A primer on the use of machine learning to distil knowledge from data in biological psychiatry.

Applications of machine learning in the biomedical sciences are growing rapidly. This growth has been spurred by diverse cross-institutional and interdisciplinary collaborations, public availability of large datasets, an increase in the accessibility of analytic routines, and the availability of powerful computing resources. With this increased access and exposure to machine learning comes a responsibility for education and a deeper understanding of its bases and bounds, borne equally by data scientists seeking to ply their analytic wares in medical research and by biomedical scientists seeking to harness such methods to glean knowledge from data. This article provides an accessible and critical review of machine learning for a biomedically informed audience, as well as its applications in psychiatry. The review covers definitions and expositions of commonly used machine learning methods, and historical trends of their use in psychiatry. We also provide a set of standards, namely Guidelines for REporting Machine Learning Investigations in Neuropsychiatry (GREMLIN), for designing and reporting studies that use machine learning as a primary data-analysis approach. Lastly, we propose the establishment of the Machine Learning in Psychiatry (MLPsych) Consortium, enumerate its objectives, and identify areas of opportunity for future applications of machine learning in biological psychiatry. This review serves as a cautiously optimistic primer on machine learning for those on the precipice as they prepare to dive into the field, either as methodological practitioners or well-informed consumers.

Predicting childhood and adolescent attention-deficit/hyperactivity disorder onset: a nationwide deep learning approach.

Attention-deficit/hyperactivity disorder (ADHD) is a heterogeneous disorder with a high degree of psychiatric and physical comorbidity, which complicates its diagnosis in childhood and adolescence. We analyzed registry data from 238,696 persons born and living in Sweden between 1995 and 1999. Several machine learning techniques were used to assess the ability of registry data to inform the diagnosis of ADHD in childhood and adolescence: logistic regression, random Forest, gradient boosting, XGBoost, penalized logistic regression, deep neural network (DNN), and ensemble models. The best fitting model was the DNN, achieving an area under the receiver operating characteristic curve of 0.75, 95% CI (0.74-0.76) and balanced accuracy of 0.69. At the 0.45 probability threshold, sensitivity was 71.66% and specificity was 65.0%. There was an overall agreement in the feature importance among all models (τ > .5). The top 5 features contributing to classification were having a parent with criminal convictions, male sex, having a relative with ADHD, number of academic subjects failed, and speech/learning disabilities. A DNN model predicting childhood and adolescent ADHD trained exclusively on Swedish register data achieved good discrimination. If replicated and validated in an external sample, and proven to be cost-effective, this model could be used to alert clinicians to individuals who ought to be screened for ADHD and to aid clinicians' decision-making with the goal of decreasing misdiagnoses. Further research is needed to validate results in different populations and to incorporate new predictors.

Daily cannabis use may cause cannabis-induced hyperalgesia.

BACKGROUND AND OBJECTIVES: Public opinion about cannabis as a medical treatment is generally favorable. As many as 35% of primary care patients report medical use of cannabis, most commonly for pain treatment. We designed a way to test whether cannabis helps chronic pain. METHODS: A retrospective cohort study was conducted to explore whether daily long-term cannabis use was associated with increased pain sensitivity using the cold pressor test (CPT) to measure pain tolerance. Patients who used cannabis every day were compared to patients who inhaled tobacco and control patients who never used tobacco or cannabis. The effect of cannabis use on CPT was assessed using a generalized linear model. RESULTS: Patients using cannabis daily had a median CPT of 46 s, similar to those who did not use cannabis but who inhaled tobacco (median CPT 45 s). Patients who used both cannabis and tobacco had the lowest CPT (median 26 s). The control group had a median CPT of 105 s. Cannabis use was associated with a significantly decreased pain tolerance (χ²(1) = 8.0, p = .004). The effect of tobacco on CPT was only marginally significant (χ²(1) = 3.8, p = .052). CONCLUSION AND SCIENTIFIC SIGNIFICANCE: This suggests a phenomenon similar to opioid-induced hyperalgesia; a drug that reduces pain short term, induces pain long term-opponent process. Daily cannabis use may make chronic pain worse over time by reducing pain tolerance. In terms of risk/benefit, daily cannabis users risk addiction without any long-term benefit for chronic pain.

Consortium neuroscience of attention deficit/hyperactivity disorder and autism spectrum disorder: The ENIGMA adventure.

Neuroimaging has been extensively used to study brain structure and function in individuals with attention deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) over the past decades. Two of the main shortcomings of the neuroimaging literature of these disorders are the small sample sizes employed and the heterogeneity of methods used. In 2013 and 2014, the ENIGMA-ADHD and ENIGMA-ASD working groups were respectively, founded with a common goal to address these limitations. Here, we provide a narrative review of the thus far completed and still ongoing projects of these working groups. Due to an implicitly hierarchical psychiatric diagnostic classification system, the fields of ADHD and ASD have developed largely in isolation, despite the considerable overlap in the occurrence of the disorders. The collaboration between the ENIGMA-ADHD and -ASD working groups seeks to bring the neuroimaging efforts of the two disorders closer together. The outcomes of case-control studies of subcortical and cortical structures showed that subcortical volumes are similarly affected in ASD and ADHD, albeit with small effect sizes. Cortical analyses identified unique differences in each disorder, but also considerable overlap between the two, specifically in cortical thickness. Ongoing work is examining alternative research questions, such as brain laterality, prediction of case-control status, and anatomical heterogeneity. In brief, great strides have been made toward fulfilling the aims of the ENIGMA collaborations, while new ideas and follow-up analyses continue that include more imaging modalities (diffusion MRI and resting-state functional MRI), collaborations with other large databases, and samples with dual diagnoses.

Predicting suicide attempt or suicide death following a visit to psychiatric specialty care: A machine learning study using Swedish national registry data.

BACKGROUND: Suicide is a major public health concern globally. Accurately predicting suicidal behavior remains challenging. This study aimed to use machine learning approaches to examine the potential of the Swedish national registry data for prediction of suicidal behavior. METHODS AND FINDINGS: The study sample consisted of 541,300 inpatient and outpatient visits by 126,205 Sweden-born patients (54% female and 46% male) aged 18 to 39 (mean age at the visit: 27.3) years to psychiatric specialty care in Sweden between January 1, 2011 and December 31, 2012. The most common psychiatric diagnoses at the visit were anxiety disorders (20.0%), major depressive disorder (16.9%), and substance use disorders (13.6%). A total of 425 candidate predictors covering demographic characteristics, socioeconomic status (SES), electronic medical records, criminality, as well as family history of disease and crime were extracted from the Swedish registry data. The sample was randomly split into an 80% training set containing 433,024 visits and a 20% test set containing 108,276 visits. Models were trained separately for suicide attempt/death within 90 and 30 days following a visit using multiple machine learning algorithms. Model discrimination and calibration were both evaluated. Among all eligible visits, 3.5% (18,682) were followed by a suicide attempt/death within 90 days and 1.7% (9,099) within 30 days. The final models were based on ensemble learning that combined predictions from elastic net penalized logistic regression, random forest, gradient boosting, and a neural network. The area under the receiver operating characteristic (ROC) curves (AUCs) on the test set were 0.88 (95% confidence interval [CI] = 0.87-0.89) and 0.89 (95% CI = 0.88-0.90) for the outcome within 90 days and 30 days, respectively, both being significantly better than chance (i.e., AUC = 0.50) (p < 0.01). Sensitivity, specificity, and predictive values were reported at different risk thresholds. A limitation of our study is that our models have not yet been externally validated, and thus, the generalizability of the models to other populations remains unknown. CONCLUSIONS: By combining the ensemble method of multiple machine learning algorithms and high-quality data solely from the Swedish registers, we developed prognostic models to predict short-term suicide attempt/death with good discrimination and calibration. Whether novel predictors can improve predictive performance requires further investigation.

An integrated analysis of genes and functional pathways for aggression in human and rodent models.

Human genome-wide association studies (GWAS), transcriptome analyses of animal models, and candidate gene studies have advanced our understanding of the genetic architecture of aggressive behaviors. However, each of these methods presents unique limitations. To generate a more confident and comprehensive view of the complex genetics underlying aggression, we undertook an integrated, cross-species approach. We focused on human and rodent models to derive eight gene lists from three main categories of genetic evidence: two sets of genes identified in GWAS studies, four sets implicated by transcriptome-wide studies of rodent models, and two sets of genes with causal evidence from online Mendelian inheritance in man (OMIM) and knockout (KO) mice reports. These gene sets were evaluated for overlap and pathway enrichment to extract their similarities and differences. We identified enriched common pathways such as the G-protein coupled receptor (GPCR) signaling pathway, axon guidance, reelin signaling in neurons, and ERK/MAPK signaling. Also, individual genes were ranked based on their cumulative weights to quantify their importance as risk factors for aggressive behavior, which resulted in 40 top-ranked and highly interconnected genes. The results of our cross-species and integrated approach provide insights into the genetic etiology of aggression.

Machine-Learning prediction of comorbid substance use disorders in ADHD youth using Swedish registry data.

BACKGROUND: Children with attention-deficit/hyperactivity disorder (ADHD) have a high risk for substance use disorders (SUDs). Early identification of at-risk youth would help allocate scarce resources for prevention programs. METHODS: Psychiatric and somatic diagnoses, family history of these disorders, measures of socioeconomic distress, and information about birth complications were obtained from the national registers in Sweden for 19,787 children with ADHD born between 1989 and 1993. We trained (a) a cross-sectional random forest (RF) model using data available by age 17 to predict SUD diagnosis between ages 18 and 19; and (b) a longitudinal recurrent neural network (RNN) model with the Long Short-Term Memory (LSTM) architecture to predict new diagnoses at each age. RESULTS: The area under the receiver operating characteristic curve (AUC) was 0.73(95%CI 0.70-0.76) for the random forest model (RF). Removing prior diagnosis from the predictors, the RF model was still able to achieve significant AUCs when predicting all SUD diagnoses (0.69, 95%CI 0.66-0.72) or new diagnoses (0.67, 95%CI: 0.64, 0.71) during age 18-19. For the model predicting new diagnoses, model calibration was good with a low Brier score of 0.086. Longitudinal LSTM model was able to predict later SUD risks at as early as 2 years age, 10 years before the earliest diagnosis. The average AUC from longitudinal models predicting new diagnoses 1, 2, 5 and 10 years in the future was 0.63. CONCLUSIONS: Population registry data can be used to predict at-risk comorbid SUDs in individuals with ADHD. Such predictions can be made many years prior to age of the onset, and their SUD risks can be monitored using longitudinal models over years during child development. Nevertheless, more work is needed to create prediction models based on electronic health records or linked population registers that are sufficiently accurate for use in the clinic.

Sodium hydrogen exchanger 9 NHE9 (SLC9A9) and its emerging roles in neuropsychiatric comorbidity.

Variations in SLC9A9 gene expression and protein function are associated with multiple human diseases, which range from Attention-deficit/hyperactivity disorder (ADHD) to glioblastoma multiforme. In an effort to determine the full spectrum of human disease associations with SLC9A9, we performed a systematic review of the literature. We also review SLC9A9's biochemistry, protein structure, and function, as well as its interacting partners with the goal of identifying mechanisms of disease and druggable targets. We report gaps in the literature regarding the genes function along with consistent trends in disease associations that can be used to further research into treating the respective diseases. We report that SLC9A9 has strong associations with neuropsychiatric diseases and various cancers. Interestingly, we find strong overlap in SLC9A9 disease associations and propose a novel role for SLC9A9 in neuropsychiatric comorbidity. In conclusion, SLC9A9 is a multifunctional protein that, through both its endosome regulatory function and its protein-protein interaction network, has the ability to modulate signaling axes, such as the PI3K pathway, among others.

Blood transcriptomic comparison of individuals with and without autism spectrum disorder: A combined-samples mega-analysis.

Blood-based microarray studies comparing individuals affected with autism spectrum disorder (ASD) and typically developing individuals help characterize differences in circulating immune cell functions and offer potential biomarker signal. We sought to combine the subject-level data from previously published studies by mega-analysis to increase the statistical power. We identified studies that compared ex vivo blood or lymphocytes from ASD-affected individuals and unrelated comparison subjects using Affymetrix or Illumina array platforms. Raw microarray data and clinical meta-data were obtained from seven studies, totaling 626 affected and 447 comparison subjects. Microarray data were processed using uniform methods. Covariate-controlled mixed-effect linear models were used to identify gene transcripts and co-expression network modules that were significantly associated with diagnostic status. Permutation-based gene-set analysis was used to identify functionally related sets of genes that were over- and under-expressed among ASD samples. Our results were consistent with diminished interferon-, EGF-, PDGF-, PI3K-AKT-mTOR-, and RAS-MAPK-signaling cascades, and increased ribosomal translation and NK-cell related activity in ASD. We explored evidence for sex-differences in the ASD-related transcriptomic signature. We also demonstrated that machine-learning classifiers using blood transcriptome data perform with moderate accuracy when data are combined across studies. Comparing our results with those from blood-based studies of protein biomarkers (e.g., cytokines and trophic factors), we propose that ASD may feature decoupling between certain circulating signaling proteins (higher in ASD samples) and the transcriptional cascades which they typically elicit within circulating immune cells (lower in ASD samples). These findings provide insight into ASD-related transcriptional differences in circulating immune cells. © 2016 Wiley Periodicals, Inc.

Genetic architecture for human aggression: A study of gene-phenotype relationship in OMIM.

Genetic studies of human aggression have mainly focused on known candidate genes and pathways regulating serotonin and dopamine signaling and hormonal functions. These studies have taught us much about the genetics of human aggression, but no genetic locus has yet achieved genome-significance. We here present a review based on a paradoxical hypothesis that studies of rare, functional genetic variations can lead to a better understanding of the molecular mechanisms underlying complex multifactorial disorders such as aggression. We examined all aggression phenotypes catalogued in Online Mendelian Inheritance in Man (OMIM), an Online Catalog of Human Genes and Genetic Disorders. We identified 95 human disorders that have documented aggressive symptoms in at least one individual with a well-defined genetic variant. Altogether, we retrieved 86 causal genes. Although most of these genes had not been implicated in human aggression by previous studies, the most significantly enriched canonical pathways had been previously implicated in aggression (e.g., serotonin and dopamine signaling). Our findings provide strong evidence to support the causal role of these pathways in the pathogenesis of aggression. In addition, the novel genes and pathways we identified suggest additional mechanisms underlying the origins of human aggression. Genome-wide association studies with very large samples will be needed to determine if common variants in these genes are risk factors for aggression. © 2015 Wiley Periodicals, Inc.

Autism spectrum disorder traits in Slc9a9 knock-out mice.

Autism spectrum disorders (ASDs) are a group of neurodevelopmental disorders which begin in childhood and persist into adulthood. They cause lifelong impairments and are associated with substantial burdens to patients, families, and society. Genetic studies have implicated the sodium/proton exchanger (NHE) nine gene, Slc9a9, to ASDs and attention-deficit/hyperactivity disorder(ADHD). Slc9a9 encodes, NHE9, a membrane protein of the late recycling endosomes. The recycling endosome plays an important role in synapse development and plasticity by regulating the trafficking of membrane neurotransmitter receptors and transporters. Here we tested the hypothesis that Slc9a9 knock-out (KO) mice would show ADHD-like and ASD-like traits. Ultrasonic vocalization (USV) recording showed that Slc9a9 KO mice emitted fewer calls and had shorter call durations, which suggest communication impairment. Slc9a9 KO mice lacked a preference for social novelty, but did not show deficits in social approach; Slc9a9 KO mice spent more time self-grooming, an indicator for restricted and repetitive behavior. We did not observe hyperactivity or other behavior impairments which are commonly comorbid with ASDs in human, such as anxiety-like behavior. Our study is the first animal behavior study that links Slc9a9 to ASDs. By eliminatingNHE9 activity, it provides strong evidence that lack of Slc9a9leads to ASD-like behaviors in mice and provides the field with a new mouse model of ASDs.

Genetics of aggressive behavior: An overview.

The Research Domain Criteria (RDoC) address three types of aggression: frustrative non-reward, defensive aggression and offensive/proactive aggression. This review sought to present the evidence for genetic underpinnings of aggression and to determine to what degree prior studies have examined phenotypes that fit into the RDoC framework. Although the constructs of defensive and offensive aggression have been widely used in the animal genetics literature, the human literature is mostly agnostic with regard to all the RDoC constructs. We know from twin studies that about half the variance in behavior may be explained by genetic risk factors. This is true for both dimensional, trait-like, measures of aggression and categorical definitions of psychopathology. The non-shared environment seems to have a moderate influence with the effects of shared environment being unclear. Human molecular genetic studies of aggression are in an early stage. The most promising candidates are in the dopaminergic and serotonergic systems along with hormonal regulators. Genome-wide association studies have not yet achieved genome-wide significance, but current samples are too small to detect variants having the small effects one would expect for a complex disorder. The strongest molecular evidence for a genetic basis for aggression comes from animal models comparing aggressive and non-aggressive strains or documenting the effects of gene knockouts. Although we have learned much from these prior studies, future studies should improve the measurement of aggression by using a systematic method of measurement such as that proposed by the RDoC initiative.

Recovery from opioid use on a neuropsychoanalytic service.

Background: Little is known about recovery from opioid use disorder (OUD) or outcomes of detoxification and drug-free treatment of chronic opioid therapy (COT). Harm reduction with medications for opioid use disorder (MOUD) is regarded as the only legitimate treatment. Methods: The Institutional Review Board (IRB) approved reporting deidentified outcomes. Patients seen over a 10-year period whose records suggested recovery were called and interviewed. Results: Overall, 69/86 (80%) confirmed that they had been sober for at least a year, including 41 patients with OUD (75%) and 28 COT patients (90%). 91% were drug-free, and 9% were on MOUD. 79% preferred a psychotherapy approach. 21% preferred MOUD. Coming for more treatment and abstinence from tobacco were significantly correlated with recovery. Conclusion: This is the first report that we are aware of regarding the frequency of recovery from OUD and COT. We have complicated the discussion about what is the best treatment for patients with OUD and patients on COT. Advising that maintenance is the only legitimate treatment for patients who suffer from OUD or who are on COT seems both premature and jeopardizes the ability of treaters to individualize treatment recommendations.

Post-COVID-19 Mental Health Distress in 13 Million Youth: A Retrospective Cohort Study of Electronic Health Records.

OBJECTIVE: To investigate the impact of the SARS-CoV-2 infection on the rates of mental disorders in youth. METHOD: The study involved 7,519,465 children and 5,338,496 adolescents from the TriNetX Research Network, all without prior mental disorder histories. Among them, 290,145 children and 223,667 adolescents had SARS-CoV-2-positive tests or confirmed COVID-19 diagnoses. Kaplan-Meier survival analysis was used to evaluate the probability of developing new mental disorders (any codes in International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) F01-F99 category and suicidal behaviors) within 2 years post infection, compared to the propensity score-matched youth who were never infected. RESULTS: Within 2 years post SARS-CoV-2 infection, children had a probability of 0.15 in acquiring new psychiatric diagnoses, compared to 0.026 for matched non-infected children; adolescents had a 0.19 probability against 0.05 for their non-infected counterparts. The hazard ratio (HR) was 6.0 (95% CI = 5.8-6.3) for children and 4.2 for adolescents (95% CI = 4.1-4.4), with children vs adolescents HR of 1.4 (95% CI = 1.36-1.51). Elevated HRs were observed for almost all subcategories of mental disorders and suicidal behaviors, with variations based on sex, severity of SARS-CoV-2 infection, and viral variants. COVID-19 was similar to other respiratory infections in increasing the rate of mental disorders in adolescents, but had a significantly higher effect on children (HR = 1.57, 95% CI =1.53-1.61). CONCLUSION: This study revealed significant mental health distress following SARS-CoV-2 infection in youth, which was more pronounced in children than in adolescents. These findings underscore the urgent need to support at-risk youth, particularly those who contracted SARS-CoV-2 at younger ages and had more severe infections. DIVERSITY & INCLUSION STATEMENT: One or more of the authors of this paper self-identifies as a member of one or more historically underrepresented racial and/or ethnic groups in science.

Effects of ADHD and ADHD treatment on glycemic management in type 1 diabetes: A systematic review and meta-analysis of observational studies.

AIMS: Studies suggested a higher prevalence of Attention-deficit/hyperactivity disorder (ADHD) in individuals with Type 1 Diabetes Mellitus (T1D). However, it is unclear how ADHD impacts glycemia and diabetes-related complications. This systematic review and meta-analysis aimed to investigate the effect of ADHD and ADHD medications on HbA1c and acute complications in T1D. METHODS: A literature search was conducted in PubMed, EMBASE, CINAHL, Scopus, PsycINFO, CENTRAL, and Web of Science collections up to November 22, 2023. Seventeen studies were selected for the systematic review by independent reviewers, with twelve included in the meta-analysis. RESULTS: Mean HbA1c levels were significantly higher in T1D individuals with ADHD compared to those without ADHD (MD = 0.60; 95 % CI: 0.41, 0.79; I2 = 90.1 %; p-value < 0.001). The rates of suboptimal HbA1c levels, hospitalization, diabetic ketoacidosis, and hypoglycemia were all substantially higher in T1D individuals with ADHD than those without ADHD. No difference was found in mean HbA1c between those who received ADHD treatment and those who did not (mean difference = -0.52; 95 % confidence interval: -1.16, 0.13; I2 = 78.6 %; p-value = 0.12). CONCLUSIONS: ADHD is associated with higher HbA1c and increased acute diabetes-related complications. More research is needed to assess the effects of ADHD treatments on T1D management.

ADHD medication discontinuation and persistence across the lifespan: a retrospective observational study using population-based databases.

BACKGROUND: Although often intended for long-term treatment, discontinuation of medication for ADHD is common. However, cross-national estimates of discontinuation are missing due to the absence of standardised measures. The aim of this study was to determine the rate of ADHD treatment discontinuation across the lifespan and to describe similarities and differences across countries to guide clinical practice. METHODS: We did a retrospective, observational study using population-based databases from eight countries and one Special Administrative Region (Australia, Denmark, Hong Kong, Iceland, the Netherlands, Norway, Sweden, the UK, and the USA). We used a common analytical protocol approach and extracted prescription data to identify new users of ADHD medication. Eligible individuals were aged 3 years or older who had initiated ADHD medication between 2010 and 2020. We estimated treatment discontinuation and persistence in the 5 years after treatment initiation, stratified by age at initiation (children [age 4-11 years], adolescents [age 12-17 years], young adults [age 18-24 years], and adults [age ≥25 years]) and sex. Ethnicity data were not available. FINDINGS: 1 229 972 individuals (735 503 [60%] males, 494 469 females [40%]; median age 8-21 years) were included in the study. Across countries, treatment discontinuation 1-5 years after initiation was lowest in children, and highest in young adults and adolescents. Within 1 year of initiation, 65% (95% CI 60-70) of children, 47% (43-51) of adolescents, 39% (36-42) of young adults, and 48% (44-52) of adults remained on treatment. The proportion of patients discontinuing was highest between age 18 and 19 years. Treatment persistence for up to 5 years was higher across countries when accounting for reinitiation of medication; at 5 years of follow-up, 50-60% of children and 30-40% of adolescents and adults were covered by treatment in most countries. Patterns were similar across sex. INTERPRETATION: Early medication discontinuation is prevalent in ADHD treatment, particularly among young adults. Although reinitiation of medication is common, treatment persistence in adolescents and young adults is lower than expected based on previous estimates of ADHD symptom persistence in these age groups. This study highlights the scope of medication treatment discontinuation and persistence in ADHD across the lifespan and provides new knowledge about long-term ADHD medication use. FUNDING: European Union Horizon 2020 Research and Innovation Programme.

Genetic architecture of Wistar-Kyoto rat and spontaneously hypertensive rat substrains from different sources.

The spontaneously hypertensive rat (SHR) has been widely used as a model for studies of hypertension and attention deficit/hyperactivity disorder. The inbred Wistar-Kyoto (WKY) rat, derived from the same ancestral outbred Wistar rat as the SHR, are normotensive and have been used as the closest genetic control for the SHR, although the WKY has also been used as a model for depression. Notably, however, substantial behavioral and genetic differences among the WKY substrains, usually from the different vendors and breeders, have been observed. These differences have often been overlooked in prior studies, leading to inconsistent and even contradictory findings. The complicated breeding history of the SHR and WKY rats and the lack of a comprehensive understanding of the genetic background of different commercial substrains make the selection of control rats a daunting task, even for researchers who are mindful of their genetic heterogeneity. In this study, we examined the genetic relationship of 16 commonly used WKY and SHR rat substrains using genome-wide SNP genotyping data. Our results confirmed a large genetic divergence and complex relationships among the SHR and WKY substrains. This understanding, although incomplete without the genome sequence, provides useful guidance in selecting substrains and helps to interpret previous reports when the source of the animals was known. Moreover, we found two closely related, yet distinct WKY substrains that may provide novel opportunities in modeling psychiatric disorders.

Can sodium/hydrogen exchange inhibitors be repositioned for treating attention deficit hyperactivity disorder? An in silico approach.

Medications for attention deficit hyperactivity disorder (ADHD) are only partially effective. Ideally, new treatment targets would derive from a known pathophysiology. Such data are not available for ADHD. We combine evidence for new etiologic pathways with bioinformatics data to assess the possibility that existing drugs might be repositioning for treating ADHD. We use this approach to determine if prior data implicating the sodium/hydrogen exchanger 9 gene (SLC9A9) in ADHD implicate sodium/hydrogen exchange (NHE) inhibitors as potential treatments. We assessed the potential for repositioning by assessing the similarity of drug-protein binding profiles between NHE inhibitors and drugs known to treat ADHD using the Drug Repositioning and Adverse Reaction via Chemical-Protein Interactome server. NHE9 shows a high degree of amino acid similarity between NHE inhibitor sensitive NHEs in the region of the NHE inhibitor recognition site defined for NHE1. We found high correlations in drug-protein binding profiles among most ADHD drugs. The drug-protein binding profiles of some NHE inhibitors were highly correlated with ADHD drugs whereas the profiles for a control set of nonsteroidal anti-inflammatory drugs (NSAIDs) were not. Further experimental work should evaluate if NHE inhibitors are suitable for treating ADHD.

[Considerations regarding animal research of traditional Chinese medicine on attention deficit hyperactivity disorder].

Attention-deficit/hyperactivity disorder (ADHD) is a common developmental neuropsychiatric disorder. Although ADHD can often be treated with stimulant class medications to alleviate symptoms, such treatments may cause undesirable side effects. Recently, Traditional Chinese medicine (TCM) has been gaining interest with treatment potential for ADHD and the lack of the stimulant-associated undesirable side effects. Animal models are useful for study the efficacy and mechanisms of TCM treatment for ADHD, however, previous studies of TCM on ADHD animal models, in general, have not considered appropriate experiemental designs. There were many concerns regarding the choice and source of the model and control animals, drug administration methods, behavioral and biochemical testing criteria, humane use of animals, and statistical power, etc. In this review, we discuss these issues present in the previous literature of animal research, and propose guidelines for future studies in particular consideration with the unique characteristic of Chinese medicine itself.

Association of ADHD symptoms with type 2 diabetes and cardiovascular comorbidities in adults receiving outpatient diabetes care.

Background: The relationship between attention-deficit/hyperactivity disorder (ADHD) symptoms and type 2 diabetes mellitus (T2D) and its cardiovascular outcomes have not been sufficiently studied. Methods: 2,986 adults with T2D from the Joslin Diabetes Center at Upstate Medical University were assessed for ADHD-like symptoms, executive dysfunction, and emotional control using the Adult Self-Report Scale V1.1 (ASRS) expanded version. Surveys were sent electronically, and clinical data were obtained from the electronic medical record. Pearson chi-square test was used for categorical variables association. When ASRS scores were the dependent variable, negative binomial regression correcting for demographic variables that were associated with the ASRS scores was used. Results: 155 (49.2%) of respondents met DSM-5 criteria for ADHD using the ASRS scores; Only ten (3.6%) of respondents had an ICD10 diagnosis of ADHD in their medical record; Forty-three (13.7%) had either a diagnosis of ADHD in the medical history or were taking medications used by people with ADHD. Higher levels of ADHD-like symptoms were found in patients with T2D compared with population norms. There was a modest association of the ASRS executive dysfunction subscale with overall cardiovascular comorbidities (p = 0.03). However, the p-value did not survive the multiple testing correction. Both ADHD-like symptoms and symptoms associated with emotional control, however, were not associated with specific cardiovascular diseases, hypertension, or with HbA1c, LDL-cholesterol, triglycerides, ALT, creatinine, or eGFR. Conclusion: Our results suggest that adults with T2D attending a tertiary care diabetes clinic are at risk for having ADHD-like symptoms, highlighting the importance of screening for ADHD symptoms in this specialty setting and referring undiagnosed adult patients for further assessment and treatment of ADHD. Larger studies are needed to clarify the relationship between ADHD-like symptoms, executive dysfunction, and emotional control with diabetic control and comorbidities.