Attention-Deficit/Hyperactivity Disorder (ADHD) is a prevalent psychiatric condition characterized by developmentally inappropriate symptoms of inattention and/or hyperactivity/impulsivity, which leads to impairments in the social, academic, and professional contexts. ADHD diagnosis relies solely on clinical assessment based on symptom evaluation and is sometimes challenging due to the substantial heterogeneity of the disorder in terms of clinical and pathophysiological aspects. Despite the difficulties imposed by the high complexity of ADHD etiology, the growing body of research and technological advances provide good perspectives for understanding the neurobiology of the disorder. Such knowledge is essential to refining diagnosis and identifying new therapeutic options to optimize treatment outcomes and associated impairments, leading to improvements in all domains of patient care. This review is intended to be an updated outline that addresses the etiological and neurobiological aspects of ADHD and its treatment, considering the impact of the “omics” era on disentangling the multifactorial architecture of ADHD.The online version contains supplementary material available at 10.1007/s44192-022-00030-1. Attention-Deficit/Hyperactivity Disorder (ADHD) is characterized by developmentally inappropriate symptoms of inattention and/or hyperactivity/impulsivity, which leads to impairments in the social, school/academic, and professional contexts. The worldwide prevalence of ADHD is among the highest for neurodevelopmental disorders, estimated at ≈5–7% for children/adolescents , 2 and ≈ADHD diagnosis requires a careful clinical assessment based on symptom evaluation and functional impairments. Diagnostic classification practices such as the Diagnostic and Statistical Manual of Mental Disorders—5th edition  offer critical standard criteria to guide clinicians for an accurate diagnosis. According to DSM-5, to fulfill the diagnosis criteria, patients should present: (a) at least six (for children and adolescents) or five (for adults) out of the nine symptoms of inattention and/or hyperactivity/impulsivity for a minimum of 6 months; (b) symptoms onset before 12 years of age; (c) symptoms that cause impairments in living; (d) symptoms that affect different life contexts . Notably, the manifestation of symptoms should not be better explained by other conditions . DSM-5 describes three clinical presentations of ADHD: predominantly inattentive, predominantly hyperactive, or combined. In general, the inattentive and combined presentations are the most frequently observed among patients, except for young children (3–5 years old), for which hyperactivity is more pronounced [ADHD diagnosis is sometimes challenging due to the substantial heterogeneity of the disorder in terms of clinical and pathophysiological aspects, a fact that impacts research on the etiological and neurobiological specificities of ADHD . The higAnother source of heterogeneity is the diverse clinical profile of children and adults with ADHD. For example, the comorbidity profile mentioned above also varies across development. In children, the most common comorbidities include externalizing  and learning disorders. In contrast, mood, anxiety, and especially personality and substance use disorders become more prevalent in adult life , 14. In Considering the consequences of ADHD symptomatology in different life contexts and its high social and economic impact, the clinical, epidemiological, and neurobiological aspects of this disorder have been widely studied. Despite the challenges related to clinical heterogeneity, as mentioned above, promising perspectives to understanding the neurobiological basis of ADHD have emerged in the last few years. In this literature review, we provide an updated appraisal of the etiological and neurobiological aspects of ADHD and its treatment in light of the “omics” era for disentangling the multifactorial architecture of ADHD. The search was based on published research collected from the PubMed database using general search keywords for each topic followed by a critical evaluation of existing research to include the most relevant evidence .Among the environmental variables proposed as risk factors predisposing to ADHD, pre-, peri- and postnatal factors are the most frequently associated, including low birth weight and prematurity , maternaAlthough investigations on environmental risk factors for ADHD have yielded some reproducible findings, causality inferences remain elusive since unmeasured factors may confound the results . The potADHD has a multifactorial etiology, in which genetic and environmental factors are involved in its development. The genetic contribution to ADHD is among the highest for psychiatric disorders. A high family aggregation is reported for ADHD and data from a large population sample demonstrated that the hazard ratio for ADHD relatives, compared to the frequency of ADHD in relatives of unaffected people, is 70 for monozygotic twins, around 8 for dizygotic twins and full siblings, between 2 and 3 for half-siblings, 2.2 for full cousins, and 1.5 for half cousins . The tot2SNP) has been estimated for ADHD. The ADHD GWAS that reported the first genome-wide significant findings (2019-ADHD) estimated the ADHD h2SNP at ≈22% [2SNP was lower than in the previous GWAS, valued at 14% [2SNP, confirming the extensive polygenic architecture of ADHD and that many hits, in addition to those already discovered, will become significant as the sample size increases. Although the h2SNP estimated for ADHD is far from reaching the heritability estimated in twin studies, it should be noticed that the h2SNP of psychiatric phenotypes is usually much lower than the total heritability because of unmeasured factors in GWAS, such as rare and structural variants, gene–gene and gene-environmental interactions, and epigenomics.The understanding of the genetic architecture of ADHD has significantly evolved with the technological advances in the molecular genetics field, especially with the availability of large-scale genome-wide association studies (GWAS). Based on these studies, the proportion of the phenotypic variation attributed to common genetic variants .The 2019-ADHD GWAS comprised a sample of 20,183 cases and 35,191 controls (the first wave of data from the Danish iPSYCH15 cohort (iPSYCH1) plus 11 ADHD cohorts collected by the Psychiatric Genomics Consortium (PGC)) and revealed 12 significant hits. The 2022-ADHD GWAS, including the extended Danish iPSYCH cohort (iPSYCH1 plus new iPSYCH2 data), the Icelandic deCODE cohort, and the PGC, totalizing a sample of 38,691 individuals with ADHD and 186,843 controls, showed 27 genome-wide loci associated with ADHD, of which 21 were new . The assDRD2, DRD4), the dopamine transporter (DAT1) and the enzymes monoamine oxidase (MAOA), catechol-O-methyltransferase (COMT) and dopamine beta-hydroxylase (DBH) [2SNP is explained by a much higher number of SNPs than those significantly associated. Therefore, genetic variants pointed out by candidate genes can still become significant as the sample size increases, as observed for other disorders, such as schizophrenia and depression, for which the significance in variants of candidate genes only emerged when GWAS reached huge sample sizes  [Initially, it was expected the first significant hits to be revealed in these GWAS would be dopaminergic and noradrenergic genes, which were previously implicated in ADHD by meta-analysis of candidate gene studies, such as those encoding the dopamine receptors (e.g. se (DBH) , 55. Intviduals) –58.Genomic studies revealed an extensive genetic overlap of ADHD with a wide range of phenotypes, including psychiatric, cognitive, behavioral, and metabolic domains, suggesting that the clinically observed association of ADHD with these phenotypes can be explained, at least in part, by shared genetic factors , 53, 59.The genomic studies of ADHD also reinforce the high polygenic nature of ADHD and the dose-dependent effect of the PGS for ADHD, where the increase in PGS deciles was associated with a corresponding increase in the odds ratio for ADHD in the target samples . BesidesRelevant findings from genome-wide studies have also helped to disentangle the clinical heterogeneity of ADHD observed in different stages of life and provided insights into the debate about the neurodevelopmental conceptualization of the disorder. A GWAS comparing the genetic background between children and adults showed a shared genetic contribution to ADHD in these groups, supporting the neurodevelopmental nature of persistent ADHD in adults . A subseDespite the great advances in understanding the biological aspects related to ADHD, as summarized above, the causal effects underlying the reported associations are still largely unknown. In this regard, Mendelian Randomization (MR) has been used as a promising approach to investigate potential causal relationships between an exposure  and an outcome  by using genetic variants as instrumental variables. Since genetic variants are randomly determined at conception, using them as instruments has the potential to eliminate confounding effects behind associations while evaluating causal effects between a risk factor and a clinical phenotype , 71.Several associations with ADHD have been explored through MR. Concerning the links with other psychiatric phenotypes, it has been suggested a causal effect of ADHD genetic liability with an increased risk for posttraumatic stress disorder , autism Still, the limitations of this approach should be considered while interpreting the results. MR relies on major assumptions that when violated can introduce bias to the analyses, especially regarding the absence of horizontal pleiotropy and potential confounders affecting the exposure-outcome relationship . This isDespite evidence demonstrating high polygenicity in ADHD, it is possible that many variants at different loci may converge on common mechanisms. A putative pathophysiological mechanism that has been long implicated in ADHD involves the dysregulation of monoaminergic neurotransmission systems, mainly dopaminergic and noradrenergic. These neurotransmitters exert their functions by a mechanism with a curve pattern in an ‘inverted U’ shape, that is, both very intense activity  and very low activity  impair the functioning of these systems .The physiological changes induced by the binding of dopamine and norepinephrine to their respective receptors involve the modulation of several cognitive and executive processes usually impaired in ADHD . Additional evidence for the involvement of these neurotransmission systems in ADHD is summarized in Table Pathophysiological insights can also be inferred from neuroimaging studies, which has reported differences in structural and functional brain architecture between patients with ADHD and neurotypical individuals, especially in children. Large meta-analyses from the ADHD working group of the ENIGMA  consortium reported that children with ADHD present smaller volumes in different subcortical brain regions  and total intracranial volume , as wellStructural connectivity differences in ADHD have been investigated using Diffusion Tensor Imaging (DTI), a technique based on the observation of the directionality and coherence of water diffusion that allows inferences on the microstructure of brain white matter (WM). Meta-analyses based on fractional anisotropy (FA) measures, a common DTI parameter that is considered a marker of white matter microstructural integrity, support that the most robust findings related to ADHD involve the frontostriatal circuitry and the interhemispheric connection through the corpus callosum , 123. MiFunctional Magnetic Resonance Imaging (fMRI) studies associate ADHD with alterations in connectivity in multiple brain networks involved mainly in executive functions, attention, emotions, and sensory and motor activities. A broadly studied model for ADHD suggests an impaired synchronization between the DMN (Default Mode Network), which is normally more active when tasks are not performed, and the TPN (Task-Positive Network), which is activated while performing tasks. In individuals with ADHD, inadequate DMN hyperactivity and TPN hypoactivity while performing tasks have been suggested as an explanation for ADHD symptoms . In restAlthough methodological issues challenge the identification of brain aspects related to ADHD, such associations are supported by genomic studies showing a high genetic correlation between ADHD and several neuroimaging measures, including intracranial and total brain volumes and cortical surface area –136. AddVIPR2  as a differentially methylated gene in ADHD [ST3GAL3 gene  in the epigenome-wide significance level [Some studies evaluating differential methylation in candidate genes were conducted in ADHD , 138. Ho in ADHD , 147. Ince level , which wce level , 53.ERC2—ELKS/RAB6-interacting/CAST family member; CREB5—cAMP responsive element binding protein 5; ZBTB38—zinc finger and BTB domain containing 38; PPIL1—peptidylprolyl isomerase like 1; and TRERF1—transcriptional regulating factor 1) [TNXB, a genome-wide hit in the schizophrenia and neuroticism GWASs [The two meta-analyses involved different study designs , 148. Onactor 1)  was a mesm GWASs –152.GART—phosphoribosylglycinamide formyltransferase, phosphoribosylglycinamide synthetase, phosphoribosylaminoimidazole synthetase, and SON—DNA and RNA binding protein) [Although the results summarized here are promising, this set of studies is still small, especially involving samples of adult individuals and looking at clinical aspects of ADHD (only three EWASs). Further studies with even larger samples are needed to elucidate the role of epigenetic mechanisms in ADHD throughout life. Additionally, the relationship between epigenomics and genomics should be better studied since epigenetic markers also present significant heritability estimates . A recenprotein) . TherefoThe bidirectional communication between the gut microbiota and the nervous system is known as the gut-brain axis (GBA). The main mechanisms through which the gut microbiota could affect the nervous system involve the production and metabolism of neurotransmitters, the vagus nerve, the hypothalamic–pituitary–adrenal axis, and inflammation pathways , 156. ThEnterococcus, Bifidobacterium, and Odoribacter has been reported in ADHD patients. These genera are involved in pathways related to the production and release of the neurotransmitters, such as GABA and dopamine, suggesting that such gut dysbiosis could be related to the pathogenesis of ADHD through neurotransmitter dysregulation [Faecalibacterium is decreased in ADHD and is inversely related to ADHD severity [Faecalibacterium has been related to anti-inflammatory properties [The role of microbiota in the pathogenesis of ADHD has been suggested by studies showing differences in microbiota composition and/or abundance between patients with ADHD and controls . An incrgulation –160. On severity , 162. Faoperties . Unbalanoperties , 165.Blautia in patients with ADHD compared to healthy controls [Blautia is known to interfere with the regulation and function of inflammation and immunity pathways, reinforcing the hypothesis that the link between gut microbiota and ADHD pathogenesis might involve inflammation mechanisms.The specific differences in gut microbiota mentioned above describe the most common findings that have been highlighted by recent reviews; however, a meta-analysis that assessed the relationship between gut microbiota and ADHD found no significant differences at the phylum and family levels, reporting as the only significant association the higher within-group diversity of controls . BlautiaRegarding the general composition of the microbiota, while some individual studies have found differences in patients with ADHD in diversity indices  , 168, thEarly and adequate intervention has the potential to reduce the risk of negative ADHD-related outcomes in mental and physical health . The treTherapeutic guidelines, including those from the British Association of Psychopharmacology  and the The safety and efficacy of commonly used ADHD medications are supported by metanalyses , which provide the basis for the guidelines’ recommendations mentioned above. Besides being efficacious in reducing the core symptomatology of ADHD, there is congruent evidence indicating their potential to improve related outcomes, such as quality of life, academic performance, social aspects, comorbid neuropsychiatric conditions, as well as reduce the risk of other functional impairments, including criminality, incidents of self-harm, suicidality, and risk-taking behaviors , 176–179In general, stimulants present superior efficacy compared to other medications for all age ranges. Among the stimulants, amphetamines have shown greater effect sizes in reducing ADHD symptoms than methylphenidate . AlthougMethylphenidate, the most used medication worldwide, is derived from piperidine and structurally similar to amphetamine. Both methylphenidate and amphetamines produce similar effects in increasing the availability of dopamine and norepinephrine in the synaptic cleft, leading to enhanced neurotransmission, but their mechanisms of action differ in some respects. The pharmacological activity of methylphenidate results mainly from blocking the dopamine and norepinephrine transporters , thus inhibiting the reuptake of these neurotransmitters into presynaptic neurons . On the Amphetamines and methylphenidate present with a similar profile of adverse effects, which are usually mild and transient. The most common are decreased appetite, dry mouth, irritability, sleep disturbances, tachycardia, and headache; however, amphetamines are associated with a higher rate of side effects, such as weight loss and insomnia . ConcernFor patients who do not respond, do not tolerate, or present specific conditions for which stimulants are contraindicated, the non-stimulant medications atomoxetine, clonidine, and guanfacine are alternative FDA-approved treatment options. Meta-analyses confirm that all these medications are safe and superior to placebo in reducing ADHD symptoms, and the specificities of each drug are described below.In clinical assessments, atomoxetine presents efficacy of modest effect size  but is sAtomoxetine is a potent and selective inhibitor of norepinephrine reuptake that acts through the blockade of the NET. Such presynaptic NET inhibition leads to increased norepinephrine extracellular levels and occurs mainly in the prefrontal cortex but not in the mesolimbic and mesocortical pathways up to the nucleus accumbens. Therefore, atomoxetine presents minimal abuse or dependence potential . AlthougExtended-release formulations of the α2-adrenergic agonists clonidine and guanfacine are also second-choice alternatives for ADHD treatment, both as monotherapy and as an adjunct to stimulants. Although monotherapy with these agents has shown superior efficacy to placebo, when compared to stimulant medications, they have smaller effect sizes and their treatment effects take longer to be observed , 195. HoGuanfacine and clonidine have similar pharmacological properties and, differently from atomoxetine, their actions to potentiate the noradrenergic transmission occur through the direct agonism of the postsynaptic α2-adrenergic receptors rather than via NET blockade . HoweverAlthough not included in clinical guidelines, therapeutic alternatives, such as the antidepressant bupropion, have also been used in clinical practice to treat ADHD symptoms. In this sense, there is evidence of its effectiveness, especially at high doses, although the consistency across studies is not too strong , 199. ThAlthough the approved medications are generally safe, efficacious, and lead to an important relief of ADHD core symptoms, they are not curative, and a considerable proportion of patients do not reach an optimal response. Besides, their effectiveness is further reduced due to high rates of poor drug adherence and persistence . Such heAs briefly reviewed above, the known pharmacological actions of common medications used to treat ADHD involve only a few biological pathways, which seems inconsistent with the high complexity of the pathophysiological mechanisms proposed for ADHD. In this sense, GWAS arises as an important tool to advance knowledge for a more comprehensive understanding of the molecular underpinnings of ADHD neurobiology and the medications used for its treatment, helping to guide the development of better strategies for patient care .As GWAS of ADHD susceptibility increases in power, they can reveal additional potential pathways involved with the disorder’s pathophysiology and yield new candidates for prospective drug development. Nonetheless, no significant genome-wide results have been found in GWAS for the response to treatment in patients with ADHD , 205. ThOn the other hand, in a study investigating the druggable genome, the genes targeted by the FDA-approved medications for ADHD were not associated with ADHD at the genome-wide level, indicating that these drugs act through alternative pathways different from those underlying ADHD neurobiology . AnotherSLC6A2/NET, COMT, ADRA2A, SLC6A3/DAT1 and DRD4 genes as potential predictors of the efficacy of methylphenidate in children [DAT1 gene, for which no significant association with the response to methylphenidate was found [Although the evidence from candidate gene studies suggests a genetic contribution related to the dopaminergic and noradrenergic pathways in the treatment response of ADHD, especially in children, few robust and replicated results have been observed across these studies. A meta-analysis conducted by Myer et al. (2017) suggests polymorphisms in the children . For aduas found .The current limited knowledge provided by genetic studies of ADHD treatment so far reinforces the need to improve the understanding of the molecular basis of ADHD treatments. As further insights on the molecular biology of ADHD and medication used to treat it emerge, advancements in the pharmacotherapeutic options for ADHD improve, potentially benefiting the care of patients with ADHD. The above-mentioned exploration of the druggable genome in ADHD based on GWAS yielded processes of signal transduction and cell adhesion as novel possibilities for intervention in ADHD treatment, presenting possibilities of drug repurposing and novel targets for drug development . A potenADHD is an important impairing condition of public health due to its prevalence and persistence across the lifespan, and because it leads to a higher risk of adverse outcomes, such as academic underachievement, substance use and abuse, other psychiatric disorders, somatic diseases, risky behaviors, and premature death. Although the existing medications are considered safe and efficacious in relieving symptoms and preventing many negative consequences, there are groups of individuals with inappropriate responses, and these drugs’ long-term therapeutic effects are not robust. Research aiming to provide a more comprehensive understanding of the disorder’s pathophysiology is warranted to improve the diagnosis and management of the disorder.Hundreds of studies have attempted to identify the underlying neurobiology or biomarkers for ADHD. These studies have faced several challenges, especially those related to the high heterogeneity of the disorder. Regardless, there is no doubt about the role of biology in the etiology and course of ADHD, and some facts are already a consensus in the area  Fig. 1)1). For eThe study of brain biology has been somehow boosted by the development of neuroimaging techniques. The combined effects of risk factors may affect brain regions and networks that control cognitive, executive, and emotional processes usually impaired in ADHD, which can be evaluated through neuroimaging. These studies have helped to understand the functional and structural aspects of ADHD and added to our understanding of which brain areas are modulated and/or modified during the pharmacological treatment of ADHD. Despite the growing progress, the findings should be interpreted with caution due to the small sample sizes evaluated in these studies added to the high heterogeneity of both neuroimaging study designs and ADHD presentation.https://newbrainnutrition.com).The role of the environment in ADHD has also been the focus of several studies. Still, the reported associations should be viewed as correlated rather than causal risk factors. Since both environment and genetics interact in a complex manner in the contribution to the etiology of ADHD and the high probability of unmeasured factors in the studies, it is a challenge to identify environmental risk factors robustly associated with ADHD without the interference of confounders. Epigenomics and metagenomics will certainly become important tools in studying the role of the environment in the etiology and course of ADHD. While these approaches are promising, the available sample sizes are still very small. From this perspective, international consortia will be essential to meet this need for larger sample sizes. An important example is Eat2BeNice, a multicentric and international project whose main goal is to investigate the effects of nutrition and lifestyle on ADHD using genomics, epigenomics, and metagenomics as research tools  are important initiatives to advance psychiatric genetics research in populations other than European.The growing body of research and technological advances provide good perspectives for understanding the neurobiology of ADHD, refining diagnosis, and identifying new therapeutic options to optimize treatment outcomes and associated impairments, leading to improvements in all domains of patient care.Additional file 1: Table S1. Meta-analyses summarizing effects from exposures to several substances . Table S2. Meta-analyses summarizing associations with biomarkers and nutritional factors. Table S3. Meta-analyses summarizing associations with parental, newborn, and lifespan-related factors.Below is the link to the electronic supplementary material.


Although heritability estimates suggest a role for genetic components, environmental risk factors have been described as relevant in the etiology of attention deficit/hyperactivity disorder (ADHD). Several studies have investigated the role of toxicological pollution, i.e., air pollution, heavy metals, POPs, and phthalates. Clear evidence for association of ADHD and environmental factors has not been provided yet. To answer this, we have assessed all available systematic reviews and meta-analyses that focused on the association between pollutant exposure and either ADHD diagnosis or symptoms. More than 1800 studies were screened of which 14 found eligible. We found evidence of a significant role for some pollutants, in particular heavy metals and phthalates, in the increased risk of developing ADHD symptoms. However, at the current stage, data from existing literature also do not allow to weight the role of the different environmental pollutants. We also offer a critical examination of the reviews/meta-analyses and provide indications for future studies in this field. PROSPERO registration: CRD42022341496The online version contains supplementary material available at 10.1007/s11356-023-30173-9. Attention deficit/hyperactivity disorder (ADHD) is a complex neurodevelopmental condition with a known persistence into adulthood. It is characterized by inattention, motor hyperactivity, and impulsivity that are inconsistent with age or developmental level , mercury (Hg), and manganese (Mn) and ADHD risk/symptoms  and ADHD, in particular measuring the exposure to POPs  during pregnancy and ADHD in offspring  terms and ADHD. Second, we were also interested in exploring the methodological quality of earlier studies by overviewing systematic reviews and meta-analyses. Although correlational studies does not indicate causality, the present approach  guidelines were used to conduct this overview  who excluded studies that did not meet the eligibility criteria . The same authors proceeded with the full-text screening of retained papers according to the inclusion criteria. In case of discordant opinions, the three authors voted to reach a decision. Based on AMSTAR 2 recommendations  extracted data from the full texts independently. For each systematic review or meta-analysis, we extracted data on: the year of publication, size, and type of population, study design, pollutants considered and means of exposure, modality of ADHD assessment, and main findings on the association between pollutant exposure and ADHD (diagnosis or ADHD-related symptomatology). Table The AMSTAR 2 (A MeaSurement Tool to Assess Systematic Reviews) checklists  approach .Our search strategy returned 1802 studies , of which 1644 were excluded using automated filters. Searching on Cochrane Library did not produce any results. For PubMed we used the filter for meta-analyses and systematic reviews, whereas in Web of Science and Scopus we used the filter for reviews. Twenty-five duplicates have been further excluded manually, leading to a total of 133 studies screened.n = 2), did not focus on ADHD as an outcome measure (n = 3), and were not a systematic review or included less than 25% of articles focused specifically on ADHD (n= 7). After the selection, 14 studies were retained in the present overview: 6 systematic reviews, 3 meta-analyses, and 5 systematic reviews and meta-analyses. Process: Figure Four authors  screened titles and abstracts, excluding an additional 107 studies. The remaining 26 studies were then screened in their full-text by three authors . Studies were excluded at this stage, if they included animal samples  and for meta-analyses ranged from 3.5 to 1.5 . Except for one study, which has high methodological quality, the overall quality was rated as critically low. According to Shea et al.’s (2017) classification, the items that most affect the quality of studies were the absence of a written protocol or guide to follow before the start of the review (item 2), the lack of a comprehensive literature search strategy (item 4), the lack of a list of excluded articles and a justification for their exclusion (item 7), the absence of a technique for assessing the risk of bias in individual studies (item 9), inappropriate methods for the statistical combination of results (item 11), no consideration of the risk of bias when interpreting the results of the review (item 13), and a lack of an evaluation of publication bias (item 15). Most of the included studies presented weaknesses in several crucial items, with particular respect to items 2 and 7. Only one study indeed reported the presence of a written protocol and a list of excluded studies  or International Classification of Diseases (ICD) criteria  of 2.57 (95% CI = 1.75–3.78).Three systematic reviews considered the association between air pollution and ADHD  across 27 studies. The correlation between hyperactivity/impulsivity and Pb was 0.13 across 23 studies . For associations between ADHD and Pb, Cohen’s d across 9 studies was 0.51 . He et al. (RD) of 0.22 . Furthermore, they reported an odds ratio (OR) of 0.35  across 4 cross-sectional studies and an OR of 1.47  across 7 case-control studies. Nilsen and Tulve ; when considering specific ADHD subtypes and Pb, the OR varied from 2.89 to 5.23 .Only one systematic review considered the association between Pb and ADHD, showing 12 positive associations in 17 investigations  for Hg exposure and all ADHD outcomes. Moreover, the authors reported an OR of 2.63  for Mn exposure and all ADHD outcomes. Yoshimasu et al. .Two meta-analyses examined the association between Mn and Hg and ADHD. Nilsen and Tulve’s  meta-anau et al.  conducteOnly one systematic review considered the association between arsenic and ADHD reporting a positive association in 1 investigation  to 1.30 (95% CI = 0.98–1.73)), and below 1 in males (ranging from 0.92 (95% CI = 0.81–1.03) to 1.03 (95% CI = 0.85–1.25)). Moreover, Qu et al. .Five systematic reviews investigated the association between POPs and ADHD. Overall, 22 positive associations out of 28 investigations (78.57%) were observed. Lam et al. (2017) showed 9 weak associations for 9 investigations; Roth and Wilks  5 positiu et al.  observedOR of 3.31  for the association between phthalates and ADHD. However, the considerable heterogeneity of the included studies needs to be considered when interpreting the results.Three systematic reviews considered the association between phthalates and ADHD  terms and, for the sake of critical investigation and discussion, we categorized them in 4 groups with relative sub-groups: air pollution (both particulate and gaseous air pollution); heavy metals ; persistent organic pollutants; and phthalates. Some articles concurrently focused on multiple pollutants, whereas some others analyzed only one kind of pollutant.With respect to the first group, air pollution, we found a positive association between air pollution and increased risk of ADHD/symptoms of ADHD in about 60% of the total investigations included


Background: The present report tries to understand the possible relationship between working memory (WM) and intelligence measurements, using the direct scores of the Working Memory Test Battery for Children (WMTBC) and Kaufman’s Brief Intelligence Test (K-BIT), in normal development (ND) and diagnosed attention deficit hyperactivity disorder (ADHD) children and adolescents. Results: Partial correlations, discounting the effect of age, showed a significant correlation in ND subjects between the central executive (CE) component of WM and the WM visuospatial sketchpad (VSS) component and the WM phonological loop (PL); also, significant correlations were obtained for the WM VSS with the K-BIT Matrices scores, the WM PL with the K-BIT Vocabulary, and the K-BIT Matrices scores with the K-BIT Vocabulary. For ADHD subjects, there were significant correlations between WM VSS and WM CE, and WM VSS and K-BIT Matrices. We tested the robustness of these correlations by selecting a small number of subjects through permutations; a robust correlation between WM CE and WM PL in ND, and between WM VSS and WM CE and WM VSS and K-BIT Matrices scores was obtained. These results were also supported by mediation analysis. Conclusions: There is a relationship during development between WM as measured with WMTBC and general intelligence as measured with K-BIT in ND and ADHD subjects. The dysexecutive character of ADHD has been shown, given that by controlling for intelligence, the differences in WM performance between ND and ADHD disappear, except for WM CE. The results suggest that in ADHD subjects, the WM VSS component presents a more pivotal role during cognitive processing compared to ND subjects. Working memory (WM) is a psychological process that is responsible for the processes of retention, perception, and manipulation of information to make changes in behavior . One of Concerning the structural development of the WM, the PL and VSS develop independently in the 5–8-year-old period . A moderThe relationship between working memory and intelligence has been studied and found to be strongly related ,13. TherThe WM construct is statistically related to that of fluid intelligence ,18 and iThe Kaufman’s Brief Intelligence Test (K-BIT)  is used Attention deficit hyperactivity disorder (ADHD) is a pervasive developmental disorder that includes attention and/or impulsivity deficits. Poor success in curricular activities and performance in ADHD is associated with poorer adjustment in school, family, and various social contexts . WM defiThe aim of the present report is to show whether there is a relationship between WM, as measured with WMTBC , and flup = 0.045); however, the low number of females in this group raises doubts about the latter result. Given the low influence of gender and for the sake of simplicity, the gender factor will not be considered in the analysis.Two different samples participated in the present study: normal development (ND) and attention deficit hyperactivity disorder (ADHD) subjects. The ND sample is composed of 84 participants with corresponding ages between 6 and 18 years , including 45 males (53.5%) and 39 females (46.5%). The sample of ADHD subjects is composed of 36 subjects with corresponding ages between 6 and 17 years , including 28 males (77.8%) and 8 females (22.2%). Age did not show significant differences between groups. In the ND group, controlled for age, no significant gender differences were obtained for any of the analyzed empirical variables after False Discovery Rate (FDR) correction . In the Initially, the ADHD sample was composed of 41 children and adolescents. They were evaluated through two different methods: a structured interview and “Nesplora Aula” ,53. All The participants had a medium socioeconomic status, and they were in the academic year that corresponds to their age. No psychological, psychiatric, or neurological disease was reported (except for the diagnosis of ADHD). When ADHD subjects were taking medication, it was suppressed 48 h before the experiment to carry out the assessment, and the parents or tutors of the participants signed a written consent (parents/tutors in the case of the children and adolescents) following the Helsinki protocol. The study was approved by the Bioethical Committees of Seville University and Jaen University.Digit Recall: The examiner speaks sequences of digits at a rate of one per second. The child recalls each sequence in the correct order.Word List Matching: The examiner speaks a sequence of words twice. The child decides whether the order of the words in the second sequence is the same as in the first. In some trials, the two lists are identical (the words are in the same order), in which case the child is asked to respond by saying “same”. In other trials, the word in the second list is in a different order to the first, more specifically, two adjacent words have been swapped in order. In these trials, the child should respond “differently”.Word List Recall: The examiner speaks sequences of short (two syllable) words for immediate recall. Each sequence must be recalled in exactly the same order as it was heard.Block Recall: The examiner taps sequences using the blocks on the block board. Initially, children are asked to recall the location of only one block. Then, sequences of two or more blocks are presented. It is important that each sequence is recalled in exactly the same order as was seen.Non-Word List Recall: The examiner speaks sequences of one-syllable non-sense words. The child recalls each sequence in the correct order.Listening Recall: The examiner reads a series of short sentences, some of which make sense and some of which do not. The child is asked to judge whether each sentence makes sense or not and to answer ‘true’ or ‘false’ accordingly. After the total number of sentences in a trial (ranging from 1 to 6) has been heard, the child is asked to recall the last word of each sentence in the order in which they were heard. It is important that each target word is recalled in exactly the same order as it was heard.Counting Recall: The examiner presents the child with arrays of dots to be counted on a series of cards. Once the dots have been counted, the child is asked to recall the total number of dots on each of the cards seen, in the order in which they were seen.Mazes Memory: The examiner traces a route through each maze by following a red line with his finger. Once the route has been demonstrated, the child is asked to recall it by drawing it in pencil in a Maze Memory answer booklet.Backward Digit Recall: The examiner speaks a sequence of digits for immediate recall. The child is asked to recall the list in reverse order, i.e., the recalled list should begin with the last digit heard and end with the first digit heard .The participants were administered with the WMTBC . This te-For the Word List Matching and Word List Recall subitems, disyllabic words were collected from a list of frequency of words used by children, with certain similar criteria such as the length between the original words and high frequency of use, to facilitate the completion of the test to younger age groups .-For the subtest of Non-Word List Recall, the same words were used as in the previous tests. To transform words into non-words, we changed the order of the vowels within the words and the order of the syllabi that made up each word.-For the subtest of Listening Recall, due to the characteristics of the subtest, the original sentences were translated from English to Spanish to keep the semantic meaning and the possible interference between words as in the original English sentences.The battery used was adapted from English to Spanish, following a series of criteria such as:K-BIT measures verbal and non-verbal intelligence in children, adolescents, and adults . The tesThe order of testing K-BIT and WMTBC was counterbalanced. Due to difficulties in arranging the time schedules, the K-BIT was only administered to 21 ADHD subjects and the whole sample of ND subjects (84). The direct scores of the K-BIT for verbal (Vocabulary) and non-verbal (Matrices) in both groups were obtained.All the statistical analyses were computed with IBM SPSS Statistics 26.p-values were corrected for multiple comparisons using the False Discovery Rate (FDR) [Univariate ANOVAs of the groups as a between-subject factor were computed for the PL, VSS, and CE of the WMTBC, and Vocabulary and Matrices of the K-BIT, using age and gender as covariates. The obtained te (FDR) .p-values were corrected for multiple comparisons using the FDR. The mediational analysis will be used to explore the possible role of WM in mediating between age and K-BIT scores. In this analysis, a series of coefficients quantify the relationship between the different variables . This analysis was computed with JASP [To study a possible relationship between the components of the WM and the K-BIT components of intelligence, bivariate Spearman correlations were performed between the scores of each of the subtests. Additionally, the same correlations were computed, controlling for age. The obtained ith JASP . Only thGiven the different number of subjects in the ND (84 subjects for WMTBC and K-BIT) and ADHD (36 subjects for WMTBC and 21 subjects for K-BIT) groups, 10,000 correlations were computed with the selection of 17 subjects by permutation without replacement. This procedure will permit the elimination of the biases that would appear from the different numbers of subjects in ND and ADHD, while not discarding the information from any single subject. The values of the selected subjects during permutations were selected from the residuals of the regressions of the WM and K-BIT variables vs. age to minimize possible different ages of the ND and ADHD subjects. This method of selecting subjects permitted the validation of which correlations were not affected by the sample size and should be considered highly robust.p < 0.001, η2 = 0.119), VSS  = 6.49, p = 0.012, η2 = 0.053), CE  = 28.08, p = 0.007, η2 = 0.195), Matrices  = 43.21, p < 0.001, η2 = 0.300), and Vocabulary  = 25.62, p < 0.001, η2 = 0.202). In these five direct scores of variables, ND presented significantly higher scores than ADHD. An additional univariate ANOVA to compare the WM direct scores of ND and ADHD, with the Matrices and Vocabulary covariates as additional covariates, showed that only the CE was statistically significant after FDR correction  = 7.55, p = 0.021, η2 = 0.071). K-BIT and WM direct scores were Spearman correlated with the impulsivity and inattention scores of the DuPaul questionnaire, but no significant correlations were obtained.The FDR-corrected ANOVA mean comparisons, with gender and age as covariates, showed significant differences between the groups’ PL  = 15.64, p-values were corrected for multiple comparisons using FDR.To study the age dependence of WM and intelligence in both samples, linear regressions of the direct scores of the three WM components and the two K-BIT components, with respect to age (measured in days), were computed. All the linear regressions were significant  for ND, To study a possible relationship between the components of WM and the K-BIT components, bivariate Spearman correlations were performed between the scores of each of the subtests in both samples . For theTo analyze the possible correlations between the components of WM and the components of intelligence, discounting age as a determining factor, partial correlations were computed between the direct scores of the subtests, controlling for the variable “age measured in days” . For thep = 0.012; CE for Matrices, p = 0.025; VSS for Matrices, p = 0.005; and PL for Vocabulary, p = 0.018. The mediation of VSS for Vocabulary and of PL for Matrices were not significant. For ADHD, the a × b significant effect was more limited, only the mediation of VSS between age and Matrices was significant, p = 0.044.The mediation analysis showed that in ND, the a × b coefficients were significant for the following mediations: CE mediating between age and Vocabulary p-values histogram for each correlation. For ND, only the correlation between CE–PL showed the most correlations obtained with the sampled subjects being significant, while for ADHD, CE–VSS and VSS–Matrices were highly significant.To test for the robustness of the obtained significant correlations and try to compute correlations with the same number of subjects in ND and ADHD, 17 subjects were selected from each sample without replacement and correlated 10,000 times. Although not specified as an aim, the results show statistically significant differences in direct scores between ND and ADHD children on the WM and K-BIT measures. This supports previous findings indicating poorer performance in ADHD children in WM  and geneThe first aim of this report was to show whether there is a relationship between WM as measured by the WMTBC, and general intelligence, as measured by the K-BIT, during development in two different samples: ND and ADHD subjects. Initially, it was hypothesized that the CE of WM would be related to the direct scores on the K-BIT Matrices and Vocabulary scales of K-BIT, while the VSS would be related to the Matrices scale and the PL would be related to the Vocabulary scale. These relationships would be more functionally associated if the age component was removed, as the common factor of maturation would be eliminated. The results obtained in the ND sample show that, as predicted, significant correlations were established between PL with Vocabulary and VSS with Matrices, and a trend for statistical significance was obtained for CE with Vocabulary and Matrices (FDR-corrected). These results suggest that WM is also related to other cognitive functions, such as intelligence. Previous findings seem to show that the short-term storage is mainly responsible for the relationship between intelligence and WM, and other cognitive processes, such as attention, are discarded as main protagonists . These fA positive correlation between WM components (CE–PL and CE–VSS) was also found in ND. Gathercole et al.  showed aWith regard to ADHD, not only were lower WM and intelligence measures presented, as previously described ,58, but Permutation analysis with a small resampled collection of subsamples showed a very robust pattern of correlations in WM CE–PL in ND, and CE–VSS and VSS–Matrices in ADHD. This finding supports a different pattern of interrelationships between cognitive processes in ND and ADHD, one that is more biased towards verbal control in ND and visually biased in ADHD. The small sample size in the ADHD group suggests that although permutation analysis indicates that the visually biased processing mode in ADHD appears to be statistically very robust, a large sample size may be needed to confirm the present results. It should be noted that the lack of robustness that appears in the ND correlations does not mean that these correlations are not present when a larger sample is tested, as described in In summary, the results in the ND correlations showed that the two intelligence subscales, Vocabulary and Matrices, were related to PL and VSS, respectively, suggesting a modular specific pattern in the relationship between WM and intelligence. In ADHD, however, the pattern of relationship between WM and intelligence (as measured by K-BIT) was limited to the relationship of CE with VSS, and VSS with Matrices, suggesting a more central role for visual information in cognitive processing. However, the results for ADHD should be treated with caution, given the small sample size of the ADHD group.An important limitation of the present report is the wide range of ages of the subjects, which is due to recruitment limitations rather than to theoretical considerations, given that age differences have been found in the causal directionality of the WM PL vs. Vocabulary relationships  and in tDuring the developmental process, an association is established between working memory (WM), as assessed by WMTBC, and general intelligence, as measured by K-BIT, in normally developing subjects and those with ADHD. The executive deficient nature of ADHD has been evidenced, as when adjusting for intelligence, disparities in working memory performance between typically developing individuals and those with ADHD disappear, except for the CE working memory component. The findings suggest that in individuals with ADHD, the VSS component of working memory plays a more crucial role during cognitive processing compared to typically developing individuals.


The aim was to develop and test a novel screen of adult ADHD, with a specific focus on clinical use. We designed a series of three studies to accomplish this aim.n = 155) and Study Two (n = 591) collected data via surveys to conduct exploratory and confirmatory factor analyses, respectively. Study Three analyzed the scale’s psychometrics in a clinical sample (n = 151).Study One (r (131) = .76, p < .001, and the Conner’s Adult ADHD Rating Scales r (131) = .71, p < .001.Study One and Study Two identified a 10-item scale with a two-factor structure. Study Three found good discriminant validity, sensitivity = 80.0%, specificity = 80.2%, and convergent validity with both the Brown Executive Function/Attention Scales, The scale demonstrated effectiveness in screening for ADHD in a psychiatric outpatient population. Its results may be used to identify patients that may benefit from thorough ADHD diagnostic procedures.The online version contains supplementary material available at 10.1186/s12888-023-05295-6. Attention Deficit Hyperactivity Disorder (ADHD) emerges in childhood and may continue throughout adulthood . Across Arriving at an accurate ADHD diagnosis poses challenges for psychiatric treatment providers. First, ADHD symptoms may be easily attributable to other mental health disorders . For exaDiagnosing ADHD is further complicated by every patient’s unique psychological and social developmental history. Across age groups, the measured prevalence of ADHD in adults indicates that men are more likely than women to have ADHD; however, this finding may be inaccurate due to pervasive societal norms and expectations pertaining to gender. In general, girls, more often than boys, are coached by teachers and parents to practice social-emotional regulation skills and not engage in externalizing behaviors . Thus, ATo adapt to challenges caused by symptom presentation and varied biopsychosocial impacts on ADHD presentation, clinicians often use validated psychological assessments in addition to clinical interviews and observations in order to construct an accurate clinical conceptualization of a patient’s presenting symptoms. There are multiple ADHD rating scales used to identify and measure the symptoms of ADHD. However, clinicians face a need for easily accessible short rating scales created for use in identifying adult ADHD within a clinical population. These assessments differ in terms of construct operationalizations ; how results are appropriately interpreted , the perspective of the person rating ADHD , and for what types of psychiatric assessments they are most appropriately used .The current availability of multiple ADHD assessment rating scales, which offer a wide range of valid approaches and operationalizations of ADHD symptoms, benefits clinical practice. A well-selected set of independent ADHD assessment scales increases the likelihood that an ADHD assessment will result in an accurate conceptualization of an individual patient’s unique presentation and reduces risks of either a type I or type II error compared to the use of just one scale . A lack Yet, the set of available ADHD measures that would be practicable for wide administration to screen patients at initial clinical contact is limited. The Adult Attention Deficit/Hyperactivity Disorder Self-Report Scale (ASRS V1.1) was developed for use by the general public by the World Health Organization and is widely used for initial screening purposes . An updaADHD Symptom and Side Effect Tracking - Baseline Scale (ASSET-BS), for the use of screening for ADHD in a population of patients seeking psychiatric treatment.Developing a valid and reliable short screening rating scale with a straightforward interpretation, established for use within a psychiatric population, would promote accuracy in ADHD diagnosis and treatment decisions. To this end, here we report on the results of a series of three studies undertaken to develop a brief self-report screening instrument for ADHD, entitled the Study One was designed to collect data to perform an exploratory factor analysis (EFA) on proposed items for the new measure to identify a factor structure. Study Two was designed to collect data in a large sample representative of the U.S.A. general population to perform a confirmatory factor analysis of the factor structure identified in Study One. Finally, Study Three was designed to evaluate the ASSET-BS’ performance in terms of convergent validity and discriminant validity in screening for ADHD in a population of patients starting outpatient psychiatric treatment for a wide range of potential DSM-5 problems. To do so, we analyzed ASSET-BS results in the context of a wide-ranging psychiatric assessment battery normally administered prior to initiating outpatient treatment for psychiatric disorders at a large outpatient psychiatric practice.The initial item set was taken from a clinician’s tool developed at a large outpatient psychiatric practice located in the midwestern U.S.A. The clinician’s tool, titled “ADHD Symptom and Side Effect Tracking” (ASSET) was created in 2001, and consisted of a set of DSM-IV criterion-derived items relating to ADHD-related symptoms and common side effects of ADHD pharmacological treatment. The items were changed over time in accordance with DSM-5 changes and as clinicians at the practice requested updates. As of 2019, the ASSET contained 16 items that measured patient-reported change using a scale of -3 (worsening) to +3 (improving).As the ASSET was purported to be a useful clinical tool to inform treatment decisions and help structure clinical interviews, we hypothesized that if items derived from it were used to measure overall ADHD severity, it would be an ideal short screener for ADHD within a clinical population. Further, a measurement of ADHD symptom severity using a tool that emerged from clinical practice may be uniquely attuned to certain facets of ADHD symptomatology that are responsive to psychiatric treatment.Sleep Quality was included due to the DSM-5’s reference to the impact of sleep disorders on ADHD symptoms, plus strong cross-cultural evidence reported in peer-reviewed literature that ADHD is highly related to problems with sleeping for both adults and children [Anxiety was included because anxiety may be a moderating factor between childhood ADHD and decreased quality of life in adulthood [Mood was included to coincide with clinical reports of the presentation of Adult ADHD. For example, the Mayo Clinic [We determined that 10 of the original 16 items had good criterion agreement with the DSM-5, and 3 of the other 6 items had strong clinical rationale for inclusion as proposed items for a novel ADHD measure. The item children –21. The dulthood . Anxietydulthood . Furtherdulthood . Therefoo Clinic . Indeed,o Clinic .Fatigue and Muscle/Joint Pain represent symptoms commonly present at intake for adult ADHD patients [Appetite and Dry Mouth were not included as they primarily reference common side effects of front-line ADHD medications [The items patients , 27. Howications . The cliAfter the initial proposed items were selected, we adopted a 6-point Likert-type scale to measure each item. The scale asks the participant to rate the level of the impact on daily life functioning they may have experienced due to problems with the sign or symptom of ADHD referenced by the item . We chose to measure the impact of symptoms on daily life functioning to ground the scale in the DSM-5’s diagnostic criteria for clinical significance.Participants completed the study via an online survey developed and hosted on a Qualtrics platform. Once individuals consented, the online survey asked participants to identify their gender, race, education level, and if they have ever received a diagnosis of ADHD or have been treated for ADHD. Participants were then directed to answer the 13 initially proposed items for the ASSET-BS.The respondent panel selection and survey administration was completed via Qualtrics. Participants were asked to answer the ASSET-BS items compiled in Study One, along with an expanded set of demographic items to confirm stratified sampling targets were reached in terms of race/ethnicity, urban/rural residency, gender, and geographic region of the United States. Two demographic variables were collected but not used as sampling sufficiency targets: educational attainment level, and self-reported history of ADHD diagnosis or treatment.The study design was an analysis of an outpatient psychiatric clinical sample’s archived psychological test battery results. The ASSET-BS was included in an assessment battery administered at a large Midwestern United States psychiatric practice. Its results were not used to interpret a patient’s condition. During the assessment, all patients were given the opportunity to decline to answer the ASSET-BS items, even if they had already provided broad consent for research use of their testing data. The wide-range assessment included dozens of possible measures with screeners selected to measure a wide swath of the DSM-5. From the archived assessment battery results, we selected for analysis the Epworth Sleepiness Scale (ESS) , the DimSD = 12.22) submitted complete survey responses. Out of those 155 participants, the sample was 83.9% female, 15.5% male, and 0.6% transgender female. Reported race and ethnicity was 83.9% Caucasian, 5.8% African American, and 4.5% Asian. For education background, the sample reported 11.0% doctorate-level degree, 47.7% master-level degree, 17.4% bachelor-level/4-year degree, 19.3% associate-level/two-year degree or some college, and 4.5% reported a high school diploma or had not completed high school. For self-reported ADHD incidence, 26.5% self-reported having been treated for or diagnosed with ADHD as an adult, and 71% indicated that they had not been diagnosed or treated for ADHD as an adult, whereas 2.6% were unsure or declined to disclose whether they had been diagnosed with or treated for ADHD.One hundred and seventy participants were recruited through convenience sampling to complete a brief survey comprising the proposed ASSET-BS questions and demographic items. The participants were invited purposefully by the research team either individually or through email through select listservs to select adults who would thoughtfully answer the questions and offer any critiques or suggested changes. To this end, the sample consisted of adult colleagues, coworkers, other psychiatric care providers, graduate students, and classmates of the research team members. Graduate student email listservs were used if members of the research team had pre-existing permission to post research invitations to the listservs . All parSD = 12.54). About 52% of the participants were female, 46.2% were males, and 1.1% of the participants reported a non-cisgender identity such as transgender or non-binary. 65.8% reported being White/Caucasian, 15% Hispanic, 9.5% African American, 3.9% Asian, 2.9% reported being of two or more races, 1.2% American Indian/Alaska Native, and 0.3% endorsed a race/ethnicity of native Hawaiian/other pacific islander. Seventy-five percent of the study population reported living in an urban area of the United States, and 25% reported a rural living location. Education levels varied greatly, with 22.7% of those having graduated high school, 21.3% having a 4-year college degree, about 14% having some college education, about 12% having a professional degree, and 12% also having a graduate, masters level degree. Additionally, a small percentage had either a 2-year college degree or a doctorate. Lastly, 61.6% of the participants reported not having an ADHD diagnosis, whereas 35.2% reported either having an ADHD diagnosis or having received treatment for ADHD, and 3.2% were unsure if they had ever been assigned a diagnosis of ADHD or been treated for ADHD.To capture a nationally representative sample, we partnered with Qualtrics to administer a survey to 591 adult participants. The participant set was selected to coincide with U.S. census-reported percentages of ages, gender identities, geographic locations, ethnicity, and race. The sample ranged from 18 to 65 years of age  who had completed a wide-range psychiatric assessment battery within a 2-month timeframe in 2021. To be included in the sample, the assessment battery must have been ordered following an intake session with a clinician, and the patient must have provided broad consent for research use of their psychological testing data. The demographic profile of the resultant clinical sample was as follows: 91.0% were Caucasian, 3.7% Asian, 3.0% African American, and 1.5% self-identified as “other” for race/ethnicity. 64% were female, and 33% were male, and 3% elected to not disclose their gender when taking the psychological assessment battery.We selected a clinical sample of 151 outpatient adult psychiatric patients , generalized anxiety disorder (60.89%), major depressive disorder (55.45%), social phobia (23.76%), panic disorder (20.79%), bipolar 2 disorder (15.34%), and bipolar 1 disorder (14.85%). As the study datasheet was deidentified to protect privacy during the study analyses, more exact information about the diagnostic outcomes for the entire sample is unavailable.t-test for difference in means.Study One’s primary analysis was an exploratory factor analysis (EFA) of participant responses to test the interrelationships among proposed ASSET-BS items to derive a parsimonious factor structure. We set a priori criteria for minimally acceptable Kaiser-Meyer Olkin score (0.70), communality with all other items (0.40), minimal factor loading within a factor (0.40), and highest acceptable cross loading onto a secondary factor (0.32). As exploratory analyses, we utilized the participant self-reported ADHD positive or negative statuses to assess preliminary discriminant/construct validity via an independent sample Study Two’s primary analysis was a confirmatory factor analysis (CFA) of the model indicated by the EFA in Study One. We then tested for possible covariate effects of demographic factors via calculating Pearson’s correlation coefficients and then, if significant, examining if the discovered covariate caused a significant difference in model fit or item loading. This test for covariate effects on factor structure was done via structural equation modeling by groups, comparing model fit and factor loading for women versus men participants.Study Three’s primary analyses were calculations of Pearson correlation coefficients to assess convergent and divergent validity. As secondary analyses, we conducted a binary logistic regression to test for effect of gender on clinical sample ASSET-BS results, and we conducted an ROC curve analysis to assess discriminant validity strength and provide a clinical cut-off score based on optimized sensitivity and specificity. To assign ADHD positive and negative classifications to study participants, three of the authors  reviewed each participant’s testing data and used the following criteria to classify the participants as ADHD positive or negative: ADHD positive status was defined as a participant that (1) had elevated T-Scores across the Brown EF/A, CAARS Self-Report, and CAARS Observer-Report Scales; (2) did not trigger any validity flags on the CAARS assessments; and, (3) the psychological assessment was ordered by a psychiatric clinician to test a hypothesized diagnosis of ADHD. If validity flags were triggered on the CAARS assessments, a participant was excluded from classification altogether.excessive talking, sleep quality, and brain fog. Specifically, the item excessive talking failed to achieve a 0.40 communality, the item sleep quality failed to load onto a factor at a 0.40 level, and brain fog loaded onto both two factors over the 0.32 cut-off. After removing excessive talking, sleep quality, and brain fog, the EFA identified a two-factor model explaining 68.40% of variance between participants. The pattern matrix demonstrated loadings of six items onto one factor and the other four items onto a second factor. We reviewed the items belonging to each factor for conceptual fit with ADHD DSM 5 criteria. We named the first factor Inattentive, as the resultant items correspond conceptually with an attention deficit aspect of ADHD per criteria in the DSM 5. We named factor two Hyperactivity and Impulsivity due to alignment with DSM-5 criteria concerning behavioral and emotional dysregulation in an ADHD clinical presentation.Results of the EFA identified three items that did not align with factor loadings appropriately. These included 2(26) = 85.69, p < 0.001. The factors correlated with each other significantly, r (155) = 0.66, p < 0.001, and the magnitude of the correlation coefficient indicated good convergent validity between the subscales.Table Inattentive subscale was α = 0.91, and the Hyperactivity and Impulsivity subscale’s internal reliability was α = 0.81. An independent-sample t-test found that participants who self-reported carrying a current ADHD diagnosis (n = 41) scored significantly higher on the ASSET-BS than participants who self-reported not carrying an ADHD diagnosis (n = 111), t (150) = 8.745, p < 0.001, Cohen’s d = 1.55.Internal reliability for the 10-item ASSET-BS was α = 0.91. Internal reliability for the 2 (79) = 84.17, p = 0.324. A total of 0.66% (33/5910) of all possible item answers were missing. Missing item answers were imputed with a maximum likelihood method to facilitate an effective structural equation model analysis in IBM’s AMOS software package. The confirmatory factor analysis (CFA) performed via a structural equation model analysis of the two-factor item-loading model discovered in Study 1  found strong standardized regression weights for factor loadings of all items onto their hypothesized factors. In assessing model fit, the analysis supported a finding of an acceptable-fitting model according to some indices , but the RMSEA index was slightly elevated into a range indicating a degree of fit between a poor and acceptable fitting model, χ2 (34) = 180.19, p < 0.001, CFI = 0.974, RMSEA = 0.085 (90% CI of 0.073 and 0.098), and PNFI = 0.731. Additionally, the covariance between the latent factors (β = 0.96), was strong enough to raise a question of whether a one-factor model may be the optimal way to configure the items.Internal reliability for the 10 ASSET-BS items was α = 0.961. Little’s MCAR test indicated that any missing values were at random, χanxiety and mood were allowed to covary, model fit may improve noticeably. Conceptual support for allowing these items to covary was that both items load onto the same factor, and anxiety and mood are rationally highly related in that changes in one construct could logically coincide to a high degree with a change in the other. A structural equation model analysis of an adjusted model adopting covariance between anxiety and mood (Modified Model), found improved global and comparative fit statistics over the Theoretical Model with a slight reduction in parsimony, χ2 (33) = 110.39, p < 0.001, CFI = 0.986, RMSEA = 0.063 (90% CI of 0.054 and 0.076) and PNFI = 0.718. Figure Inspecting the standardized residual matrix revealed no significant measurement error was introduced by any item. The modification indices identified that if the error terms of the items 2 (34) = 126.37, p < 0.001, CFI = 0.983, RMSEA = 0.068 (90% CI of 0.55 and 0.081) and PNFI = 0.739. As the two-factor model was identified in Study One’s findings, had conceptual alignment with the DSM-5’s two-dimensional ADHD nomenclature, and the one-factor model failed to unambiguously improve goodness of fit, we opted to retain the two-factor model as represented in Fig. Next, given the strong level of covariance calculated between the two latent variables, we compared a hypothesized one-factor model of the ASSET-BS to the Modified Model. The one-factor model achieved minimal differences in fit statistics in comparison to the Modified Model’s fit statistics, χTo allow for future administrations of the ASSET-BS to capture the Modified Model’s improvements in measurement accuracy, factor score coefficients were calculated and are presented in Table F (1) = 19.10, p < 0.001, Cohen’s d = 0.37. As non-cisgender participants made up a very small portion of the sample, only scores from participants who identified as cis-gender were entered into an ANOVA to identify differences in mean ASSET-BS scores based on gender .The demographic variables were re-coded as categorical  or continuous (age and level of education). Of the possible covariate variables, initial correlation (continuous variables) and ANOVA  analyses, found that gender may possibly impact ASSET-BS scores significantly with a non-negligible effect size, 2 (33) = 105.35, p < 0.001, CFI = 0.976, RMSEA = 0.084 (90% CI of 0.66 to 0.102) and PNFI = 0.708. The model’s fit statistics for the cis-gender men group were χ2 (33) = 68.33, p < 0.001, CFI = 0.986, RMSEA = 0.063 (90% CI of 0.41 and 0.084) and PNFI = 0.714. For the cis-gender women group, covarying the error terms of the executive functioning items productivity and misplacing daily items improved model fit χ2 (32) = 87.63, p < 0.001, CFI = 0.981, RMSEA = 0.075 (90% CI of 0.56 to 0.094) and PNFI = 0.691, but had negligible effect on model fit when applied to the cis-gender men group, χ2 (32) = 65.71, p < 0.001, CFI = 0.987, RMSEA = 0.062 (90% CI of 0.41 to 0.084) and PNFI = 0.693. Recalculating scores for cis-gender women participants using adjusted factor weights . For the two groups analyzed, all items loaded onto their hypothesized latent variables without noticeable variation from the CFA tests of the Theoretical Model and Modified Model. The model’s fit statistics for the cis-gender women group were χr (143) = 0.232, p = 0.005. The cis-gender men’s mean ASSET-BS score was 3.47, SD = 1.23 and the cis-gender women’s mean ASSET-BS score was 4.04, SD = 1.10. A binary logistic regression analysis found that gender accounted for 5.2% of the variance between participants in ASSET-BS scores, Cox & Snell R2 = 0.052, χ2 (1) = 7.07, p = 0.006. Adjusting the women’s ASSET-BS scores by using Study 2’s alternative factor weighting for women had no impact on the effect size of gender on difference in mean ASSET-BS scores.Internal reliability of the ten ASSET-BS items was ⍺ = 0.899. In this sample, Gender was found to have a small but significant correlation with ASSET-BS scores Using the classification criteria outlined in the Analyses section, the three-author team classified 30 participants as ADHD positive, 93 as ADHD negative, and 12 participants were excluded from discriminant validity analyses due to internal-validity flags contained in their CAARS results reports.Table CI = 0.835 to 0.954), and a Gini index coefficient of 0.789. An ASSET-BS factor score of 4.04 achieved 96.7% sensitivity and 65.9% specificity, positive predictive value (PPV) = 47.54%, negative predictive value (NPV) = 98.39%. To better balance sensitivity and specificity, a score of 4.40 achieved 80.0% sensitivity and 80.2% specificity, PPV = 57.14%, NPV = 92.60%. Figure The receiver operating characteristic (ROC) curve analysis of the ASSET-BS calculated an AUC coefficient of 0.895, (95% r (131) = 0.76, p < 0.001, the CAARS Self-Report ADHD Index, r (131) = 0.71, p < 0.001, and the CAARS Self-Report DSM-5 ADHD Symptoms Index, r (131) = 0.68, p < 0.001. The ASSET-BS moderately correlated with the CAARS Observer-Report ADHD Index (r (104) = 0.49, p < 0.001), as well as the CAARS Observer-Report ADHD Symptoms Score, r (104) = 0.45, p < 0.001. The correlation coefficients of the relationships between the ASSET-BS and the CAARS Observer-Report scores were similar in magnitude to the correlation coefficients describing the relationship between the CAARS Self-Report ADHD Index and the CAARS Observer-Report ADHD Index, r (107) = 0.55, p < 0.001, and the CAARS Observer-Report DSM 5 Symptoms Score, r (107) = 0.51, p < 0.001.The ASSET-BS strongly correlated with published self-report measures of ADHD, specifically the Brown EF/A Index Score r (143) = 0.57, p < 0.001. It weakly correlated with the Epworth Sleepiness Scale (ESS), r (143) = 0.22, p = 0.009, and had no significant correlation with the Dimensions of Obsessions and Compulsions Scale (DOCS), r (83) = 0.111, p = 0.319.To assess divergent validity, correlations between the ASSET-BS and measures for sleepiness, depression and OCD were calculated. The ASSET-BS moderately correlated with the PHQ 9, Inattention and Hyperactivity, aligned with diagnostic criteria [Results from the three studies indicate that the ASSET-BS is a valid and reliable ADHD screener with a focus on clinical use. To understand how the ASSET-BS fits within the current set of ADHD scales developed for clinical use, the Adult ADHD Quality of Life Scale (AAQOL)  and the criteria . HoweverWe found encouraging results in support of convergent validity, as the ASSET-BS strongly correlated with the Brown EF/A and the CAARS Self Report Index and the CAARS Self Report DSM-5 Symptoms Subscale. For divergent validity, correlations with the ESS and the DOCS were low, showing that the ASSET-BS measurements are not overly impacted by divergent constructs from ADHD. However, the moderate correlation with the PHQ-9 shows that depressive symptoms may impact ASSET-BS scoring, and clinicians should likewise interview for depression signs and symptoms when reviewing an elevated ASSET-BS score to ensure the score is reflective of ADHD and not a major depressive disorder.ASSET-BS scores are impacted to a small degree by gender identity. However, the impact of gender on ASSET-BS results requires further exploration due to differing results between Study 2 and Study 3. In Study 2, the general population study, the mean male score was elevated above the mean female score, whereas in Study 3, the clinical sample study, the female mean score was elevated above the male mean score. Because ADHD is a neurodevelopmental disorder with multiple biopsychosocial elements at play in its causation and presentation, it is unsurprising that gender identity would have measurable effects on symptomatology . We encoA key caution is that an ADHD diagnosis cannot be supported based on an ASSET-BS result alone. The ASSET-BS is intended to aid in screening for ADHD via measuring how severely ADHD symptoms are impacting daily life functioning. If a patient’s ASSET-BS score is elevated at or above the clinical cut-off score, further clinical assessment procedures should follow to establish whether or not ADHD is a consistent and parsimonious explanation of the patient’s clinical presentation, and to assess possible malingering or drug-seeking behavior . FurtherGeneralizability limitations exist for the clinical sample study results. The clinical sample was homogenous in terms of race/ethnicity and so future studies should evaluate the ASSET-BS’ clinical performance within diverse clinical populations. Study Two, the general population study, did not find significant differences in ASSET-BS scores by race/ethnicity. Even so, we cannot rule out the possibility of race/ethnicity, or a variety of other demographic variables, i.e. nationality, religion, region, social economic status, etc., being significant covariates within clinical populations. Thus, a key clinical use caution is to ensure that ASSET-BS results are considered in the context of intersecting multicultural factors possibly impacting symptoms.The ASSET-BS is designed to be used at intake in an outpatient psychiatric setting to conduct an effective screening for probable ADHD positive cases. These results establish that the ASSET-BS is a valid tool for clinical screening use. Study One identified a 10-item scale, with two factors, that combined explained 68.40% of the variance in participant answers. Study Two confirmed the two-factor model identified in Study One in a large sample whose demographic characteristics were representative of the U.S.A. general population. Study Two also identified measurement improvements through incorporating into scoring expected covariance between two items, and provided factor scoring coefficients so that future ASSET-BS scoring can be consistent with the improved measurement model. Study Three established that the ASSET-BS, as scored in accordance with the factor scoring identified in Study Two, had good convergent validity and acceptable divergent validity. Study Three’s results also demonstrated good discriminant validity for use within a clinical sample, with 80.0% sensitivity and 80.2% specificity found at a cut-off factor score value of 4.40. All three studies’ results supported high internal reliability with α values in Studies One, Two and Three ranging from α = 0.899 to α = 0.961. Future research should investigate the ASSET-BS’ ability to measure change due to psychiatric treatment, evaluate test-retest reliability, how the ASSET-BS performs in more diverse clinical samples, and further explore the effects of gender on scoring. Lastly, as a clinical use caution, to counter any effects of gender or other demographic variables on ASSET-BS scores, we strongly advise considering ASSET-BS results in the context of a biopsychosocial interview to understand how a multitude of biopsychosocial factors, such as gender norms, roles, and expectations may impact item-level scores.Additional file 1: Appendix A. ADHD Symptom and Side Effect Tracking - Baseline Scale.Additional file 2: Appendix B. Alternative Factor Score Calculation for Women.


Background and objectives: Attention deficit hyperactivity disorder (ADHD) is a childhood-onset neurodevelopmental disorder characterized by two dimensions: inattentiveness and hyperactivity/impulsivity. ADHD may be the result of complex interactions between genetic, biological and environmental factors possibly including fluoride exposure. Materials and methods: A literature search was performed on 31 March 2023 in the following databases: PubMed, Embase and Web of Science. We defined the following inclusion criteria according to the PECOS statement: a healthy child and adolescent population (P), fluoride exposure of any type (E), comparison with low or null exposure (C), ADHD spectrum disorder (O), and ecological, cross-sectional, case–control and cohort studies (S). Results: We found eight eligible records corresponding to seven different studies investigating the effect of fluoride exposure on children and adolescents. One study had a cohort design and one a case–control one, while five were cross-sectional. Only three studies applied validated questionnaires for the purpose of ADHD diagnosis. As regards exposure assessment, levels of fluoride in urine and tap water were, respectively used in three and two studies, while two used both. Three studies reported a positive association with ADHD risk, all assessing exposure through fluoride levels. By using urinary fluoride, conversely, a positive correlation with inattention, internalizing symptoms, cognitive and psychosomatic problems was found in three studies, but no relation was found in the other one. Conclusions: The present review suggests that early exposure to fluoride may have neurotoxic effects on neurodevelopment affecting behavioral, cognitive and psychosomatic symptoms related to ADHD diagnosis. However, due to the heterogeneity of the studies included, current evidence does not allow to conclusively confirm that fluoride exposure is specifically linked to ADHD development. Attention deficit hyperactivity disorder (ADHD) is a childhood-onset neurodevelopmental disorder characterized by two dimensions: inattentiveness and hyperactivity/impulsivity . The defAmong children and adolescents, ADHD is one of the most common psychiatric disorders, with an estimated prevalence of 3.4% worldwide ,9. NatioADHD is the result of complex interactions between genetic, biological and environmental factors. Although the precise etiology of ADHD is still unknown, it has been suggested to be an inherited disorder, with heritability ranging between 70% and 80% ,12. NeurEnvironmental factors might also play a role in the manifestation of ADHD as suggested by the increased prevalence of the disease over the last decades , possiblAmong other compounds, the trace element fluoride has been investigated for its possible neurotoxic effects ,28. InorThis potentially harmful effect can be explained at two levels. First of all, the ability of fluoride to cross the less efficient blood–brain barrier in prenatal and early life. Secondly, the ability to concentrate in the brain areas responsible for memory and learning abilities, affecting the metabolism and physiology of neuronal and glial cells through oxidative stress ,35. FluoThis systematic review was registered in PROSPERO (no. CRD42022321899) and was conducted in accordance with the recommendations of the PRISMA  statement. The research question following the PECOS  statement was “What is the association between early or prenatal fluoride exposure on ADHD risk in non-adult populations as assessed in non-experimental studies?”. As a consequence, inclusion criteria were as follows: a healthy child and adolescent population (P), fluoride exposure of any type, e.g., water, food, biomarker (E), comparison to null or low exposure  (C), ADHD spectrum disorder as an outcome (O), and ecological, cross-sectional, case–control and cohort studies (S).We conducted a literature search in the following databases on March 31, 2023: PubMed, Embase and Web of Science. We used MeSH terms and keywords related to “ADHD” and “fluoride”. Full search strategies are reported in No limits were set to data or language. Reference lists from the retrieved articles have also been searched. Title and abstracts of the remaining records were screened against inclusion criteria by one author (GF), involving another (TF) in case of uncertainty, while those found to be irrelevant were discarded. Full texts of the remaining records were independently obtained and reviewed by two authors (GF and TF). In case of disagreement between the two reviewers, this was resolved by discussion until consensus was reached.A total of 43 records were retrieved after duplicate removal. We excluded 24 records based on title and abstract screening and further excluded 11 records due to full-text evaluation . Finally, eight eligible papers were included in this review. These eight papers referred to seven studies. One had a cohort design and another a case–control one, while five studies were cross-sectional. Two studies were conducted in the USA, two in Canada, one in Mexico, one in China and one in India. Each study used different methods to assess fluoride exposure and diagnose ADHD. This also leads to remarkable heterogeneity in the results presented. In particular, Bashash et al., 2018 , RiddellIn the studies by Riddell et al., 2019 and Barberio et al., 2017, urinary analyses were performed using an Orion PH meter with a fluoride ion selective electrode after dilution with an ionic adjustment buffer. Urinary fluoride concentration was adjusted for creatinine and specific gravity. Moreover, tap water samples were analyzed for fluoride concentrations through basic anion exchange chromatography procedure. Wang et al., 2022 collected urinary samples of the recruited children and, using a fluoride ion-selective electrode, measured the fluoride concentration and urinary creatinine. Adkins et al., 2022 examined urine samples, with ionic fluoride diffused from the specimens through the hexamethyldisiloxane (HDMS) diffusion method. Then, urinary specific gravity was calculated to correct for dilution. Bashash et al., 2018 used maternal urinary samples as a marker of prenatal fluoride exposure: in particular, the average of all available creatinine-adjusted maternal urinary fluoride (MUFcr) concentrations was calculated.Regarding ADHD diagnosis, only 3 studies applied specifically validated questionnaires for ADHD ,43,47. MThe heterogeneity of study designs has an impact on the results of the articles we included in our review: Malin and Till 2015, Riddell et al., 2019 and Khairkar et al., 2021 suggest there is a positive correlation between fluoride exposure and ADHD diagnosis. Bashash et al., 2018 draws attention to a relation between maternal fluoride exposure and inattention, whereas no association is found between maternal fluoride exposure and hyperactivity or impulse control dysfunctions. Conversely, Barberio et al., 2017 and Perrott 2018 observe no correlation between fluoride exposure and ADHD. Adkins and Wang note a positive correlation between fluoride exposure and the development of internalizing symptoms such as somatization, but they do not find any significant connection with ADHD.The aim of our review was to sharpen our knowledge of how fluoride can affect the risk of ADHD development in children and adolescents. The studies included here investigate the effect of early fluoride exposure on children and adolescents with different designs and methods. Chronologically, Malin and Till 2015 were the first to outline a relation between the artificial fluoridation of community water and an increase in ADHD prevalence in children and adolescents in the United States, using data from the Centers for Disease Control and Prevention (CDC) . NeverthKhairkar et al., 2021, conducted a case–control study in India, identifying three different areas based on fluoride levels in drinking water. They found a relation between water fluoride levels and the development of psychiatric manifestations such as ADHD, childhood disruptive mood disorder, defiant disorder and arithmetic specific scholastic skill disorder . HoweverBarberio et al., 2017 used data from the Canadian Health Measures Survey (Cycle 2 and 3) to investigate the relation between fluoride exposure  and learning disabilities. For each subsample, the Authors were able to obtain not only urinary fluoride, but also creatinine-adjusted urinary fluoride and gravity-adjusted urinary fluoride, which are thought to be more accurate due to the correction of urinary dilution . HoweverA common limitation to most of the studies is the lack of a specific validated test for ADHD diagnosis. ADHD diagnosis is mainly clinical, made by a qualified health care provider and based on patients’ symptoms, general psychosocial situation and interpersonal as well as psychiatric history. The use of tests such as Conners’ Rating Scale can also be helpful in confirming and endorsing the diagnosis .Riddell et al., 2019 used the Strengths and Difficulties Questionnaire (SDQ) to objectify ADHD diagnosis by physicians. The SDQ consists of 25 items, subdivided into five subscales: emotional problems, conduct problems, hyperactivity-inattention, peer problems, and prosocial behavior . Using dWang et al., 2022 asked parents to complete the Conners’ Parent Rating Scale-Revised (CPRS-R) to support ADHD diagnosis in children aged 7–13 years. The CPRS-R uses six subscales to assess different behavioral domains: conduct problems, learning problems, psychosomatic problems, impulsive-hyperactive behaviors, anxiety, and ADHD index. Wang found no correlation between urinary fluoride and ADHD symptoms or diagnosis. However, there was a link between fluoride exposure and somatization symptoms, suggesting a neurotoxic effect of fluoride on the human brain . This fiUnlike the studies described above, Bashash et al., 2018. used data from the Early Life Exposures to Environmental Toxicants (ELEMENT) cohort to verify an association between prenatal fluoride exposure and the development of ADHD. Maternal urinary fluoride samples were used as biomarkers of prenatal fluoride exposure. For ADHD assessment, CPRS-R was completed by mothers, while the Conners’ Continuous Performance Test (CPT-II) was administered to enrolled children. Moreover, the DSM-IV Inattention Index, DSM-IV Hyperactive-Impulsive Index, and DSM-IV Total Index (inattentive and hyperactive-impulsive behaviors combined) were used to confirm the diagnosis. Bashash et al., 2018. noted that higher prenatal fluoride exposure, as measured by MUFcr, corresponded to more ADHD-like symptoms on the CRS-R, particularly related to inattention: a 0.5 mg/L-higher level of fluoride in maternal urine resulted in higher scores on the CRS-R for DSM-IV Inattention and Cognitive Problems and Inattention. On the other hand, maternal urinary fluoride was not associated with hyperactivity and impulse control .Differences in study designs, fluoride exposure measures and methods for diagnosing ADHD may account for the heterogeneity in our results. However, evidence of neurotoxic effects on brain development has been repeatedly supported, particularly in terms of IQ performance ,32,57,58Current epidemiological evidence indicates that fluoride exposure may have neurotoxic effects on neurodevelopment, including behavioral alterations, cognitive impairment and psychosomatic issues. However, the heterogeneity in study designs and results from human studies did not allow us to reliably identify fluoride exposure as a risk factor for ADHD development. More rigorous studies are needed to provide conclusive evidence of an etiologic association between pre- or post-natal fluoride exposure and ADHD.


Acute ischemic stroke (AIS) is a serious neurological disease. Normobaric hyperoxia (NBHO) is both a non-invasive and easy method that seems to be able to improve outcomes after cerebral ischemia/reperfusion. In clinical trials, normal low-flow oxygen has been shown to be ineffective, but NBHO has been shown to have a transient brain-protective effect. Today, NBHO combined with recanalization is the best treatment available. NBHO combined with thrombolysis is considered to improve neurological scores and long-term outcomes. Large randomized controlled trials (RCTs), however, are still needed to determine the role they will have in stroke treatment. RCTs of NBHO combined with thrombectomy have both improved infarct volume at 24 hours and the long-term prognosis. These two mechanisms most likely play key roles in the neuroprotective actions of NBHO after recanalization, including the increase in penumbra oxygen supply and the integrity of the blood-brain barrier (BBB). Considering the mechanism of action of NBHO, oxygen should be given as early as possible to increase the duration of oxygen therapy before recanalization is initiated. NBHO can further prolong the existence time of penumbra, so that more patients may benefit from it. Overall, however, recanalization therapy is still essential. Stroke is the leading cause of functional disability and mortality worldwide. Ischemic stroke accounts for 80% of all stroke types. Therefore, the treatment of ischemic stroke is essential. Although intravenous thrombolysis and intravascular mechanical thrombectomy have been proven effective and are the current recommended treatments, clinical outcomes are worse than expected. This is despite successful endovascular treatment (EVT), indicating the need for a novel adjuvant therapy to improve brain protection and reduce treatment complications -3. CurreNBHO commonly refers to administering 40-100% oxygen continuously to patients under normal atmospheric pressure. In clinical trials, proper usage of NBHO has been correlated with efficacy outcomes. Compared with normobaric low-flow inhalation, which was proven ineffective, NBHO seems to have a clear neuroprotective effect. Brain damage after ischemic stroke can be ameliorated by rescuing the "ischemic penumbra," which is the severely hypoperfused and hypoxic, electrically silent brain tissue. NBHO can freeze the penumbra and prevent the ischemic core from growing, leading to improved neurological outcomes within 24 hours -8. HowevIn 2017, Shi et al. conducted a clinical study that not only used NBHO in combination with intravenous thrombolysis but also included biomarker analysis and neurological function score assessment. Patients with anterior circulation stroke (<4.5 hours) were randomly assigned to either oxygen therapy (10 L/min) or room air for 4 hours. The study found that NBHO therapy could reduce peripheral blood occludins levels and protect the BBB in AIS patients. NBHO could also improve the outcomes of AIS patients who have already been treated with intravenous tPA thrombolysis . HoweverIn 2021, Li et al. conducted an observational study that included patients with the same criteria and treatment intervention. The multivariate logistic regression analysis showed that NBHO combined with intravenous thrombolysis benefited from 90-day functional independence . AlthougThe European PROOF study by Poli et. al. included 460 AIS patients (<3hours) with anterior circulation macrovascular occlusion. The oxygen inhalation group received mask oxygen inhalation of ≥ 40 L/min 100% concentration. However, NBHO cannot reduce the volume progression of the infarction on an MRI in 24 hours . This maAnother RCT conducted by Cheng et. al. followed 233 patients receiving endovascular treatment with large vessel occlusion in their anterior circulation. They were given either 6 hours of high-flow Venturi mask oxygen therapy (FiO2 50% 15 L/min) or conventional low-flow nasal catheter oxygen support therapy (3 L/min) immediately after recanalization. This study found that NBHO can improve functional prognosis, decrease mortality, and reduce the infarct volume of AIS patients .In the OPENS-1 study by Li et. al., 86 patients who had anterior circulation macrovascular occlusive stroke with an indication for interventional therapy were randomly selected within 24 hours of onset. These selected participants were given a face mask used to inhale 10L/min oxygen for 4 hours in the EVT+NBHO group, while the EVT group was given room air. It was found that the EVT+NBHO group significantly reduced infarct volume, improved their NIHSS score in 7 days, and increased rates of functional independence at 90 days compared with the control group . After tTaking this into consideration, along with the current results, we see that NBHO should be started as early as possible, even before vascular recanalization, to achieve the best results and continued for a reasonable period, maybe 1-2 hours, to achieve "penumbra frozen" . In the Although intravenous thrombolysis and endovascular thrombectomy have been verified to be effective, recanalization after cerebral ischemia often still leads to reperfusion injury, which may cause dysfunction of the BBB. Additionally, no efficient therapy can be administered before recanalization. Based on many animal studies, two major mechanisms have been emphasized as follows.2 at the pre-ischemic level by administering 95% O2 during ischemia resulted in a smaller volume of cerebral infarction in the hyperoxia group compared to the normoxic group. Additionally, the neurological function of the hyperoxia group was considered improved. Further research has shown that improving penumbra oxygenation leads to a decrease in the production of reactive oxygen species (ROS), matrix metalloproteinase-9 (MMP-9), and caspase-8 in the penumbra, providing neuroprotection [Acute ischemic stroke occurs due to vessel occlusion which leads to low oxygen delivery to tissues. Liu et al. conducted a study which found that maintaining penumbral POotection , 16. ThiThrombolysis and mechanical thrombectomy are time-restricted treatments , 18. TheCIR refers to an unfavorable clinical outcome despite "near complete" recanalization (mTICI2b-3) . CIR mayOverall, NBHO appears to be a promising neuroprotective strategy that can assist vascular recanalization and improve patient outcomes. The mechanisms underlying penumbra freezing and BBB protection have been shown to protect the BBB,reduce cerebral edema and reperfusion injury, all of which can contribute to better reperfusion. To explore its potential for wider use, perfusion scans can be used to identify patients who are beyond the standard time window for reperfusion therapy but still meet the indications for treatment. By administering NBHO before reperfusion therapy, patients may experience even better protective effects and a more favorable prognosis. In fact, NBHO may even be administered in the ambulance to prolong the existence of the penumbra, thereby allowing more AIS patients to benefit from recanalization therapy and reducing the risk of mortality and disability.