Objectives: This paper reviews the literature on intentional discontinuation of psychostimulants in ADHD to summarize what is known about clinical course of controlled discontinuation and guide practitioners who are considering stopping these medications for youth with ADHD.Methods: A systematic search was executed in Cochrane CENTRAL, EMBASE, Psychinfo, and MEDLINE databases to identify all articles that addressed the topic of deprescribing of psychotropic medications in children and adolescents. Keywords and search strings were developed using “PICO” framework, involving Population of interest (<18 y.o.), Intervention , Comparator (continuation of specific medications), and Outcomes. Ten reviewers conducted the initial screen via a single reviewer system. Articles that met a set of three inclusionary criteria were selected for full text review and identification as specific to discontinuation of stimulants in ADHD.Results: The literature review identified 35 articles specifically addressing intentional deprescribing, discontinuation, tapering, or withdrawal of stimulants for children and adolescents with ADHD. In addition to providing broad support for the efficacy of stimulants to treat ADHD and reduce negative outcomes, there is a distinct population of children and adolescents with ADHD who do not relapse or deteriorate when taken off medications for ADHD. The majority of articles addressed either the re-emergence of ADHD symptoms or side effects, both desired and adverse, following discontinuation of stimulants. While confirming the ability of stimulants to treat ADHD in youth, our results support periodic consideration of trials of stopping medications to determine continued need.Conclusions: This systematic review summarizes the literature on deprescribing stimulants for ADHD in children and adolescents. Further research is needed to determine the optimal duration of treatment, identify patients that may benefit from medication discontinuation, and inform evidence-based guidelines for discontinuation when appropriate. More research is needed to understand and define the subgroup of youth who may succeed with stimulant discontinuation. Attention-deficit/hyperactivity disorder (ADHD) is a common childhood psychiatric condition. It is generally considered a long-term condition with up to two-thirds of individuals diagnosed in childhood continuing to experience the condition or symptoms in adulthood –3. ReseaPsychostimulants are the medications with the highest established efficacy in treating youth with ADHD and are recommended as first-line treatment options –7. WhileThese questions have added pertinence as rates of psychiatric medication prescriptions have increased dramatically in the child and adolescent population over the last 20 years . In addiDeprescribing is a structured approach to identifying and discontinuing medications when existing or potential harms outweigh existing or potential benefits. Such deprescribing may be motivated by a variety of reasons, not limited to concerns about polypharmacy, managing adverse effects, changing evidence base or best practices, changing clinical need, or patient preference . First iIn clinical practice, medication may be discontinued by either patient or provider for many reasons, at times due to adverse effects, lack of symptom control, client or family preference, when diagnostic formulation changes, changing health status, or when it is believed medication may no longer be necessary to maintain functioning . This paThis effort was initiated by the American Academy of Child and Adolescent Psychiatry (AACAP) Adoption and Foster Care Committee to develop a resource on deprescribing psychotropic medications in youth. A systematic search was executed in Cochrane CENTRAL, EMBASE, Psychinfo, and MEDLINE databases to identify all articles that addressed the topic of deprescribing of psychotropic medications in children and adolescents. Keywords and search strings were developed using “PICO” framework, involving Population of interest (<18-year-olds), Intervention , Comparator (continuation of specific medications), and Outcomes. Ten reviewers conducted the initial records screen of title and abstract via a single reviewer system.Given the large number of articles to screen, exclusion by a single reviewer was allowed at this step. Reviewers could choose to include, exclude, or tag the article for a second review. In the latter case, two reviewers (WM and MB) reviewed the title, journal, and abstract. Both reviewers were required to agree on either inclusion or exclusion, with discordant responses addressed in conversation. Articles included at this step were identified for full-text review. Full-text review included review of references to detect any potentially relevant articles that had not be identified in the initial database search, and pertinent citations were identified and added to the initial screening step for full review. Additionally, articles were similarly added if identified by reviewers from other mechanisms such as reading of literature or suggestions from experts in the field.There were three inclusionary criteria, and included studies met all three criteria. First, the topic of the article relates to provider initiated deprescribing, discontinuation, tapering, withdrawal, or reduction of psychiatric medications. Studies focused only on patient non-adherence were not included. Additionally, the article included a psychotropic medication with behavioral health/mental health indications . Medications with psychiatric indications  being studied for non-psychiatric reasons  were included if the outcome measure was pertinent to psychiatry (cognition), as opposed to only neurological (seizure relapse). Finally, the population studied was <18 years of age, or if spanning youth and adult populations, the <18-year-old subset is analyzed independently.The initial search returned 12,520 citations. An additional 75 (46 new articles and 29 duplicates) references were identified via review of reference or authors knowledge of pertinent studies, including those published after the initial search that met inclusion criteria and came to authors' awareness see .N = 14), as were those focusing on non-stimulants (N = 10). Discontinuation case studies were excluded if the primary diagnosis was not ADHD. Case studies that described side effects that emerged in the process of discontinuation were included. Additionally, a hand search of the bibliographies of full text articles and updates from the published literature yielded one additional study.We identified 58 articles specific to ADHD as a primary diagnosis had a second full-text review by two authors (WL and JW) to identify themes and data pertinent to intentional discontinuation, tapering, or withdrawal of medications treating ADHD. Studies relating to medication non-adherence or discontinuation without physician involvement were excluded  on MPH and two on dextroamphetamine  proceeding to a 2-week randomized, double-blind, placebo-controlled withdrawal phase . The impN = 157) then enrolled in a 6-week randomized double-blind placebo-controlled withdrawal study. The primary outcome in Coghill et al. was treatment failure, defined as >50% increase in ADHD-RS total score and ≥2-point increase in the CGI-S score at any single assessment point during the withdrawal period. Rates of treatment failure were 15.8% in the lisdexamfetamine group and 67.5% in the placebo group  and absolute difference in total scale score (<3 points) were small. Differences were significant for ADHD-RS total score and inattention subscale score, but not hyperactivity–impulsivity subscale score. Differences were significant only for the younger cohort (below median age of 13.8), and when applied to an older subset of youth, no difference was noted. For the secondary outcome measure CGI-I, those who discontinued medication worsened about 40% of the time, compared to nearly 16% in those who continued medication. More participants dropped out due to worsened functioning in the placebo group, though all were included in the analysis above. No serious adverse events were noted; change in appetite and weight change were more commonly reported with medication discontinuation. Authors concluded that a subset of youth was discontinued from methylphenidate without exacerbation of ADHD symptoms and that their data support existing guidelines recommending trial discontinuation on a periodic basis. One limitation is that authors noted that many qualifying participants who declined to participate cited ongoing benefit from MPH, suggesting that the study population may have been biased toward those suspecting limited benefit from ongoing medication and wanting trial discontinuation.In the most recent and best-designed study, Matthijssen et al. conducted a randomized, placebo-controlled discontinuation study to characterize the ongoing benefit of methylphenidate and withdrawal effects of discontinuation . They enThree RCTs described were designed to determine if concomitant stimulant use with various therapy regimens attenuated ADHD symptom re-emergence when the stimulant was discontinued. Researchers found no difference in youth who received up to 1 year of non-pharmacologic interventions when stimulants were discontinued. Each trial is described in more detail below.The first study randomized 50 children to continue their home stimulants as usual , home stimulants with cognitive training, or home stimulants with attention control for 16 weeks before switching to placebo . The autA similar study randomized 34 children to 3 months of cognitive training plus MPH, attention training plus MPH, cognitive training plus placebo, or attention training plus placebo . In the The largest and most robust RCT addressing the effect of non-pharmacologic intervention on discontinuation of a stimulant randomized a group of 103 children to 1 year of MPH, MPH plus multimodal psychosocial treatment , or MPH plus attention control psychosocial treatment . After aSome auxiliary data can be gleaned from the RCTs on stimulant discontinuation in children with ADHD. A common motivation for medication discontinuation is adverse effects. Stimulants are well-known to contribute to weight loss and blood pressure; it is also hypothesized that stimulant withdrawal can exacerbate tics in at-risk youth. The RCTs with a more adverse effect focus show nominal but often statistically benefit when stimulants are discontinued.Within these RCTs, there was evidence for and against mitigation of adverse effects when stimulants are discontinued. Weight loss, one of the most common side effects of stimulants, was measured throughout the Coghill et al. trial . During A second study noted that abrupt discontinuation of dexmethylphenidate was not associated with rebound or withdrawal symptoms . In thosp = 0.0037).Finally, one study examined if stimulant discontinuation would exacerbate or improve tics in those with premorbid chronic motor tic disorder or Tourette's disorder . Based oThree additional RCTs were included in this review that addresses summer and weekend stimulant holidays. While the clinical goals of “stimulant holidays” may not ostensibly coincide with stimulant discontinuation, there is nonetheless clinically relevant data to be gleaned. The below studies support the use of “stimulant holidays” to manage common stimulant side effects without clinical deterioration.Fifty-eight children on long-term MPH for ADHD were randomly assigned to continue their stimulants throughout the summer or to discontinue for two consecutive summers . At baseA more restrictive “stimulant holiday” can be found in a double-blind placebo-controlled RCT . The 40 Finally, a recent RCT examined weekend drug holidays as a weight recovery treatment and found this practice along with caloric supplementation and increased monitoring effective in increasing weight velocity in children taking stimulants . AdherenTwo observational studies assessed effects of stopping stimulants, the first on neuropsychological testing performance and the second on ADHD symptoms . Both haA prospective study assessed effects of acute methylphenidate discontinuation on neuropsychological performance, as evaluated by the Cambridge Neuropsychological Test Automated Battery (CANTAB) test battery, in 15 youth ages 4 to 14 with ADHD . Prior tThe other observational study completed a prospective, non-controlled trial on 42 youth, followed more than 1 year (13 followed 2 years) after diagnosis of ADHD and treatment with MPH . Each yoIn summary, short-term (>24 h) discontinuation of MPH affected youth performance on neuropsychological testing. Worsening of teacher-rated ADHD symptoms was seen in a majority (60%) but not all youth who were given placebo for 1 month during treatment with methylphenidate.A number of case reports describe events related to discontinuation of stimulant medications and provide guidance for the clinician . Four seWithdrawal dyskinesias with discontinuation of antipsychotic in children on stimulants are reported in three other case reports. The mechanisms in this case are also felt to reflect competing actions on the dopamine system by antipsychotic medications and stimulants. The abnormal movements were precipitated by sudden or gradual withdrawal of the antipsychotic. In these cases, it appears that the dyskinesias can persist for several weeks after the stimulants are stopped. To minimize this adverse effect, the authors suggested a gradual taper over several weeks of the antipsychotic with prompt discontinuation of the stimulant if abnormal involuntary movements appear –35.Additional case reports highlight withdrawal symptoms that impact both the gastrointestinal and neuromuscular systems. One report involves a 13-year-old female who developed painful muscle cramps in her legs when immediate-release methylphenidate was stopped for a summer holiday. Cramps occurred in the morning after a drug-free day . Acute wSeveral case reports describe mental health adverse effects from stopping psychostimulants. Psychiatric complications are reported in two case reports; the first involved severe depression in a child taken off pemoline , and theThese case reports provide guidance to the clinician for managing potential adverse effects related to stopping medications in a child treated with both products.While confirming the efficacy of psychostimulants for ADHD, three national guidelines for the evaluation and treatment of ADHD suggest consideration of periodic trials of stopping medications to determine continued need. Discontinuing ADHD medications in children requires a plan to monitor for return of symptoms . The AACA systematic literature review of 53 articles on how long to treat ADHD provides additional support for the efficacy of treating youth with ADHD with medications for up to a period of 2 years. There is limited evidence for the long-term advantage of medications beyond “mere” symptom control, and information on long-term adverse effects is limited. While acknowledging the substantial clinical experience that many children with ADHD continue to benefit from long-term medication treatment to control symptoms, the authors support annual medication free periods lasting several days to 1 week to determine ongoing benefit .An additional literature review addresses the use of drug holidays as a procedure to minimize or reverse adverse effects of these medications, . This report found 22 studies surveying drug holidays to manage side effects such as child growth and insomnia or reduce tolerance of medications. The review finds the practice to be common in 25 to 70% of families. The practice of drug holidays can be useful as a periodic trial of medication discontinuation to manage risk: benefit ratio and increase voice of youth and families to guide treatment. However, provider's opinions on the value of drug holidays are mixed and more evidence is needed .This review has implications in several areas for consumers and clinicians addressing optimal duration of stimulant treatment for youth with ADHD and potential outcomes if medications are stopped. Few trials set out to answer the question of “how/when/should we discontinue stimulants for ADHD?,” so this review attempted to synthesize the available information to help answer this question. Nevertheless, there are important points to guide the consideration of discontinuation.All reviewed randomized withdrawal studies support the use of medications to reduce symptoms, improve quality of life, or reduce relapse rates. Most studies show early re-emergence of ADHD symptoms for most children discontinuing stimulants. Despite these considerations, there is a significant subpopulation of youth in these RCTs (~30%) who may tolerate discontinuation without relapse of ADHD , 22, 25.One limitation of these RCTs is that several are industry sponsored, designed to evaluate the efficacy of medications and may be biased toward children who have shown significant early responses and good tolerance to medication , 17, 26.In addition to controlled intentional discontinuation studies, analyses of administrative databases can offer insights into continued ADHD medication effects. These studies compare outcomes during periods after prescriptions are filled to periods when prescriptions are not filled. After filling ADHD medication prescriptions, youth are less likely to suffer from unintentional injuries and substance-related events and visit EDs for unintentional injuries including traumatic brain injuries and trauma-related events –52. In SThe long-term observational MTA study may offer clues to identifying children with ADHD who may tolerate discontinuation of stimulants. Latent classes were identified by the trajectories of long-term response to treatment, and by 6 to 8 years, the type of treatment at 14 months did not predict functioning . AdherenBrain maturation and/or the development of compensatory strategies may facilitate discontinuation of medications for ADHD. As children age into adults, working memory, planning, and problem solving become more efficient . SimilarDeprescribing as a systematic approach to providing the minimum effective dose or number of medications identifying can be applied to youth with ADHD. The strength of the diagnosis and natural course of the condition along with previous responses to psychostimulants are considered along with an assessment of their risks and benefits. Periodic discontinuation trials of psychostimulants in ADHD are supported by the practice guidelines , 45, 59.It is important to have a shared decision discussion on whether stimulants should be continued with the family and youth in which one reviews comorbid conditions, adverse effects, timing with school or other important events, and if they still feel the medications are needed , 22. DifThe drug holiday trials support intentional short periods of discontinuation to not only identify youth who may no longer require medications to succeed but manage side effects as well. Stimulants tend to have quick onset of action and short half-life allowing for short-term “drug holidays,” such as weekends or school holidays, to mitigate adverse effects including growth retardation, weight loss, and insomnia , 24, 47.The literature supports careful monitoring as necessary for any discontinuation trial. Many of the case reports describe the emergence of side effects associated with stopping stimulants used to treat ADHD and provide clinical guidance on safety of discontinuation or withdrawal of medications. Temporary movement disorders may emerge when stimulants are discontinued and may occur more frequently with concomitant antipsychotics. Psychostimulants have been associated with motor tics, but in RCT, abrupt withdrawal of medications did not exacerbate tics  and led In most cases, relapse is noticed within 2 weeks, so a discontinuation trial could be brief , 18. OneWhile the recent inclusion of deprescribing as a PubMed search term provided some reference, a comprehensive search of the desired search was difficult to design, and studies with relevant data often used different descriptors. By necessity, the review evolved from the systematic review to a targeted review as initial results revealed little guidance on informing the question of deprescribing or planned discontinuation of stimulants. It is possible that some relevant articles have not been located. Also, our focus was on intentional discontinuation of stimulants by providers so literature on patient adherence with ADHD medications was not included in the review . Due to n ≥ 75) and showed discontinuation of stimulants (with or without therapies), resulting in rapid re-emergence of ADHD symptoms. Also, the study populations included many more males in their cohorts.Industry sponsorship and study design may influence the findings. Six RCTs were sponsored grants. Three of the RCTs were sponsored by pharmaceutical companies, and these coincided with the three of the four largest RCTs (This systematic review summarizes the literature on deprescribing stimulants for ADHD in children and adolescents, in particular characterizing rates of symptom re-emergence and medication withdrawal-emergent side effects. In summary, our review indicates that a significant group of youth may tolerate discontinuation of stimulants, but more research is needed to clearly understand and identify them. Further research is also needed to determine the optimal duration of treatment and inform evidence-based guidelines for discontinuation when appropriate.WL, JW, MB, EG, WM, DC, and TL were involved in the conception and design of the work, which includes drafting and revising the intellectual content. All authors approve the version now submitted and agree to be accountable for all aspects of the work.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


These putatively pleiotropic SNPs were tested for additional associations across a broad range of phenotypes. Fourteen linkage disequilibrium-independent SNPs were associated with each disorder separately (p < 5 × 10–4) and in the cross-disorder meta-analysis (p < 5 × 10–8). Nine of these SNPs had not been highlighted previously in either individual GWAS. Evidence supported nine of the fourteen SNPs acting as eQTL and two as brain eQTL. Index SNPs and their genomic regions demonstrated associations with other mental health phenotypes. Through conducting meta-analysis on ADHD and MDD only, our results build upon the previously observed genetic correlation between ADHD and MDD and reveal novel genomic regions that may be implicated in this overlap.Attention deficit/hyperactivity disorder (ADHD) demonstrates a high level of comorbidity with major depressive disorder (MDD). One possible contributor to this is that the two disorders show high genetic correlation. However, the specific regions of the genome that may be responsible for this overlap are unclear. To identify variants associated with both ADHD and MDD, we performed a meta-analysis of GWAS of ADHD and MDD. All genome wide significant ( Attention deficit/hyperactivity disorder (ADHD) is one such neurodevelopmental disorder and is associated with multiple long-term poor health outcomes, including depression2. Depression is a leading cause of disability worldwide3 and its incidence rapidly increases during adolescence and peaks in early adulthood5. Evidence of the association between ADHD in childhood or adolescence and increased risk of subsequent depression has been found in both general population6 and clinical samples9. Moreover, clinical outcomes for those with comorbid ADHD and depression are often worse than in individuals with depression alone10. The factors that underlie this strong association between ADHD and depression are unclear.DSM-5 defined neurodevelopmental disorders that typically onset early in development demonstrate a high level of comorbidity with later-onset psychiatric disorders14, one contributing factor that could partly explain the relationship between ADHD and depression is shared genetic risk. Early evidence for this came from twin studies, which observed a genetic correlation ranging between 0.23 and 0.77 for ADHD and depression diagnoses or symptoms in children and adolescents19. More recently, genome-wide association studies (GWAS) have supported the genetic overlap of ADHD and major depressive disorder , with an estimated ADHD-MDD genetic correlation of rg = 0.42 (SE = 0.03)14. Moreover, studies have shown that polygenic scores derived from the ADHD GWAS are associated with depression diagnosis in an adult population sample20, depression symptoms in an adolescent population sample21 and depression symptoms in a twin sample of children22. In line with these findings, the most recent GWAS meta-analysis of eight psychiatric disorders by the Cross Disorder Group of the Psychiatric Genomics Consortium (PGC)23 reported a similar robust genetic correlation between ADHD and depression . Given the focus by Lee et al.23 on exploring cross-disorder effects including eight psychiatric disorders and reporting based on statistics across all eight diagnoses, regions impacting only ADHD and MDD are likely to be omitted from the key findings due to insufficient evidence across the remaining six diagnoses. Risk mechanisms that explain the link between ADHD and depression might also involve other phenotypes that have not been previously studied; for example, psychiatric phenotypes such as early irritability and anxiety are known antecedents of depression, including in those with ADHD26.As ADHD and depression are both heritable and familialIn this paper we conducted a GWAS meta-analysis of ADHD and MDD, aiming to identify novel associated SNPs with contribution to both disorders. Our secondary aim was to investigate associations between SNPs implicated by this cross-disorder meta-analysis and a range of additional GWAS phenotypes.14 and MDD13. GWAS data were downloaded from https://www.med.unc.edu/pgc/results-and-downloads/. Additional local quality control of the downloaded data was performed to align genome strand, genome build (hg19) and marker nomenclature (HRC1.1) across the studies. All analysis was limited to autosomal SNPs.We used published GWAS summary statistics of ADHD27. Thus, we restricted our analysis to samples of European ancestry only, leaving 19,099 cases and 34,194 controls.Demontis and colleagues identified 12 genome-wide significant loci associated with ADHD by combining 12 cohorts , mainly of European and North American ancestry and one of Chinese ancestry. The Danish population-based cohort (iPSYCH) used health records from the Danish Psychiatric Central Research Register to identify those individuals diagnosed with ICD-10 ADHD. The remaining 11 cohorts consisted of 4 parent–offspring trio cohorts and 7 case–control cohorts in which individuals with ADHD were recruited from clinics, hospitals, or medical registers, and were diagnosed using standard tools administered by trained researchers or clinicians. Imputation of non-genotyped markers was conducted using the 1000 Genomes Project Phase 3 reference panel. Heterogenous ancestry in samples has been shown to give rise to population stratification bias in genome-wide analysesWray and colleagues identified 44 genome-wide significant loci associated with MDD by combining 7 case–control cohorts . The first cohort was a mega-analysis of 29 European ancestry samples where MDD cases were required to meet DSM-IV, 1CD-9 or ICD-10 criteria using structured diagnostic interviews, clinician-administered checklists or a review of medical records. The 6 additional cohorts were also of European ancestry where cases were defined according to DSM-IV, ICD-9 or ICD-10 diagnoses of MDD derived via interviews, self-report, receiving medical treatment and national or hospital treatment records. The published data includes an MDD cohort from the 23andMe study. Due to data restrictions, we excluded the 23andMe cohort from our analysis, leaving a sample of 59,851 cases and 113,154 controls.2) and the genetic correlation of ADHD and MDD GWAS were calculated using the LD-score approach28. Estimates were calculated on the liability scale using a population prevalence of 0.05 for ADHD and 0.15 for MDD. Summary data were harmonised to common build, strand and nomenclature and SNP h2 estimates calculated limited to the HapMap-3 SNP subset provided by the LDSC software. Local genetic correlations at specific regions of the genome were also investigated using Rho-HESS29, which can account for the phenotypic correlation of two traits and thus potential sample overlap. Rho-HESS that accounted for an estimate of the phenotypic correlation of ADHD and MDD (0.3418) was conducted.SNP-based heritability (SNP h30 with ADHD used as the index. A fixed-effects model rather than a random-effects model was chosen to maximise power to detect SNPs associated with ADHD and MDD31. A genome-wide significance (GWS) threshold of 5 × 10–8 was used.Our aim was to identify regions of common association between ADHD and MDD, regardless of the direction of the effect. Strong associations in opposing directions are of biological interest and may also be expected where one trait could act as a counter measure of the second (e.g. wellbeing and depressive symptoms). Therefore, we meta-analysed the maximum SNP effect in a GWAS summary-data-based meta-analysis. We performed the meta-analysis using a fixed-effects inverse variance-weighted model using METALp value for ADHD GWAS is smaller than both the MDD GWAS and meta-analysis p values, thus appearing to be more robustly implicated in ADHD than the combination of both phenotypes. Model 2 is alike to Model 1, but with the MDD p value as the smallest. Model 3 is defined as a GWS meta-analysis hit where the meta-analysis p-value is smaller than both the ADHD and MDD GWAS p values. Model 3 is suggestive of a variant contributing to both phenotypes and highlights potential regions of joint association between ADHD and MDD. Thus, while all Model 1 and 2 SNPs would have been highlighted previously by the individual ADHD and MDD GWAS, Model 3 is our model of interest and in the current study we report all SNPs meeting Model 3 criteria. To limit the identification of putatively stochastic Model 3 associations, we limited reporting to SNPs that show an ADHD and MDD maximum p value of 5 × 10–4.For each of the identified SNPs from the MDD-ADHD meta-analysis, we compared the association findings against the original ADHD and MDD GWAS. Based on these comparisons we describe three classes of association. Model 1 is defined as a GWS meta-analysis hit where the 32). LD statistics for pruning were derived from the European super population 1000 genomes project phase 3 reference genotypes. “LD ranges” were defined as regions around the index association containing SNPs with r2 ≥ 0.2 with the primary SNP and p < 0.05.SNPs that met Model 3 criteria were Linkage Disequilibrium (LD) pruned using PLINK  to define Model 1, 2 and 3 SNPs  were mapped to strong associations (LD ranges containing index association of p < 1 × 10–5) for a set of 37 reference GWAS of human phenotypes. These GWAS are heritable, reasonably well-powered, complex human traits that could be of relevance to both ADHD and MDD. The studies covered 5 categories of traits: mental health (13 traits), personality (5 traits), cognitive (3 traits), anthropometric (4 traits) and ‘other’ (12 traits). The ‘other’ category included traits relating to physical activity, smoking and health conditions of the skin, digestive, genitourinary, metabolic, immune and nervous systems. All GWAS LD ranges were calculated as described previously and overlaps were mapped using bedtools (https://bedtools.readthedocs.io/). Moreover, we directly assessed the association at the index SNPs against all 37 GWAS. All SNPs were p-corrected < 0.05. Bonferroni correction based on 37 independent tests was applied . After quality control of the MDD summary data, 7,104,680 SNPs remained. MDD SNP h2 on the liability scale was h2 = 0.15 (0.01). Genetic correlation between ADHD and MDD was estimated as rg = 0.52 (0.04) . Local genetic correlations by genomic region are reported in Supplementary File After quality control of the ADHD summary data, 5,907,045 SNPs remained. ADHD SNP hp < 5 × 10–4 for ADHD and MDD separately  meta-analysis signal (rs12658032) and the strongest signal found to act as a brain eQTL (rs8084351)—we also present detailed figures of key results. Detailed results for all other SNPs are in the Supplementary Materials.We identified 14 SNPs that met Model 3 criteria in the combined analysis ; that is they were associated at GWS level in the combined analysis, the p-value was lower in the combined analysis compared to the individual GWAS, with meta = 1.06(1.04–1.07); Pmeta = 1.54 × 10–16) was the strongest association observed, with GWS association observed in MDD and strong association for ADHD (see Table p < 10–5) (Supplementary File RP11-6N13.1 (ENSG00000251574.6) and RP11-6N13.4 (ENSG00000253776.1)  had previously shown strong evidence for association with the individual ADHD or MDD GWAS. The first of these SNPs, rs12658032, (ORmeta = 1.04(1.03–1.06); Pmeta = 4.9 × 10–12) and is similarly located in a region showing no protein-coding annotation and no evidence of brain related eQTL (Table RP4-598G3.1 [A.K.A LINC01360 (ENSG00000233973.6)] is mapped to the region ; Pmeta = 5.9 × 10–9) is mapped to chromosome 3p21.31 and is physically co-located over many genes  cis-eQTL ; Pmeta = 2.4 × 10–9) is mapped to chromosome 18q21.2 and is physically co-located over the DCC gene  , rs8084351 is an example of a novel finding having been observed below the standard GWS reporting threshold in both the MDD GWAS and ADHD GWAS. These SNPs when combined in this meta-analysis reveal a strong combined signal (see Table p < 0.05) at genomic regions overlapping with 7 of the 14 regions that were identified as containing shared SNPs in the meta-analysis  in both ADHD and MDD GWAS individually.We performed a GWAS meta-analysis to identify SNPs with a joint contribution to ADHD and MDD. We identified 14 such LD-independent SNPs. All 14 SNPs demonstrated concordant directions of effect for ADHD and MDD. Five of the SNPs were in high LD with nearby SNPs reported as GWS in the individual MDD or ADHD GWASWe examined association across 37 GWAS selected to represent well-powered GWAS of mental health, cognition, personality, anthropometric and other heritable traits to explore the pleiotropy of each identified SNP. In general, for each of the identified SNPs, we observed deviation from the null across traits within the mental health category.RP11-6N13.1 and RP11-6N13.4) do not show evidence of brain tissue expression. The region that includes rs12658032 is associated with multiple traits and therefore warrants further interrogation. A recent study reported a SNP in this region  that was associated with ADHD, MDD, ASD and anorexia23.Annotation of GWAS findings can often add supporting data through revealing functionally relevant SNPs, thus moving from associated SNPs to genes and potential biological processes. For the traits under investigation in this study, we annotated the SNP and the associated region (including other SNPs in high LD to the index SNP) for transcripts, eQTL and SNP consequences. Essentially, our approach was to identify SNPs showing evidence of functional impact in trait relevant tissues (i.e. brain) or showing a relationship to genes related to brain function and/or neurodevelopment. Our top finding (rs12658032) revealed none of these properties. The only transcripts in the region  was found to be an eQTL for brain expression. rs2029591 was shown to be an eQTL for multiple genes across multiple tissues. With respect to brain tissue, rs2029591 is an eQTL for 39. We identified regions of pleiotropy based on GWAS of ADHD and MDD. However, overlap exists in the symptoms of different mental health disorders and overlapping genetic associations may not be limited to ADHD and MDD. In order to explore the extent of pleiotropy, we screened identified regions against 37 additional GWAS. As expected, we identified regions that show common association across multiple psychiatric disorders. Some of the relationships may be in part due to examining highly correlated traits . Some may be subject to correlation through misclassification bias or confounding. Misclassification may arise between related psychiatric presentations. For example, misdiagnosis of bipolar disorder as MDD in the early stages of treatment seeking is common40. Confounding might arise where a common association with, for example, cognition and ADHD arises only because of ADHD being associated with both the SNP and cognition independently and not through a causal pathway. Models such as vertical pleiotropy offer an alternate explanation through the role of intermediate traits. For example, rs8084351 was associated with numerous mental health phenotypes as well as cognition. Vertical pleiotropy would be where rs8084351 directly influences traits related to ADHD, which influences cognition, which could in turn impact upon MDD risk in a causal cascade, for example.The phenomenon whereby we observe common association between two traits is called pleiotropy. There are several types of pleiotropy and mechanisms that give rise to it39. Additionally, the MDD GWAS used in this study has a much larger sample size than the ADHD GWAS used. This statistical power imbalance may potentially bias meta-analysis results in favour of the better-powered discovery GWAS (MDD). Indeed, for 11 of 14 reported associations we found lower p-values for MDD than ADHD. To reduce over interpretation of GWS SNPs due to stochastic processes driven by the better statistically powered GWAS, we limited our interpretation to SNPs whereby a lower but modest minimum association (p < 5 × 10–4) was observed in both studies individually. There are concerns in the literature regarding reliance on p values33 and thus we repeated our meta-analysis using effect size estimates (z-scores) to define Model 1, 2 and 3 SNPs. Results were very similar, with the z-score approach detecting 13 Model 3 SNPs—all of the 14 SNPs found using the p-value approach except the weakest association (rs17775184).There were five instances where our associated regions identified from the meta-analysis overlap with regions found to harbour GWS associations in the individual ADHD or MDD GWAS. Although we focus on the SNP with the strongest association in the meta-analysis in addition to strong associations with ADHD and MDD individually, the assumption that the shared association is driven by a common SNP could be a potential limitation in our interpretation. Specifically, the SNP with the strongest association may not represent the same causal variant being shared between ADHD and MDD. Our associations could potentially represent ‘spurious pleiotropy’, whereby be two different SNPs in the ADHD and MDD GWAS are in high LD with one another41, however, the exact number of ADHD cases with comorbid MDD was not specified in published work using these data41. Sample overlap could theoretically inflate effect sizes, but given the small extent of overlap, any potential inflation is likely be small. We were unable to adjust the meta-analysis for sample overlap due to the focus of this study on summary statistics. However, we did conduct an additional investigation of regional genetic correlation using Rho-HESS, which can account for the phenotypic correlation of two traits and therefore potential sample overlap29. Rho-HESS that accounted for an estimate of the phenotypic correlation of ADHD and MDD (0.3418) detected local genetic correlation (non-corrected p < 0.05) at genomic regions overlapping with 7 of the 14 regions identified as containing shared SNPs in the meta-analysis (the other 7 regions had observed genetic correlations p < 0.6), thereby supporting the meta-analysis results. Additional issues to consider are the choice and availability of GWAS data. Some of the GWAS are primarily based on clinical diagnosis14 whereas others used data sourced solely from the UK Biobank42. Classification and diagnosis in the UK Biobank are often based on self-report. Although not necessarily a limitation, it is important to note that the 23andMe cohort of the MDD GWAS was not included in this study. With this cohort removed, MDD heritability [h2 = 0.15 (0.01)] was higher than reported in the original GWAS13 [h2 = 0.09 (0.004)]. The genetic correlation between ADHD and MDD was also higher (rg = 0.52) relative to the genetic correlation reported when 23andMe is included (rg = 0.42)14. One explanation is that the self-reported phenotype used by 23andMe affords a broader spectrum of clinical severity and increases heterogeneity, in comparison to clinician derived diagnosis used by contributors to the non-23andMe MDD GWAS cohorts13. Finally, it is important to note that the ADHD and MDD GWAS summary data used here were of a European ancestry only, restricting the generalisability of our findings.A known limitation of meta-analysis of GWAS summary data is the potential bias arising from sample overlap. The ADHD and MDD GWAS data were generated from multiple cohorts from PGC and iPSYCH and it is possible that the contributing cohorts share individuals. Our understanding of the extent of sample overlap in this study is that, for PGC samples, there is no evidence of case overlap and there appears to be nominal overlap of 4% of MDD controls with ADHD controls . For iPSYCH, the numbers of overlapping individuals between the ADHD and MDD GWAS are not publicly available, but there may be some overlap of cases and of controls. 17,841 of the MDD controls were from iPSYCH, meaning that up to 15% of MDD controls could overlap with ADHD iPSYCH controls. Some of the ADHD cases in iPSYCH  may have comorbid psychiatric disorders13 and ADHD14 in an adapted meta-analysis, which provides a novel approach for investigating SNPs that make a joint contribution to the two disorders, compared to previous cross disorder GWAS of numerous psychiatric disorders . The exploration of multiple phenotypes in the study by Lee et al.23 requires evidence across multiple GWAS and thus might omit regions only impacting ADHD and MDD. Indeed, only 5 of the 14 genomic regions identified in the current study were identified in the study by Lee et al.23 , and thus 9 additional regions were identified in the current study. The screening of other GWAS associations of index SNPs and regions, instead of standard literature review approaches, allowed investigation free from reporting-bias of the associations of the SNPs identified in the meta-analysis with a broad range of phenotypes.Advantages of this study include the use of the most recent and well powered GWAS of MDDFuture research directions include the use of in-depth functional analysis of the genomic regions identified here to further explore their biological mechanisms in the overlap of ADHD and MDD. Additionally, the use of Mendelian Randomisation-based techniques to infer whether or not identified variants are causal in ADHD, MDD and/or their overlap would be a useful next step.In conclusion, this study highlights 14 LD-independent SNPs contributing to the genetic overlap of ADHD and MDD, 9 of which are novel in that they did not meet GWS reporting thresholds in either the ADHD or MDD GWAS alone. Compared to existing cross disorder papers, this paper focuses specifically on ADHD and MDD using a method to highlight shared SNPs regardless of direction of effect. The results build upon existing evidence of the genetic architecture of ADHD and MDD, while revealing the regions of the genome that potentially explain some of the overlap observed between the disorders. Findings suggest that there might be some unique genetic architecture to the overlap of ADHD and MDD. eQTL results support the biological relevance of certain identified SNPs to mental health phenotypes. These SNPs seem to be largely specific to mental health related phenotypes rather than other trait classes, supporting the existence of common genetic pathways amongst psychiatric disorders. A greater understanding of the divergence and commonalities of genetic contributions to psychiatric disorders may better inform current diagnostic boundaries.Supplementary Information 1.Supplementary Information 2.Supplementary Information 3.


We aimed to understand the association between MW frequency andclinical measures, context regulation of MW and groupdifferences in task performance.27 adults with ADHD and 29 controls performed tasks manipulatingdemand on working memory and sustained attention, and recordedtheir MW frequency using probes.A significant association between MW frequency and the clinicalmeasures was demonstrated. Along with increased MW frequency,individuals with ADHD reported decreasing MW frequency duringincreasing demands on working memory (context regulation), butnot on sustained attention (deficient context regulation).Controls, however, maintained continuous task focus across allconditions. Group differences in task performance were no longersignificant after adding MW frequency as a covariate.Deficient context regulation during increasing demands on sustainedattention suggests that sustained attention deficits may play amore important role in regulation of MW in ADHD. MW frequencymight also underpin performance deficits in ADHD. ADHD is a common neurodevelopmental disorder affecting 5% to 7% of children. PrevaleAn important aspect of MW is context regulation, which occurs when MW frequencydecreases as task demands increase, in order to allow for an optimal taskperformance . ContextAnother important aspect of context regulation of MW is its relationship withexecutive control . One proposal is thatexcessive MW results from a failure in executive control to preventautomatic MW from becoming conscious . In lineNeuroimaging and electrophysiological evidence suggest that individuals withADHD experience deficient context regulation of neural activity . In partTo address this question, we studied adults with and without ADHD adopting anexperience sampling approach during two cognitive tasks: the Mind WanderingTask (MWT) and Sustained Attention Task (SAT). The MWT was previously usedto demonstrate context regulation of MW in population-based samples , whereasOur first aim was to test the association between the experimental experiencesampling measures of MW with clinical measures of MW, ADHD, executiveskills, and functional impairment . Our second was to studyfrequency of MW during changing task demands and context regulation of MW inindividuals with ADHD compared to controls . Our third aim wasto compare cognitive performance between groups and test whether MW wouldexplain statistically any between-group differences .The sample consisted of 56 individuals (27 with ADHD and 29 controls) ofmixed gender and between the ages of 18 and 65 years. The groups werematched on age, sex, and IQ .The adults with ADHD were recruited from the South London and MaudsleyNHS Trust ADHD clinic, the Barnet, Enfield and Haringey Mental HealthTrust clinics, online advertisements via adult ADHD networks andprimary care physicians. Control adults without ADHD and no priordiagnosis or treatment for any mental health condition were recruitedvia online recruitment advertisements from all over London.Participants in both groups were excluded if they had a current orpast diagnosis of major physical illness , severe recurrent mental health problems other than ADHD, current or past substance abuse , or anIQ < 80.All ADHD participants had a formal diagnosis of ADHD based on clinicalrecords and met both DSM-4 and DSM-5 criteria for ADHD, confirmedduring assessments for this study. Fourteen participants with ADHDwere receiving pharmacological treatment for ADHD. Twelve werereceiving stable treatment with stimulant medication and two withatomoxetine. Seven participants with ADHD experienced comorbiddifficulties with depression and anxiety and were taking a low dose ofa concomitant medication for anxiety or depression (SSRIs). Twoindividuals with ADHD also had a suspected autism spectrum disorder.All these nine individuals were included in the final sample.All participants were invited for a test session lasting approximately 3to 4 hr, which involved a diagnostic interview for ADHD , a cognitive task battery comprising two tasks , IQtesting  and self-report questionnaires. Participants on ADHD medication (bothstimulants and non-stimulants) were asked to refrain from taking theirADHD medication for 48 hr before the assessment. All participants wereasked to refrain from consuming caffeine, alcohol, illicit andnon-illicit substances or smoking on the day of assessments and thepreceding evening.The 0-back (choice reaction) condition measures general alertnessand motor speed, whereas the 1-back condition measures visualworking memory performance Figure. In the The order of conditions was counterbalanced. For each trial,between 2 and 6 non-targets preceded the target. The non-targetslasted for 1 to 3 s with increasing steps of 0.1 s in each trial. The totalnumber of stimuli was 128 targets (64 in each condition) and 580non-targets (290 in each condition). Each target lasted for 4 s,allowing the participant 4 s to respond until their responseended it immediately. The fixation appeared before and after alltask stimuli crosses ranged from 2 to 4 s with increasing stepsof 0.1 s.There was a total of eight trials in each block for each condition.There were eight blocks, with a varying duration from 40 to120 s. At the end of each block, participants were informed thatthey were about to start a new block with either the samecondition with the word “STAY” or that they were about to switchto the other condition with the word “SWITCH.” Both messagewords “SWITCH” and “STAY” appeared on the screen for 5 s. Thetotal duration of task was approximately 30 min divided into two15-min sessions.The SAT is a vigilance task, which has three levels of aprogressively increasing sustained attention load . The participants are required to respond as quickly aspossible to the appearance of a counter  ofmilliseconds, via a right button response within 1 s. The visualstimuli appeared either after short, frequent consecutiveintervals of 1 s, in series of 3 to 5 stimuli , or after longer, less frequent time delays of2, 5, or 8 s ,pseudo-randomly interspersed into the blocks of 3 to 5 trials of1 s   Figure. The lonThe SAT design and approach contrasts other attentional task  ; Fast TaWherewas your attention just before this probe?” withtwo response options “On task” and “Offtask.” If they had responded “Off task,” anotherquestion enquired “Were you aware of your attentiondrifting away from the task?” with two responsesoptions “Aware” and “Unaware.”The use of thought probes to measure MW has been validated inprevious neuroimaging studies contrasting changes in neuralfunction between periods of task-focus and off task thoughtsat approximately 1-min intervals. The probe appeared in theplace of the targets in the MWT and in the place of the stimulusfollowing the infrequent delays in the SAT. We included 26delays per session  contrasting 20 delays  in the original version of the SAT. Most of these extradelays  were followed by thought probes  rather than the task stimulus (black digits), ensuringconsistency in the number of delays between our and the originalversion of the SAT. Participants were first asked “thoughts.MW frequency was calculated as a proportion using the number probesindicating MW divided by the total number of probes. The valuesranged from 0 to 1, equivalent to 0% to 100% of the time.For each task and condition, cognitive performance was measuredusing mean reaction time (MRT), intra-subject reaction timevariability (RTV), and error rate. For the MWT, we measuredaccuracy/errors , based on previous work reportinFor the SAT, participants had only one response option during,before and after the appearance of task stimulus. We thereforemeasured the proportion of non-responses out of the number of trials for each delay type separately, asan index of sustained attention. After the end of eachdelay/interval, there was a stimulus and a response . We calculated all SAT measures based onthis trial (a stimulus and a response following straight afterthe duration of the delay) to study the effect of delay type.Unlike in a previous study of children with ADHD , prematuMRT and RTV variables showed a normal distribution. Error data  were positively skewed and transformed using a logtransformation. In order to report standardized beta coefficients, allvariables were also standardized before analyses.Analysis 1: To test the relationship between the experimental experiencesampling of MW and the clinical measures, we carried out linearregressions using total MW frequency during each task as anindependent variable and ADHD symptoms ,self-reported MW (MEWS), executive skills (BRIEF-A), and functionalimpairment (WFRIS) separately as dependent variables. We hypothesizedthat MW frequency would be associated with all these clinicalmeasures.Analysis 2: To test our hypothesis for differences in the frequency of MWand in the context regulation of MW under increasing demand on workingmemory in the MWT, we tested the effects of condition (0-back vs.1-back), group (ADHD vs. control) and group-by-condition interactionon MW frequency with repeated measures general linear models. Wepredicted a significant interaction whereby controls would show lessfrequent MW during the difficult (1-back) compared to the easy(0-back) condition (context regulation), whereas individuals with ADHDwould mind wander to a similar degree during both conditions(deficient context regulation).Similarly, to test our hypothesis of group differences in the overallfrequency of MW and in context regulation of MW under increasingdemand on sustained attention (SAT), the effects of condition , group (ADHD vs. controls) and group-by-condition interaction onMW frequency were examined with repeated measures general linearmodels. The frequent 1-s delays were not included in the analysisbecause there were no MW thought probes during these intervals. Weexpected a significant group by delay difficulty interaction, wherebycontrols would maintain continuous task focus/low MW frequency(context regulation) whereas individuals with ADHD would maintain highMW frequency across increasing delays (deficient context regulation).Further, we predicted an overall higher frequency of MW in ADHDindividuals compared to controls during both tasks.We also controlled for the effect of working memory capacity (differencein accuracy/incorrect responses between the 0-back and 1-backconditions in the MWT) on MW frequency  because it hasbeen proposed as a modulator of MW frequency .Analysis 3: In analyses of cognitive performance ,we tested the effect of condition (1-back vs. 0-back) in the MWT andthe effect of delay (1 s vs. 2 s vs. 5 s vs. 8 s) in the SAT, group(ADHD vs. control) and group-by condition interaction with repeatedmeasures general linear models for each task separately. Toinvestigate the hypothesis that MW frequency explains measures of taskperformance statistically, we repeated these analyses using theprobe-derived MW frequency during the MWT for the analysis of MWTperformance and during the SAT for the analysis of SAT performance.After the end of each delay/interval, there was a stimulus and aresponse . We calculated all SAT measuresbased on this trial (a stimulus and a response following straightafter the duration of the delay) to study the effect of delay.p-values were equal or lowerthan 0.032 for the MWT and equal or lower than 0.039 for the SAT.Given the large number of hypotheses tested in Analyses 1, 2, and 3,results were corrected for multiple testing using a false discoveryrate (FDR) threshold based on the total number of comparisons in eachtask. FDR significant Although there were no group differences for age between groups ,individMW frequency in both tasks was associated positively and strongly withall measures of ADHD symptoms, self-reported MW-D, executive skillsand functional impairment , but notp < .001), group(p < .001) and condition-by-groupinteraction (p = .026) (p = .001), whereas the difference betweenconditions in controls was not statistically significant(p = .090). After adding working memorycapacity as a covariate, the main effect of condition(p = .001) and group(p < .001) as well as the condition-by-groupinteraction (p = .020) remained unchanged.There was a significant effect of condition( = .026) . The mai = .026) . Howeverp = .004), group(p < .001) and a significant delay-by-groupinteraction (p = .020) (p = .007) and 8 s(p = .009) than during 2 s, while therewas no difference between 8 and 5 s (p = .740).Individuals with ADHD reported mind wandering more frequentlycompared to controls during the task (p = .020) and 8 s delay(p = .040) compared to the 2 s delay, butthere was no difference between 5 and 8 s(p = .580) . In contrast, in controls MW frequency did notchange significantly as function of increasing delays . After adding working memory capacityas a covariate, the main effect of condition(p = .038) and group(p < .001) remained significant, while thedelay-by-group interaction did not survive correction formultiple comparisons (p = .050).There was a significant main effect of delay( = .020) . The maithe task . Post-hothe task . Individp < .001) and group(p < .001) emerged, but therewas no significant condition-by-group interaction(p = .951) (p = .013) and theinteraction also remained non-significant(p = .820), while the group effectwas no longer statistically significant(p = .080).A significant effect of condition( = .951) . Allpar = .951) . Afterap = .009) and group(p < .001), but there was nosignificant condition-by-group interaction(p = .632) (p = .030), whereas theeffect of greater RTV in the ADHD group was no longerstatistically significant (p = .080).There was a main effect of condition( = .632) .Responsp = .005) and a borderline significantcondition-by-group interaction(p = .051), but no main group effect(p = .361). There were moreincorrect responses in the 1-back compared to the 0-backcondition. Unlike controls, who did not show differencesbetween conditions (p = .777),individuals with ADHD made more incorrect responses duringthe 1-back compared to the 0-back(p = .001). This effect was no longersignificant (p = .680) after adding MWfrequency as a covariate.There was a main effect of condition(p < .001) and group(p = .009), as well as a borderlinesignificant condition-by-group interaction(p = .054) (p < .001),after 2 s compared to the 5 s (p = .001)and 8 s (p < .001) delay, and after5 s compared to 8 s delay (p = .030).Individuals with ADHD had slower responses across theentire duration of the task (p < .001), 5 s(p < .001), 8 s(p < .001), but response speeddid not change after 2 s compared to 5 s(p = .080) and 8 s(p = .122), or 5 s compared to 8 s(p = .691). In contrast,individuals with ADHD made slower responses withincreasing delays . Both the effect of group(p = .480) and condition-by-groupinteraction (p = .451) were no longersignificant after adding MW frequency as a covariate,while the effect of delay remained significant(p < .0001).There was a main effect of delay( = .054) .Responsthe task .Controlp < .001), group(p = .039) and condition-by-groupinteraction (p = .001). Responses weremore variable after 1 s delays compared to the 2, 5, and8 s delays (p < .001). However, therewas no difference in RTV after 2 s intervals compared to5 s delays (p = .930), 2 s compared to8 s delays (p = .921), 5 s compared to8 s (p = .811). Compared to controls,individuals with ADHD had more variable responses duringthe 1 s intervals, but there was no difference betweengroups for 2, 5, and 8 s , whereas individuals withADHD had more variable responses after 1 s delays comparedto 2, 5, and 8 s delays (p < .001).The ADHD group also did not show modulation in RTV after2 s compared to 5 s (p = .630) and 8 s(p = .450), or 5 s versus 8 s(p = .572). After adding MWfrequency as a covariate, the effect of group(p = .100) and condition-by-groupinteraction (p = .171) were no longersignificant, but the main effect of condition remainedsignificant (p = .010).There was a main effect of delay( and 8 s . Thegrop < .001), group(p = .004), but no significantcondition-by-group interaction(p = .242). There were more OEs withincreasing delays (2 s vs. 1 s(p < .001), 5 s vs. 1 s(p < .001), 8 s vs. 1 s(p < .001), 5 s vs. 2 s(p < .001), 8 s vs. 2 s(p < .001)), but there were nodifferences between 5 and 8 s delays(p = .08). Compared to controls,individuals with ADHD made more omission errors during alldelays (p = .320) was no longersignificant (p = .090), but the effect ofcondition (p = .010) and the interaction(p = .230) remained unchanged.There was a significant main effect of condition, providing clear evidence of increased MWfrequency in ADHD compared to controls during both tasks (MWT and SAT).However, the frequency of MW in controls (10%–20%) was considerably lowerthan the frequency of MW (50%–55%) in population-based samples during theMWT  compared to controls (10%–20%) across both tasks. The size ofthese effects was large  compared to the low cognitive demand condition in the MWT,demonstrating context regulation of MW in response to higher demands onworking memory, similar to that observed in population-based samples . In contUnlike the MWT, context regulation in the SAT reflects maintenance of increasedtask focus as demands on sustained attention increase. In that context,individuals with ADHD compared to controls experienced greater MW frequencywith increasing inter-stimulus delays, indicating poor context regulation ofMW in response to increasing demands on sustained attention. In contrast,controls maintained increased task focus with increasing inter-stimulusdelays, suggesting effective adjustment to task demands (contextregulation).From these findings, we conclude that there is impaired context regulation ofMW in response to demands on sustained attention, but not working memory.One possible explanation could be that processes underpinning sustainedattention/vigilance might reflect a core deficit in ADHD, and/or potentiallybe more strongly related with MW. In contrast, working memory might reflectan additive impairment to ADHD, which does not play such a direct causalrole in the maintenance of MW and ADHD symptoms. This view is supported byprevious findings showing that sustained attention/vigilance measures show astronger overlap in familial/genetic influences with ADHD than higher-levelexecutive functions such as working memory . MeasureConsistent with these findings, a large adult outcome study found that theneural markers of attention processes and MW   were theAlthough working memory deficits are well-established in ADHD and viewed, insome models, as a core deficit leading to the inattentive symptoms of ADHD, alternaAnother key point is that increasing sustained attention and working memorydemands are both associated with deficient context regulation of neuralactivity in individuals with ADHD compared to neurotypical individuals. This suIn line with findings from population-based  and ADHDBased on our previous hypothesis that MW may explain underlying cognitiveperformance deficits of inattentive behaviors , we testOur findings have key implications for neurocognitive models of ADHD. Theincreased MW in response to demands on sustained attention in the ADHD groupcompared to controls suggests a core problem of allocating resources inresponse to increasing demands on sustained attention. This is in keepingwith the cognitive-energetic  models oOur findings may also have implications for the previous context regulationmodels: executive control failure , andexeA potential explanation is that working memory capacity may moderate MWfrequency and task performance only in tasks requiring restraint of habitualactions such as response inhibition. In line with this hypothesis,previously working memory capacity was found to predict MW frequency andtask performance during a task probing response inhibition but not during atask probing vigilance and sustained attention  or evenThis study has three main limitations. First, the sample size is relativelysmall, and could only detect medium-to-large effects as significant. Second,we used differences in error rate only, as a proxy of working memorycapacity, which limits our interpretation. Third, the tasks were notsufficiently difficult for controls. Future research should include a largersample size, more difficult WM conditions such as 3 or 4-back conditions, oran additional measure of working memory capacity  and an Individuals with ADHD showed context regulation of MW frequency in response toincreasing working memory load, but not in response to increasing sustainedattention load. In contrast, controls maintained low levels of MW frequencyduring both tasks with no evidence of context regulation, presumably due toa floor effect. Working memory capacity did not account for these findings,which might be a task-dependent effect. Alternatively, a deficient contextregulation of MW during increasing demands on sustained attention mayreflect a core process in ADHD and give rise to other neurocognitivedeficits. These findings suggest implications for neurocognitive models ofADHD/MW.Click here for additional data file.Supplemental material, Supplementary_material for Context Regulation ofMind Wandering in ADHD by Natali Bozhilova, Giorgia Michelini,Christopher Jones, Jonna Kuntsi, Katya Rubia and Philip Asherson inJournal of Attention Disorders


Objective: Evidence regarding comedication among individuals with ADHD is lacking, especially in adults. This study investigated comedication and polypharmacy with ADHD medications in adults. Method: We identified adults dispensed with ADHD medications during 2013 in Sweden and matched them to controls. Logistic regression was used to calculate odds ratios (ORs) of receiving other medications. Results: Individuals receiving ADHD medications had higher risk of receiving any major classes of somatic medications . They were more likely to receive respiratory system, alimentary tract and metabolic system, and cardiovascular system medications. In addition, they had higher risk of receiving any other psychotropic medications. The proportion of polypharmacy with five or more medication classes increased from 10.1% to 60.4% from 18 to 64 years. Conclusion: Comedication was more common in adults receiving ADHD medications. Potential benefits and harms of comedication and polypharmacy require further research.  ADHD is a common neurodevelopmental disorder, affecting 5% to 8% of children  and 3% oIt is well established that patients with ADHD have high rates of psychiatric comorbidities . EmerginBy taking advantage of Swedish national registers, this nationwide study investigated the comedication (including both somatic [nonpsychotropic] and psychotropic medications) and polypharmacy patterns with ADHD medications among adults.Data were available through linkage of the Prescribed Drug Register (PDR), the Total Population Register (TPR), and the National Patient Register (NPR) in Sweden based on the unique personal identification numbers . The PDRThe target population included all individuals aged 18 to 64 years who were residing in Sweden during 2013. Among whom, all individuals who had at least one dispensed prescription of ADHD medication during the study period were identified. A randomly selected 1:1 control group of individuals without any ADHD medication prescriptions in 2013 was created by matching on year of birth and sex as individuals with ADHD medication prescriptions.Information on ADHD medication was defined from the PDR. Four stimulants  and two nonstimulants (atomoxetine [N06BA09] and guanfacine [C02AC02]) were included in this study. For sensitivity analysis, information on ADHD diagnosis was obtained from the NPR, where diagnostic codes in ICD-9  and ICD-10  were used.To describe prescription patterns, we retrieved information on the source of prescription  and the duration of prescriptions from the PDR. In accordance with previous studies , an indiSupplemental Table S1.Comedication with ADHD medication was defined as dispensed prescriptions of any other medication within 6 months before and after an ADHD medication prescription . MedicatSupplemental Table S1).In addition to these main drug classes, we investigated dispensation of other psychotropic medications among individuals receiving ADHD medications, including antipsychotics (N05A); anxiolytics, hypnotics, and sedatives (N05B or N05C); antidepressants (N06A); antiepileptic drugs (N03A); drugs used in addictive disorders ; and opioid pain medications  for receiving each drug class in those receiving ADHD medications versus controls, with adjustment for sex and age as a categorical variable . ORs andSupplemental Table S2).We identified 41,840 adults aged 18 to 64 years residing in Sweden who dispensed at least one prescription of ADHD medications in 2013, including 20,629 young adults , 16,889 middle-aged adults , and 4,322 older adults . Overall, the percentage of individuals coprescribed with any somatic medications increased with age, from 76.7% in young adults to 93.4% in older adults . The proSupplemental Table S3 lists the 10 most commonly dispensed medications in the respiratory system medication class among individuals receiving ADHD medications.For each specific class of somatic medications, individuals receiving ADHD medications had higher odds of receiving them compared with controls for all age groups . Among aSupplemental Table S4 lists the 10 most commonly dispensed medications in this class among individuals dispensed with ADHD medications.Alimentary tract and metabolic system medications are associated with the second highest ORs of being dispensed, in individuals with ADHD medication prescriptions versus controls. The ORs ranged from 4.0  among older adults to 4.7  among middle-aged adults. Supplemental Table S5 lists the 10 most commonly dispensed medications in this class among individuals dispensed with ADHD medications.Cardiovascular system medications have the third highest ORs of being dispensed when comparing individuals with ADHD medication prescriptions with controls. The OR of receiving cardiovascular system medications was the highest among young adults . The association attenuated with age, but remained significant among older adults . Supplemental Figure S1). Men and women receiving ADHD medications showed similar comedication patterns except that men had a lower proportion but higher OR of receiving genitourinary and reproductive hormone medications .Sensitivity analyses showed that the comedication patterns among individuals with both ADHD diagnosis and ADHD medication versus controls are similar to the main analysis . Supplemental Tables S6 to S11 list the most commonly dispensed medications in each type of psychotropic medications among individuals receiving ADHD medications.Compared with population controls, individuals receiving ADHD medications had higher degree of polypharmacy from young adulthood to older age. They received on average 2.5 classes of other medications at age 18 to 5.0 classes at age 64, with the corresponding figures of 0.9 to 2.7 among population controls . The proThis Swedish nationwide study provides a comprehensive overview of comedication and polypharmacy patterns associated with ADHD medication in adults. Comedication with somatic medications was more common in adults who received ADHD medications compared with the general population, in particular with respiratory medications, alimentary tract and metabolic system medications, and cardiovascular system medications. Comedication with psychotropic medications was also common. Individuals receiving ADHD medications presented high degree of polypharmacy across age.Supplemental Table S3). These medications are mainly used for allergic reactions and asthma. The increased odds of receiving respiratory medications is in line with findings that ADHD is associated with chronic conditions, such as eczema . These medications are indicated for gastric/duodenal ulcers and gastroesophageal reflux disease (GERD). However, no studies to date have evaluated the potential link between ADHD and gastric/duodenal ulcers or GERD. It is possible that proton pump inhibitors could shorten the time to maximum plasma concentration of ADHD medications, though the evidence is scarce and there are variations between different types of ADHD medications . This might be explained by cardiovascular events or symptoms associated with ADHD . The high rate of opioids prescription is an important public health concern in the United States and many European countries , which relaxed the age limit of symptom onset from age 7 to 12 years in diagnosis of ADHD (The results of this study should be considered in light of its limitations. First, we used dispensed prescriptions from the PDR when assessing medication utilization, but cannot verify whether the dispensed medications were consumed. Second, we did not have information on the underlying indications for dispensations of medications, which means that we were unable to determine whether any of the ADHD or comorbid medications were dispensed for other problems or off-label use. However, there is no indication other than ADHD for which the use of ADHD medications is licensed in Sweden, though off-label use of ADHD medications to alleviate symptoms caused by narcolepsy and multiple sclerosis is possible but rare . In addi of ADHD . Finally of ADHD . Thus, tWe found elevated rates of comedication and polypharmacy with somatic and psychotropic medications in adults receiving ADHD medications. Our findings provide new insights into the complex nature of comedication and polypharmacy with somatic medications among adults receiving ADHD medications, and call for future research to inform guideline development on optimal clinical practice.Click here for additional data file.Supplemental material, Supplementary_material for Comedication and Polypharmacy With ADHD Medications in Adults: A Swedish Nationwide Study by Le Zhang, Andreas Reif, Ebba Du Rietz, Tyra Lagerberg, Agnieszka Butwicka, Brian M. D’Onofrio, Kristina Johnell, Nancy L. Pedersen, Henrik Larsson and Zheng Chang in Journal of Attention Disorders


SP1 included landraces from eastern countries, the domestication region of the “Fertile Crescent.” SP2 and SP3 consisted of landraces from the north and the south Mediterranean shores, where durum wheat spread during its migration westward. Decreases in NS, grain-filling duration and HI, but increases in early soil coverage, days to heading, biomass at anthesis, grain-filling rate, plant height and peduncle length occurred during this migration. SP4 grouped modern cultivars gathering the CIMMYT/ICARDA genetic background, and SP5 contained modern north-American cultivars. SP4 was agronomically distant from the landraces, but SP5 was genetically and agronomically close to SP1. GWAS identified 2,046 marker-trait associations (MTA) and 144 QTL hotspots integrating 1,927 MTAs. Thirty-nine haplotype blocks (HB) with allelic differences among SPs and associated with 16 agronomic traits were identified within 13 QTL hotspots. Alleles in chromosomes 5A and 7A detected in landraces were associated with decreased yield. The late heading and short grain-filling period of SP2 and SP3 were associated with a hotspot on chromosome 7B. The heavy grains of SP3 were associated with hotspots on chromosomes 2A and 7A. The greater NGS and HI of modern cultivars were associated with allelic variants on chromosome 7A. A hotspot on chromosome 3A was associated with the high NGS, earliness and short stature of SP4.A panel of 172 Mediterranean durum wheat landraces and 200 modern cultivars was phenotyped during three years for 21 agronomic and physiological traits and genotyped with 46,161 DArTseq markers. Modern cultivars showed greater yield, number of grains per spike (NGS) and harvest index (HI), but similar number of spikes per unit area (NS) and grain weight than the landraces. Modern cultivars had earlier heading but longer heading-anthesis and grain-filling periods than the landraces. They had greater RUE (Radiation Use Efficiency) up to anthesis and lower canopy temperature at anthesis than the landraces, but the opposite was true during the grain-filling period. Landraces produced more biomass at both anthesis and maturity. The 120 genotypes with a membership coefficient  Triticum turgidum ssp. durum) is an important cereal crop grown on around 17 million ha worldwide with a global production of 33.6 million tons in 2020 . Its production is concentrated in the variable, often low rainfall regions of the Mediterranean basin and in the northern plains of Canada and the United States (https://ec.europa.eu/eurostat/data/database). The Mediterranean Basin embraces countries between 27° and 47°N and between 10°W and 37°E extending over three continents and a coastline of 46,000 km  breeding programs found that 15 ancestors contributed to 72% of the genetic makeup of improved cultivars q > 0.80 to these subpopulations were used.We tested a panel of 372 durum wheat genotypes including 172 landraces from 21 Mediterranean countries and 200 modern cultivars of diverse origins . The aim2 and comprised eight rows spaced 0.15 m apart. Sowing density was adjusted to 250 viable seeds m−2. Planting dates were 4-December 2012, 27-November 2013 and 21-November 2014. Average minimum and maximum monthly temperatures and rainfall were calculated from daily data recorded for a weather station close to the experimental fields. Soil moisture was monitored in one of the repeated checks from the seedling stage by means of soil probes  located at three depths  (2O5 and 125 u.K2O were applied. Arthropod pests were controlled with Chlorpyrifos 48% (0.2 l/ha), Deltamethrin 2.5% (3 l/ha) and Esfenvalerate 5% (0.3 l/ha). For disease control Trifloystrobin 37.5% + Cyproconazole 16% (0.35 l/ha) and Epoxiconazole 12.5% (1 l/ha) were used. Weeds were controlled with Ioxynil 7.5% + Bromoxynil 7.5% + MCPP 37.5% (3 l/ha) and Fluroxypyr 20% (1 l/ha).Field experiments were conducted during the 2013, 2014, and 2015 seasons in Lleida , north-eastern Spain. The experiments were carried out in a non-replicated modified augmented design with two replicated checks (cultivars “Avispa” and “Euroduro”) at a ratio of 1:5 between checks and tested genotypes. The plots measured 3.6 m5–40 cm) . Given t−2) was determined at GS65 by weighing samples from a 0.5-m-long section of a central row from each plot after oven-drying them at 70°C for 48 h. Before plot harvesting, samples from a 1-m-long section were pulled from a central row of each plot and weighed, and the number of spikes per m2 (NS), grains per spike (NGS) and the weight of grains in the sample were recorded on a whole sample basis. Harvest index (HI) was computed by dividing the grain weight by the total weight of the plants contained in 1-m-long row sample. Plant height  was measured at GS87 in three main stems per plot from the tillering node to the top of the spike, excluding the awns. The length of the peduncle  was measured on the same stems. Grain yield was determined by mechanically harvesting the plots at ripening and was expressed at a 12% moisture level. Grain weight  was determined by counting the grains in 10 g drawn randomly from harvested grains of each plot. The mean grain-filling rate  was calculated for each plot as the ratio between grain weight and the number of days from anthesis to physiological maturity. Canopy temperature was measured around noon on sunny days with an infrared thermometer  and air temperature was simultaneously measured using a digital thermometer (Boneco 7041). Canopy temperature depression (CTD) was determined at anthesis (CTDA) and again two weeks after it, when the crop was at milky-dough grain stage (CTDMi), as CTD = Ta − Tc, where Ta was the air temperature and Tc the canopy temperature mean of three measures per plot. Photosynthetically active radiation intercepted by the crop canopy (IPAR) was determined on clear days from 12:00 to 15:00 h  every three-weeks from emergence to physiological maturity using a portable ceptometer , using the following equation  from emergence to heading (cPAREH), from heading to anthesis (cPARHA) and from anthesis to maturity (cPARAM) were determined. Radiation use efficiency  from emergence to anthesis (RUEEA) and during grain filling or anthesis to maturity (RUEAM) were determined using the following equation  was then calculated by adding the GA daily values obtained by linear interpolation.where: DM is the above-ground biomass measured at dates http://www.diversityarrays.com) with the DArTseq genotyping by sequencing platform. A total of 46,161 markers were used to genotype the association mapping panel, including 35,837 PAVs (presence/absence variants) and 10,324 SNPs. Markers were ordered according to the consensus map of wheat v4 available at https://www.diversityarrays.com/technology-and-resources/genetic-maps/ .DNA isolation was performed from leaf samples following the method reported by Doyle and Doyle . High-thP < 0.05 using the Tukey-Kramer correction with the SAS statistical package. Mean genotypic values across years were used to perform a hierarchical cluster analysis by the Ward method of the JMP V.14 software (SAS Institute Inc.) Principal component analysis was performed on the correlation matrix using the mean values across years of genotypes with a membership coefficient q > 0.80 to the five genetic subpopulations determined by Soriano et al.  was used to estimate the variance components and to produce the best linear unbiased predictors (BLUPs) of each accession in each year. The MIXED procedure of the SAS statistical package  was used with the Kenward-Roger correction due to the unbalanced number of genotypes within subpopulations. Multiple linear regression was performed for the 372 genotypes among the genetic structure coefficients  with TASSEL software version 5.0  matrix with 6 principal components as the fixed effect and a kinship (K) matrix as the random effect (PCA + K) at the optimum compression level following the equation:where y is the trait value, β is the fixed effect for the marker and u is a vector of random effects not associated for the markers; X and Z are incidence matrices linking y to β and u. Finally, e is the undetected vector of random residual.P < 0.05 for considering a Marker-Trait Association (MTA) as significant and the results were expressed with the associated P-values on a -log10 scale.A false discovery rate (FDR) threshold  correlation observed for both landraces and modern cultivars (data not shown). On average, landraces were 30 cm taller than the modern cultivars, with a peduncle that was 11 cm longer. They also produced more aboveground biomass, both at anthesis and at physiological maturity. CTD at anthesis was significantly higher in modern cultivars but this trend was reversed at the milky-dough grain stage. RUE up to anthesis was significantly higher in modern cultivars, but the opposite was true during the grain-filling period. Diagrams of frequency distribution for each trait and germplasm type are shown in EH, DAM and ultimately yield.The ANOVAs showed statistically significant differences between landraces and modern cultivars for most traits . The higEH, cPAREH, PL, PH and DMA, while cluster D grouped genotypes with more yield, HI, DAM, cPARAM, RUEEA, DHA and cPARHA. Three modern cultivars were identified in cluster A: the Canadian cultivars “Macoun” and “Waskana” that were placed jointly with the landraces “Entrelargo de Montijo” and “Trigo Glutinoso” from Spain and France, respectively, and the US cultivar “West Bred Laker,” which clustered in the same branch than the Lebanese landrace “IG-84856.” Conversely, four landraces were grouped in cluster D jointly with modern cultivars. The Italian landrace “Capeiti” was close to the modern Syrian cultivars “Awalbit 7” and “Omrabi 3.” A phenotypic similarity was also detected between the Israeli landraces “Etith” and “JM-3987” and the modern cultivars “Annouar” and “Fjord” from Morocco and USA, respectively. Finally, the Italian landrace “Hymera” clustered close to the modern American “Duraking.” Cluster C in The bi-dimensional clustering shown in EH, and between NGS with W and GFR. Similarly, consistent associations were identified in landraces and modern cultivars between NGS and RUEAM and between GFR and W. Correlation coefficients with yield showed that the traits that mostly influenced it were W  and GFR  in landraces, but HI  in modern cultivars. It was also noticeable the different sign in landraces and modern cultivars of the correlation coefficients between CTDA and DEH .Correlation coefficients between traits for landraces and modern cultivars are shown in R2 values ranged from 0.0471 for RUEAM to 0.7416 for HI  and “IG-83920,” q = 0.907). SP2 included 25 landraces from northern Mediterranean countries  and one landrace, “Douro Boukowo” cataloged as Moroccan. SP3 included 11 landraces from western Mediterranean countries , and the modern Canadian cultivar “Macoun” (q = 0.999). SP4 grouped 52 modern cultivars related to the CIMMYT and ICARDA germplasm pools. The origins more represented in this SP were Syria (33%), Spain (19%), Morocco (11%), Mexico (10%) and Tunisia (8), with a minor number of accessions from Argentina, Chile, Ethiopia, France, Germany, India, Italy and Pakistan (q = 0.949), which is very likely genetically related with the north-American germplasm pool. These results revealed a different origin for each subpopulation i.e., SP1, SP2, and SP3 containing landraces from eastern, northern and western Mediterranean countries, respectively, SP4 grouping modern cultivars derived from the international centers CIMMYT and ICARDA, and SP5 grouping modern north-American cultivars  conducted with the mean data across years of the 120 genotypes accounted for around 49% of the variability contained in the phenotypic data, with PC1 explaining 36.0% of it . The maiirection . The disirection . The genEA) was significantly higher in both modern cultivar SPs than in the landrace SPs. EH and GA90d, as already suggested by the multivariate analysis. Average values for SP2 were reduced by 6% for NS and 10% for HI with no significant changes in NGS or W relative to those for SP1. Landraces from SP3 were characterized by a greater average yield compared to those from SP1 and SP2 and that superiority was paralleled by a higher W. The analysis of crop development revealed that the average time to heading of SP1 was lower than those observed for SP2 and SP3, while the opposite was true for the duration of the period from heading to maturity. This was associated with a greater average GFR for SP2 and SP3. Landraces from SP2 and SP3 were, on average, significantly taller than those from SP1 and produced more aboveground biomass at anthesis. Differences between SP in cPAR followed a similar trend than that observed for phenological traits. In terms of green leaf are accumulated, GA90d, the average value for landraces from SP2 and SP3 were greater than the values for landraces from SP1, the latter being similar to those for the modern cultivars SPs.Mean values of phenotypic traits for each subpopulation are shown in A total of 46,161 DArTseq markers were used to genotype the durum wheat collection as reported by Soriano et al. . After rP = 3, with 273 of these MTAs being above the FDR threshold at −log10 P>4.6  differences among SPs within the QTL hotspots for the analyzed traits was carried out defining HBs when at least one SP have a different haplotype with a frequency higher than 60% than the other SPs . A totalAM, whereas the HB6 is related to NGS and W. In total we found 39 HBs associated with 16 agronomic traits. Among the different traits, DAM was associated with variation in 18 HBs whereas NS was linked to only one. Six traits were not associated with haplotype differences among SPs: CPAREH, GFR, CTDMi, RUEEA, and RUEAM.A 3-years field experiment was conducted to evaluate a panel of 172 ancestral landraces from 21 Mediterranean countries and 200 modern cultivars of diverse origins. The 21 traits determined included detailed phenological indicators, relevant physiological parameters, yield and all its components. Given the substantial differences in phenology, height and general plant type between landraces and most modern cultivars, comparing their agronomic performance in the same experiment is not straightforward and cannot be reliably done at any location and under any conditions. This is also the case when comparing performance of modern cultivars with photoperiod sensitivity (Upper Midwest of USA and Canada) to those with none (most CIMMYT/ICARDA-derived germplasm). In order to adequately assess their performance potential, landraces need to be grown under weather conditions that accommodate their general lateness and long pre-heading growth periods  while allowing for grain-fill to occur under relatively optimal conditions (no excessive heat or water stress at the end of the season). Because of their considerable height, they need to be evaluated under conditions that prevent excessive lodging, including low-to-moderate water input and avoiding excess fertilization during plot management. At the same time, the experimental conditions need to also allow for the proper expression of performance potential of the generally shorter, earlier modern cultivars in order for the comparison between the two groups to be fair and reliable. The testing site used in the present study, namely the north-eastern Spanish location of Lleida, satisfies all these prerequisites, in addition to being highly representative of rainfed Mediterranean environments  more than the early-heading ones, which could result from higher transpiration rates associated with a larger potential evapotranspiration  to a particular SP > 0.8. While the classification was, for the great majority of the 120 genotypes, consistent with their geographical or breeding origin, it yielded some apparent incongruences. The Italian landrace “Aziziah 17/45” was classified in the middle-eastern group SP1, instead of logically be included is SP3 with most of the Italian landraces. However, it was developed in Italy from an early maturing pure line selected from Syrio-Palestinian landraces , it is conceivable that it retained a large part of the genetic background from western Mediterranean landraces. Finally, the French cultivar “Auroc” was classified in SP5, along with the US and Canadian modern cultivars. No information could be obtained to clearly explain this apparent geographical discrepancy, but it is conceivable that it may have a north American parent in its ancestry. Some of these unexpected clustering could be also due to errors in accession identification and labeling.A more granular subdivision, beyond landraces This analysis further subdivided the landraces group into three SPs, with SP1 corresponding to landraces from the eastern Mediterranean, in or around durum wheat's center of origin, SP2 including landraces from the northern Mediterranean coast and SP3 representing the western Mediterranean region. These results agree with a recent study involving a worldwide durum wheat collection  and with the regions surrounding the Mediterranean Basin that wheat colonized during its migration westward (SP2 and SP3). SP2 represent landraces established along the migration path that wheat took before entering the Iberian Peninsula from the north, about 7,000 years BP, after spreading through Turkey and the Balkan Peninsula Feldman, . A seconThe two SPs involving modern cultivars (SP4 and SP5) include two of the most widely and currently grown genetic pools of spring durum wheat  was not unexpected given the massive adoption of the germplasm developed in these international centers worldwide. Pfeiffer and Payne  reportedPpd-A1 and Ppd-B1, the vernalization response genes Vrn-A1 and Vrn-B3, and the dwarfing genes Rht-B1, Rht12 and Rht25. The incorporation of favorable alleles at these loci by breeding programs shortened the cycle length of durum wheat  and Ppo-B2 (chromosome 2B); peroxidase activity, the Pod-A1 gene on chromosome 3A; grain protein content (GPC-B1) on chromosome 6B; Finally, the phytoene synthase gene Psy-B1 located in QTL hotspot 7B.11 involved in the biosynthesis of carotenoid pigments, the biochemical basis of yellow color in durum wheat. The identification of functional genes incorporated in the modern durum wheat cultivars suggest these QTL hotspots were target genomic regions during selection in modern breeding programs.A GWAS allowed the identification of molecular markers associated with the agronomic traits analyzed in the current study. As previously reported by Soriano et al.  LD decayà et al.  the imprEH, DHA, PL and PH found in cultivars developed from CIMMYT/ICARDA. Among the landrace SPs, the Western Mediterranean landraces (SP3) showed haplotypes associated with higher grain weight in HB6 hotspot 2A.2 and HB1 and HB2 in hotspot 7A.4, where known loci increasing grain weight were found, namely, TaSus2-2A and TaTEF-7A, respectively. A previous study reported higher grain weight in western durum wheat Mediterranean landraces  and increased biomass at maturity . Western Mediterranean landraces showed haplotypes associated with lower cPAR from heading to anthesis in hotspots 7B.3 and 7B.11, whereas northern landraces had haplotypes decreasing cPAR at maturity both eastern and western Mediterranean landraces harbored alleles increasing the latter parameter . Among these hotspots affecting PAR, known loci for grain weight (TaTEF-7A) and vernalization requirements (Vrn-B3) were found in 7A.4 and 7B.11, respectively.Moreover, within the QTL hotspots, HBs were defined to search for haplotype patterns differentiating the SPs. The use of specific haplotypes affecting agronomic performance will allow the selection of the most favorable allelic combinations for durum wheat breeding in Mediterranean environments. Landraces showed haplotypes on chromosomes 5A and 7A, associated with decreased yield. The different haplotype variants identified in HB4 and HB9 in hotspot 7A.4 could be associated with the greater NGS of modern cultivars compared to that of the landraces. HB2 and HB5 in hotspot 3A.1 could be associated with the greater values for NGS and the lowest values for DA, traits different than the ones separating the two subpopulations involving modern cultivars: yield, HI, grain weight, phenology, PL, PH, CTDA, and CPAREH. Landraces from the east of the Mediterranean Basin were the most phenotypically similar to modern cultivars, particularly the North-American ones, but yield and days to heading allowed distinguishing both subpopulations.The conjunction of a profuse molecular analysis with a comprehensive phenotyping of the germplasm panel allowed a reliable distinction of genetic groups and the identification of phenotypic traits differentiating landraces from modern cultivars and among genetic subpopulations. Except for the number of spikes and grain weight, the remainder 19 phenotypic traits assessed in the current study properly differentiated old germplasm from the improved one. Landraces from different Mediterranean geographical regions could be distinguished by their different NS, GFR, PH and DMAt molecular level, the identification of different haplotypes among SPs affecting agronomic performance within QTL hotspots will be of special interest for designing future breeding lines through MAS including germplasm from different Mediterranean regions.The original contributions presented in the study are included in the article/CR and JS conceived the project and carried out the statistical analyses and outlined the manuscript. CR, DV, and KA assembled and purified the germplasm collection. CR and DV performed field evaluations. JS performed molecular analyses. All authors wrote the manuscript and approved the final version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.