHD. Current problems in pediatric and adolescent health care. 2026-10-16

PubMed ↗ DOI / publisher ↗

Oct 13, 2026

PubMed

Brain Waste-Clearance Marker Is Lower Across Psychiatric Disorders

Researchers pooled 24 brain-imaging studies and found that people with psychiatric conditions, including ADHD among several others, scored lower than healthy people on a scan marker linked to how the brain clears waste. The studies varied a lot, and the result isn't broken out for ADHD alone. The marker is an indirect estimate, so it can't show cause.

What you can do

Nothing to change today: treat this as an early research signal about shared brain mechanisms, not something that applies to ADHD specifically or that you can act on.

Evidence

Study design: meta-analysis
Population: Patients with psychiatric disorders versus healthy controls in 32 studies in the systematic review; 24 studies (n = 2855; 1503 patients, 1352 controls) in the meta-analysis. The abstract does not give ADHD-specific numbers or ages.
Sample size: Total: 2855; Exposure / intervention group: 1503; Comparator group: 1352; Meta-analysis of 24 studies reporting bilateral DTI-ALPS; 'intervention' is the patient count and 'comparator' the healthy control count. The systematic review included 32 studies. The number of ADHD studies and participants is not reported in the abstract.
Intervention / exposure: Not reported
Comparator: Healthy controls (patient-control comparisons of DTI-ALPS index)
Key results: Bilateral DTI-ALPS index (diffusion tensor imaging along the perivascular space), a non-invasive proxy for glymphatic-related processes: Significant transdiagnostic reduction in DTI-ALPS index in psychiatric populations compared with healthy controls (Hedges' g = -0.78, 95% CI -1.01 to -0.55, p < 0.0001).
Between-study heterogeneity: Substantial heterogeneity (I² = 86.3%).
Small-study effects: There was evidence of small-study effects.
Effect estimates: standardized mean difference: Hedges' g = -0.78 · 95% CI -1.01 to -0.55 · p < 0.0001 · Pooled random-effects comparison of bilateral DTI-ALPS index in psychiatric populations versus healthy controls (24 studies, transdiagnostic, not ADHD-specific)
other: I² = 86.3% · Between-study heterogeneity of the pooled DTI-ALPS estimate
Limitations: Between-study heterogeneity was substantial (I² = 86.3%).
There was evidence of small-study effects, which can inflate pooled estimates.
The pooled effect is transdiagnostic; the abstract reports no ADHD-specific effect size, so it cannot be assumed to apply to ADHD alone.
DTI-ALPS is an indirect, non-invasive proxy for glymphatic-related processes, not a direct measure of glymphatic function.
Authors state that harmonised imaging pipelines and multimodal validation are needed to clarify the biological and clinical significance of the findings.
Patient-control comparisons imply cross-sectional data, so the findings cannot establish causation or direction. This is an inference, not an author statement.
Abstract-only input: risk-of-bias results and moderator analyses are not reported in the abstract.
Source reviewed: Abstract

References

Pascucci A, Saccaro LF, Forrer S et al. Transdiagnostic reductions in glymphatic-related perivascular diffusion across psychiatric disorders: A systematic review and meta-analysis. Neuroscience and biobehavioral reviews. 2026-10-13

PubMed ↗ DOI / publisher ↗

Sep 26, 2026

PubMed

Kids Newly Diagnosed With ADHD Ate More Processed Food and Had Lower Diet Quality

Researchers compared 64 children newly diagnosed with ADHD, not yet treated, to 64 children without ADHD of the same age and sex. The ADHD group ate more highly processed food, had a stronger desire to drink, and scored lower on a diet quality measure. This was a one-time comparison, so it can't show whether diet affects ADHD or the reverse.

What you can do

If you're a parent, use this as a prompt to look at your child's overall diet quality and processed food intake for its own sake, without expecting it to treat ADHD symptoms, since this study didn't test that.

Evidence

Study design: observational
Population: Children with ADHD (n=64) and age- and sex-matched healthy controls (n=64); matched by sex and age (±6 months). Specific age range not stated in abstract.
Sample size: Total: 128; Exposure / intervention group: 64; Comparator group: 64; 64 children with ADHD and 64 individually matched healthy controls; 'intervention' refers to the ADHD case group, as there was no intervention.
Intervention / exposure: Not reported
Comparator: Healthy controls individually matched by sex and age (±6 months)
Key results: Desire to drink (Children's Eating Behaviour Questionnaire): Children with ADHD had higher desire to drink scores than matched controls; higher scores were associated with higher odds of ADHD (adjusted OR = 1.88); remained significant after Holm correction.
Highly processed food consumption (sQ-HPF): Children with ADHD had higher sQ-HPF scores; higher scores associated with higher odds of ADHD (adjusted OR = 2.44); remained significant after Holm correction.
Diet quality (MIND Diet Index): Children with ADHD had lower MIND Diet Index scores; higher scores associated with lower odds of ADHD (adjusted OR = 0.77); remained significant after Holm correction.
Healthy dietary pattern score (principal component analysis): Children with ADHD had lower healthy dietary pattern scores, but the association did not remain significant after Holm correction.
Eating behaviors (Behavioral Pediatrics Feeding Assessment Scale) and other CEBQ subscales: Not reported in the abstract beyond desire to drink; no other significant differences are specified.
Effect estimates: odds ratio: 1.88 · 95% CI 1.37-2.58 · Desire to drink score and odds of ADHD; primary adjusted conditional logistic model (adjusted for maternal education). Increment per score unit not specified in abstract.
odds ratio: 2.44 · 95% CI 1.61-3.71 · sQ-HPF score and odds of ADHD; primary adjusted model. Scaling/increment unknown from abstract.
odds ratio: 0.77 · 95% CI 0.66-0.89 · MIND Diet Index score and odds of ADHD; primary adjusted model. Scaling/increment unknown from abstract.
Limitations: Authors describe the study as exploratory.
"The observational design precludes temporal or causal inference."
"Larger independent prospective studies are needed."
The healthy dietary pattern association did not remain significant after Holm correction.
Small sample (64 per group); results limited to children without major comorbidities and treatment-naïve, so may not generalize to other ADHD presentations.
Dietary intake and eating behaviors were assessed by questionnaires (food frequency and parent/caregiver-type scales), which are subject to reporting bias; only maternal education is named as an adjustment variable.
Each exposure was examined separately rather than jointly, so independent contributions are unknown.
Increment/scaling of odds ratios not specified in the abstract.
Source reviewed: Abstract

References

Şen N, Acar Tek N. Eating Behaviors, Dietary Patterns and Diet Quality in Newly Diagnosed, Treatment-Naïve Children with ADHD: An Age- and Sex-Matched Case-Control Study. Nutrients. 2026-09-26

PubMed ↗ DOI / publisher ↗

Jul 16, 2026

PubMed

Kids With Smokers Inside the Home Had 58% Higher Odds of ADHD

In a US survey of about 6,800 children aged 4 to 15, those living in homes where someone smoked indoors were more likely to have ADHD. This held after accounting for other differences between families, and across age, sex, and race groups. It was a one-time snapshot, and ADHD was identified through questionnaires, so it can't show that smoke causes ADHD.

What you can do

If you're a parent, keeping smoking out of your home is worth doing for children's health anyway, but don't read this study as proof it prevents or causes ADHD.

Evidence

Study design: cross-sectional
Population: US children aged 4-15 years from NHANES 1999-2004 (n=6790 analyzed; 452 [7.97%] classified as having ADHD)
Sample size: Total: 6790; 452 children (7.97%) classified as having ADHD. Exposed and non-exposed group sizes are not given in the abstract.
Intervention / exposure: Exposure (not an intervention): household secondhand smoke, defined as any household member smoking inside the home
Comparator: Children with no household member smoking inside the home (non-exposed group)
Key results: ADHD status (questionnaire-based) by household SHS exposure: Household SHS exposure remained significantly associated with ADHD after adjustment for multiple confounders (OR=1.583; 95% CI: 1.166-2.150).
Prevalence of household SHS exposure in children with vs without ADHD: 36.25% in children with ADHD vs 22.77% in children without ADHD (p<0.001).
Subgroup analyses (sex, age, race, PIR, BMI, birth weight): Association remained generally consistent across all strata (all p for interaction >0.05).
Sensitivity analyses: Confirmed the positive association between household SHS exposure and ADHD (all p<0.05).
Effect estimates: odds ratio: 1.583 · 95% CI 1.166-2.150 · Adjusted odds of ADHD in children with household SHS exposure vs non-exposed, from weighted multivariate logistic regression
other: 36.25% vs 22.77% prevalence of household SHS exposure · p < 0.001 · Unadjusted exposure prevalence in children with ADHD vs without ADHD
Limitations: Cross-sectional design: the authors state 'Future prospective investigations are required to clarify the causal relationship underlying this association.'
ADHD cases were identified through questionnaire-based assessments rather than a clinical diagnostic evaluation.
Exposure was defined only as whether any household member smoked inside the home; no biomarker or dose measure is mentioned in the abstract.
Secondary analysis of pooled NHANES 1999-2004 data, so the data are old and the variables available were not designed for this question.
Abstract only: the full list of adjusted confounders and the sensitivity analysis details are not reported.
Source reviewed: Abstract

References

Bao Y, Zhang W, Wang C et al. Association between household secondhand smoke exposure and ADHD in US children aged 4-15 years: Evidence from NHANES 1999-2004. Tobacco induced diseases. 2026-07-16

PubMed ↗ DOI / publisher ↗ Open full text ↗

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Latest research

ADHD Medication Use in Pregnancy Was Not Linked to More Major Birth Defects

Genetic Tests Can't Tell You Which ADHD Medication Will Work Best for Your Child

Brain Waste-Clearance Marker Is Lower Across Psychiatric Disorders

Kids Newly Diagnosed With ADHD Ate More Processed Food and Had Lower Diet Quality

Kids With Smokers Inside the Home Had 58% Higher Odds of ADHD