Evidence suggests that the poorer mental health associated with attention deficit hyperactivity Disorder (ADHD) is partially explained by adverse psychosocial correlates of the condition. As recent studies show that self‐compassion is negatively associated with ADHD, this study investigates if levels of self‐compassion may explain the mental health outcomes in people with ADHD compared to people without ADHD.A total of 543 adults with ADHD , and 313 adults without ADHD  completed questionnaires online to measure levels of self‐compassion and mental health. A Structural Equation Model assessed the mediating effect of self‐compassion on the relationships between ADHD and well‐being , and ADHD and ill‐being .Findings suggest that low self‐compassion contributes to poorer mental health in adults with ADHD compared to adults without ADHD.Thus, self‐compassion may be a potential target to improve mental health in this population.This study shows that self‐compassion is an important factor in the mental health of adults with ADHD and provides preliminary evidence for the use of self‐compassion interventions to improve mental health outcomes in adults with ADHD. The No‐ADHD group was aged between 18 and 82 years old , 84.35% were Caucasian, and 66.45% were female. Further details of the sample's demographics are displayed in Table Participants were recruited between January and March 2019, via social media sites, online forums, posters displayed in shops, university disability services, and at ADHD support group venues across the United Kingdom. Data collected from this study have been partially presented previously . Demographic questions were presented first and were always followed by measures of ADHD and self‐compassion. Measures of stress, well‐being, perceived criticism,Participants completed a series of questionnaires online via the survey platform Qualtrics (2.32.3.1α = 0.87 and ω = 0.87) was similar to previous studies   .The SCS Neff,  is a 26‐2.3.3α = 0.83) psychological well‐being (α = 0.83), and social well‐being (α = 0.74) , psychological well‐being , and social well‐being .The MHC‐SF Keyes,  is a sel2.3.4α = 0.71) and HADS‐anxiety (α = 0.84) when used with an ADHD population , and adequate internal consistency for HADS‐anxiety (α = 0.76 and ω = 0.77).The HADS  and test–retest reliability (intraclass correlation > 0.70) .The PSS  Lee, . In this2.3.6An attention check question was included to ensure that respondents were attending to the questions and responding accurately. The question read: “People vary in the amount they pay attention to these kinds of surveys. Some take them seriously and read each question, whereas others go very quickly, and barely read the questions at all. If you have read this question carefully, please write the word yes in the blank box below labelled 'other'. There is no need for you to respond to the scale below.” Respondents were required to respond in an empty text box labeled “Other” to pass the attention check.2.4The data were cleaned, coded, and analyzed in Excel and R , root mean square error approximation (RMSEA) (<0.08), standardised root mean square residual (SRMR) (<0.08), comparitive fit index (CFI) (≥0.90), and Tucker‐Lewis index (TLI) (≥0.90).SEM was used to model the mediating effect of self‐compassion on the relationship between ADHD (IV) and two latent variables, ill‐being , and well‐being . Each of the dependent variables  were also loaded onto a global latent factor of “Mental Health” to control for any shared variance across variables. This structure was theoretically driven by the two continua model , thus, robust maximum likelihood estimators were used for the SEM, as these estimators do not require assumptions of normality . Modification indices were assessed but no changes were made to the model. The linear relationships between variables in the model are presented in Figure The model presented in Figure β = −0.6, p < 0.01, [−0.71 to −0.48]), lower well‐being , and higher ill‐being . Although having a diagnosis of ADHD still had a significant direct effect on mental health after self‐compassion was entered into the model as a mediator , levels of self‐compassion did partially mediate the relationship between having an ADHD diagnosis and increased ill‐being  as well as partially mediate the relationship between having ADHD and decreased well‐being . Gender was not significantly associated with well‐being  or ill‐being , however, having a comorbid condition was associated with well‐being , ill‐being , and self‐compassion .The results from the SEM demonstrated that having a diagnosis of ADHD was significantly associated with lower self‐compassion levels  and lower levels of well‐being  associated with ADHD. This is the first study to provide evidence that the way in which adults with ADHD respond to themselves during times of suffering or failure partially explains the poorer mental health that they have in comparison to adults without ADHD. Consequently, the findings support and extend theory and knowledge about the mental health of adults with ADHD in several important ways, and highlight potential avenues for future research. The findings also highlight the potential of self‐compassion as a positive addition to clinical practice that could complement other empirical interventions to improve self‐regulation, resilience, and mental health in adults with ADHD.First, the current findings add to the growing evidence base that considers individual differences in adults with ADHD, and how these may interact with the outcomes of ADHD to impact factors of mental health. The results of this study demonstrate that levels of psychological, emotional, and social well‐being are all lower in adults with a diagnosis of ADHD compared to adults without ADHD. This is the first study that we know of, to show that all elements of wellbeing are reduced in people with ADHD and that both ill‐being and well‐being differ significantly in people with ADHD compared to people without ADHD. What is more, the results identified that lower levels of self‐compassion associated with ADHD were a partial contributor to both the lower well‐being and higher ill‐being observed in the participants with ADHD. This is consistent with the findings of Geurts et al.  who founPeople with ADHD, and traits of neurodiversity, are more likely to experience failure (Fredriksen et al., Overall, these results highlight the potential self‐compassion could have for promoting well‐being in adults with ADHD. ADHD is a complex condition that is highly heterogeneous and can differ significantly from person to person (Faraone et al., 4.1A notable strength of the current study is the large sample size, which permitted advanced statistical analysis and allowed us to be more stringent with our exclusion criteria by screening participants based on their ASRS scores. This meant that we could clearly define the ADHD and No‐ADHD groups by diagnosis and trait level and that the sample was diverse, so the findings are both representative and generalizable. The full sample included people from a range of ages, demographics, and ethnicities, and the ADHD sample was representative of the commonly co‐occurring conditions, and of people who received a diagnosis in childhood and adulthood. However, the study relied on self‐reported measures, which may make the data vulnerable to common method bias which occurs when similarity in response style, social desirability, and priming effects result in spurious correlations between items (Podsakoff et al., 5The current study suggests that lower levels of self‐compassion in adults with ADHD partially explain why adults with ADHD have lower levels of psychological, social, and emotional well‐being and higher levels of depression, anxiety, and stress compared to adults without ADHD. Longitudinal research is needed to verify if increasing self‐compassion can improve mental health outcomes associated with ADHD. Nevertheless, the current findings demonstrate that self‐compassion may be an important factor in many different aspects of mental health for people with ADHD. It, therefore, provides preliminary support for the use of self‐compassion interventions in adults with ADHD, in conjunction with existing empirically supported interventions.https://publons.com/publon/10.1002/jclp.23354The peer review history for this article is available at Supporting information.Click here for additional data file.


Attention deficit/hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders, and around two-thirds of affected children report persisting problems in adulthood. This negative trajectory is associated with high comorbidity with disorders like obesity, depression, or substance use disorder (SUD). Decreases in cortical volume and thickness have also been reported in depression, SUD, and obesity, but it is unclear whether structural brain alterations represent unique disorder-specific profiles. A transdiagnostic exploration of ADHD and typical comorbid disorders could help to understand whether specific morphometric brain changes are due to ADHD or, alternatively, to the comorbid disorders. In the current study, we studied the brain morphometry of 136 subjects with ADHD with and without comorbid depression, SUD, and obesity to test whether there are unique or common brain alterations. We employed a machine-learning-algorithm trained to classify subjects with ADHD in the large ENIGMA-ADHD dataset and used it to predict the diagnostic status of subjects with ADHD and/or comorbidities. The parcellation analysis demonstrated decreased cortical thickness in medial prefrontal areas that was associated with presence of any comorbidity. However, these results did not survive correction for multiple comparisons. Similarly, the machine learning analysis indicated that the predictive algorithm grouped most of our ADHD participants as belonging to the ADHD-group, but no systematic differences between comorbidity status came up. In sum, neither a classical comparison of segmented structural brain metrics nor an ML model based on the ADHD ENIGMA data differentiate between ADHD with and without comorbidities. As the ML model is based in part on adolescent brains, this might indicate that comorbid disorders and their brain changes are not captured by the ML model because it represents a different developmental brain trajectory. Attention deficit/hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders and has prevalence in children of 5–7%, of which around two-thirds report persisting problems in adulthood , 2. A maAlterations in brain morphometry have been widely reported in both children and adults with ADHD , 10 and We used two distinct analytic approaches to investigate this topic. First, we conducted a standard univariate analysis of FreeSurfer-based volume segmentations of the structural brain scans of subjects with ADHD with and without comorbidities for testing our specific hypotheses. Second, we employed a machine learning (ML) algorithm trained to classify subjects with ADHD in a larger dataset and used it to calculate the ADHD brain risk score  of the We address three separate research questions by both types of analyses: 1) is there a general effect of comorbidities on structural brain morphometry and/or predictive accuracies of the ML algorithm? 2) Is there a cumulative effect of comorbidities? 3) Is there a specific effect of each of the comorbidities?We hypothesized that the subjects with ADHD and comorbid disorders will show smaller subcortical brain volumes and lower cortical thickness, particularly in the frontal-striatal pathways than the patients with ADHD only . We hypoThe study includes 133 subjects with ADHD with and without the comorbid presence of depression, obesity, or SUD . The recruitment took place at Donders Center for Cognitive Neuroimaging, Nijmegen, Netherlands and Goethe University, Frankfurt am Main, Germany.via the short Wender-Utah-Rating scale . Adult e [WURS; , which uThe project was carried out in accordance with the provisions of the Declaration of Helsinki and the European Guidelines on Good Clinical Practice, and was approved by the Ethics Committee of the Medical Faculty of the J.W. Goethe University Frankfurt am Main (reg. no. 256/16) and in Nijmegen by Radboud University (reg. no. 2018-4364).http://enigma.ini.usc.edu/protocols/imaging-protocols/) based on automatic outlier detection and visual inspection of segmentation quality.Structural T1 MPRAGE sequences were acquired on a Siemens PrismaFit scanner (Nijmegen) or a Siemens Trio Syngo scanner (Frankfurt). T1 scans were segmented by FreeSurfer cortical and subcortical segmentation. Quality Control (QC) was performed using the standardized ENIGMA QC pipeline (http://enigma.ini.usc.edu/protocols/imaging-protocols/). The automated protocols, based on FreeSurfer (version 5.3) segmentations, are fully validated and allow for maximal uniformity and comparability across sites. For each participant, left and right subcortical volumes, cortical thickness, and cortical surface area measures were calculated. Quality control depended on the visual inspection of all volume segmentations. Poorly segmented regions were removed from further analyses.In order to investigate brain morphometry, all structural MRI scans were segmented into subcortical volumes, cortical thickness, and cortical surface area by FreeSurfer segmentation. Structural T1-weighted MRI scans acquired at the two contributing sites were segmented using standardized and publicly available ENIGMA imaging protocols (p = 0.002). Full details of the model training and operation are available in Zhang-James et al.  data from 4,183 subjects (adults and children with and without ADHD) from 35 participating sites . Images were processed using the consortium's standard segmentation algorithms in FreeSurfer (V5.1 and V5.3). Details about the processing of the training data sample are provided in Zhang-James et al. . FreeSurFor the current study, the algorithm was applied to our new comorbidity cohort, rendering a BRS for each subject, indicating the likelihood that the subject was affected by ADHD. Average BRS scores were used in the current study to test whether predictive accuracies for the ADHD diagnostic status would differ between subjects with ADHD and comorbid diagnoses of SUD, obesity, or depression.The machine learning classifier reported by Zhang-James et al.  was usedThe final ensemble multilayer perceptron (MLP) neural network models reported in Zhang-James et al.  were tesThe primary outcome measure in the current study was therefore the BRS for each subject, indicating a likelihood that each subject would be classified as affected by ADHD. Given that all our subjects have an ADHD diagnosis (both in the comorbid and non-comorbid groups), correct prediction would mean the algorithm classifies all the subjects as “affected”.t-tests and the third one as F-test.As stated in the introduction, we formulated three hypotheses to test for the effects of comorbidity on structural brain morphometry, as operationalized by FreeSurfer segmentations of subcortical volumes, cortical thickness, and cortical surface area. First, we compared patients with and without comorbidity. Second, we looked for a linear effect of comorbidity . Third, we compared by F-test contrast all eight subgroups of different combinations of depression, SUD, or obesity. All tests included age, gender, and site as covariates. Intracranial volume (ICV) was added as an additional covariate in all the models, with subcortical volume as the dependent variable. The first two tests were defined as We calculated differences in medication use between the comorbidity subgroups with a univariate general linear model in R (R Core 27). Comorbidity  was used as a dependent variable, whereas medication type  was used as an independent variable with age, sex, and site as covariates.In accordance with the three research questions, three sets of models were run in R. Each model used each segmentation of subcortical volume, cortical thickness, and surface area as separate independent variables. The first model set tested for differences between participants with ADHD and no comorbidities and those with any comorbidity. The second model set tested for group differences between participants with ADHD and all comorbidities (cumulatively). The third model set tested for individual differences among subjects with ADHD, ADHD + depression, ADHD + obesity, and ADHD + SUD.p =0.022), left bank of the superior temporal sulcus , and middle temporal sulcus . Similarly, the model testing differences between solely ADHD and ADHD + all cumulative comorbidities showed uncorrected group differences in the right and left rostral middle frontal , superior frontal , right pars triangularis , lateral orbitofrontal  and left pars opercularis , and caudal middle frontal , indicating lower cortical thickness in frontal areas associated with more comorbidities. These effects did not survive multiple comparisons corrections with a stringent pFDR-correction.The summarized results of the models are depicted in No effects of the different comorbidities on medication use were observed. As an exploratory follow-up analysis, medication use  was also subsequently added as a predictor in all ROIs that showed an uncorrected significant effect as depicted in p < 0.001). However, no differences were observed between any of the comorbidity groups, so the addition of comorbidities did not influence the accuracy of this prediction. Neither any, all, or each of the comorbidities were associated to BRS : t = 0.879, p = 0.381, Cohen's d = 0.15, Cohen's d CI = −0.3–0.62; every: f = 0.04, p = 0.986). The post hoc power analyses indicate that the power achieved for the F-test was 1-β = 0.05, limiting the interpretability of the lack of effects. BRS was not associated with site or sex, and neither was the BRS correlated with childhood symptoms of ADHD (WURS-k) -factor  in the dSimilarly, the ML algorithm had a predictive accuracy of 0.57, indicating an above-chance level for a correct ADHD diagnosis across all the subjects. However, this did not differentiate between subjects with solely ADHD or subjects with ADHD and comorbidities. Therefore, an ML model trained to differentiate between subjects with ADHD and healthy controls in the ENIGMA-ADHD dataset does not differentiate between subjects with just ADHD and ADHD + comorbidities. This indicates that the features used by this data-driven algorithm to predict ADHD status are not the same features that are altered in subjects with comorbidities. Therefore, using this approach, we find no evidence that the underlying morphometric features underlying ADHD and comorbid disorders are similar.As the ML model was based at least in part on adolescent brains, this might indicate that comorbid disorders and their brain changes that come later in life are not captured by the ML model because it represents a different developmental trajectory. In addition, one could argue that the disorders are not simple extensions of a dysregulated ADHD-developmental trajectory; therefore, our ML model does not pick up different subgroups.post hoc power analyses also indicate low to moderate power for the FreeSurfer analyses (1-β = 0.1–0.2). Therefore, a more extensive sample may aid in uncovering subtle severity-related effects on brain structure.Several further limitations should be considered when discussing our study. First, the limitations of the ML model described by Zhang-James et al.  apply toAn innovative part of our study is the use of an ML-model for generating a predictive ADHD brain risk score. This has parallels in recent studies trying to use the ENIGMA dataset as a weighting prior for a brain risk summary measure, e.g., in schizophrenia  but not To conclude, it is notable that neither the morphometric ML-model nor the univariate FreeSurfer-Segmentation models were effective in predicting ADHD and comorbid disorders. However, we do not think that the current results should be interpreted as definitive evidence that there are no effects on individuals with ADHD depending on type or number of comorbid disorders. Future studies might further investigate specific tasks, e.g., reward-learning, and look for relationships with ADHD symptoms. For example, there are alternate approaches with specific tasks, e.g., functional connectivity that may prove better. Indeed, a recent article by our group demonstrated that striatal rs-fMRI-connectivity is able to distinguish between the number of comorbidities aka the disorder “load” , indicatThe raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by Ethics Committee of the Medical Faculty of the J. W. Goethe University Frankfurt am Main (reg.no. 256/16) and in Nijmegen by the Radboud University (reg.no. 2018-4364). The patients/participants provided their written informed consent to participate in this study.DR and OG were involved in data collection, analysis, conceptual development, and writing of the manuscript. AR and JB were invovled in writing the grant application, conceptual development, and proofreading of the mansucript. YZ-J and SF were involved in the development of the machine learning algorithm, statistical analyses, conceptual interpretation, and proofreading of the manuscript. All authors contributed to the article and approved the submitted version.Financial support for this study was received from the European Union's Horizon 2020 Research and Innovation Programme under grant agreement 667302 (Comorbid Conditions of ADHD).AR has received a research grant from Medice and served on advisory board and/or speaker's bureau for Medice, Shire/Takeda, Janssen, Servier, and Neuraxpharm. OG has served on advisory board and/or speaker's bureau for Medice Arzneimittel Pütter GmbH and Shire/Takeda and has served on advisory board and/or speaker's bureau for Medice Arzneimittel Pütter GmbH. JB declares that he has been, for the past 3 years, a consultant /member of the advisory board of/and/or speaker for Janssen Cilag BV, Eli Lilly, Medice, Shire, Roche, and Servier. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Results show that the efficacy of saffron is comparable to that of methylphenidate. Saffron is more effective for treating hyperactivity symptoms, while methylphenidate is more effective for inattention symptoms.Attention Deficit/Hyperactivity Disorder is the most prevalent neurodevelopmental disorder worldwide. Choice treatment includes psychostimulants, but parents tend to be reluctant to administer them due to side effects, and alternatives are needed. Saffron extract is a natural stimulant that has been proven safe and effective for treating a variety of mental disorders. This study compares the efficacy of saffron and the usual treatment with methylphenidate, using objective and pen-and-paper tests. We performed a non-randomized clinical trial with two groups, methylphenidate ( Attention Deficit/Hyperactivity Disorder (ADHD) is one of the most prevalent neurodevelopmental disorders among children, adolescents, and adults worldwide . ADHD syWithin nutritional supplements, saffron has recently been postulated as one of the most interesting alternative treatments for ADHD. Saffron is a spice extracted from saffron crocus (crocus sativus) that has traditionally been used as an additive and food colorant worldwide, but also as a natural remedy for several diseases given its anti-inflammatory and antioxidant properties. Accordingly, saffron has been used to treat chronic diseases such as rheumatoid arthritis, inflammatory bowel diseases, Alzheimer’s, and several cancers  . MoreoveRecently, three clinical trials tested saffron’s efficacy to treat ADHD symptoms in adolescents and adults ,21,22. A®  compared to methylphenidate in core ADHD symptoms and executive functions. Our main hypothesis is that saffron presents similar efficacy compared with methylphenidate. We based the main hypothesis on the above-mentioned studies reporting positive results about the efficacy of saffron in different ADHD populations. We operationalized this hypothesis into two more specific hypotheses. First, patients taking saffron and methylphenidate show similar improvements in objective measures for core ADHD symptoms (inattention and hyperactivity); and second, patients taking saffron and methylphenidate show similarly improved executive functioning. The second hypothesis may help to elucidate whether or not the improvements in ADHD symptoms, in the event that we do find such findings, are explained by improvements in executive functions. In this paper, we present a non-randomized study using objective measures, alongside a battery of subjective, psychometric scales (see Methods below), in order to measure both core ADHD symptoms and executive functions. We compared the efficacy of Saffr’activA single-center, prospective, naturalistic, non-randomized, non-blind, pre–post intervention study was conducted in the Child and Adolescent Mental Health Services (CAMHS) at Puerta de Hierro University Hospital in Majadahonda, Spain. After a thorough explanation, written reported consent was signed by the patients and their parents or legal tutors.Participants were recruited from outpatients aged 7 years old or above, who met the criteria for ADHD diagnosis following the Diagnostical and Statistical Manual (DSM-5), confirmed by a child psychiatrist. For 20% difference between treatments, 80% statistical power, and 2.5% statistical significance, the sample size was calculated as 35 participants per group. All the patients were naïve .The sample was divided into two groups: Group 1 received psychoeducation and extended-release methylphenidate (tritiated up to a dose of 1 mg/kg per day), while Group 2 received psychoeducation and saffron (30 mg/day). The patients and their parents could choose their treatment group after a full explanation of the study and the properties of both methylphenidate and saffron. For each participant, the treatment duration was 3 months. Group 1 received one dose of extended-release methylphenidate in the morning , whereas patients of group 2 were recommended to take one dose of saffron in the evening . We based this recommendation on the well-known properties of saffron in improving sleep quality, latency, and duration . MeasureSimilar to previous studies ,21, we uThe severity of ADHD was measured using two scales. First, the SNAP-IV, Spanish version  was usedFurthermore, we evaluated executive functions using the Behavioral Rating Inventory of Executive Function—Second Edition (BRIEF-2) , SpanishFinally, sleep quality was measured with the Sleep Disturbance Scale for Children (SDSC) . The SDSIn addition to these subjective measures, we used the Conners’ Continuous Performance Test, version 3 (CPT-3) . The CPTFirst, Kolmogorov–Smirnov tests were performed to assess the normality assumption for the variables. For most variables, normality could not be assumed. Thus, we employed non-parametric tests when possible. Mann–Whitney tests were performed to test the main effects of group (methylphenidate vs. saffron) and time (pre–post) factors. Two-way 2 × 2 ANOVAs were performed with time (pre vs. post treatment) and group (stimulant vs. saffron) as factors and each measure as a dependent variable, to test the interactions between these two factors. Statistical analyses were performed in SPSS v. 26.0.p = 0.552).Most patients (32 out of 36 in the saffron arm and 24 out of 27 in the methylphenidate arm) completed the study. Furthermore, 7 patients from the methylphenidate arm (25.9%) and 10 patients from the saffron arm (31.2%) reported side effects . It is also noteworthy that both treatments were well tolerated, and no significant side effects were reported. Finally, a pronounced difference was found in the saffron arm, in the sense that the parents of the children treated with saffron reported a pronounced improvement in the time to fall asleep. Consistent with our hypothesis, we found a comparable improvement in the mean vs. the baseline score in both parent-rated, subjective scales and the CPT-3 in children either using methylphenidate or saffron. This is in keeping with the recent literature. Several recent clinical trials examined the efficacy and safety of saffron extract to treat ADHD symptoms ,21,22. AWe found that saffron and methylphenidate were both comparably effective for treating ADHD symptoms in children, as pointed out by the non-significant interaction effects from the ANOVAs. Nonetheless, the graphical interpretation of the ANOVAs showed interesting trends: while methylphenidate tended to be more effective for inattention, saffron tended to be more effective for hyperactivity, as measured by the SNAP-IV scale. Additionally, another interesting finding was that it not only improved ADHD core symptoms, but also executive functions. Furthermore, the improvement in the executive functions was moderate, compared with the mild improvements in core ADHD symptoms. Executive functions include some cognitive processes  that are core to ADHD . It is pFurthermore, our results from SDSC suggest that, although not reaching statistical significance, saffron has an impact on improving time to fall asleep, which methylphenidate does not have. Both groups improve sleeping time similarly, which is usually an issue when treating ADHD patients. This finding is clinically relevant given the well-known association between ADHD and sleep problems . FurtherRegarding the CPT-3, patients with saffron improved most in Block Change measures, a measure related to sustained attention, while patients with methylphenidate improved most in Commissions, a parameter more closely related to impulsivity. Interestingly, none of the samples improved in measures of Inter-Stimulus Interval changes, another measure related to sustained attention. These results contrast those obtained by Khaksarian et al. , who fouThe discrepancy between objective and subjective measures for the hyperactivity–impulsivity domain can be explained in two ways: first, objective and subjective measures tend to show low correlations ,43; and Although our results are in line with previous trials, these results have to be interpreted taking into account several limitations. First, unlike previous studies in this field, this pilot study was neither randomized nor blind; rather, patients were able to choose between treatments and the PI was aware of their selection. This limitation makes our study more sensitive to selection bias. However, as displayed in On the other hand, the most relevant strengths of the present study are, first, the use of objective measures, and second, that the improvements in both groups included both ADHD core symptoms and executive functions, and both were observed using subjective and objective measures, thus giving consistency to our study. In conclusion, this pilot study presents evidence of the efficacy and safety of saffron extract compared to methylphenidate in a sample of children and adolescents with ADHD. Although comparable in general terms, saffron tends to be more effective for hyperactivity symptoms, while methylphenidate is more effective for inattention symptoms. Furthermore, both treatments improved the number of sleeping hours, but only saffron made it easier to fall asleep.


Attention-deficit/hyperactive disorder (ADHD) is a neurodevelopmental disorder that commonly occurs in children with a prevalence ranging from 3.4 to 7.2%. It profoundly affects academic achievement, well-being, and social interactions. As a result, this disorder is of high cost to both individuals and society. Despite the availability of knowledge regarding the mechanisms of ADHD, the pathogenesis is not clear, hence, the existence of many challenges especially in making correct early diagnosis and provision of accurate management.We aimed to review the pathogenic pathways of ADHD in children. The major focus was to provide an update on the reported etiologies in humans, animal models, modulators, therapies, mechanisms, epigenetic changes, and the interaction between genetic and environmental factors.References for this review were identified through a systematic search in PubMed by using special keywords for all years until January 2022.DRD1, DRD2, DRD4, DAT1, TPH2, HTR1A, HTR1B, SLC6A4, HTR2A, DBH, NET1, ADRA2A, ADRA2C, CHRNA4, CHRNA7, GAD1, GRM1, GRM5, GRM7, GRM8, TARBP1, ADGRL3, FGF1, MAOA, BDNF, SNAP25, STX1A, ATXN7, and SORCS2. Some of these genes have evidence both from human beings and animal models, while others have evidence in either humans or animal models only. Notably, most of these animal models are knockout and do not generate the genetic alteration of the patients. Besides, some of the gene polymorphisms reported differ according to the ethnic groups. The majority of the available animal models are related to the dopaminergic pathway. Epigenetic changes including SUMOylation, methylation, and acetylation have been reported in genes related to the dopaminergic pathway.Several genes have been reported to associate with ADHD: The dopaminergic pathway remains to be crucial in the pathogenesis of ADHD. It can be affected by environmental factors and other pathways. Nevertheless, it is still unclear how environmental factors relate to all neurotransmitter pathways; thus, more studies are needed. Although several genes have been related to ADHD, there are few animal model studies on the majority of the genes, and they do not generate the genetic alteration of the patients. More animal models and epigenetic studies are required. Attention-deficit/hyperactive disorder (ADHD) is a neurodevelopmental disorder that is characterized by hyperactivity, inattention, impulsivity, and problems in social interaction and academic performance . The predominantly hyperactive/impulsive, predominantly inattentive, and combined types are the major three forms that exist (DSM-5). It most commonly occurs in children with prevalence rates ranging from 3.4 to 7.2% . It affeThe phenotype is complex and heterogeneous, presenting with variable clinical features, developmental course, and outcome. The diagnosis of ADHD is typically determined in the school-age years; however, it can occur from early childhood. It is very difficult to diagnose young children due to their inability to cooperate well, and this happens because diagnosis relies much on interviews and observations according to the rating scale. There is no available standardized protocol for the auxiliary examinations which can assist in diagnosis despite the presence of many studies. Moreover, ADHD is frequently comorbid with other conditions in about 75% of the cases , includivia multiple receptors influence the functioning of the prefrontal cortex, cerebellum, and caudate, which are responsible for the regulation of attention, thoughts, emotions, behavior, and actions , and the interaction between genetic and environmental factors. This update will deepen the understanding of the pathogenesis, the interaction between genetic and environmental factors, and possibly provide molecular targets for the development of accurate therapies.References for this review were identified through a systematic search in PubMed. Keywords used were the combination of Attention-Deficit/Hyperactivity Disorder or ADHD and gene or copy number variations or microRNAs or environmental factors or social factors or diet or epigenetics or neuroimaging or animal model or modulators, or pharmacology or mechanisms or pathways for all years until January 2022. This review included only papers published in English regarding ADHD, animal models, gene mutations, copy number variations, micro-RNAs, neuro-imaging changes, environmental factors, epigenetic changes, pathways, dietary, and pharmacological treatments. It excluded abstracts, patents, book chapters, and conference papers. The final reference list was generated based on the originality and relevance to the broad scope of this review.Several genes have been related to ADHD in human beings from different parts of the world as shown in Attention-deficit/hyperactive disorder is a heterogeneous and highly heritable disorder resulting from complex gene–gene and gene–environment interactions . MultiplMultiple genes have been reported to associate with ADHD in children. All reported genes have a significant association with neurotransmission and neurodevelopment which can explain ADHD etiology and pathophysiology . They faDRD1, DRD2, DRD4, and DAT1.Dopamine exerts its effects in the frontal cortex through GABAergic neurons , noradreDRD1 gene encodes for DRD1 (dopamine receptor D1) receptor. DRD1 is found in the postsynaptic membrane where it regulates the postsynaptic transmission of dopamine. It is expressed in excitatory neurons in the prefrontal cortex  receptor. DRD2 is highly expressed in the prefrontal cortex, responsible for locomotion, reward, reinforcement, memory, and learning  which in turn activates the calcium signaling pathway which influences synaptic plasticity, affects downstream gene regulation, long- and short-excitability as well as cell death or survival. It is also responsible for postsynaptic neurotransmission of NE  (DRD2-12 (rs7131465) is related to a higher risk for ADHD and autism spectrum disorder overlap   has been1800497) . DRD2-12 overlap . The int overlap . T allel overlap . The intlescence . There ilescence . CurrentDRD4 gene encodes DRD4 (dopamine receptor D4) receptor. This receptor is a GPCR that inhibits adenylyl cyclase, found in the dopaminergic synapse. DRD4 is expressed in the prefrontal cortex including the anterior cingulate and orbitofrontal cortex, which are regions predominantly affected in ADHD  in exon 3 of the DRD4 gene has been implicated in the etiology of ADHD  and phthalate metabolite levels can affect the attention level in ADHD patients  showed that reduced DRD4 expression increases extracellular levels of glutamate neurotransmission in the striatum of DRD4–/– mice  which is located in the presynaptic membrane  in midbrain dopaminergic neurons demonstrated a hyperactivity phenotype of ADHD  display reduced dopamine uptake and cell surface DAT expression levels in the striatum. DAT1 gene 5′-UTR methylation has been suggested as a new approach for the exploration of an epigenetic biomarker in ADHD diagnosis. Paternal nicotine exposure prompts hyperactivity in C57BL/6 mice next-generation through down-regulating the expression of DAT which results from hyper-methylation of DAT  gene in DAT (Slc6a3) expressing cells leads to hyperactivity but sparing motor function as shown in DAT-specific Slc6a8 knockouts (dCrt-/y)  . SUMOyladCrt-/y) .There is a close relationship between levels of circulating miRNAs and ADHD. Iron helps in the homeostasis of the hemoglobin structure, antioxidants, genetic repair, and in particular, central nervous system function . To dateZinc is a very important cofactor for more than 100 enzymes and is required for modulating melatonin and dopamine. Dopamine plays a major role in the pathogenesis of ADHD as has been described before . Animal Ethanol affects dopamine release and impairs the binding of glutamate to mGluRs in the postsynaptic membrane . ParentaExposure to pyrethroid pesticides has been linked with ADHD both in human beings and animals. There are associations between pyrethroid insecticide use and urinary 3-phenoxybenzoic acid concentrations among Korean preschool-age boys . Mice exDAT-KO mice was attenuated when fluoxetine (a selective serotonin re-uptake inhibitor) was administered  which is a rate-limiting enzyme in the synthesis of serotonin from tryptophan. Tryptophan hydroxylase is found in the cytosol of the presynaptic membrane and is highly expressed in the frontal cortex. TPH2 rs11179027 and TPH2 rs1843809 alleles are linked with the genetic predisposition to ADHD in Egyptian children (TPH2 (rs17110747) is related to abnormal behaviors and impaired cognition in female ADHD children according to the family based association tests  encodes the 5-HT1A receptor that binds to the endogenous neurotransmitter serotonin. This receptor is a GPCR that is found in the postsynaptic membrane and is highly expressed in limbic areas, hypothalamus, and cortex. It plays a role in the regulation of dopamine and 5-HT levels in the brain. Activation of 5-HT1A receptors stimulates the opening of G-protein-coupled inwardly rectifying K+ (GIRK) channels in CA1 pyramidal cells of the hippocampus  which is also a GPCR for serotonin. It is found in the postsynaptic and glial cell membranes, highly expressed in the neocortex, caudate nucleus, nucleus accumbens, and hippocampus. It facilitates the reuptake of serotonin from the synapse to the glial cell and postsynaptic membrane. HTR2A plays a role in the frontal cortex, where it mediates the activation of a subpopulation of GABAergic neurons  which is also found in the presynaptic membrane and highly expressed in the striatum, basal ganglia, and hippocampus. It inhibits the activity of adenylate cyclase and controls the release of dopamine, serotonin, and acetylcholine in the brain. Two serotonergic genes: HTR1B and SLC6A4 were found to associate with ADHD according to the review  is encoded by the SLC6A4 gene. SLC6A4 is an integral membrane protein that transports the neurotransmitter serotonin from synaptic spaces into presynaptic neurons. Functional 5HTTLPR 44-bp insertion/deletion ‘long’ allele was identified as a risk allele for ADHD according to the meta-analysis  is responsible for the synthesis of norepinephrine through oxidative hydroxylation of dopamine and it is released into the circulation during synaptic transmissions from sympathetic neurons . It is eunctions . DBH is s1611115 , rs11085s1611115  as well s1611115 . Moreoves1611115 . The DBHrisoners . Neverthrisoners .NET1/SLC6A2  encodes for the norepinephrine transporter. It is found in the presynaptic membrane and is highly expressed in the brain cortex, responsible for the reuptake of norepinephrine and epinephrine into presynaptic nerve terminals, and is a regulator of norepinephrine homeostasis. This gene regulated intrinsic brain activity, visual memory, and attention in ADHD children  is encoded by the ADRA2A rs553668 AA and ANKK1 rs1800497 AA genotypes emphasizing the importance of the gene-gene interactions in the occurrence of the ADHD  . ADRA2A the ADHD . Gene-en to ADHD . Notably to ADHD .The neurotransmitter acetylcholine (ACh) plays a critical role in brain circuits mediating motor control, learning, attention, and memory. Cholinergic dysfunction is associated with multiple brain disorders including ADHD . There aCHRNA4 and CHNRA7) that encode α4 and α7 subunits of nicotinic acetylcholine receptors, respectively, have been reported  encodes for the nAChR α4 subunit, which is found in the postsynaptic membrane and highly expressed in the central nervous system. CHRNA4 is a ligand-gated ion channel that mediates fast signal transmission at synapses. Several genetic studies have focused on the CHRNA4 gene as a candidate gene for ADHD due to its involvement in the nicotinic acetylcholine system; however, they have conflicting results. Comings et al. found a dinucleotide repeat in intron 1 as a risk for ADHD, Todd et al. identified polymorphism in the exon 2–intron 2 junction as a risk for severe inattention problems, Brookes et al. found the association between the 5’ flanking regions of CHRNA4 with combined subtype of ADHD; nevertheless, Kent et al. and Bobb et al. found no significant evidence of association in their studies (Two cholinergic genes (reported . CHRNA4  studies .CHRNA7) is a gene coding for the a7 subunit of the neuronal acetylcholine receptor (CHRNA7). CHRNA7 is also ligand-gated ion channels that mediate fast signal transmission at synapses and is found in both presynaptic and glial cell membranes. It is more prominent in the hippocampus and GABAergic interneurons of stratum oriens, stratum radiatum, and on pyramidal neurons  enzyme. Glutamate plays a role in ADHD through bidirectional regulation of dopamine signaling while GABA modulates dopamine metabolism. A study done based on proton magnetic resonance spectroscopy revealed higher levels of glutamate and low levels of GABA in children with ADHD than in controls . GAD1 (Glutamate decarboxylase 1) encodes a key enzyme of GABA biosynthesis (GAD67). GAD67 catalyzes the conversion of glutamic acid to gamma-aminobutyric acid (GABA) and is found in the presynaptic membrane and highly expressed in the frontal cortex. GAD1 gene and ADHD.Glutamate is the major excitatory neurotransmitter in the brain and is involved in multiple functions relevant to ADHD: brain development, modulation of neuronal activity, regulation of dopamine signaling, synaptic plasticity, memory formation, and learning. GABA is an inhibitory neurotransmitter in the brain which is produced from the conversion of glutamate controls . This imGRM1 knockout mice demonstrated the involvement of this receptor in associative learning due to reduced hippocampal long-term potentiation (GRM4 rs1906953 and GRM7 rs9826579 are associated with ADHD in the Chinese population (GRM7 rs37952452 polymorphism was reported to play a role in the treatment response to methylphenidate in children with ADHD (GRID2 (Glutamate Ionotropic Receptor Delta Type Subunit 2) increases the risk of ADHD too  are GPCRs involved in the modulation of excitatory synaptic transmission . There antiation  as well ntiation . Impairentiation . GRM7 anntiation  and is tntiation . GRM4 rspulation . GRM7 rsith ADHD , and it ith ADHD . GRID2  or in genes known to interact with them (Approximately 10% of the children with ADHD carried CNVs in the glutamate metabotropic genes (ith them . A high ith them  and replith them . Most ofith them .Manganese is an important cofactor in the synthesis of anti-oxidative enzymes  and is aTARBP1) DNA methylation is related to ADHD symptoms in adulthood and childhood  and ADGRL3 haplotype CCC (markers rs1565902-rs10001410-rs2122642) are associated with ADHD in the Caribbean community , DRD4, and dopamine D5 receptor (DRD5) . Animal This is a comprehensive review covering genetics, neurotransmitters as well as neuroanatomical structures involved in ADHD. Nevertheless, ADHD being a heterogeneous illness with a varying number of comorbidities, it is still unclear how environmental factors relate to all neurotransmitter pathways. Future studies should explore more the associations between environmental factors and neurotransmitter pathways. Although several genes have been related to ADHD, there are few animal model studies on the majority of the genes. More epigenetic studies are required for other genes on top of those related to the dopaminergic pathway.MK, HD, and JX performed a literature review, drafted the article, and gave final approval of the version to be published. BC and YM assisted in the preparation of the table, revised the manuscript, and gave final approval of the version to be published. LY, FH, OB, and JP revised the manuscript critically for important intellectual content and gave final approval of the version to be published. FY conceptualized, designed, and revised the manuscript critically for important intellectual content and gave final approval of the version to be published. All authors contributed to the article and approved the submitted version.


S > 0 is assigned to individuals who develop disease and a score of 0 is assigned to uninfected individuals. In this article, we derive the vaccine efficacy statistic  based on BOI scores, and we extend the method to adjust for baseline covariates. Also, we illustrate it with data from a clinical trial in which the efficacy of a Herpes Zoster vaccine was evaluated.In recent years, many vaccines have been developed for the prevention of a variety of diseases. Although the primary objective of vaccination is to prevent disease, vaccination can also reduce the severity of disease in those individuals who develop breakthrough disease. Observations of apparent mitigation of breakthrough disease in vaccine recipients have been reported for a number of vaccine‐preventable diseases such as Herpes Zoster, Influenza, Rotavirus, and Pertussis. The burden‐of‐illness (BOI) score was developed to incorporate the incidence of disease as well as the severity and duration of disease. A severity‐of‐illness score  This method, called burden‐of‐illness (BOI) test, is attractive for two main reasons; first, it is simple and second it is consistent with the intent‐to‐treat (ITT) principle in clinical trials.One of the main objectives of vaccine efficacy (VE) trials is to compare the proportion of individuals who are infected between vaccinated and unvaccinated groups. However, a vaccine may affect the incidence, duration and severity of a disease. In order to consider incidence, duration, and severity, Chang et alS by excluding most of the 0's. This approach is more powerful than BOI in case of no VE. Tu et alS's with a mixture of point mass at zero and a log‐normal distribution. LachenbruchOther statistical methods have been proposed in the literature. Follmann et alBOI). VEBOI is defined as the relative reduction in the BOI score in the vaccinated group compared to the unvaccinated or control group and is calculated as 1 minus the relative risk . VEBOI is a simple, useful, and interpretable statistic in vaccine development and was chosen as a primary endpoint in the Shingles Prevention Study, which evaluated the VE and safety of a live zoster vaccine.BOI and the following statistic (which is a function of VEBOI)The VE is the standard statistic in vaccine development studies. For this reason, in this article, we derive a VE measure based on the BOI scores (VEWe illustrate our methods using data from a Herpes Zoster (HZ) clinical trial2NV volunteers are randomized to receive a vaccine, NC are randomized to placebo (or control), and that infections are recorded along with a postinfection outcome, X. Chang et alS as 0 for the uninfected patients and X for the infected patients. Chang et al proposed to test the null hypothesis that the distribution of S is the same in the two groups using the following t test like statistic,S¯j=1/Nj∑i=1NjSi,j, for treatment group j = V, C, and V ar0 is the variance computed under the null hypothesis (score test).Let us suppose that BOI statisticIn this article, we consider instead a VEn events are observed. Results of fixed event designs are provided in Appendix A. In case of fixed time design ES¯j=pjμj, j = V, C, where pj is the probability of infection and μj is the expectation of X of those infected in treatment group j. The variance is VarS¯j=pjσj2+1−pjμj2/Nj, where σj2 is the variance of X of those infected in treatment group j. Using the Delta method, it follows thatTwo different designs are considered: (a) the fixed time design, where the trial is stopped after an fixed duration of follow‐up, and (b) the fixed event design, where the trial is stopped when 2.1prop) the test which compares the infection rates between the two groupsinf) the t test in infected individuals only,nC and nV are the number of infected individuals in each group and XGi is the ith measurement in group G. Note that inf test can be affected by selection bias because it is based on infected individuals only. The two statistics described above can be combined using the Fisher's test (FCM).P‐value1{1}inf and P‐valueprop are the one‐sided P‐values of Zinf and Zprop, respectively, and χ42 represents a random variable with chi‐square distribution and 4° of freedom. Since FCM is a function of inf, it does not assess a causal effect of vaccine. Mehrotra et alFCM for postrandomization selection bias using the potential outcomes framework for causal inference. In a similar spirit, Callegaro et alIn this section, we describe in more detail some published approaches and compare these methods via a simulation excercise. We denote by . We considered the rank version of this test (CH − LW) because it is expected to be more powerful.Finally, we describe in more details the Chop‐Lump test.3Z=logRR/VarlogRR and RR = (pVμV)/(pCμC). If we denote k, the randomization ratio (NV = kNC), thenThe asymptotic power under local alternatives is given byThe sample size is given by3.1BOI is consistent with the ITT principle because all randomized participants are explicitly included in the analysis. A principal stratum estimator could be considered as well.VE4Z represents the matrix of baseline covariates. To adjust BOI scores for covariates, we propose a nonlinear regression model where the expectation (E) of the BOI score for the ith subject isGi is the vaccination group . The VE adjusted for covariates is given byR [glm], or SAS [GLIMMIX]) that does not require integer S values. To adjust for the exposure, we included the log of the follow‐up time as an offset. SAS code to fit this model is provided in Appendix B.In this section, we consider the possibility of adjusting the BOI scores for baseline covariates, such as age, sex. In the following, 5In this section, we illustrate our method using data from the GSK HZ clinical trial (NCT01610414) that evaluated the VE of RZV vaccine in the prevention of HZ in autologous HSCT recipients 18 YOA and older.5.1The study was a phase III, observer‐blind randomized, placebo‐controlled, multicenter, multicountry study with two parallel groups to evaluate the VE of RZV vaccine in the prevention of HZ in autologous HSCT recipients 18 YOA and older. Eligible subjects were randomized to RZV or placebo according to a 1:1 ratio. The primary endpoint of this study was to demonstrate the VE of RZV to reduce the number of HZ episodes. However, a secondary objective was to demonstrate a reduction in the severity of pain  associated with HZ for those subjects experiencing a HZ episode. Pain and other types of discomfort  can have a substantial adverse impact on the functional status and quality of life of affected individuals; therefore, relief of acute and chronic HZ pain and discomfort is an important goal.5.2The zoster brief pain inventory (ZBPI) was used to quantify HZ pain and discomfort, and was adapted from the Brief Pain Inventory to make it a HZ‐specific measure of pain severity that captures pain and discomfort caused by HZ.5.3All subjects with a HZ episode were required to complete the ZBPI questionnaire at the onset of the suspected HZ episode (identified by the presence of rash or pain) on a daily basis from onset of the HZ episode (day HZ‐0) to day HZ‐28, and then weekly onward until a 4‐week pain‐free period was documented. If pain reappeared in the same area after a 4‐week pain‐free period and was not accompanied by a new HZ rash, it was assigned to the previous HZ‐episode.The HZ BOI scores were calculated from the ZBPI worst pain scores and the HZ burden‐of‐interference score were calculated from the ADL score, over the 182 days from the first day of HZ rash (day HZ‐0) using area under the curve (AUC) methods. The scores were defined as 0 for participants who did not develop an evaluable case of HZ during the study.m is the number of ZBPI assessments between days 0 and 182, Yk is the score at timepoint k and tk is the number of days relative to day 0 at timepoint k.The calculation of AUC was based on the trapezoidal rule.5.45.4.1%) in the vaccinated group and 135 subjects (15.9%) in the control group developed a case of Herpes Zoster. Of the 184 subjects who developed HZ, 5 did not have a ZBPI score (three in the vaccinated group and two in the placebo group) and were thus removed from the analysis. The distribution of worst ZBPI pain scores is as follows, using the notation in section V = RZV and C = Placebo.A total of 1721 subjects were included in the analysis cohort. A total of 870 were vaccinated with RZV and 851 subjects were included in the placebo group. About 49 subjects (5.6α¯j is the mean follow up time (years) in vaccination group j = V, CHence, the 95% confidence interval (CI) of the VE based on the normal distribution is . The 95% CI for the Burden‐of‐Interference is calculated similarly. The VE was also calculated separately for the baseline age categories . The results for BOI are presented in Table 5.4.2The VE for both the BOI and Interference were also calculated adjusting for baseline covariates, using a regression model as described in section % CI of .Using the notation from section Table BOI estimate is:The overall VE6NC = NV = 858 and pC = 0.15. Different values of VE were considered , with pV = pC(1 − VE). The log pain score for infected individuals were generated according to a normal distribution with variance σ2 = 1.52 and mean μC =  or μV = (μC − Δ) for patients in the placebo or vaccine group, respectively. For each scenario, 10 000 clinical trials were simulated.In order to compare the performance of the proposed method with existing approaches, we simulated Zoster Vaccine Trials similar to the real data described above. The number of infections in the placebo group was generated according to a binomial distribution with BOI) with the following approaches: the BOI t testprop), the t test comparing postinfection outcomes in infected individuals (inf), the Fisher's test (FCM) combining prop and infCH − LW).We compared the proposed approach . This is not surprising, the advantage of VEBOI over BOI being the interpretability of the estimated effect. When Δ = 0  these three tests have similar power, and no method performs very well. At larger Δ values, VEBOI performs better, but generally has lower power than the Chop‐Lump test, unless the VE is “large” (VE = 30%). This is due to the ratio μC/σ = 3. In fact, asymptotic and simulation results of Follmann et alBOI as well) decreases with larger values of μC/σ. The last row of Table μC is 2.25 instead of 4.5 so the ratio is much smaller (μC/σ = 1.5). As expected,BOI (and BOI) is more powerful, even more powerful than CH − LW.Table BOI with FCM test. With the exception of small Δ or” large” VE (VE = 30%), FCM is more powerful than VEBOI, however this test is not consistent with the ITT principle because Zinf is restricted to subjects who are selected based on a postrandomization event. Note that Zinf (and consequently FCM) can lead to the conclusion that a harmful vaccine generating more infections with low pain is beneficial.Finally, we compare VE7In vaccine clinical trials, the VE estimate is the standard statistic for presenting efficacy outcomes. In this article, we proposed a VE statistic based on the BOI score.As a motivating example, we analysed HZ data from a vaccine clinical trial. The overall HZ VE for HZ incidence was reported as 68.2%.onTOP is conditional on developing disease, that is, it can easily be shown that VEonTOP corresponds to 1 − μV/μC. As such, the statistic is subject to selection bias which could lead to the conclusion that a harmful vaccine generating more infections with low pain is beneficial. Consequently the VEonTOP statistic should (a) be adjusted for selection bias, or (b) always be reported in conjunction with the VEHZ and VEBOI statistics to provide a complete overview of results. Further research needs to explore how this statistic can be interpreted and used in future research.However, it should be noted that VESimilarly, the overall VE for reducing the Burden‐of‐Interference of HZ on activities of daily living was estimated to be 82.8%. The results from this analysis would suggest that in addition to preventing HZ, vaccination with RZV also reduces the BOI and Burden‐of‐Interference in subjects who develop HZ. Plausible hypotheses are that memory CD4+ T cells would be capable of mounting a rapid antiviral response upon reactivation of the varicella zoster virus. In some cases, this anamnestic immune response may not be able to prevent a HZ episode but in vaccine recipients with breakthrough disease, the response may be sufficient to more rapidly control the reactivated virus, leading to reduced severity of disease.R [glm], or SAS [GLIMMIX]) that does not require integer S values. It is straightforward to extend the model, for example by including the interaction between treatment and covariates. In this way, it is possible to test if the VEBOI depends on baseline covariates (testing if the interaction is significant).We proposed a quasi‐Poisson regression model on the scores to adjust the BOI VE for baseline covariates. The overdispersion parameter allows proper estimation of the variance even in presence of many zeros and the model can be implemented in standard software  to reduce the ratio E(fSC/Var(fSC).Simulation results based on our real data showed that the power of VEIn summary, the proposed BOI VE is a useful and interpretable measure of efficacy for clinical trials where the vaccine may affect both the incidence and the severity of disease. The adjustment for baseline covariates can further improve the efficiency of the analysis and avoid conditional bias from chance covariate imbalance.AC and DC are employees of and holds shares in the GSK group of companies. SM is a freelance consultant on behalf of GSK.All authors contributed equally to this work.


The diagnostic process of attention deficit hyperactivity disorder (ADHD) is complex and relies on criteria sensitive to subjective biases. This may cause significant delays in appropriate treatment initiation. An automated analysis relying on subjective and objective measures might not only simplify the diagnostic process and reduce the time to diagnosis, but also improve reproducibility. While recent machine learning studies have succeeded at distinguishing ADHD from healthy controls, the clinical process requires differentiating among other or multiple psychiatric conditions. We trained a linear support vector machine (SVM) classifier to detect participants with ADHD in a population showing a broad spectrum of psychiatric conditions using anonymized data from clinical records (N = 299 participants). We differentiated children and adolescents with ADHD from those not having the condition with an accuracy of 66.1%. SVM using single features showed slight differences between features and overlapping standard deviations of the achieved accuracies. An automated feature selection achieved the best performance using a combination 19 features. Real-world clinical data from medical records can be used to automatically identify individuals with ADHD among help-seeking individuals using machine learning. The relevant diagnostic information can be reduced using an automated feature selection without loss of performance. A broad combination of symptoms across different domains, rather than specific domains, seems to indicate an ADHD diagnosis. Overall, this extensive diagnostic process relies on criteria highly sensitive to subjective biases  and may result in delays in treatment initiation. This is particularly unfortunate given that effective treatments for ADHD are readily available. Thus, it should be paramount to streamline, shorten and specify the diagnostic process. To achieve this, it is necessary to identify the most relevant aspects of data that predict the diagnostic outcome. This may be possible via the application of machine learning techniques.The diagnostic process in the case of suspected attention deficit hyperactivity disorder (ADHD) commonly entails collecting a substantial amount of data and is thus complex, time-consuming and costly. A substantial amount of data, however, is necessary to distinguish whether ADHD underlies the particular pattern of observed symptoms as opposed to norm variants of behavior, possible differential diagnoses, or comorbidities occurring in addition to ADHD4. Concerning ADHD, previous studies based on clinical and/or neuroimaging data have performed automated classifications to distinguish between ADHD and typically developing individuals with classification accuracies ranging from 62 to 89.5%9. Unfortunately, this dichotomous distinction between the labels of "typically developing" and "ADHD" does not reflect the question typically asked in the clinical setting. Even though this question is clinically much more relevant, to our knowledge no study so far has attempted to apply machine learning in order to predict whether the diagnostic outcome will be "ADHD" or "something else"  in a broad spectrum of clinical conditions within a help-seeking population.In recent years, machine learning has made remarkable progress, from its application in detecting between-group differences to making predictions on the individual level10 and may result in lower classification accuracy than clinical measures6, it is currently still necessary to focus on readily available behavioural/clinical data including demographic information, subjective symptom ratings, and objective neuropsychological data.Since neuroimaging or genetic data are not (yet) part of the routine diagnostic process for ADHD due to limitations in cross-sample reliability/validity as well as in sensitivity and specificity11 are associated with a higher risk for ADHD. Self-report symptom rating scales, are less reliable than informant ratings12 with studies further reporting low to medium correlations between parent and teacher ratings13. To account for these differences, it has been suggested to use the degree of consistency between them as an indicator of ADHD symptom severity14.Demographic data like male gender, severe early onset and familial predispositions15, as well as difficulties in working memory1 and processing speed17, have been proposed to distinguish between individuals with ADHD and typically developing controls. Verbal comprehension and logical reasoning, in turn, are not systematically reduced in children with ADHD18. Overall, reductions in general or subscale-specific IQ are not specific to ADHD. Instead, the label "ADHD" explicitly points to difficulties in attentional processes . Evidence for impairments in terms of accuracy and reaction time variability20 in tests pertaining to inhibition21 seems to be particularly strong. Similarly, this is the case regarding the intensity domain of attention22. Specifically, this concerns omission errors occurring in tests of sustained attention/tonic alertness23. Evidence is rather mixed concerning the selectivity domain of attention22.Neuropsychological tests are a further important component of the data collected during the diagnostic process. Lower overall IQIn this proof-of-concept study, we attempted to train a machine learning model to predict the diagnostic outcome of "ADHD" in a help-seeking clinical sample. To our knowledge, this is the first study that attempted to train a machine learning classifier on anonymized real-world clinical data and to distinguish children/adolescents with an ADHD diagnosis from those with none or other diagnoses. In addition to well-established neuropsychological measures and individual symptom ratings, we included features capturing the degree of consistency between parents' and teachers' ratings. In order to test possible implications for shortening the diagnostic process, we assessed the predictive information of every single feature. Moreover, we attempted to reduce the necessary diagnostic information using a data driven, automated feature selection.The standardized diagnostic process included several consultations with the child and caregivers together and individually. Parents and (nursery) school teachers completed general and ADHD-specific rating scales. Further, general intelligence and attention were assessed via standardized testing batteries. In addition, somatic conditions which may contribute to any existing attention problems were excluded . The final diagnostic decision was given strictly based on ICD-10 clinical criteria assessed by a senior specialist in child and adolescent psychiatry or psychology.2 = 6.871, p = .009) and younger .This was a study based exclusively on data from a clinical records. We extracted the data of help-seeking individuals who were referred to our secondary care outpatients unit with a suspected ADHD diagnosis, or in whom an ADHD diagnosis was the suspected diagnosis after the initial consultation. The group labeled "ADHD" included patients who had received one of the following diagnoses: attention deficit hyperactivity disorder (F90.0), hyperkinetic conduct disorder F90.1), or attention deficit disorder without hyperactivity (F98.80). Importantly, not all psychiatric comorbidities constituted an exclusion criterion for the ADHD group (see below). The "non-ADHD" group contained patients who did not fulfill diagnostic criteria for ADHD. Socio-demographic and clinical characteristics of the sample (N = 299) are presented in Table , or atte24. Data sets were excluded if neurological or genetic disorders, endocrine disorders (incl. not corrected hypo- or hyperthyroidism), or other severe documented medical comorbidities on Axis IV had been identified.Data sets were included in the study if ADHD had been suspected at the beginning of the diagnostic process, patients were younger than 18 years at the beginning of the diagnostic procedure, and if at least 2 out of 3 attention tests scores from the TAP diagnostic battery  were availableI.Demographic variables (age and gender);II.25; parent version of the Child Behavior Checklist (CBCL) and its school equivalent, the Teacher's report form (TRF)26; Strengths and Difficulties Questionnaire parents (SDQ-P) and teacher (SDQ-T) versions27). To account for age and gender differences amongst patients, we used normed T-values as features in all cases. Additionally, we computed a set of 'consistency indices' describing the consistency between parent and teacher ADHD specific Conners-3 ratings (for details see Supplementary note Symptom ratings (Conners-3 parent/teacher ratingsIII.24 was used to assess inhibition (GoNogo subtest), divided attention (Divided Attention subtest) and Alertness (Alertness subtest). The Wechsler Intelligence Scale for Children IV or V29 was used to measure general intelligence. To generate compatibility between versions IV and V, we used the average of the visual-spatial index and the fluid reasoning index as 'perceptual reasoning' in participants who completed the WISC V. For the attention measures, we used the T-values as features. For the intelligence measures, we used the standardized IQ scores as features.Neuropsychological measures , who also approved the study. The study was performed in accordance with the Declaration of Helsinki. Briefly, we extracted the following 92 features from the clinical records , as well as all participants with > 20% of missing features  from the ADHD group). We determined the 20% cutoff as a compromise solution to preserve a diverse set of features without too strongly negatively impacting the accuracy due to too many missing valuesnon-ADHD groups in three ways. First, to assess the SVM classifier's performance on the whole dataset, we used the complete set of 30 features for training and testing. Second, we assessed the importance of single features for the classification by performing the classification using each one single feature at a time . We chose this procedure rather than reporting the SVM weights, as those cannot be interpreted regarding the importance of single features31. Finally, to try and optimize the algorithm's performance, we aimed to eliminate irrelevant features in a data-driven way. Similar to our previous work32, we used the sequential floating forward selection (SFFS)34 implemented in MATLAB 2017a (Mathworks Inc.) for this purpose. In an SFFS feature selection, two separate algorithms are combined. The sequential forward selection (SFS) starts from an empty set of features and sequentially adds features that result in the highest classifier accuracy when combined with the already selected features. Sequential backward selection (SBS) works in the opposite direction by removing the feature, leading to higher accuracy. In SFFS, each feature selection step comprises SFS and SBS32. After adding each feature, we performed an SVM classification using the selected set of features. We performed the train and test procedures using a standard k-fold crossvalidation method (k = 10)36 , we compared correctly vs. incorrectly predicted participants using a t test and a chi-square test, where applicable. A significant difference in some demographic variables  would indicate that the classifier would have a limited validity/range of applicability. To further assess the contribution of demographic variables to the classification, we also performed a secondary analysis repeating the primary SVM classification using all the features listed in Table Since this was a population-based study, the ADHD and p value = .001). The correctly and incorrectly classified participants did not significantly differ in age , gender (χ2(1) = .171, p = .679) and total IQ .The classification using the complete set of 30 features yielded an average accuracy of 66.1% (obtained from the true label) . The classifier falsely identified 18.2% of ADHD patients as not having ADHD (type 2 error). Conversely, 15.8% of patients without ADHD were falsely identified as having the condition (type 1 error). The permutation test showed that the accuracy is higher than randomly assigned labels (We ranked the features according to the achieved classification accuracy when exclusively one feature was used for testing and training Table . For a gThe automated feature-selection procedure achieved a maximum classification accuracy of 68.1% using a set of 19 features (Supplementary table p = .001).In order to determine the predictive value of non-demographic features, we excluded the demographic features (age and sex) from training and classification in a secondary analysis. The model achieved an accuracy of 65.1% . A permutation test revealed this performance was significantly above chance  resulted in a comparable accuracy. An automated feature selection achieved the best performance using a combination of 19 most predictive features across attention and intelligence domains and symptom ratings. The accuracy might be further increased using datasets without missing data. The consistency index of parent and teacher ratings did not outperform conventional features. Our study suggests that ADHD can be identified using data from clinical records even in a mixed, help-seeking population of children and adolescents.31 which may be challenging to collect by recruiting participants for a specific study but are readily available in clinical databases. Moreover, the results from experimental studies might not generalize to a clinical setting, where clinicians are commonly confronted with multiple/concurrent disorders and/or various potential differential diagnoses. Thus, we showed that SVM in combination with real-world, comprehensive clinical data could yield an above-chance classification accuracy and detect individuals with ADHD among those having none or different condition(s).Machine learning studies require large amounts of data8. Although we used more features than this study, the resulting accuracy was lower. This might be because many help-seeking individuals in our sample received other diagnoses associated with symptoms that may mimic ADHD . Thus, the two groups (ADHD vs. "something else") are not as clearly differentiated from each other as it would be the case when distinguishing between individuals with confirmed ADHD diagnoses and those not showing any symptoms at all. Previous studies aiming at distinguishing more than one disorder from typically developing controls reported lower classification accuracies than studies aiming at classifying typically developing individuals and patients with one condition37.To our knowledge, the highest achieved accuracy in studies of ADHD patients and healthy individuals were 89.5%39. This was also the case in the current study. In this study, instead of identifying physiological patterns typical of ADHD, we aimed to train a classifier to identify ADHD based on data available from medical records. As typical age and gender distributions of ADHD may naturally be reflected in this data structure, which may constitute a sampling bias, conducting a second analysis without these features was essential. This analysis without age and gender still revealed a significant classification accuracy, demonstrating that the neuropsychological features and ADHD-specific ratings on their own are sufficient to identify ADHD in a mixed patient sample.Age and gender were shown to be useful for diagnostic and prognostic tools based on machine learning in previous studies8. We addressed this issue by using the consistency index above, which did not outperform conventional ADHD-specific features like parent/teacher-rated symptoms. The automatic feature selection also only emphasized a rather unspecific symptoms like peer relations, aggression, and teacher negative impression bias. These results suggest that clinical ratings capturing broader ADHD-related behavioral irregularities  as reported by different sources using the Conners-3 questionnaire are informative when aiming to identify ADHD amongst a help-seeking clinical population. This may reflect the notion that the rather qualitative "clinical impression" of ADHD plays a significant role in the diagnostic process40. Similarly, this may also be interpreted as showing that a rather broad functional impairment associated with ADHD symptoms  is indicative of diagnostic classification in the clinical setting. This issue could be examined further by including clinician rating scales or those capturing the degree of functional impairment41.Previous studies have opted not to include clinical ratings in the analysis to avoid possible subjective biases6 included IQ scores as part of an overall "phenotypic" feature that also contained aspects like age and gender, making a specific interpretation impossible. In addition, these studies only focused on the distinction between individuals with ADHD and typically developing controls, thus reducing the validity of the results for clinical practice. It is the goal to distinguish ADHD from disorders or norm variants of behavior mimicking ADHD symptoms. Interestingly, the processing speed subscale ranked highest of all IQ-related features in the single feature classification. This may reflect the previously reported relevance of this aspect of neuropsychological processing16 when comparing individuals with ADHD and healthy controls. Within the automatic feature selection, reaction time variability and accuracy in tests capturing tonic/phasic alertness and inhibition ranked numerically higher than mean reaction times. While a previous study suggested that objective neuropsychological measures considerably underscored rating scales in distinguishing ADHD from healthy participants8, our results show that these scores in general indeed contribute to classification when identifying individuals with ADHD in a mixed help-seeking population. This supports the notion that objective measures like those employed in the current context are indeed important elements of the diagnostic process of ADHD as has been suggested previously42.Among the neuropsychological measures, the total IQ score did not rank among the most predictive features. Previous machine learning studies suggesting IQ to be a predictive featureThis study has the following limitations. First, we could not include broader clinical measures such as the CBCL as possible features due to too many missing values. These measures might have provided more specific information on differences between diagnostic entities. Similarly, father ratings also needed to be excluded due to missing data . Retraining the classifier without missing data achieved a further increase in the classification accuracy (imputation 66.1% vs no missing data 68.8%). This suggests, that an effort to simplify the diagnostic process in order to reduce the probability of missing data might increase the performance of automated classifiers. Second, although we tested generalizability indirectly using the permutation test, an independent validation sample would provide more precise information on the generalizability of our classifier. Third, the relative importance of single features needs to be interpreted carefully while considering the low classification accuracy differences between the features and the relatively high standard deviations of the achieved accuracies. As the Fig. 3). Although the classification performed above chance (i.e. accuracy of 66.1%), the performance did not reach a level suggestive of possible clinical utility (i.e. 80% accuracy43). Multimodal data (particularly neuroimaging and genetic data) might improve the recognition of psychiatric disorders using machine learning44. In order to validate such recognition tools, multicentric data are necessary. In order to arrive at firm conclusions in this matter, there is a need for standardized recommendations for ADHD diagnostic procedures, such as specification of the obligatory attention domains, cognitive assessments and assessment scales used. These recommendations should also take into account the risk of missing data finding compromises between a broad assessment and feasibility.In conclusion, we provide a proof-of-concept that real-world clinical data from medical records might contribute to identification of ADHD among help-seeking individuals. In this context, age, gender, and accuracy/reaction time variability seem to play a marginally more critical role than other features. Further, ADHD core symptoms reported by parents and/or teachers do not seem to carry the degree of importance as it may be assumed. Instead, results suggest a relatively broad combination of symptoms across different domains to indicate an eventual ADHD diagnosis. Overall, this implies that research endeavors aiming to identify biological and less subjective markers of ADHD need to be continued (see Faraone et al.Supplementary Information.


Epidemiological studies suggest a link between eczema and attention deficit hyperactivity disorder (ADHD), but underlying mechanisms have not been examined.We aim to investigate the association between eczema and subsequent ADHD symptoms in the Growing Up in Singapore Towards healthy Outcomes cohort and explore the role of pro-inflammatory cytokines and gut microbiome.The modified International Study of Asthma and Allergies in Childhood questionnaire and Computerized Diagnostic Interview Schedule for Children Version IV were administered to assess reported eczema within the first 18 months and presence of ADHD symptoms at 54 months, respectively. Skin prick testing at 18 months, cytokines in maternal blood during pregnancy and cord blood and the mediating role of the gut microbiome at 24 months were assessed.After adjusting for confounders, eczema with or without a positive skin prick test was associated with doubling the risk of ADHD symptoms. No differences in maternal and cord blood cytokines were observed in children with and without eczema, or children with and without ADHD. Gut microbiome dysbiosis was observed in children with eczema and children with ADHD. Children with eczema also had lower gut bacterial Shannon diversity. However, the relationship between eczema and ADHD was not mediated by gut microbiome.Early life eczema diagnosis is associated with a higher risk of subsequent ADHD symptoms in children. We found no evidence for underlying inflammatory mechanism or mediation by gut microbiome dysbiosis. Further research should evaluate other mechanisms underlying the link between eczema and ADHD.https://clinicaltrials.gov/ct2/show/NCT01174875], identifier [NCT01174875].[ The increasing incidence of eczema is paralleled by an increasing incidence of attention deficit hyperactivity disorder (ADHD) . Both ecEvidence from a number of studies suggests a link between eczema and ADHD. In a United States study including 354,416 children from 19 population-based surveys, eczema was associated with a 50% increased risk of developing ADHD compared to children without eczema . SimilarDespite the epidemiological associations between eczema and ADHD, studies have not evaluated the underlying mechanisms and common early-life factors that may mediate the association. Pro-inflammatory cytokines and the gut microbiome have been proposed to be involved in the development of both allergic and neurodevelopmental disorders . Eczema Hence, the aim of our study is to investigate the association between eczema and subsequent ADHD symptoms in the Growing Up in Singapore Towards healthy Outcomes (GUSTO) cohort in a confirmatory analysis. We also explore associations of pro-inflammatory cytokines in maternal blood during pregnancy and cord blood with eczema and ADHD and the possible role of the gut microbiome in linking eczema and ADHD.The GUSTO study is a prospective cohort study which recruited pregnant women attending their first-trimester antenatal dating ultrasound scan clinics at two major public maternity units in Singapore, KK Women’s and Children’s Hospital and National University Hospital from June 2009 to September 2010 . PregnanTrained interviewers gathered information on demographic characteristics, family history of allergy, socioeconomic data and lifestyle factors. Birth outcomes were measured and recorded after delivery. Ethical approval was obtained from the Domain Specific Review Board of Singapore National Healthcare Group (D/2009/021 approved on 26 February 2009) and the Centralised Institutional Review Board of SingHealth (2018/2767 approved on 2 March 2019). The conduct of this study was based on the guidelines in the Declaration of Helsinki. Written informed consent was obtained from the mothers after a detailed explanation of the study.Dermatophagoides pteronyssinus, Dermatophagoides farina , and Blomia tropicalis (developed in-house)  questionnaire at ages 3, 6, 9, 12, 15, and 18 months for evaluation of eczema, which was defined as maternally reported physician-diagnosed eczema based on the question: “Has your child ever been diagnosed with eczema?” at any timepoint, which included children with eczema ranging from mild to severe. The ISAAC questionnaire has been featured in several birth cohorts studying allergic outcomes , 15. We n-house) . A positDiagnostic and Statistical Manual of Mental Disorders-Fourth Edition Text Revised (DSM-IV) , a highly structured diagnostic interview designed to be administered by lay interviewers to assess most of the commonly occurring mental health disorders in children . The men(DSM-IV) . CDISC i(DSM-IV) , 19–23. (DSM-IV) . If sympMaternal blood samples were obtained between 26 and 28 weeks of pregnancy. Maternal and cord blood cytokines were assayed in plasma using customized Human ProcartaPlex Panels , which uses Luminex xMAP technology in combination with DropArray bead plates . Interferon gamma (IFNγ) and tumor necrosis factor-α (TNFα) were measured using single-molecule array assays on the SP-X platform . All values were corrected for plate-specific effects by centering at the median.1 (Accession Number PRJNA668050).Stool samples were collected at 24 months. DNA extraction, 16S rRNA gene V4 region sequencing and data processing were performed. We followed the methods for gut microbiome analysis in our previous publications, with minor modification . Brieflyn (%) for categorical variables. Associations between maternally-reported diagnosis of eczema alone and in combination with a positive SPT, as well as presence of ADHD at 54 months as defined according to the CDISC structured interview, were assessed using Poisson regression, adjusting for demographic and other relevant covariates including mother history of allergy, maternal age, parity, child gender, ethnicity, childcare attendance in first year, exposure to tobacco smoke during pregnancy, gestational age, pre-pregnancy body mass index, child weight at birth, maternal education which have been shown to influence eczema and ADHD outcomes  and Stata version 16. Descriptive statistics for continuous variables are presented as mean (SD) when normality and homogeneity assumptions were satisfied, otherwise as median (IQR), and outcomes , 29. DifA total of 288 mother–child pairs were included in the study after limiting the sample to children whose mothers completed the CDISC-YC at 54 months and the modified ISAAC questionnaire within the first 18 months. Of the 288 children, 158 (54.9%) were boys. Seventy-four (25.7%) children had at least once a reported diagnosis of eczema in the first 18 months. Seventy-two (25.0%) of the children had ADHD symptoms according to the CDISC at 54 months. The mothers’ median age at recruitment was 30.8 years (IQR 27.0–34.8). The majority of the mothers had 12 or fewer years of education [187 (65.6%)], were of Chinese ethnicity [142 (49.3%)], had no history of allergy [159 (56.8%)], and were not exposed to tobacco smoke during pregnancy [163 (60.4%)] .No significant differences were observed in the maternal blood cytokines C-reactive protein (CRP), IFNγ and TNFα between children with and without eczema or between children with and without ADHD . Nor didA reported diagnosis of eczema within the first 18 months with and without a positive SPT was significantly associated with an increased risk of ADHD symptoms at 54 months  after adjusting for demographic and other covariates .p < 0.05). No differences were observed between the 65 children with ADHD symptoms at 54 months and the 196 without such symptoms (adj p < 0.05)  .Lachnoclostridium spp. and Tyzzerella spp. (Lachnospiraceae family); and Cronobacter spp. (Enterobacteriaceae family) and decreases in Faecalibacterium spp. (Ruminococcaceae family) and Fusicatenibacter spp. (Lachnospiraceae family) were observed in children with reported eczema compared to those without (adj p < 0.05) (Escherichia or Shigella spp. (Enterobacteriaceae family) and Haemophilus spp. (Pasteurellaceae family) was decreased, while that of Enterococcus devriesei (Enterococcaceae family), Streptococcus spp. (Streptococcaceae family), Eggerthella spp. (Coriobacteriaceae family), and Actinomyces spp. (Actinomycetaceae family) was increased in children with ADHD symptoms at 54 months compared to those without (adj p < 0.05)  were further analyzed and those which showed significant differences between groups were plotted in term of relative abundance, as shown in  < 0.05) . The rel < 0.05) .No mediation effect  was observed for any gut microbiota in the association between eczema reported within the first 18 months and ADHD symptoms at 54 months .In the prospective GUSTO cohort, we observed that maternally reported diagnosis of eczema within the first 18 months of life with or without a positive SPT was associated with a doubling of the risk of ADHD symptoms. These findings are consistent with previous reports of this association , 31. WhiA cross-sectional study by Schmitt et al. found that children with eczema were approximately 1.5 times more likely to have ADHD . AnotherEczema and ADHD are linked to the release of pro-inflammatory cytokines . In our Lachnoclostridium spp. was substantially higher in the gut microbiome of subjects with eczema in comparison to subjects without eczema. The pro-inflammatory role of Lachnoclostridium had been shown in murine models in which gut Lachnoclostridium abundance was higher in wild-type eczema mouse models as compared to anti-inflammatory interleukin-37b knock-in mice  producers which serve as an important energy source and exhibit anti-inflammatory properties in the host  (accession number PRJNA668050).The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions. All raw microbiome data are available at NCBI  and the Centralised Institutional Review Board of SingHealth (2018/2767 approved on 2 March 2019). The conduct of this study was based on the guidelines in the Declaration of Helsinki. Written informed consent was obtained from the mothers after a detailed explanation of the study. Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.EL conceptualized and designed the study, designed the methodology, performed data analysis, drafted the initial manuscript as well as reviewed and revised the manuscript. DO, MO, HL, and MT drafted the initial manuscript as well as reviewed and revised the manuscript. LT performed the data analysis, drafted the initial manuscript as well as reviewed and revised the manuscript. QY designed the methodology, performed data analysis, drafted the initial manuscript as well as reviewed and revised the manuscript. YHC designed the methodology, performed data analysis as well as reviewed and revised the manuscript. ET, AG, HV, OT, JE, YSC, PG, FY, KMT, KHT, NK, BL, LS, and MM conceptualized and designed the study as well as reviewed and revised the manuscript. JX performed the data analysis as well as reviewed and revised the manuscript. MK designed the methodology, drafted the initial manuscript as well as reviewed and revised the manuscript. BB conceptualized and designed the study, supervised the project as well as reviewed and revised the manuscript. All authors agree to be accountable for the work.YSC and NK are part of an academic consortium that has received research funding from Abbot Nutrition, Nestle and Danone. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.