ObjectivesThis systematic review and meta-analysis provides a quantitative summary of the literature exploring the relationship between maternal diet quality during pregnancy and child cognitive and affective outcomes. We investigate whether there are indications for robust associations and aim to identify methodological strengths and challenges of the current research to provide suggestions of improvement for future research.Design and participantsRelevant studies were identified through a systematic literature search in relevant databases. All studies investigating maternal diet quality during pregnancy in relation to child cognitive or affective functioning in children of elementary school age or younger were assessed for inclusion.Results18 relevant studies, comprising 63 861 participants were identified. The results indicated a small positive association between better maternal diet quality during pregnancy and child functioning. We observed publication bias and significant heterogeneity between studies, where type of diet classification, publication year and outcome domain together accounted for about 30% of this heterogeneity. Trim and fill analysis substantiated the presence of publication bias for studies in the affective domain and showed an adjusted effect size of Hedge’s g=0.088 (p=0.0018) (unadjusted g=0.093 (p=0.03)). We observed no publication bias in the cognitive domain, where results indicated a slightly larger effect size (g=0.14 (p<0.0001)) compared with that of the affective domain. The overall summary effect size was g=0.075 (p<0.0001) adjusted for publication bias (unadjusted g=0.112 (p=0.0001)). Child diet was not systematically controlled for in the majority of the studies.ConclusionThe results indicated that a better maternal diet quality during pregnancy has a small positive association with child neurodevelopment, with more reliable results seen for cognitive development. These results warrant further research on the association between maternal diet quality during pregnancy and cognitive and affective aspects of child neurodevelopment, whereby it is crucial that future studies account for child diet in the analysis.

Background Attention Deficit Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental disorder. Effective long-term treatment options are limited, which warrants increased focus on potential modifiable risk factors. The aim of this study was to investigate associations between maternal diet quality during pregnancy and child diet quality and child ADHD symptoms and ADHD diagnosis. Methods This study is based on the Norwegian Mother, Father and Child Cohort Study (MoBa). We assessed maternal diet quality with the Prenatal Diet Quality Index (PDQI) and Ultra-Processed Food Index (UPFI) around mid-gestation, and child diet quality using the Diet Quality Index (CDQI) at 3 years. ADHD symptoms were assessed at child age 8 years using the Parent Rating Scale for Disruptive Behaviour Disorders. ADHD diagnoses were retrieved from the Norwegian Patient Registry. Results In total, 77,768 mother-child pairs were eligible for studying ADHD diagnoses and 37,787 for ADHD symptoms. Means (SD) for the PDQI, UPFI and CDQI were 83.1 (9.3), 31.8 (9.7) and 60.3 (10.6), respectively. Mean (SD) ADHD symptom score was 8.4 (7.1) and ADHD diagnosis prevalence was 2.9% (male to female ratio 2.6:1). For one SD increase in maternal diet index scores, we saw a change in mean (percent) ADHD symptom score of − 0.28 (− 3.3%) (CI: − 0.41, − 0.14 (− 4.8, − 1.6%)) for PDQI scores and 0.25 (+ 3.0%) (CI: 0.13, 0.38 (1.5, 4.5%)) for UPFI scores. A one SD increase in PDQI score was associated with a relative risk of ADHD diagnosis of 0.87 (CI: 0.79, 0.97). We found no reliable associations with either outcomes for the CDQI, and no reliable change in risk of ADHD diagnosis for the UPFI. Conclusions We provide evidence that overall maternal diet quality during pregnancy is associated with a small decrease in ADHD symptom score at 8 years and lower risk for ADHD diagnosis, with more robust findings for the latter outcome. Consumption of ultra-processed foods was only associated with increased ADHD symptom score of similar magnitude as for overall maternal diet quality, and we found no associations between child diet quality and either outcome. No causal inferences should be made based on these results, due to potential unmeasured confounding.

Our aim in this study was to estimate the strength of associations between prenatal diet quality and child behavioral, language, and motor functions in the Norwegian Mother and Child Cohort Study (1999–2008). We created a prenatal diet quality index (PDQI) based on adherence to Norwegian dietary guidelines. Child outcomes were defined as sum scores on the Child Behavior Checklist, the Ages and Stages Questionnaire, and the Child Development Index at ages 18, 36, and 60 months. Using a longitudinal cohort study design and Bayesian hierarchical modeling, we estimated association strengths using inverse probability weighting to account for selection bias. In total, 27,529 mother-child pairs were eligible for inclusion. A 1–standard-deviation increase in PDQI score was associated with an absolute reduction in outcome sum scores of 0.02–0.21 and a 3%–7% relative decrease, with larger decreases seen for language and motor functions than for behavioral functions. PDQI scores were inversely associated with all child functions, but the estimated strength of each association was low. The results indicate that the observed variations in PDQI scores in an industrialized Western society may not profoundly influence the child functions studied.

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Background Machine learning (ML) tools exist that can reduce or replace human activities in repetitive or complex tasks. Yet, ML is underutilized within evidence synthesis, despite the steadily growing rate of primary study publication and the need to periodically update reviews to reflect new evidence. Underutilization may be partially explained by a paucity of evidence on how ML tools can reduce resource use and time-to-completion of reviews. Methods This protocol describes how we will answer two research questions using a retrospective study design: Is there a difference in resources used to produce reviews using recommended ML versus not using ML, and is there a difference in time-to-completion? We will also compare recommended ML use to non-recommended ML use that merely adds ML use to existing procedures. We will retrospectively include all reviews conducted at our institute from 1 August 2020, corresponding to the commission of the first review in our institute that used ML. Conclusion The results of this study will allow us to quantitatively estimate the effect of ML adoption on resource use and time-to-completion, providing our organization and others with better information to make high-level organizational decisions about ML.