Cumulative maternal exposures of inflammation and attention‐deficit, hyperactivity disorder risk in children: Does one size fit all?
Bibliographic record
Abstract
Attention-Deficit/Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder involving impaired attention, hyperactivity and impulsive behaviour; the condition affects over 3% of children worldwide.1 ADHD not only poses potential life-threatening risks for individuals but also exerts a significant economic burden on society—estimated to cost $31.6 billion annually in the United States.2 Identifying modifiable factors in the aetiology of neurodevelopmental disorders and psychiatric illness is paramount for risk prediction and the design of preventive interventions. Although our understanding of environmental origins for the development of ADHD is incomplete, it is recognised that many aetiologic determinants operate during foetal life and infancy, the earliest and most vulnerable stages of brain development. Given the absence of a cure for ADHD, identifying modifiable risk factors for ADHD remains critical. In this issue of Paediatric and Perinatal Epidemiology, Nielsen and colleagues3 explore the role of multiple maternal exposures related to inflammation during pregnancy with ADHD in the offspring. The study was based on 908,770 children born from July 2001 to December 2011 in New South Wales, Australia, and followed up until December 2014. ADHD was identified in 16,297 children (incidence 3.5 per 1000 person-years) at a median age of 7 years at first treatment. Seven chronic maternal complications and risk factors (exposures) were identified from birth data and hospital admissions during pregnancy: autoimmune disease, asthma, hospitalisation for infection, mood or anxiety disorder, smoking, hypertension and diabetes. They found that each exposure was independently associated with an increased risk for ADHD (range of hazard ratios [HR] 1.19 to 1.89); the risks increased with cumulative exposures to inflammatory markers, suggesting dose–response relationships. Children born to women with asthma, infection, mood or anxiety disorder and smoking carried the greatest burden of ADHD risk (HR 6.12, 95% confidence interval [CI] 3.47, 10.70). This study is unique in many ways. It provides a population-based characterisation of ADHD risks by maternal exposures to inflammation-related conditions; an examination of single and multiple conditions and, importantly, cumulative effects through a dose–response relationship. It also offers much-needed clarity in understanding how maternal inflammation may affect ADHD in children. In this commentary, we highlight how this study advances the field and points to areas where nuanced data are lacking but may provide important insights into understanding the role of inflammation in ADHD risk in children. The findings in the study add to the accumulating evidence that shows increased risks of neurodevelopmental disorders in relation to maternal obesity, cardiovascular disease, chorioamnionitis and epilepsy.4-6 These risks could be either caused by the chronic disease itself or by medication used to treat the chronic disease. The pathways that connect maternal illness and neurodevelopmental outcomes are complex and, for the most part, largely remain unknown; among them, maternal factors play an important role in shaping ADHD risks. Nielsen and colleagues3 emphasise that the maternal exposures considered are heterogeneous and several conditions have important non-inflammatory aspects that may influence the observed association. It is important to acknowledge a limitation inherent in the heterogeneity of the maternal conditions studied, each possessing distinct biological pathways leading to offspring ADHD. While the study demonstrates an increased risk of ADHD with an escalating number of maternal exposures, the complexity of each condition poses a challenge in formulating a unified intervention strategy. For instance, with autoimmune disease, it is imperative to closely monitor and manage the condition and medication use during pregnancy. Conversely, addressing smoking habits requires targeted behavioural interventions. Optimal blood glucose monitoring is crucial for maternal diabetes management,7 while interventions for hypertension during pregnancy may include daily low-dose aspirin, antihypertensive medications, foetal monitoring and timed delivery. This study begins to uncover the cumulative effects of maternal chronic conditions on ADHD risk. However, it prompts critical questions about how these conditions should be approached: as a constellation of interconnected conditions or as individual entities. Does smoking fall in the same group as a chronic condition? While extended periods of regular smoking qualify as chronic exposure, we argue against the inclusion of smoking as a chronic maternal illness. Instead, we regard smoking as a potential risk factor for ADHD. Recent studies have documented that the previously thought maternal smoking-ADHD risk in children is best described as an association that is strongly underscored by unmeasured confounding,8 and residual confounding. For instance, in this study, residual confounding by socioeconomic status (which is associated with inflammatory conditions in general) could potentially bias the associations. Although the authors adjusted for area-level average income as a proxy for the socio-economic status, the lack of adjustment for the specific markers of socio-economic status may have resulted in residual confounding by socio-economic status. In our view, despite the shared goal of preventing ADHD, these differing approaches highlight the need for tailored strategies based on the unique characteristics of each maternal condition. Future investigations may benefit from exploring each maternal chronic condition individually (i.e. diabetes, obesity, smoking) to disentangle the specific mechanisms underlying these associations to better understand the pathways through which maternal conditions may be causally associated with ADHD in children. This approach could provide insights into the causal pathways, facilitating the development of targeted interventions. A second issue relates to how some pre-existing conditions and pregnancy complications are associated with preterm birth or SGA. Nielsen and colleagues3 found that children exposed to increasing maternal exposures were more likely to be born preterm, experiencing severe neonatal morbidity. This suggests that preterm birth or SGA may lie on the causal pathway between maternal inflammation manifesting as chronic conditions and ADHD. Assessing the extent to which these factors mediate the effects of maternal inflammation on offspring ADHD is crucial.9 A recent study revealed that 48% of the total effect of maternal chronic cardiovascular or metabolic disorders on offspring cerebral palsy was mediated by preterm delivery.10 In short, Nielsen et al.'s findings stress the complexity of maternal factors, and the study's diverse maternal conditions pose a challenge for a unified approach, emphasising the need to explore each condition individually. We recommend that an understanding of how specific maternal inflammatory mechanisms may affect ADHD risk in children, and how potential mediating factors shape these risks may be worthy of future investigations. Only when all the factors causing a condition are clearly understood can any effective attempts at prevention be targeted. Neda Razaz and Cande V. Ananth conceived of and wrote this article. The authors have nothing to report. Dr. Razaz is supported, in part, by the Swedish Research Council (DNR 4-2979/2020) and the Canadian Institute of Health Research (PJT-183598). Dr. Ananth is supported, in part, by the National Heart, Lung, and Blood Institute (R01-HL150065) and the National Institute of Environmental Health Sciences (R01-ES033190), National Institutes of Health. The authors declare no conflicts of interest. Neda Razaz is an Assistant Professor in the Clinical Epidemiology Division at Karolinska Institutet in Stockholm, Sweden. Her research program aims to understand the role of maternal and paternal chronic illness during pregnancy, neurodevelopment and other long-term outcomes in childhood and early adulthood. She is the commissioning editor of Paediatric and Perinatal Epidemiology. Cande V. Ananth is a Professor and Chief of the Division of Epidemiology and Biostatistics in the Department of Obstetrics, Gynecology, and Reproductive Sciences at Rutgers Robert Wood Johnson Medical School, NJ. His research is directed at (i) Ischemic placental disease, preterm delivery and long-term risks of coronary heart disease and stroke outcomes along the life course; (ii) Impact of air pollution and weather exposures on ischemic placental disease; and (iii) Applications of innovative analytic approaches, including causal models and mediation methods, to studies in human reproduction. He is the editor-in-chief of Paediatric and Perinatal Epidemiology. Not Applicable.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".