A reliable diagnosis of adult Attention Deficit/Hyperactivity Disorder (ADHD) is challenging as many of the symptoms of ADHD resemble symptoms of other disorders. ADHD is associated with gambling disorder and obesity, showing overlaps of about 20% with each diagnosis. It is important for clinical practice to differentiate between conditions displaying similar symptoms via established diagnostic instruments. Applying the LightGBM algorithm in machine learning, we were able to differentiate subjects with ADHD, obesity, problematic gambling, and a control group using all 26 items of the Conners’ Adult ADHD Rating Scales (CAARS-S: S) with a global accuracy of .80; precision  ranged between .78 (gambling) and .92 (obesity), recall (sensitivity) between .58 for obesity and .87 for ADHD. Models with the best 5 and best 10 items resulted in less satisfactory fits. The CAARS-S seems to be a promising instrument to be applied in clinical practice also for multiclassifying disorders displaying symptoms resembling ADHD. According to the European Consensus Statement on diagnosing and treating adult ADHD2 the gold standard in diagnosis has four components: (a) a DSM-based clinical interview entailing the specific assessment of adult ADHD symptoms, (b) standardized questionnaires for assessing adult ADHD symptoms, (c) comorbidity assessment, and (d) the appraisal of school- or work certificates. This elaborate procedure might not be readily translatable in everyday clinical practice for various factors, e.g., economic restrictions, time limits regarding the diagnostic process or simply too little knowledge about adult ADHD2.Today there is general agreement that Attention Deficit/Hyperactivity Disorder (ADHD) often persists into adulthood with a prevalence rate of ~ 2.8% for adult ADHD2. Impaired concentration, a symptom shared with many disorders such as major depression, dysthymia, posttraumatic stress disorder or generalized anxiety disorder, might resemble the inattentiveness of adult ADHD. Restlessness and excessive talking, elements of ADHD’s hyperactivity component, may be difficult to differentiate from the psycho-motor agitation associated with mania, hypomania, major depressive disorder or generalized anxiety disorder. Impulsivity as apparent in ADHD may be difficult to distinguish from characteristics like those in manic or hypo-manic episodes, or from impulsive behavior inherent to borderline personality disorder and other disorders related to poor impulse control .Another reason why adult ADHD is so hard to diagnose reliably is that many ADHD symptoms resemble those of other disorders5. Specifically, disorders such as developmental disability, depressive disorder, and anxiety disorders3 as well as disorders associated with emotion dysregulation, a common symptom in ADHD6, co-occur with adult ADHD. For instance, personality disorders, especially borderline personality disorder (BPD) is closely associated with ADHD according to a recent review7. ADHD is also associated with pathological gambling, as about 20% of people with gambling disorder also experience ADHD8 and path analysis revealed emotion regulation to be a mediator between ADHD and gambling disorder9. To differentiate between pathological gambling and ADHD is important, as impulsivity due to ADHD might respond to psychostimulant treatment and thus attenuate the impairment associated with pathological gambling. A recent review on the connection between obesity and adult ADHD revealed that individuals with adult ADHD have higher odds of being overweight, a higher-than-average body mass index (BMI) score, and are significantly more often affected by obesity than subjects without ADHD; among patients seeking bariatric surgery, 20.9% had also been diagnosed with ADHD10. Again, reducing impulsivity through psychostimulant treatment could enhance obesity treatment. In summary, we highlight that the older the patient, the more difficult it might be to establish whether a patient with a history of inattention, hyperactivity, impulsivity, low self-esteem, and deficits in executive functions has ADHD, another disorder or both, since various other disorders might be associated with the deficits observed. Further, other disorders such as obesity and pathological gambling might be comorbid with ADHD and respond to psychostimulant treatment if diagnosed correctly.In fact, adult ADHD is a highly comorbid disorder13. However, problems arise when the aim is to distinguish adult patients with ADHD from other patient groups exhibiting similar symptoms, for instance anxiety. Under those circumstances, the specificity of rating scales is impaired when employed to inform a differential diagnosis14. With respect to BPD, common rating scales can differentiate patients with adult ADHD on the domain of severe impulsiveness, but not with respect to disturbed impulse control, disinhibition, hyperactivity or attentional control15. In general, false positive rates increase dramatically when using rating scale assessments. McCann and Roy-Byrne12 tested the ability of three different rating scales—the Adult Rating Scale (ARS), the Attention-Deficit Scale for Adults (ADSA), and the Symptom Inventory for ADHD (SI-ADHD)—to discriminate adult patients with ADHD from adult patients with major depression, bipolar disorder, anxiety disorders, and substance abuse/dependence disorders. Based on criterion cut-off scores of those inventories, individuals diagnosed with major depression or dysthymia yielded up to 73.9% false positives . Solanto et al.16 tested the predictive value of the Brown Attention Deficit Disorder Scale and a Continuous Performance Test and concluded that sensitivity and specificity parameters provided no meaningful contribution to the differential diagnosis of ADHD and internalizing disorders.There is a large body of literature on differentiating adult ADHD from healthy/community control groups—efforts that usually result in satisfying differentiation rates for rating scales17. Similar results have been found for translations of the CAARS, i. e. the German adaptation13. However, van Voorhees et al.18 examined the ability of the CAARS to differentiate between ADHD and other axis I disorders associated with attention problems, and found that patients with ADHD were likely to be indistinguishable from patients with anxiety and mood disorders when solely relying on the CAARS ratings-scales for diagnosis. Furthermore, there are gender effects demonstrating that females with ADHD are harder to differentiate from other patient groups than males19.The Conners’ Adult ADHD Rating Scales (CAARS) are well established and assess specific adult ADHD symptoms based on DSM-IV criteria with norms for males/females and four different age groups. The reported psychometric properties of the CAARS are highly satisfying, and they discriminate patients from healthy control subjects well The goal of the current study was thus to establish whether the CAARS could discriminate between different patient groups presenting with symptoms similar to and/or frequently/accompanying adult ADHD, specifically disorders associated with emotion dysregulation and the lack of behavioral inhibition like obesity and problematic gambling. For control purposes, a healthy control group was added to the sample.Figure 20 Precision  ranged between .86 (problematic gambling) and .96 (obesity), recall (sensitivity) between .83 for obesity and .95 for ADHD, and the F1 score, which is the harmonic mean between precision and recall, ranged between .87 (problematic gambling) and .93 (ADHD). We can consider all these values high. Some misclassification occurred in that 33 (9.1%) of individuals with problematic gambling had been classified as controls and 41 (9.9%) of controls as individuals with problematic gambling.In the training data, individual AUC values were very high in all groups. The confusion matrix of the multiclassification problem in Fig. 20. Precision  ranged between.78 (problematic gambling) and .92 (obesity), recall (sensitivity) between .58 for obesity and .87 for ADHD, and the F1 score, which is the harmonic mean between precision and recall, ranged between .71 (obesity) and .82 (controls & ADHD). These values can be regarded fair to high. Some misclassification occurred in that 26 (16.8%) of individuals with problematic gambling had been classified as controls and 21 (11.8%) of controls as individuals with problematic gambling.In the test data, all groups revealed very high individual AUC values. The confusion matrix of the multiclassification problem in Fig. The feature importance plot in Fig. We also applied logistic regression and support vector machines (SVM) to compare our results to other models. We report detailed results for our best fitting model. Among the three models, SVM and LightGBM demonstrate the best performance with slight fluctuations in metrics in favor of one or another, which partially depends on the split of the dataset Table . It shou20. Precision  ranged between .55 (obesity) and .72 (controls), recall (sensitivity) between .32 for obesity and .77 for ADHD, and the F1 score, which is the harmonic mean between precision and recall, ranged between .41 (obesity) and .73 (ADHD & controls). These values can be considered low to moderate. A considerable amount of misclassification occurred in the obesity group. There was also misclassification in that 88 (24.3%) of individuals with problematic gambling were classified as controls, and 74 (17.9%) of controls as individuals with problematic gambling.To optimize the model and eliminate redundant information, we calculated a model with just the best five overall features and reclassified the subjects in our sample. In the training data, individual AUC values were again very high in all groups. The confusion matrix of the multiclassification problem in Fig. 20. Precision  ranged between .52 (obesity) and .73 (controls), recall (sensitivity) between .40 for obesity and .75 for ADHD, and the F1 score, which is the harmonic mean between precision and recall, ranged between .45 (obesity) and .71 (controls). These values can be considered low to moderate. Some misclassification occurred in that 16 (40.0%) of subjects with obesity had been classified as having ADHD, 35 (22.6%) of individuals with problematic gambling were classified as controls, and 38 (21.3%) of controls as individuals with problematic gambling.In the test data, individual AUC values were again very high in all groups. The confusion matrix of the multiclassification problem in Fig. 20. Precision  ranged between .74 (obesity) and .81 (ADHD & controls), recall (sensitivity) between .53 for obesity and .89 for ADHD, and the F1 score, which is the harmonic mean between precision and recall, ranged between .61 (obesity) and .85 (ADHD). These values can be regarded as fair to high. Misclassification occurred in the group with obesity, as 21 (22.1%) had been classified as individuals with ADHD. Misclassification also occurred in that 54 (14.9%) of individuals with problematic gambling had been classified as controls and 70 (16.9%) of controls as individuals with problematic gambling.To optimize the aforementioned model, we calculated one with the best 10 overall features and reclassified the subjects in our sample. In the training data, all groups’ individual AUC values were again very high. The confusion matrix of the multiclassification problem in Fig. 20. Precision  ranged between.53 (obesity) and .77 (controls), recall (sensitivity) between .40 for obesity and .80 for ADHD, and the F1 score, which is the harmonic mean between precision and recall, ranged between .46 (obesity) and .75 (controls & ADHD). These values can be considered fair to good. Some misclassification occurred in that 15 (37.5%) of subjects with obesity had been classified as individuals with ADHD, 31 (20.0%) of those with problematic gambling had been classified as controls, and 31 (17.4%) of controls had been classified as individuals with problematic gambling.In the test data, individual AUC values were again very high in all groups. The confusion matrix of the multiclassification problem in Fig. A summary of model evaluation parameters for the three presented models can be found in Supplementary Table 10 were very well distinguishable from each other as well as from the control group. The first data analysis using all 26 items of the CAARS:S and age and gender resulted in very high AUC values for all groups and revealed high accuracy of .80 as well as a high kappa coefficient of .71. These positive predictive values were fair to high in this analysis, as were the sensitivity values and harmonic mean F1. Correct classification rates were acceptable; 16.8% of individuals with problematic gambling had been falsely classified as belonging to the control group and 11.8% of the control group falsely classified as belonging to the group with problematic gambling. All further analyses revealed less satisfying classification rates, that is, attempts to optimize the model with just the best five and ten overall features were not as successful. With respect to the four different groups, various items in the CAARS-S: S proved important for classification. We found that for the control group, the items “failures in the past make it hard for me to believe in myself”, “being mentally absent in everyday activities” and “keeping focused on things” were most relevant for classification; the three items that emerged broadly for the group with ADHD target the core symptom domains of ADHD ; for people with obesity, temper-bouts and concentration were also most relevant, but further items concerning self-confidence (wish for more trust in one’s own abilities); for individuals with problematic gambling it was interference in other peoples´ activities and being highly self-critical were the most important classification items, as was male sex. The latter is probably due to the very high rate of males in this group (> 80%), one that exceeded those of the other three groups. In fact, the representative population-based study that provided the data for this study revealed that problematic gambling is a primarily male disorder, with significantly more males fulfilling more of this disorder’s criteria21. In childhood, significantly more boys (14%) than girls (6.3%) are affected by ADHD22; this difference remains significant in adult ADHD (OR 1.6), but the difference is not nearly as large (3.4% to 2.2%)1. With our study’s rate of 60.5% males with ADHD, this reflects the smaller difference in adulthood well, and contrary to former studies19 gender was not a relevant factor for classification. While other studies have demonstrated that differentiating females with ADHD in comparison to other patient groups with the CAARS was difficult19, we achieved such differentiation successfully in this study. The prevalence rates of obesity for men and women in Germany are similar23, though concerning morbid obesity (BMI ≥ 40 kg/m2), females’ rates (2.8%) are more than twice that of males (1.2%) in Germany according to a recent meta-analysis24, and our data. More females were enrolled in our control group, as is usually the case in voluntary studies25 like ours. Although the latter groups would have accommodated the expectation of sex differences, those only appear relevant when classifying problematic gambling.Our results demonstrate that with machine learning analyses, the various groups of patients we assumed to be similar in terms of impulse control and emotion regulationStructured clinical interviews were applied to assess a broad range of disorders and were used in the different patient samples to establish primary disorders. As no specific information on ADHD symptoms is available apart from CAARS ratings, we performed additional descriptive analyses on groups with obesity, problematic gambling and healthy controls , which can be avoided by using LightGBM. Besides extra work this can make the quality of the forecast some kind of unstable and will highly depend on the values of the train dataset. To maintain good performance, SVM must be recalibrated and recalculated more often and much more attention must be paid to the value range of a newly incoming data set.Based on our experience and results of many competitions, we would not use SVMs for really challenging real world problems. Anything beyond a lab problem might be better addressed with a different algorithm. We are not asserting that SVM is a poor algorithm, but the probability of it failing on a real-world problem is higher compared to some other more sophisticated algorithms.27. In a visual GO/NOGO task, special ERP components were able to differentiate between adult ADHD subjects and controls with an accuracy of 92% using SVM with tenfold cross-validation28. Evaluating PET imaging and genetic predictors within the serotonergic system, an accuracy of .82 could be achieved for classification of ADHD and controls using Random Forest with fivefold cross-validation29. The Random Forest approach was used to investigate how multiple genetic and environmental factors jointly contribute to ADHD, or to examine whether hyperactivity persists in male and female adults with ADHD31. Teicher et al. were able to demonstrate that a computerized activity measure was best able to differentiate between adults with ADHD and controls (AUC = .83)31. This study’s promising results demonstrate that such analyses can be successfully applied to differentiating various patient groups with ADHD-like symptoms. Studies explicitly based on self-rating scales could not be found which stresses the importance for further research in this area. Connecting the literature to our analyses, we conclude that research groups in the area of ADHD relied predominantly on SVM, however the Random forest approach was also used29. It is known that in some challengeable cases GBMs may outperform Random Forest models. Based on the literature we therefore decided to go deeper and apply an even more sophisticated algorithm (LightGBM) trying to solve this issue.Machine learning has been used in ADHD in order to differentiate subgroups with the help of neurophysiological measures. Based on power spectra EEG recordings during four different tasks, the support vector machine (SVM) method with tenfold cross-validation resulted in about 90% correct classifications between ADHD subtypes while the correct classifications between ADHD and controls was just about 70%. ADHD subgroups were regarded as being more homogeneous than the controls which should explain the differences9. Overall, only 13 patients with ADHD (5%) were misclassified in the first test data analysis using the complete feature set of the CAARS-S. This is an excellent rate and highly important, as the CAARS is known to have successfully differentiated patients from healthy controls13, but differential diagnoses proved much more difficult18, especially for females19 or other rating scales16. The classification with the best 5 and 10 features was not as successful, but that is not surprising due to the overlap between ADHD and obesity and between ADHD and gambling disorder that are both around 20% and the information based on only 5 or 10 features does then not prove to be sufficient. Nevertheless, the CAARS-S: self-rating seems to be a promising instrument to apply in clinical practise also for multiclassifying disorders displaying symptoms resembling those of ADHD.To summarize our findings: the short version of the CAARS self-rating proved to be a useful instrument to classify different groups of patients that reveal overlapping symptoms in ADHD’s emotion-regulation and impulse-control domains2 and overlaps, for instance, with disorders in the internalizing 3 and externalizing spectrum  as well as with personality disorders4. It would have been beneficial to have included such disorders. However, mental health disorders are prevalent both in patients seeking bariatric surgery 32 as well as in people with problematic gambling (with high comorbidity of axis I and axis II disorders). Of the individuals with problematic gambling in this study, the majority (> 80%) had another mental disorder according to axis I and about a third of this sample also on axis II33. As mentioned before, empirical data also reveal ADHD’s high prevalence among individuals with obesity10. Further, we only used a community convenience sample in whom we did not control for psychopathology symptoms and did not apply the DIVA-interview. We just asked whether a mental disorder had ever been diagnosed or not. Thus, a certain amount of noise is probable within this control sample. Our study results are relevant, as we were able to successfully differentiate our groups with great specificity despite our patients’ high comorbidity—thus highlighting the CAARS-S: S specificity.We were unable to recruit patient groups to participate in this study apart from those with obesity and problematic gambling. As stated in our introduction, ADHD is highly comorbid16. Future studies should thus aim to include groups of patients with a range of different disorders and diagnostic measures to test differential diagnostic properties.Second, we only used the CAARS short self-rating version. Applying the long self- and observer CAARS version would also have been interesting, as would using different diagnostic instruments  have beenA total of n = 1629 subjects participated in the study and completed the short form of the CAARS (CAARS-S: S) consisting of 26 items. Of those, n = 1037 belonged to one of the clinical groups , while n = 592 had no diagnosed disorder (referred to as our control group).https://www.divacenter.eu/Content/VertalingPDFs/German%20DIVA%202.0_FORM.pdf). The Conners’ Adult ADHD Rating Scales (CAARS-L self- and observer-ratings), and the Qb+35 were also used to confirm the diagnosis. The Amsterdam Short Term Memory Test (AKGT) was additionally applied as a symptom validity measure and to identify patients with severe attention problems36. The diagnosis was based on the DIVA 2.0 results in order to fulfill DSM-IV diagnostic criteria.Patients with ADHD were individuals newly diagnosed at the adult ADHD outpatient clinic at Philipps University Marburg. They were all medication-naïve and examined by experienced, licensed clinical psychologists relying on a detailed clinical history, and the structured diagnostic interview for ADHD in adults (DIVA 2.0), a DSM-IV based clinical interview assessing the ADHD core symptoms in childhood and adulthood, as well as psychological domains often impaired in adult ADHD (2) and were examined during their routine preoperative psychosomatic evaluation. This group’s mean BMI was 47.1 kg/m2 with a standard deviation of 8.47. Data regarding depressive symptoms were available for 123 of the 135 patients with obesity. Of those, 47.2% scored above the threshold for major depressive disorder of the Patient Health Questionnaire depression module. Overall, 41.5% of the sample met the criteria for binge eating disorder assessed with the German version of the Eating Disorder Examination interview. Gambling disorder as well as other potential comorbidities were assessed with the German version of the ICD module of the Structured Clinical Interview for DSM-IV research version (SCID-ICD). No gambling disorder was observed in any of the patients with obesity.Data on people with obesity were gathered at the Department of Psychosomatic Medicine and Psychotherapy of the Hannover Medical School (MHH). They consisted of bariatric surgery candidates presenting morbid obesity  and from different populations with a high risk of gambling problems All members of the community control group were gathered as a convenience sample at Philipps University Marburg; there was no thorough assessment, but participants were just asked whether a mental disorder was ever diagnosed or not. Table 39: assesses ADHD core-symptoms and related problematic behavior in adults 18 years of age and older. Symptoms are rated on a Likert-scale from “0”  to “3” (very much/very frequently). The short forms of the instrument (CAARS-S/O: S) used in this study consist of 26 items assessing ADHD core-symptoms and self-concept (see Table tt = .84–.91). We obtained factorial, concurrent and criterion validity for all scales13. Cronbach’s alpha values for this sample ranged from α = .76 to .81 and were satisfactory.The German adaptation of the CAARS  algorithm and the hyperparameter tuning algorithm hyperopt. This is a gradient boosting framework that uses tree based learning algorithms. As LightGBM uses tree-based learning algorithm, the way of feature selection is identical to one of decision trees. In our model we used a Gradient Boosting Decision Tree algorithm that in turn typically used the classical CART (Classification and Regression Trees) algorithm that selects the split predictor, which maximizes the split-criterion gain over all possible splits of all predictors used on iteration. The exact parameters used are found in our Supplementary material Table .40. To assess model performance of the multiclass problem, confusion matrices of the training and test data were calculated and parameters like accuracy, kappa coefficient, precision , recall (sensitivity), and the F1 score as a harmonic mean between precision and recall were extracted20. Feature importance per group was examined and the most important five and ten features were used to build a classification model in the form of a further shortened test which was then evaluated as described above.The data were split into training data  and test data . We split our data on training and test datasets with a classical relationship 70/30 considering the stratification of our classes. The analysis was performed in such a way that neither during the training nor during the hyperparameter tuning the model has seen the test dataset, which served as “new” incoming data. The train dataset underwent an additional split under k-Fold cross validation during the hyperparameter tuning. K-Fold cross-validation by itself was not an object of a hyperparameter tuning, it was used in order to evaluate the generalization capacity of the selected hyperparameters. Number of folds was selected as 5 (default), which in this case corresponds to our empirical observation considering the desired generalization capacity and the sample size. Log loss for multi-class classification was used as an evaluation metric, 100 Monte Carlo simulations were undertaken. Individual Receiver Operating Characteristic Curves (ROC) were calculated per group and the Area under the Curve (AUC) was used to assess model performanceThe authors assert that all procedures contributing to this work comply with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975, as revised in 2008. The study was approved by the local review board of the institute of psychology, Philipps University Marburg, as well as by the review boards of the departments of medicine at the Universities of Hannover  and Greifswald. Written informed consent was obtained from all participants, and their confidentiality was assured. Subject data was collected from 2010 to 2013 through convenience sampling at the participating centers. All subjects were provided with a short study description and were asked to fill out the short CAARS self-rating questionnaire. For analyses, data were sent to the Department of Psychology at the Philipps University Marburg.Supplementary Information


Then the correlations between the expression of HSPG2 and a variety of important clinical parameters, such as median white blood cell (WBC) count and bone marrow (BM) blasts, were further analyzed. The expression level of HSPG2 was significantly upregulated in AML patients at the time of diagnosis, downregulated after complete remission and then elevated again at relapse. Moreover, HSPG2 expression was associated with median WBC count (P < 0.001), median hemoglobin (P = 0.02), median platelet count (P = 0.001), and BM blasts (P < 0.001) in AML patients. Patients with high HSPG2 expression had both worse overall survival (OS) (P = 0.001) and poorer leukemia-free survival (LFS) (P = 0.047). In the multivariate analysis model, HSPG2 was identified as an independent prognostic biomarker of AML. Taken together, these results indicate that HSPG2 overexpression was associated with poor prognosis in AML patients, and may be a prognostic biomarker and therapeutic target of AML.Heparan sulfate proteoglycan 2 (HSPG2), also known as perlecan, is a large multi-domain extracellular matrix proteoglycan, which contributes to the invasion, metastasis and angiogenesis of solid tumor. However, very little is known about the effect of HSPG2 on acute myeloid leukemia (AML). This study aims to investigate the prognostic value of the HSPG2 gene in terms of overall survival and leukemia-free survival in patients with AML. Bone marrow mononuclear cells (BMMCs) from 4 AML patients and 3 healthy controls were processed for RNA-Sequencing (RNA-seq). The mRNA expression level of HSPG2 in BMMCs and CD34 Genetic abnormalities, including chromosomal abnormalities, gene mutations and gene expression abnormalities, involved in the pathogenesis of AML. For example, mutations in NPM1, FLT3-ITD, and c-KIT, as well as aberrant expression of BCL-2, MN1, WT1, and MDR1 provide some clues for evaluating the prognosis of AML patients5. Although most AML patients achieve complete remission (CR) after chemotherapy and allogeneic hematopoietic stem cell transplantation , the 5-year overall survival rate of AML patients is still poor6. Drug resistance and relapse are the two main reasons that affect the survival of patients after transplantation. Therefore, it is of great clinical significance to identify new biomarkers for predicting prognosis and optimizing individual treatment strategies.Acute myeloid leukemia (AML) is a group of malignant myelopoietic stem/progenitor cell diseases, characterized by abnormal proliferation of primitive and immature myeloid cells in bone marrow and peripheral blood9. 1p36 is a Carcinoma Prostate Brain (CAPB) locus that hosts a series of oncogenes for prostate cancer and the brain tumor10. Functionally, HSPG2 is one of the major components of the vascular extracellular matrix and basement membranes11. Previous studies have showed that high expression of HSPG2 might be an indicator of prostate cancer grade, invasion potential and distant metastasis12. Besides, it is reported that the overexpression of HSPG2 predicts poor survival in patients with oligoastrocytoma and oligodendroglioma14. An in vitro study found that HSPG2 is actively synthesized by bone marrow derived cells and the abnormal expression of HSPG2 might play a vital role in hematopoietic cell differentiation15. However, the role of HSPG2 remains unknown in AML.HSPG2 (Perlecan), a heparan sulfate proteoglycan, is a protein encoded by HSPG2 gene (maps to 1p36.12 in the human genome)In this study, RNA-sequencing (RNA-seq) and differential expression analysis found that the overexpression of HSPG2 was higher in AML patients significantly than in healthy controls. The role of HSPG2 remains unknown in AML, although overexpression of HSPG2 has been well studied in other diseases. Our research aimed to detect the expression of HSPG2 in AML patients and assess the influence of HSPG2 expression on the clinical characteristics and prognosis, which might contribute to explore new biomarker and therapeutic target of AML.Bone marrow specimens and clinical data were obtained from patients who were diagnosed with AML between 2014 and 2017. In total 151 AML patients and 11 healthy controls were enrolled in this study. The use of bone marrow specimens was approved by the Ethics Committee of the First Affiliated Hospital of Chongqing Medical University (2020–264). Written informed consent was obtained from all participants. Bone marrow mononuclear cells (BMMCs) from the participants were separated by density-gradient centrifugation with Lymphocyte Separation Medium . The main clinical and laboratory features of the patients were presented in Table + cells and AML bone marrow CD34+ cells were isolated from BMMCs by using EasySepTM Human CD34 Positive Selection Kit II . Briefly, 1 × 108 cells were incubated with 20 μL Selection Cocktail for 30 min at room temperature. Then, the cells were incubated in a magnet with 15 μL RapidSphereTM for 5 min at room temperature. After pouring off the supernatant, cells were resuspended in 1 mL of PBS to pick up the magnet coupled with CD34+ cells in five times in order to yield a high purity (>95%). Finally, the purity of collected CD34+ cells was estimated by flow cytometry .Normal bone marrow CD34The SKM-1, an AML-MDS cell line, was provided by Professor Jianfeng Zhou working in Tongji Medical College of Huazhong University of Science and Technology . Chronic myeloid leukemia cell line K562 was provided by Chongqing Key Laboratory of Translational Medicine in Major Metabolic Diseases. The HS-5, a human bone marrow stromal cell line, was provided by Department Molecular Diagnostic Center for Clinical Medicine, the First Affiliated Hospital of Chongqing Medical University . Human peripheral blood leukemia T cells line Jurkat was provided by Children’s Hospital of Chongqing Medical University .2.HS-5 cells were cultured in DMEM medium  with 10% fetal bovine serum  and 1% penicillin/streptomycin . Three human leukemia cell lines  were cultured in RPMI 1640 medium  supplemented with 10% FBS  and 1% PS . The cells were cultured in a humid atmosphere at 37 °C with 5% CO−ΔΔCt.Total RNA was isolated from each sample using trizol reagent . Complementary DNA (cDNA) was synthesized from total RNA with reverse transcription kit  according to the manufacturer’s instructions. RT-qPCR was performed using CFX96 Real-Time PCR Detection System . The total reaction volume was 10 µL and was prepared as follows: 5 µL of TB Green , 0.4 µL of each primer (10 µmol/L), 1 µL of cDNA template (0.5 ng/µL), and 3.2 µL of ddH2O. The cycling conditions were as follows: 95 °C for 30 s, followed by 40 cycles at 95 °C for 5 s and 60 °C for 30 s. Transcript levels were normalized vs. β-actin expression. The gene expression was calculated using the formula 2The primers sequences were as follows:HSPG2 Forward 5’-GACATCGCCATGGATACCAC-3’Reverse 5’-CAGGACAAGCCAGAATAGCC-3’β-actin Forward 5’-CATTGCCGACAGGATGCAG-3’Reverse 5’-CGGAGTACTTGCGCTCAGGA-3’.Total protein was harvested from each sample using RIPA lysis buffer  supplemented with 1 µM phenylmethanesulfonyl fluoride . Then, the extract was digested with 0.01 units/ml heparinase III  at 37 °C for 3 h. Thirty microgram of protein was separated by sodium dodecyl sulfate-polyacrylamide gel electrophoresis  and transferred onto polyvinylidene fluoride membranes . Subsequently, membranes were blocked with 5% non-fat milk for 2 h at room temperature, followed by incubation with primary antibodies at 4 °C overnight. The primary antibodies used in this study were anti-HSPG2  used at 1:1000 and anti-β-actin  used at 1:5000 diluted in primary antibody dilution buffer . The membranes were washed and exposed to corresponding horseradish peroxidase (HRP)-conjugated goat anti-rat  or goat anti-rabbit  secondary antibodies diluted in TBST buffer for 1 h at room temperature. Finally, the protein bands were visualized with an enhanced chemiluminescence (ECL) kit , and the band intensity was analyzed using Vilber Fusion software .P-value <0.05, and FDR < 0.05.Paired-end libraries were synthesized by using the TruSeq™ RNA Sample Preparation Kit  following TruSeq™ RNA Sample Preparation Guide. Briefly, the poly-A containing mRNA molecules were purified using poly-T oligo-attached magnetic beads. Following purification, the mRNA is fragmented into small pieces. The cleaved RNA fragments are copied into first strand cDNA. This is followed by second strand cDNA synthesis using DNA Polymerase I and RNase H. These cDNA fragments then go through an end repair process, the addition of a single ‘A’ base, and then ligation of the adapters. The products are then purified and enriched with PCR to create the final cDNA library. Cluster was generated by cBot with the library diluted to 10 pM and then was sequenced on the Illumina NovaSeq 6000 . The library construction and sequencing were performed at Shanghai Sinomics Corporation, followed by data analysis they performed. The criteria for differential genes was set up as fold change >2 or <−2, 9/L; platelet count >100 × 109/L), and absence of clinical symptoms16. Overall survival (OS) was measured from diagnosis to last follow-up or death from any cause. Leukemia-free survival (LFS) was calculated from the day that CR was established until either relapse or death without relapse17. Mann–Whitney’s U test and Pearson Chi-square analysis/Fisher exact test were applied to compare the difference of continuous variables and categorical variables between two groups, respectively. The ROC curve and area under the ROC curve (AUC) were to assess the discriminative capacity of HSPG2 expression among patients and controls. The univariate Kaplan–Meier (KM) method and the multivariate Cox proportional hazard regression model were used to determine the survival curve and independent risk factors of AML patients. Spearman was used to analyze the correlation between the expression level of HSPG2 and clinicopathological factors. For all tests, a P-value < 0.05 was considered as statistically significant. All experiments were performed in triplicate.Data were analyzed using SPSS 22.0  and GraphPad Prism 5.01 . Complete remission (CR) was defined as normalization of peripheral blood and bone marrow , and human myeloid leukemia cell lines (SKM-1 cells and K562 cells) , but not in human T cell acute lymphoblastic leukemia cell line (Jurkat cells) . The mRNA expression of HSPG2 in CD34+/CD34− cells from AML patients was higher than that in CD34+/CD34− cells from healthy controls , as compared with the predictive capacity of BM blasts .ROC curve was used to examine the diagnostic value of HSPG2 in AML patients compared with healthy controls and found the AUC value to be 0.903 in whole-cohort AML : 0.842–0.965, low) and high HSPG2 expression (>4.39) (HSPG2high). The comparisons of clinical manifestations and laboratory features between two groups were presented in Table P < 0.001), lower hemoglobin (P = 0.002), lower platelets (P = 0.001), and higher BM blasts (P < 0.001). There were no significant differences in sex, age, French-American-British classification, karyotypes and treatment regimen between HSPG2low and HSPG2high patients. In addition, no significant correlations between HSPG2 expression and seven gene mutations were confirmed (P > 0.05).By the set cutoff value based on the ROC curve, the whole cohort of AML patients were divided into two groups, low HSPG2 expression (≤4.39) (HSPG2low patients than in HSPG2high patients (P = 0.011) . Notably, the expression level of HSPG2 was remarkably decreased in CR phase and then increased again in relapse phase Fig. . MoreoveP < 0.001, Fig. P = 0.047, Fig. P < 0.001, Fig. P = 0.025, Fig. P = 0.039, Fig. P = 0.056, Fig. In this study, the median follow-up period was 9 months. Kaplan–Meier survival analysis manifested that OS and LFS of patients with high HSPG2 expression were significantly shorter than those of patients with low HSPG2 expression in the whole cohort of AML patients (9/L vs. <30 × 109/L), BM blasts(≥70% vs. <70%), treatment regimen and gene mutations (mutant vs. wild-type). As shown in Table P < 0.001) and multivariate analysis (P = 0.004). Moreover, HSPG2 expression was associated with a poorer LFS in univariate analysis (P = 0.047). Intriguingly, as shown in the results of Spearman’s rank correlation analysis , as well as WBC count .Univariate analyses and multivariate analyses on OS and LFS in the whole-cohort AML patients were performed, including the expression level of HSPG2, age (≥60 vs. <60 years old), WBC 19, and Homeodomain-only protein homeobox (HOPX)20. However, owing to the absence of an observation of the overall process before and after disease progression of AML, those known differential expression genes are still unable to illuminate the mechanism underlying disease progression and prognosis. The factors involved in disease progression and pathogenesis in AML remain to be elucidated. The five genes with a high upregulation and the five downregulation genes from our results of RNA-seq, according to their relevance to the pathogenesis and prognosis of cancers were selected for further investigation. ABCA13 has identified as a marker of poor survival in metastatic ovarian serous carcinoma21 and was found to be highly overexpressed in our results. ADD1 has been associated with primary hypertension22 and was also highly overexpressed. CCAR1 is a transcriptional coactivator for nuclear receptors and exerts its functions as a key intracellular signal transducer of apoptosis signaling pathways23, which was overexpressed in AML patients in this study. GLT1D1 was shown to be highly upregulated and promoted immunosuppression and tumor growth in B-cell non-Hodgkin’s lymphoma24. YY1 could be regulated by CXCR4 to mediate transcriptional activation of MYC and BCLXL in AML cells25. ATXN7 was found to decrease in AML patients in our cohort, and low expression of ATXN7 was significantly associated with poor recurrence-free survival (RFS) and OS in Hepatitis B Virus-Related Hepatocellular Carcinoma26. SET protein regulated intracellular redox state and sustained autophagy in Head and Neck Squamous Cell Carcinoma (HNSCC) cells, which may play an important role in resistance to the death of HNSCC cells27. The expression of CDH4 was decreased in nasopharyngeal carcinoma cell lines, xenografts and primary tumor biopsies and may be a novel putative tumor suppressor gene28. UXT is a novel regulator of the polycomb repressive complex 2 (PRC2) and acts as a renal cancer oncogene that affects the progression and survival of clear cell renal cell carcinoma patients29. Moreover, the dysregulation expression of HSPG2 was shown to affect the growth, invasion, metastasis, and angiogenesis of several tumors32.Great progress has been obtained in our understanding of the pathogenesis of AML via the characterization of dysregulated genes, such as transcription factor SALL433. The abnormal expression of HSPG2 has been reported in some carcinomas. In accordance with our findings, previous studies have found mRNA level of HSPG2 was markedly increased in solid tumor tissue compared with that in normal tissue, such as metastatic melanomas34, oral squamous carcinomas35 and hepatocellular carcinomas36. Particularly, our data showed that HSPG2 expression decreased after complete remission and returned during relapse phase. Consequently, it is considered that HSPG2 can play a vital role in disease progression and be a new molecular maker in AML. It is noteworthy that HSPG2 is significantly highly expressed in CD34+ cells of AML patients, suggesting that perlecan is indeed secreted by the hematopoietic stem/progenitor cells in AML, even though that perlecan is known to be produced from mesenchymal stem cells37. This observation, combined with our data that the expression of HSPG2 was higher in human myeloid leukemia cell lines than in bone marrow stromal cell line, explained why HSPG2 expression in AML patients was higher than that in healthy controls as mentioned above. Collectively, this study showed HSPG2 was significantly increased in AML patients, which revealed that inhibition of HSPG2 is a potential therapeutic strategy in AML.In this study, we found that HSPG2 expression was elevated significantly in newly diagnosed AML patients compared with healthy controls, which was consistent with the results of RNA-seq. HSPG2 exists in various tissues and exerts effects on many biological processes38. Guo RJ and coworkers39 also reported that high WBC count was an independent prognostic factor for a shorter OS and EFS. It is reported that excess blasts are the strongest predictors for poor outcome of AML and associated with the disease progression41. This discovery, combined with our findings that the ability of HSPG2 for predicting 2-year survival is similar to that of BM blasts in AML based on the AUC value and that there was a positive correlation between HSPG2 expression and BM blasts, suggested that HSPG2 expression might be a prognostic factor for AML. It is well known that cytogenetic abnormalities and somatic mutations have been found at diagnosis in 20–70% of AML patients and are critical in evaluating the outcome of AML patients42. Herein, we also explored whether the expression of HSPG2 was correlated with karyotypes and gene mutations. Unfortunately, no significant correlation was observed, which may be due to limited sample size.In this study, the AUC value is preferable for HSPG2 expression to distinguish AML patients from healthy controls, including CN-AML and non-M3 AML, revealing it might be a promising diagnostic biomarker. The correlation between HSPG2 expression and several clinical and laboratory characteristics was then evaluated. One the one hand, high expression of HSPG2 was associated with lower hemoglobin and platelet count. On the other hand, high expression of HSPG2 was associated with higher WBC and BM blast count, which exhibited a positive correlation. A previous study demonstrated that high WBC count is a prognostic factor in patients with acute myeloid leukemia with the genotypic combination ‘NPMc (+) with FLT3-ITD’high patients after receiving induction chemotherapy, which indicated that HSPG2 expression might be related to the response to the treatment of AML. Furthermore, survival analysis revealed that patients with high HSPG2 expression had a poor prognosis in whole cohort AML, non-M3 AML and CN-AML. We also found that HSPG2 was an independent prognostic factor for OS based on univariate and multivariate analyses and for LFS based on univariate analysis. As reported in oligoastrocytoma and oligodendroglioma, high expression of HSPG2 could independently predict poor OS and RFS13. Hence, HSPG2 expression could be used to predict inferior survival and assess treatment outcome in AML. It is worth mentioning the treatment regimen is a crucial factor for OS and LFS in AML and we found that patients treated with hematopoietic stem cell transplantation (HSCT) had better OS and LFS compared with chemotherapy alone. Based on these results, we can draw the conclusion that the expression level of HSPG2 could be used as an important indicator of prognosis in AML. Certainly, more in-depth studies on large-scale samples of AML patients are needed to verify these findings.In addition, we observed that CR rate was significantly lower in HSPG2This study implied that the expression level of HSPG2 was upregulated in AML patients and correlated with adverse prognosis and disease progression in AML patients. To sum up, HSPG2 potentially might act as a pro-oncogene in the pathogenesis of AML, and further functional researches should be conducted to confirm its role in vivo and in vitro.In summary, this is the first study to report the relationship of HSPG2 expression with the clinical outcomes in AML patients. This study confirmed that overexpression of HSPG2 was an adverse prognostic indicator for AML, although the mechanism underlying the role of HSPG2 in AML remains to be illustrated.Table S1Fig. S1


To develop and evaluate machine learning algorithms to ascertain attention-deficit/hyperactivity (ADHD) and learning disability (LD) using diagnostic codes in the medical record.Diagnoses of ADHD and LD were confirmed in cohorts of children in Olmsted County of Minnesota based on validated research criteria. Models to predict ADHD and LD were developed using ICD-9 codes in a derivation cohort of 1057 children before evaluated in a validation cohort of 536 children.The ENET-MIN model using selected ICD-9 codes at prior probability of 0.25 has a sensitivity of 0.76, PPV of 0.85, specificity of 0.98, and NPV of 0.97 in the validation cohort. However, it does not offer significant advantage over a model using a single ICD-9 code of 314.X, which shows sensitivity of 0.81, PPV of 0.83, specificity of 0.98, and NPV of 0.97. None of the models developed for LD performed well in the validation cohort.It is feasible to utilize diagnostic codes to ascertain cases of ADHD in a population of children. Machine learning approaches do not have advantage compared with simply using a single family of diagnostic codes for ADHD. The use of medical record diagnostic codes is not feasible to ascertain LD.The online version contains supplementary material available at 10.1186/s12887-020-02411-3. Neurodevelopmental disorders such as attention-deficit/hyperactivity disorder (ADHD) and learning disabilities (LD) can have a long-term impact on learning as well as emotional, behavioral and social development , 2. KnowThe aim of this study was to develop and evaluate machine learning algorithms to ascertain ADHD and LD using diagnostic codes in the medical record. In prior work, we constructed a population-based birth cohort to study the relationship between early anesthesia exposure and developmental outcomes in children, including ADHD and LD . In thisThe derivation cohort was a propensity-matched cohort examining the association between anesthetic exposure and subsequent neurodevelopmental outcomes, including LD and ADHD . In summThe validation cohort was generated as part of an unpublished study to study the association of pediatric ICU admission and neurodevelopmental outcomes. A population-based birth cohort included children born in Olmsted County, MN during a 5 year period (1/1/2003–12/31/2007) with an ICU admission prior to age 4. Each child with an ICU admission was matched , maternal age (± 3 years) and education level) with a child who were not admitted to the ICU prior to age 4. These children were followed for up to 11 years after ICU admission (last follow up date 12/31/2013).All diagnostic codes from birth were available for all cohort members through the Rochester Epidemiology Project, a population-based medical records linkage system . For eacCases of ADHD and LD were ascertained by study personnel by manually review of clinical and school records of all children in the cohorts.ADHD cases were defined based on criteria previously described . The criLD cases were ascertained according to previously-described research criteria based on one of two formulas: an intelligence quotient (IQ)-achievement discrepancy formula and a low achievement formula . ChildreFor each outcome (ADHD or LD), we aimed to identify classifier algorithms with optimized predictive ability by comparing algorithm results with the confirmed cases. Machine learning models were developed and trained in the derivation cohort before applied to the validation cohort.We considered four methods for classification: Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression, Elastic Net (ENET) logistic regression, Classification trees (CART), and Stochastic Gradient Boosting (GBM). Inputs included ICD-9 diagnosis codes. To summarize, LASSO and ENET are regression analysis methods that perform both variable selection and regularization in order to enhance the prediction accuracy and interpretability of the statistical model produced. CART and GBM are tree-based methods, with the latter using boosting to combine many weak classifiers into a single strong classifier. Each method further implemented internal cross-validation selection of tuning parameters that affect model complexity of the classifier based on the misclassification error. For each method, we considered tuning parameters minimizing cross-validation mean misclassification error (MIN) and one maximizing regularization or pruning but still within 1 standard error (1SE) of the minimum misclassification error which may reduce overfitting.Because the prevalence of cases in the cohort is relatively low, the number of cases is less than the number of non-cases. By minimizing misclassification error misclassification is roughly equally likely to occur in those predicted/classified as case and non-case status; as a result, we would expect a higher proportion of true cases will be misclassified as compared to that of non-cases, leading to low sensitivity and high specificity. One approach to overcome this sensitivity/specificity imbalance is assigning more weight to the cases during model training, thereby increasing the cost of misclassifying a case as compared to a non-case. Several case-weight options were considered in the derivation dataset, modeling prior probabilities for cases ranging from 10 to 75% (representing the proportion of LD or ADHD cases after different weights are assigned).For each of these methods, additional factors were considered. The first was whether to include within the classifier all ICD-9 codes or a subset of codes that are plausibly related to the diagnosis in question. The former approach makes no presuppositions and uses all data, but may also increase the possibility of spurious associations or overfitting, especially as the number of events within the dataset is relatively modest and substantially less than the number of codes considered (3597). The latter approach may increase specificity, but may also miss important unanticipated associations. For the latter, experts in pediatric neurodevelopment, independently and without access to the data, developed a list of select ICD-9 codes that could conceivably indicate a diagnosis of LD or ADHD, respectively. The LD list included 38 unique ICD-9 codes and ADHD list included 34 unique codes , negative predictive value (NPV), and overall accuracy. Kappa was estimated for overall accuracy, controlling for the expected accuracy. Concordance is also described based on the numeric predicted probability of case status. We determined a priori that to identify children with these disorders from administrative data, a model with high sensitivity and PPV would be preferred.In the derivation cohort, results are presented using an internal non-parametric bootstrap approach. Only select models demonstrating strong sensitivity and PPV in the derivation cohort were carried forward to the validation cohort. Analyses were performed using R statistical software (R version 3.6.1) with the caret package which acts as a wrapper for functions in the glmnet, gbm, and rpart packages –17.The derivation cohort consisted of 1057 children who were born from 1996 to 2000, including 617 (58%) boys and 440 (42%) girls. The median  age at the time of code ascertainment was 16 (14–17) years. Among the study cohort of 1057 children, 175 (17%) children had a diagnosis of ADHD and 142 (13%) children had a diagnosis of LD as identified by previously established research criteria. The validation cohort consisted of 536 children who were born from 2003 to 2007, including 296 (55%) boys and 240 (45%) girls. The median  age at the time of code ascertainment was 9 (8–10) years. Among the validation cohort of 536 children, 62 (12%) children had a diagnosis of ADHD and 20 (4%) children had a diagnosis of LD.In the derivation cohort, the sensitivity and PPV for models using all ICD-9 codes showed little variation according to the four classification methods examined for a given prior probability  complex machine learning models using clinical diagnosis codes perform well in identifying ADHD cases but do not offer significant advantage over a simple model using a single ICD-9 code for ADHD, and 2) clinical diagnostic codes are of limited utility in ascertaining LD cases.Several previous studies evaluated the concordance between case ascertainment of ADHD using diagnostic codes with ascertainment using manual clinical records review, the latter using a variety of criteria to define ADHD. Gruschow et al. employed this design to review the medical records of patients with ICD-9 codes of 314.XX and a random sample of patients without this code among children who resided in New Jersey and received primary care from a single hospital network . One in Our analysis using a single diagnostic code as the criteria for ADHD ascertainment is most directly comparable to prior work. We find similarly high specificity (with correspondingly high NPV), showing that children without the ADHD diagnostic code are very unlikely to have ADHD. We also find that some children with the ADHD diagnostic code do not meet ADHD criteria, with PPV values consistent with Gruschow et al. if those with unknown status in their study are assumed to not have ADHD. The major difference from the prior work is the lower sensitivity in the current analysis, which likely reflects that some ADHD cases are identified through school procedures and are not reflected in the medical record. When taken together with prior work, these results suggest that study designs relying only on information from the medical record may underestimate the prevalence of ADHD. This limitation should be acknowledged in studies using such methods.The availability of all diagnostic codes in our dataset and of machine learning algorithms provides the potential for improving case ascertainment by analyzing not just the occurrence of a single family of diagnostic codes . As perhIn contrast to ADHD, the use of diagnostic codes in machine learning methods did not prove useful in ascertaining LD. Although it was possible to produce a model with good performance using all codes for the derivation cohort, performance was poor in the validation cohort. Performance was not as good in models including selected codes, and was degraded in the validation cohort. This finding shows that as a practical matter, is it not feasible to utilize diagnostic codes available in the medical record to ascertain LD cases. LD is typically diagnosed within the educational system, and it appears that these diagnoses are not accurately reflected in the medical record.Although complete access to all school and medical records in a geographically-defined population is a strength of this analysis, it also has several limitations. The study population includes one county in Minnesota that may not be representative of the United State population, with care provided by two healthcare systems, so that the results may not generalize to other settings. Criteria such as DSM definitions and clinical practices used to diagnose both ADHD and LD continue to evolve, have engendered some controversies , 21, andIn conclusion, it is feasible to utilize medical record diagnostic codes to ascertain cases of ADHD in a population, recognizing inherent limitations in sensitivity  and in PPV . Machine learning approaches do not have advantages compared with simply using a single family of diagnostic codes for ADHD. The use of medical record diagnostic codes is not feasible to ascertain LD cases.Additional file 1. Expert selected ICD-9 codes for ADHD.Additional file 2:Table S1. Performance of selected models in predicting attention deficit hyperactivity disorder diagnosis in the derivation cohort. Table S2. Performance of selected models in predicting learning disability diagnosis in the derivation cohort


N = 50 adults with DCD (n = 12), ADHD (n = 9), DCD + ADHD (n = 7), and control participants (n = 22). We replicated that there were no between-group differences for behavioral-level executive performance and motor response inhibition. However, on a physiological level, ERP components N200 and P300 differed between groups, particularly during successful response inhibition. These ERPs reflect potential endophenotypic differences not evident in overt behavior of participants with ADHD and/or DCD. This suggests a disorder specific employment of inhibition or general executive functions in groups of adults with DCD, DCD + ADHD, ADHD, or control participants.Developmental Coordination Disorder and Attention-Deficit/Hyperactivity Disorder are unique neurodevelopmental disorders with overlaps in executive functions and motor control. The conditions co-occur in up to 50% of cases, raising questions of the pathological mechanisms of DCD versus ADHD. Few studies have examined these overlaps in adults with DCD and/or ADHD. Therefore, to provide insights about executive functions and motor control between adults with DCD, ADHD, both conditions (DCD + ADHD), or typically developed controls, this study used a stop-signal task and parallel EEG measurement. We assessed executive performance via go accuracy and go reaction time, as well as motor response inhibition via stop-signal reaction time. This was complemented with analysis of event-related potentials (ERPs). Based on existing investigations of adults with DCD or ADHD, we expected (1) groups would not differ in behavioral performance on stop and go trials, but (2) differences in ERPs, particularly in components N200 (index of cognitive control) and P300 (index of attention and inhibition) would be evident. The sample included  Developmental Coordination Disorder (DCD) and Attention-Deficit/Hyperactivity Disorder (ADHD) are lifelong neurodevelopmental disorders known to co-occur in up to 50% of cases . The priAn obvious target for such research is to examine how inhibitory control and related underlying mechanisms differ in DCD and ADHD. First, inhibitory control is central to executive functioning . Second,To this end, we used the Stop-Signal Task (SST), as it places particularly high demands on motor response inhibition . ArguablThe SST is an opportune method for a closer look into inhibitory control and related executive processes . We further hypothesized that behavioral compensation among adults would relate to more robust differences in the EEG signals between groups, and more specifically, that they would be present in components P300 and N200 in line with symptoms of ADHD, DCD, or both conditions versus typically developing adults.Due to insufficient data in the DCD literature to make specific assumptions of the direction of amplitudes in P300 and N200 we aimed to examine effects reported in the literature comparing ADHD and typically developing individuals. Furthermore, we explore and report all differences found in the complete set of electrodes to build insights into patterns when comparing DCD and DCD + ADHD groups. In addition, we explored all possible distinctions between the ERPs in the DCD and ADHD groups. These comparisons provide important pilot evidence in relation to group differences in inhibition and related executive functioning processes as well.N = 59 participants were recruited at two sites (Germany and United Kingdom). Following EEG pre-processing and our criteria for the removal of outliers , a final sample of N = 50 was included in the present analyses with the same participants across behavioral and neurophysiological levels  years old. Groups included those with an existing diagnosis of ADHD (n = 9), DCD (n = 12), both ADHD and DCD (n = 7), and a control group (n = 22). In order to run more reliable analyses, we combined the participants from both sites to result in adequate group sizes . The control group reported no history of any psychiatric or other health conditions. Additionally, participants with ADHD were asked to not take ADHD-relevant medication on the day of the study session if they had the option. None of the participants included in the final sample reported taking such medication on the day of testing. The protocol was reviewed and approved by ethics committees at both sites (University of Mannheim and Oxford Brookes University).We administered the Adult Dyspraxia/DCD Checklist ADC; , it yielThe SST began with a black fixation cross at the center of the screen appearing for 500 ms. Next, a black shape cue (circle or square) was presented in the center of the screen surrounded by a black frame. On go trials, participants needed to press the key corresponding to the shape shown . On stop trials  participants were instructed to refrain from pressing a key. Here, the black frame was replaced by a blue frame after a variable delay known as stop-signal delay (SSD). The SSD ranged from 250 to 1,250 ms with an adaptive up-down staircase method  based on®  using the Psychtoolbox extensions  per shape. The stimuli were presented on computers with MATLABtensions  on a 16 Following recent guidelines on the SST set out by Prior to the main task, participants completed the ASRS v.1 for ADHD , the ADCM = 0.94  electrodes per participant needed to be corrected with topographical interpolations.A 64 channel system was utilized for electrophysiological recoding in a standard 10–20 system. FCz was used as the reference electrode and AFz as the ground  to exclude premature and late responses based on the criteria recommended by Participants were excluded if their average reaction times on go trials were larger than unsuccessful stop trial reaction times, thereby violating the independence assumption of the race model of the SST . FollowiAverage go reaction times were then computed for correct go trials and unsuccessful stop reaction times, with the latter including only responses in which a key was incorrectly pressed. The stop-signal reaction time (SSRT), or the estimated amount of time required to inhibit a response about to be executed, represents response latencies that were estimated for stop trials . The SSRTs were calculated with the block-based integration method . This meVerbruggen et al., 2013n = 5 participants .Following the calculations above, performance was further screened to increase reliability by removing outlying individuals with sub-optimal performance based on the lenient outlier criteria set by post hoc tests. We also conducted independent samples t-tests to compare the amplitudes of ERPs between participants with DCD and ADHD in particular. Kolmogorov–Smirnov tests revealed the control group had a non-normal distribution  for SSRTs, which prevents its comparison to the other groups for correct stop trials. All aforementioned group comparisons held before and after outlier removal. Statistical analysis was conducted in R (v. 3.6.2).Behavioral and neurophysiological results were compared with between-subjects One-Way ANOVAs and Tukey’s LSD M = 113.1, SD = 14.1), DCD + ADHD , ADHD , and the control group . Scores of 90 and above signify probable DCD, and scores over 80 signal potential risk for DCD , DCD + ADHD , controls , and the DCD group  where a score of 47 or higher is indicative of likely ADHD. Classifications by group and testing site are listed in As mentioned, participants were grouped based on their reported diagnostic history for DCD, ADHD, both conditions (DCD + ADHD), or no health conditions, and this was confirmed by self-reported symptoms. The average ADC scores for each group from highest to lowest were: DCD  = 4.15, p = 0.011]. As revealed in post hoc testing, this difference was driven by a significant lower accuracy in the DCD + ADHD  group compared to the control group . The average reaction times for go trials, unsuccessful stop trials, and correct  stop trials (SSRTs) were not significantly different between groups, whereby SSRTs were only compared between clinical groups  = 3.47, p = 0.024], C4 , C6 , FC2 , P4 , and P6  during successful inhibition . Post hoc tests revealed several of these differences were driven by the distinction in amplitudes between the ADHD and control groups . A further difference in C6 was driven by marginally significant differences between the DCD and ADHD groups  and ADHD and control groups . Finally, one electrode was implicated in unsuccessful inhibition : C2 , for which post hoc testing revealed differences between the ADHD  and control groups  = 3.06, p = 0.038], FC1 during unsuccessful inhibition , and T7 during correct go trials . Post hoc testing via Tukey’s HSD revealed these findings were driven by differences between the DCD + ADHD and control groups . However, in Fz this group difference was just marginally significant  = 3.29, p = 0.004] , F7  , and FT7  . During unsuccessful inhibition, the DCD group had heightened peaks in activation for P300 compared to the ADHD group for electrodes AF7  , F7  , and T7  .No significant differences between the ADHD and DCD groups were found during go trials, however, for P300 and N200 on stop trials, there were several noteworthy differences between the DCD and ADHD groups in particular. During successful inhibition, significantly higher amplitudes were present in P300 for the DCD group compared to the ADHD group in fronto-temporal electrodes AF7  , FC6  , C2  , and C6   during successful inhibition. Furthermore, there were significant differences between groups for N200 peaks in several electrodes during unsuccessful inhibition, including C1  , and C2  . In all aforementioned differences in N200, the DCD group showed a significantly more negative amplitude than the ADHD group.In addition, several differences in central and fronto-central electrodes were present between the DCD and ADHD groups for N200, including FC2 [This study lays important groundwork in the DCD literature by examining endophenotypic overlaps and differences in executive functions. This novel design includes a stop-signal task and neurophysiological measurements among adults with DCD, ADHD, both conditions, and typically developing individuals (control group). As expected, the behavioral results showed few differences between all groups. One difference was present in that the go accuracy of individuals with co-occurring DCD + ADHD was lower than for typically developing individuals. Also in line with our expectations, several differences were found as indexed by ERPs between all groups, with additional differences found in direct comparison of the DCD versus ADHD groups. The latter comparison showed that many electrodes had significantly different amplitudes for components P300 and N200 between the DCD and ADHD groups. There were several electrodes, especially in central locations, which indicated differences in amplitude for P300 and N200 components on stop trials. These differences were not present for go trials which signifies that the general presence of the stop-signal may activate a specific neurophysiological response. This is in line with other research comparing those with ADHD to typically developing individuals in various age groups e.g., . While NAside from go accuracy, those with combined DCD + ADHD did not perform significantly different from all other groups at the behavioral level. This result aligns with previous studies of adults with DCD or ADHD compared to typically developing individuals where no differences in behavioral results were present between groups . Due to The general absence of differences in overt behavior emphasizes the lack of diagnostic power of typical measures of accuracy and reaction times regarding differential diagnosis of DCD or ADHD in adults. Differences in task performance on other inhibition tasks  have previously been considered as indicators of possible neurological differences among those with ADHD in particular, but similarly are not sufficient for a diagnosis . This coEvidence at the neural level indicates there may be unique neural signatures in evoked potentials between the DCD and ADHD groups, supporting findings of other studies . In the Importantly, while behavioral performance did not differ between groups in the majority of parameters, inhibition and task engagement are not necessarily employed with the same underlying neural mechanisms across groups. It is unclear if these underlying mechanisms also translate to increased effort and/or fatigue, but this should be explored in more detail in future research. Given that compensation is more readily achieved when a task is less complex for DCD groups in particular e.g., , it may Overall, the present study provides several novel contributions to the DCD and ADHD literature. First, to our knowledge it is the first study to compare inhibition performance between adult participants with DCD versus ADHD using a SST, as well as group comparisons between those with DCD, ADHD, DCD + ADHD, and typically developing adults. It is also the first study to incorporate an additional layer of EEG measurement to examine such group differences during the inhibition of a motor response to a visual cue. While most research on symptom differentiation relies on self-report questionnaires , some stThere are several limitations of the present study which should be considered. First, the sample sizes were small, in particular those of the clinical sub-groups. A normality check performed due to unequal group sizes indicated SSRTs of the typically developing (control) group were not normally distributed, even though the typically developing adults comprised of the largest group of the four in this study. Nonetheless, normality checks for other groups passed. Small and unequal group sizes are not a new problem in DCD and ADHD research, especially when involving neuroscientific measurements e.g., . In thatA second limitation is the duration of the study sessions. Participants completed 768 trials of the stop-signal task, and this took most participants around 40 min. While the participants all took breaks between blocks, this can still be straining, especially to participants with difficulties in sustaining attention. There is evidence of this for the DCD + ADHD group in particular, who performed significantly worse than typically developing individuals in go accuracy, which are the least complex trials in the task. This could be explained by a difficulty managing competing cognitive resources of the task  and could be related to a higher symptom load in the DCD + ADHD group.Another limitation is that the mean performance measures  did not reveal many substantial differences but they do not confirm a null hypothesis for behavioral results in the present study. Our main focus was to examine if neurophysiological differences would differ between groups for components linked to attention and motor performance with the expectation that behavioral data would reveal no differences, as observed in other studies with related samples e.g., . More roAdditional limitations include the participant demographics . Also, participants were recruited in two different sites. While the laboratory equipment and practices of the researchers remained as consistent as possible, some differences between the testing locations existed, such as the visual angles for the task. On the other hand, it may be an advantage that multinational groups of individuals may help to generalize the findings and is a method that has been used before with DCD , may reflect the potential strength and success of compensatory mechanisms in individuals with DCD and/or ADHD. This study serves as an important foundation for future explorations into the overlapping executive functioning processes in DCD and ADHD.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation, to any qualified researcher.The studies involving human participants were reviewed and approved by University of Mannheim and Oxford Brookes University Ethics Committees. The patients/participants provided their written informed consent to participate in this study.EM, MM, KW, MZ, and GA were involved in the conception or design of the work, data interpretation, critical revision of article, and approval of the version submitted for publication. EM tested all participants, conducted data analysis of measures, behavioral data, electrophysiological data, conducted the literature review, and formulated the initial draft of the manuscript. MM developed the stop-signal task used in this study in Matlab and wrote R scripts for behavioral analysis and trigger recoding. MZ provided funding for participants recruited in Germany. KW provided funding for participants recruited the United Kingdom. All authors contributed to the article and approved the submitted 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.