Polygenic loading for major depression is associated with specific medical comorbidity

Advancing Mental Health Research Through Strategic Integration of Transdiagnostic Dimensions and Genomics

Genome-wide studies are yielding a growing catalog of common and rare variants that confer risk for psychopathology. However, despite representing unprecedented progress, emerging data also indicate that the full promise of psychiatric genetics-including understanding pathophysiology and improving personalized care-will not be fully realized by targeting traditional dichotomous diagnostic categories. The current article provides reflections on themes that emerged from a 2021 National Institute of Mental Health-sponsored conference convened to address strategies for the evolving field of psychiatric genetics. As anticipated by the National Institute of Mental Health's Research Domain Criteria framework, multilevel investigations of dimensional and transdiagnostic phenotypes, particularly when integrated with biobanks and big data, will be critical to advancing knowledge. The path forward will also require more diverse representation in source studies. Additionally, progress will be catalyzed by a range of converging approaches, including capitalizing on computational methods, pursuing biological insights, working within a developmental framework, and engaging health care systems and patient communities.

Topic modeling identifies novel genetic loci associated with multimorbidities in UK Biobank

Many diseases show patterns of co-occurrence, possibly driven by systemic dysregulation of underlying processes affecting multiple traits. We have developed a method (treeLFA) for identifying such multimorbidities from routine health-care data, which combines topic modeling with an informative prior derived from medical ontology. We apply treeLFA to UK Biobank data and identify a variety of topics representing multimorbidity clusters, including a healthy topic. We find that loci identified using topic weights as traits in a genome-wide association study (GWAS) analysis, which we validated with a range of approaches, only partially overlap with loci from GWASs on constituent single diseases. We also show that treeLFA improves upon existing methods like latent Dirichlet allocation in various ways. Overall, our findings indicate that topic models can characterize multimorbidity patterns and that genetic analysis of these patterns can provide insight into the etiology of complex traits that cannot be determined from the analysis of constituent traits alone.

Polygenic Liability to Depression Is Associated With Multiple Medical Conditions in the Electronic Health Record: Phenome-wide Association Study of 46,782 Individuals

BACKGROUND

Major depressive disorder (MDD) is a leading cause of disease-associated disability, with much of the increased burden due to psychiatric and medical comorbidity. This comorbidity partly reflects common genetic influences across conditions. Integrating molecular-genetic tools with health records enables tests of association with the broad range of physiological and clinical phenotypes. However, standard phenome-wide association studies analyze associations with individual genetic variants. For polygenic traits such as MDD, aggregate measures of genetic risk may yield greater insight into associations across the clinical phenome.

METHODS

We tested for associations between a genome-wide polygenic risk score for MDD and medical and psychiatric traits in a phenome-wide association study of 46,782 unrelated, European-ancestry participants from the Michigan Genomics Initiative.

RESULTS

The MDD polygenic risk score was associated with 211 traits from 15 medical and psychiatric disease categories at the phenome-wide significance threshold. After excluding patients with depression, continued associations were observed with respiratory, digestive, neurological, and genitourinary conditions; neoplasms; and mental disorders. Associations with tobacco use disorder, respiratory conditions, and genitourinary conditions persisted after accounting for genetic overlap between depression and other psychiatric traits. Temporal analyses of time-at-first-diagnosis indicated that depression disproportionately preceded chronic pain and substance-related disorders, while asthma disproportionately preceded depression.

CONCLUSIONS

The present results can inform the biological links between depression and both mental and systemic diseases. Although MDD polygenic risk scores cannot currently forecast health outcomes with precision at the individual level, as molecular-genetic discoveries for depression increase, these tools may augment risk prediction for medical and psychiatric conditions.

Exploring polygenic contributors to subgroups of comorbid conditions in autism spectrum disorder

Individuals with autism spectrum disorder (ASD) have heterogeneous comorbid conditions. This study examined whether comorbid conditions in ASD are associated with polygenic risk scores (PRS) of ASD or PRS of comorbid conditions in non-ASD specific populations. Genome-wide single nucleotide polymorphism (SNP) data were obtained from 1386 patients with ASD from the Autism Genetic Resource Exchange (AGRE) study. After excluding individuals with missing clinical information concerning comorbid conditions, a total of 707 patients were included in the study. A total of 18 subgroups of comorbid conditions (‘topics’) were identified using a machine learning algorithm, topic modeling. PRS for ASD were computed using a genome-wide association meta-analysis of 18,381 cases and 27,969 controls. From these 18 topics, Topic 6 (over-represented by allergies) (p = 1.72 × 10⁻³) and Topic 17 (over-represented by sensory processing issues such as low pain tolerance) (p = 0.037) were associated with PRS of ASD. The associations between these two topics and the multi-locus contributors to their corresponding comorbid conditions based on non-ASD specific populations were further explored. The results suggest that these two topics were not associated with the PRS of allergies and chronic pain disorder, respectively. Note that characteristics of the present AGRE sample and those samples used in the original GWAS for ASD, allergies, and chronic pain disorder, may differ due to significant clinical heterogeneity that exists in the ASD population. Additionally, the AGRE sample may be underpowered and therefore insensitive to weak PRS associations due to a relatively small sample size. Findings imply that susceptibility genes of ASD may contribute more to the occurrence of allergies and sensory processing issues in individuals with ASD, compared with the susceptibility genes for their corresponding phenotypes in non-ASD individuals. Since these comorbid conditions (i.e., allergies and pain sensory issues) may not be attributable to the corresponding comorbidity-specific biological factors in non-ASD individuals, clinical management for these comorbid conditions may still depend on treatments for core symptoms of ASD.

Development and External Validation of a Delirium Prediction Model for Hospitalized Patients With Coronavirus Disease 2019

Background

The coronavirus disease 2019 pandemic has placed unprecedented stress on health systems and has been associated with elevated risk for delirium. The convergence of pandemic resource limitation and clinical demand associated with delirium requires careful risk stratification for targeted prevention efforts.

Objectives

To develop an incident delirium predictive model among coronavirus disease 2019 patients.

Methods

We applied supervised machine learning to electronic health record data for inpatients with coronavirus disease 2019 at three hospitals to build an incident delirium diagnosis prediction model. We validated this model in three different hospitals. Both hospital cohorts included academic and community settings.

Results

Among 2907 patients across 6 hospitals, 488 (16.8%) developed delirium. Applying the predictive model in the external validation cohort of 755 patients, the c-index was 0.75 (0.71–0.79) and the lift in the top quintile was 2.1. At a sensitivity of 80%, the specificity was 56%, negative predictive value 92%, and positive predictive value 30%. Equivalent model performance was observed in subsamples stratified by age, sex, race, need for critical care and care at community vs. academic hospitals.

Conclusion

Machine learning applied to electronic health records available at the time of inpatient admission can be used to risk-stratify patients with coronavirus disease 2019 for incident delirium. Delirium is common among patients with coronavirus disease 2019, and resource constraints during a pandemic demand careful attention to the optimal application of predictive models.

Distribution of agitation and related symptoms among hospitalized patients using a scalable natural language processing method

BACKGROUND

Agitation is a common feature of many neuropsychiatric disorders.

OBJECTIVE

Understanding the prevalence, implications, and characteristics of agitation among hospitalized populations can facilitate more precise recognition of disability arising from neuropsychiatric diseases.

METHODS

We developed two agitation phenotypes using an expansion of expert curated term lists. These phenotypes were used to characterize five years of psychiatric admissions. The relationship of agitation symptoms and length of stay was examined.

RESULTS

Among 4548 psychiatric admissions, 1134 (24.9%) included documentation of agitation based on the primary agitation phenotype. These symptoms were greater among individuals with public insurance, and those with mania and psychosis compared to major depressive disorder. Greater symptoms were associated with longer hospital stay, with ~0.9 day increase in stay for every 10% increase in agitation phenotype.

CONCLUSION

Agitation was common at hospital admission and associated with diagnosis and longer length of stay. Characterizing agitation-related symptoms through natural language processing may provide new tools for understanding agitated behaviors and their relationship to delirium.

Characterizing DSM-5 and ICD-11 personality disorder features in psychiatric inpatients at scale using electronic health records

BACKGROUND

Investigation of personality traits and pathology in large, generalizable clinical cohorts has been hindered by inconsistent assessment and failure to consider a range of personality disorders (PDs) simultaneously.

METHODS

We applied natural language processing (NLP) of electronic health record notes to characterize a psychiatric inpatient cohort. A set of terms reflecting personality trait domains were derived, expanded, and then refined based on expert consensus. Latent Dirichlet allocation was used to score notes to estimate the extent to which any given note reflected PD topics. Regression models were used to examine the relationship of these estimates with sociodemographic features and length of stay.

RESULTS

Among 3623 patients with 4702 admissions, being male, non-white, having a low burden of medical comorbidity, being admitted through the emergency department, and having public insurance were independently associated with greater levels of disinhibition, detachment, and psychoticism. Being female, white, and having private insurance were independently associated with greater levels of negative affectivity. The presence of disinhibition, psychoticism, and negative affectivity were each significantly associated with a longer stay, while detachment was associated with a shorter stay.

CONCLUSIONS

Personality features can be systematically and scalably measured using NLP in the inpatient setting, and some of these features associate with length of stay. Developing treatment strategies for patients scoring high in certain personality dimensions may facilitate more efficient, targeted interventions, and may help reduce the impact of personality features on mental health service utilization.

Assessment of a Prediction Model for Antidepressant Treatment Stability Using Supervised Topic Models

Importance

In the absence of readily assessed and clinically validated predictors of treatment response, pharmacologic management of major depressive disorder often relies on trial and error.

Objective

To assess a model using electronic health records to identify predictors of treatment response in patients with major depressive disorder.

Design, Setting, and Participants

This retrospective cohort study included data from 81 630 adults with a coded diagnosis of major depressive disorder from 2 academic medical centers in Boston, Massachusetts, including outpatient primary and specialty care clinics from December 1, 1997, to December 31, 2017. Data were analyzed from January 1, 2018, to March 15, 2020.

Exposures

Treatment with at least 1 of 11 standard antidepressants.

Main Outcomes and Measures

Stable treatment response, intended as a proxy for treatment effectiveness, defined as continued prescription of an antidepressant for 90 days. Supervised topic models were used to extract 10 interpretable covariates from coded clinical data for stability prediction. With use of data from 1 hospital system (site A), generalized linear models and ensembles of decision trees were trained to predict stability outcomes from topic features that summarize patient history. Held-out patients from site A and individuals from a second hospital system (site B) were evaluated.

Results

Among the 81 630 adults (56 340 women [69%]; mean [SD] age, 48.46 [14.75] years; range, 18.0-80.0 years), 55 303 reached a stable response to their treatment regimen during follow-up. For held-out patients from site A, the mean area under the receiver operating characteristic curve (AUC) for discrimination of the general stability outcome was 0.627 (95% CI, 0.615-0.639) for the supervised topic model with 10 covariates. In evaluation of site B, the AUC was 0.619 (95% CI, 0.610-0.627). Building models to predict stability specific to a particular drug did not improve prediction of general stability even when using a harder-to-interpret ensemble classifier and 9256 coded covariates (specific AUC, 0.647; 95% CI, 0.635-0.658; general AUC, 0.661; 95% CI, 0.648-0.672). Topics coherently captured clinical concepts associated with treatment response.

Conclusions and Relevance

The findings suggest that coded clinical data available in electronic health records may facilitate prediction of general treatment response but not response to specific medications. Although greater discrimination is likely required for clinical application, the results provide a transparent baseline for such studies.

Towards precision medicine in generalized anxiety disorder: Review of genetics and pharmaco(epi)genetics

Generalized anxiety disorder (GAD) is a prevalent and chronic mental disorder that elicits widespread functional impairment. Given the high degree of non-response/partial response among patients with GAD to available pharmacological treatments, there is a strong need for novel approaches that can optimize outcomes, and lead to medications that are safer and more effective. Although investigations have identified interesting targets predicting treatment response through pharmacogenetics (PGx), pharmaco-epigenetics, and neuroimaging methods, these studies are often solitary, not replicated, and carry several limitations. This review provides an overview of the current status of GAD genetics and PGx and presents potential strategies to improve treatment response by combining better phenotyping with PGx and improved analytical methods. These strategies carry the dual benefit of delivering data on biomarkers of treatment response as well as pointing to disease mechanisms through the biology of the markers associated with response. Overall, these efforts can serve to identify clinical, genetic, and epigenetic factors that can be incorporated into a pharmaco(epi)genetic test that may ultimately improve treatment response and reduce the socioeconomic burden of GAD.

Shared Genetics of Psychiatric Disorders

Until recently, advances in understanding the genetic architecture of psychiatric disorders have been impeded by a historic, and often mandated, commitment to the use of traditional, and unvalidated, categorical diagnoses in isolation as the relevant phenotype. Such studies typically required lengthy structured interviews to delineate differences in the character and duration of behavioral symptomatology amongst disorders that were thought to be etiologic, and they were often underpowered as a result. Increasing acceptance of the fact that co-morbidity in psychiatric disorders is the rule rather than the exception has led to alternative designs in which shared dimensional symptomatology is analyzed as a quantitative trait and to association analyses in which combined polygenic risk scores are computationally compared across multiple traditional categorical diagnoses to identify both distinct and unique genetic and environmental elements. Increasing evidence that most mental disorders share many common genetic risk variants and environmental risk modifiers suggests that the broad spectrum of psychiatric pathology represents the pleiotropic display of a more limited series of pathologic events in neuronal development than was originally believed, regulated by many common risk variants and a smaller number of rare ones.

Differential associations of depression-related phenotypes with cardiometabolic risks: Polygenic analyses and exploring shared genetic variants and pathways

BACKGROUND

Numerous studies have suggested associations between depression and cardiometabolic (CM) diseases. However, little is known about the mechanism underlying this comorbidity, and whether the relationship differs by depression subtypes.

METHODS

Using polygenic risk scores (PRS) and linkage disequilibrium (LD) score regression, we investigated the genetic overlap of various depression-related phenotypes with a comprehensive panel of 20 CM traits. GWAS results for major depressive disorder (MDD) were taken from the PGC and CONVERGE studies, with the latter focusing on severe melancholic depression. GWAS results on general depressive symptoms (DS) and neuroticism were also included. We identified the shared genetic variants and inferred enriched pathways. We also looked for drugs over-represented among the top-shared genes, with an aim to finding repositioning opportunities for comorbidities.

RESULTS

We found significant genetic overlap between MDD, DS, and neuroticism with cardiometabolic traits. In general, positive polygenic associations with CM abnormalities were observed except for MDD-CONVERGE. Counterintuitively, PRS representing severe melancholic depression was associated with reduced CM risks. Enrichment analyses of shared SNPs revealed many interesting pathways such as those related to inflammation that underlie the comorbidity of depressive and CM traits. Using a gene-set analysis approach, we also revealed several repositioning candidates with literature support (e.g., bupropion).

CONCLUSIONS

Our study highlights shared genetic bases of depression with CM traits, and suggests the associations vary by depression subtypes, which may have implications in targeted prevention of cardiovascular events for patients. Identification of shared genetic factors may also guide drug discovery for the comorbidities.

Mapping the Delirium Literature Through Probabilistic Topic Modeling and Network Analysis: A Computational Scoping Review

BACKGROUND

Delirium is an acute confusional state, associated with morbidity and mortality in diverse medically-ill populations. Delirium is recognized, through both professional competencies and instructional materials, as a core topic in consultation psychiatry.

OBJECTIVE

Conduct a computational scoping review of the delirium literature to identify the overall contours of this literature and evolution of the delirium literature over time.

METHODS

Algorithmic analysis of all research articles on delirium indexed in MEDLINE between 1995 and 2015 using network analysis of citation Medical Subject Headings (MeSH) tags and probabilistic topic modeling of article abstracts.

RESULTS

The delirium corpus included 3591 articles in 874 unique journals, of which 95 were primarily psychiatric. The annual delirium publication volume increased from 40 in 1995 to 420 in 2015 and grew as a proportion of total indexed publications from 8.9 to 38.6 per 100,000. The psychiatric journals published 720 of the delirium publications. Articles on treatment of delirium (806) outnumber articles on prevention of delirium (432). Abstract topic modeling and Medical Subject Headings graph community analysis identified similar genres in the delirium literature, including: delirium in geriatric, critically ill, palliative care, and postsurgical patients as well as diagnostic criteria or scales, and clinical risk factors. The genres identified by topic modeling and community analysis were distributed unevenly between psychiatric journals and nonpsychiatric journals.

CONCLUSION

The delirium literature is large and growing. Much of this growth is outside of psychiatric journals. Subtopics of the delirium literature can be algorithmically identified, and these subtopics are distributed unevenly across psychiatric journals.

Using topic modeling via non-negative matrix factorization to identify relationships between genetic variants and disease phenotypes: A case study of Lipoprotein(a) (LPA)

Genome-wide and phenome-wide association studies are commonly used to identify important relationships between genetic variants and phenotypes. Most studies have treated diseases as independent variables and suffered from the burden of multiple adjustment due to the large number of genetic variants and disease phenotypes. In this study, we used topic modeling via non-negative matrix factorization (NMF) for identifying associations between disease phenotypes and genetic variants. Topic modeling is an unsupervised machine learning approach that can be used to learn patterns from electronic health record data. We chose the single nucleotide polymorphism (SNP) rs10455872 in LPA as the predictor since it has been shown to be associated with increased risk of hyperlipidemia and cardiovascular diseases (CVD). Using data of 12,759 individuals with electronic health records (EHR) and linked DNA samples at Vanderbilt University Medical Center, we trained a topic model using NMF from 1,853 distinct phenotypes and identified six topics. We tested their associations with rs10455872 in LPA. Topics enriched for CVD and hyperlipidemia had positive correlations with rs10455872 (P < 0.001), replicating a previous finding. We also identified a negative correlation between LPA and a topic enriched for lung cancer (P < 0.001) which was not previously identified via phenome-wide scanning. We were able to replicate the top finding in a separate dataset. Our results demonstrate the applicability of topic modeling in exploring the relationship between genetic variants and clinical diseases.

Using phenome-wide association to investigate the function of a schizophrenia risk locus at SLC39A8

While nearly all common genomic variants associated with schizophrenia have no known function, one corresponds to a missense variant associated with change in efficiency of a metal ion transporter, ZIP8, coded by SLC39A8. This variant has been linked to a range of phenotypes and is believed to be under recent selection pressure, but its impact on health is poorly understood. We sought to understand phenotypic implications of this variant in a large genomic biobank using an unbiased phenome-wide approach. Specifically, we generated 50 topics based on diagnostic codes using latent Dirichlet allocation, and examined them for association with the risk variant. Then, any significant topics were further characterized by examining association with individual diagnostic codes contributing to the topic. Among 50 topics, 1 was associated at an experiment-wide significance threshold (beta = 0.003, uncorrected p = 0.00049), comprising predominantly brain-related codes, including intracranial hemorrhage, cerebrovascular disease, and delirium/dementia. These results suggest that a functional variant previously associated with schizophrenia risk also increases liability to cerebrovascular disease. They further illustrate the utility of a topic-based approach to phenome-wide association.

Association networks in a matched case-control design - Co-occurrence patterns of preexisting chronic medical conditions in patients with major depression versus their matched controls

OBJECTIVE

We present a method for comparing association networks in a matched case-control design, which provides a high-level comparison of co-occurrence patterns of features after adjusting for confounding factors. We demonstrate this approach by examining the differential distribution of chronic medical conditions in patients with major depressive disorder (MDD) compared to the distribution of these conditions in their matched controls.

MATERIALS AND METHODS

Newly diagnosed MDD patients were matched to controls based on their demographic characteristics, socioeconomic status, place of residence, and healthcare service utilization in the Korean National Health Insurance Service's National Sample Cohort. Differences in the networks of chronic medical conditions in newly diagnosed MDD cases treated with antidepressants, and their matched controls, were prioritized with a permutation test accounting for the false discovery rate. Sensitivity analyses for the associations between prioritized pairs of chronic medical conditions and new MDD diagnosis were performed with regression modeling.

RESULTS

By comparing the association networks of chronic medical conditions in newly diagnosed depression patients and their matched controls, five pairs of such conditions were prioritized among 105 possible pairs after controlling the false discovery rate at 5%. In sensitivity analyses using regression modeling, four out of the five prioritized pairs were statistically significant for the interaction terms.

CONCLUSION

Association networks in a matched case-control design can provide a high-level comparison of comorbid features after adjusting for confounding factors, thereby supplementing traditional clinical study approaches. We demonstrate the differential co-occurrence pattern of chronic medical conditions in patients with MDD and prioritize the chronic conditions that have statistically significant interactions in regression models for depression.

High Throughput Phenotyping for Dimensional Psychopathology in Electronic Health Records

BACKGROUND

Relying on diagnostic categories of neuropsychiatric illness obscures the complexity of these disorders. Capturing multiple dimensional measures of neuropathology could facilitate the clinical and neurobiological investigation of cognitive and behavioral phenotypes.

METHODS

We developed a natural language processing-based approach to extract five symptom dimensions, based on the National Institute of Mental Health Research Domain Criteria definitions, from narrative clinical notes. Estimates of Research Domain Criteria loading were derived from a cohort of 3619 individuals with 4623 hospital admissions. We applied this tool to a large corpus of psychiatric inpatient admission and discharge notes (2010-2015), and using the same cohort we examined face validity, predictive validity, and convergent validity with gold standard annotations.

RESULTS

In mixed-effect models adjusted for sociodemographic and clinical features, greater negative and positive symptom domains were associated with a shorter length of stay (β = -.88, p = .001 and β = -1.22, p < .001, respectively), while greater social and arousal domain scores were associated with a longer length of stay (β = .93, p < .001 and β = .81, p = .007, respectively). In fully adjusted Cox regression models, a greater positive domain score at discharge was also associated with a significant increase in readmission risk (hazard ratio = 1.22, p < .001). Positive and negative valence domains were correlated with expert annotation (by analysis of variance [df = 3], R² = .13 and .19, respectively). Likewise, in a subset of patients, neurocognitive testing was correlated with cognitive performance scores (p < .008 for three of six measures).

CONCLUSIONS

This shows that natural language processing can be used to efficiently and transparently score clinical notes in terms of cognitive and psychopathologic domains.

Genome-wide association identifies a novel locus for delirium risk

We aimed to identify common genetic variations associated with delirium through genome-wide association testing in a hospital biobank. We applied a published electronic health record-based definition of delirium to identify cases of delirium, and control individuals with no history of delirium, from a biobank spanning 2 Boston academic medical centers. Among 6035 individuals of northern European ancestry, including 421 with a history of delirium, we used logistic regression to examine genome-wide association. We identified one locus spanning multiple genes, including 3 interleukin-related genes, associated with p = 1.41e-8, and 5 other independent loci with p < 5e-7. Our results do not support previously reported candidate gene associations in delirium. Identifying common-variant associations with delirium may provide insight into the mechanisms responsible for this complex and multifactorial outcome. Using standardized claims-based phenotypes in biobanks should allow the larger scale investigations required to confirm novel loci such as the one we identify.