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.

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.

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.

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.

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.

Machine Learning and MRI-based Diagnostic Models for ADHD: Are We There Yet?

OBJECTIVE: Machine learning (ML) has been applied to develop magnetic resonance imaging (MRI)-based diagnostic classifiers for attention-deficit/hyperactivity disorder (ADHD). This systematic review examines this literature to clarify its clinical significance and to assess the implications of the various analytic methods applied. METHODS: A comprehensive literature search on MRI-based diagnostic classifiers for ADHD was performed and data regarding the utilized models and samples were gathered. RESULTS: We found that, although most studies reported the classification accuracies, they varied in choice of MRI modalities, ML models, cross-validation and testing methods, and sample sizes. We found that the accuracies of cross-validation methods inflated the performance estimation compared with those of a held-out test, compromising the model generalizability. Test accuracies have increased with publication year but were not associated with training sample sizes. Improved test accuracy over time was likely due to the use of better ML methods along with strategies to deal with data imbalances. CONCLUSION: Ultimately, large multi-modal imaging datasets, and potentially the combination with other types of data, like cognitive data and/or genetics, will be essential to achieve the goal of developing clinically useful imaging classification tools for ADHD in the future.

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.

Association Between ADHD and COVID-19 Infection and Clinical Outcomes: A Retrospective Cohort Study From Electronic Medical Records.

OBJECTIVE: Though psychiatric illnesses have been associated with increased COVID-19 infection risk, limited information exists about the relationship between ADHD and COVID-19. METHODS: Using the TriNetX COVID-19 Research Network, we examined the impact of ADHD diagnosis and treatment on COVID-19 infection rates and outcomes. RESULTS: ADHD patients had greater risk of COVID-19 (risk ratio (RR) 1.11, 95% CI [1.09, 1.12]). Increased risk was higher in females than males, and highest among Asian and Black patients. Within 60 days after COVID-19 diagnosis, ADHD patients had lower rates of hospitalization (RR 0.91, 95% CI [0.86, 0.96]) and mechanical ventilation (RR 0.69, 95% CI [0.58, 0.83]), and a nonsignificant reduced death rate (RR 0.65, 95% CI [0.42, 1.02]). Patients who recently received ADHD medication had higher rates of COVID-19 (RR 1.13; 95% CI [1.10, 1.15]). CONCLUSION: ADHD poses increased risk for COVID-19, but may reduce risk of severe outcomes. ADHD medications modestly impacted COVID-19 risk.

Structural brain morphometry as classifier and predictor of ADHD and reward-related comorbidities.

Attention deficit/hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders, and around two-thirds of affected children report persisting problems in adulthood. This negative trajectory is associated with high comorbidity with disorders like obesity, depression, or substance use disorder (SUD). Decreases in cortical volume and thickness have also been reported in depression, SUD, and obesity, but it is unclear whether structural brain alterations represent unique disorder-specific profiles. A transdiagnostic exploration of ADHD and typical comorbid disorders could help to understand whether specific morphometric brain changes are due to ADHD or, alternatively, to the comorbid disorders. In the current study, we studied the brain morphometry of 136 subjects with ADHD with and without comorbid depression, SUD, and obesity to test whether there are unique or common brain alterations. We employed a machine-learning-algorithm trained to classify subjects with ADHD in the large ENIGMA-ADHD dataset and used it to predict the diagnostic status of subjects with ADHD and/or comorbidities. The parcellation analysis demonstrated decreased cortical thickness in medial prefrontal areas that was associated with presence of any comorbidity. However, these results did not survive correction for multiple comparisons. Similarly, the machine learning analysis indicated that the predictive algorithm grouped most of our ADHD participants as belonging to the ADHD-group, but no systematic differences between comorbidity status came up. In sum, neither a classical comparison of segmented structural brain metrics nor an ML model based on the ADHD ENIGMA data differentiate between ADHD with and without comorbidities. As the ML model is based in part on adolescent brains, this might indicate that comorbid disorders and their brain changes are not captured by the ML model because it represents a different developmental brain trajectory.

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.

Alcohol use disorders and ADHD.

Despite a growing literature on the complex bidirectional relationship of ADHD and substance use, reviews specifically focusing on alcohol are scarce. ADHD and AUD show a significant genetic overlap, including genes involved in gluatamatergic and catecholaminergic neurotransmission. ADHD drives risky behavior and negative experiences throughout the lifespan that subsequently enhance a genetically increased risk for Alcohol Use Disorders (AUD). Impulsive decisions and a maladaptive reward system make individuals with ADHD vulnerable for alcohol use and up to 43 % develop an AUD; in adults with AUD, ADHD occurs in about 20 %, but is vastly under-recognized and under-treated. Thus, routine screening and treatment procedures need to be implemented in AUD treatment. Long-acting stimulants or non-stimulants can be used to treat ADHD in individuals with AUD. However, it is crucial to combine medical treatment for ADHD with pharmacotherapy and psychotherapy for AUD, and other comorbid disorders. Identification of individuals at risk for AUD, especially those with ADHD and conduct disorder or oppositional defiant disorder, is a key factor to prevent negative outcomes.

A seq2seq model to forecast the COVID-19 cases, deaths and reproductive R numbers in US counties.

The global pandemic of coronavirus disease 2019 (COVID-19) has killed almost two million people worldwide and over 400 thousand in the United States (US). As the pandemic evolves, informed policy-making and strategic resource allocation relies on accurate forecasts. To predict the spread of the virus within US counties, we curated an array of county-level demographic and COVID-19-relevant health risk factors. In combination with the county-level case and death numbers curated by John Hopkins university, we developed a forecasting model using deep learning (DL). We implemented an autoencoder-based Seq2Seq model with gated recurrent units (GRUs) in the deep recurrent layers. We trained the model to predict future incident cases, deaths and the reproductive number, R For most counties, it makes accurate predictions of new incident cases, deaths and R values, up to 30 days in the future. Our framework can also be used to predict other targets that are useful indices for policymaking, for example hospitalization or the occupancy of intensive care units. Our DL framework is publicly available on GitHub and can be adapted for other indices of the COVID-19 spread. We hope that our forecasts and model can help local governments in the continued fight against COVID-19.

A seq2seq model to forecast the COVID-19 cases, deaths and reproductive R numbers in US counties.

The global pandemic of coronavirus disease 2019 (COVID-19) has killed almost two million people worldwide and over 400 thousand in the United States (US). As the pandemic evolves, informed policy-making and strategic resource allocation relies on accurate forecasts. To predict the spread of the virus within US counties, we curated an array of county-level demographic and COVID-19-relevant health risk factors. In combination with the county-level case and death numbers curated by John Hopkins university, we developed a forecasting model using deep learning (DL). We implemented an autoencoder-based Seq2Seq model with gated recurrent units (GRUs) in the deep recurrent layers. We trained the model to predict future incident cases, deaths and the reproductive number, R. For most counties, it makes accurate predictions of new incident cases, deaths and R values, up to 30 days in the future. Our framework can also be used to predict other targets that are useful indices for policymaking, for example hospitalization or the occupancy of intensive care units. Our DL framework is publicly available on GitHub and can be adapted for other indices of the COVID-19 spread. We hope that our forecasts and model can help local governments in the continued fight against COVID-19.

The Effects of Low Dose Naltrexone on Opioid Induced Hyperalgesia and Fibromyalgia.

Objectives: While opioids temporarily alleviate pain, the overshoot of balancing pain drivers may increase pain, leading to opioid induced hyperalgesia (OIH). Our goal was to find out what chronic opioid treatment does to pain tolerance as measured by the cold pressor test (CPT), an objective measure of pain tolerance, and to find an alternative effective treatment for chronic pain and FM. Materials and Methods: The setting was an academic addiction medicine service that has an embedded pain service. Patients had routine clinical care starting with an evaluation that included assessment of medical and psychiatric conditions. Participants were 55 patients with OIH and 21 patients with fibromyalgia; all had at least two CPTs. Treatment included a single dose of buprenorphine for detoxification. In this open-label case series, patients were treated with low dose naltrexone (LDN), a pure opioid receptor antagonist that, we hypothesize, treats OIH and FM by restoring endogenous opioid tone. Results: Comparing initial and last CPT times, those with OIH more than quadrupled their pain tolerance, and those with FM doubled theirs. This improved pain tolerance for OIH and FM was statistically significant (p < 0.0001 and p = 0.003, respectively) and had a large effect size (r = 0.82 and r = 0.63, respectively). Discussion: Results suggest that patients on chronic opioid therapy should have pain tolerance measured by CPT with detoxification and LDN provided to correct opioid induced hyperalgesia if found. FM may also be treated with LDN. The main limitation of the findings was lack of a randomized control group treated with placebo.

Evidence for similar structural brain anomalies in youth and adult attention-deficit/hyperactivity disorder: a machine learning analysis.

Attention-deficit/hyperactivity disorder (ADHD) affects 5% of children world-wide. Of these, two-thirds continue to have impairing symptoms of ADHD into adulthood. Although a large literature implicates structural brain differences of the disorder, it is not clear if adults with ADHD have similar neuroanatomical differences as those seen in children with recent reports from the large ENIGMA-ADHD consortium finding structural differences for children but not for adults. This paper uses deep learning neural network classification models to determine if there are neuroanatomical changes in the brains of children with ADHD that are also observed for adult ADHD, and vice versa. We found that structural MRI data can significantly separate ADHD from control participants for both children and adults. Consistent with the prior reports from ENIGMA-ADHD, prediction performance and effect sizes were better for the child than the adult samples. The model trained on adult samples significantly predicted ADHD in the child sample, suggesting that our model learned anatomical features that are common to ADHD in childhood and adulthood. These results support the continuity of ADHD's brain differences from childhood to adulthood. In addition, our work demonstrates a novel use of neural network classification models to test hypotheses about developmental continuity.

Ensemble classification of autism spectrum disorder using structural magnetic resonance imaging features.

Background: Autism spectrum disorder (ASD) is characterized by a spectrum of social and communication impairments and rigid and stereotyped behaviors that have a neurodevelopmental origin. Although many imaging studies have reported structural and functional alterations in multiple brain regions, clinically useful diagnostic imaging biomarkers for ASD remain unavailable. Methods: In this study, we applied machine learning (ML) models to regional volumetric and cortical thickness data from the largest structural magnetic resonance imaging (sMRI) dataset available from the Enhancing Neuro Imaging Genetics Through Meta-Analysis (ENIGMA) consortium (1833 subjects with ASD and 1838 without ASD; age range: 1.5-64; average age: 15.6; male/female ratio: 4.2:1). Results: The highest classification accuracy on a hold-out test set was achieved using a stacked Extra Tree Classifier. The area under the receiver operating characteristic (ROC) curve (AUC) was 0.62 (95% confidence interval [CI]: 0.57, 0.68) and the area under the precision-recall curve was 0.58. Learning curve analysis showed the good fit of the model and suggests that more training examples will not likely benefit model performance. Conclusions: Our results suggest that sMRI volumetric and cortical thickness data alone may not provide clinically sufficient useful diagnostic biomarkers for ASD. Developing clinically useful imaging classifiers for ASD will benefit from combining other data modalities or feature types, such as functional MRI data and raw images that can leverage other machine learning (ML) techniques such as convolutional neural networks.

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.

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.