Psychiatric comorbidity: fact or artifact?

Applying the Research Domain Criteria to Rodent Studies of Sex Differences in Chronic Stress Susceptibility

Women have a 2-fold increased rate of stress-associated psychiatric disorders such as depression and anxiety, but the mechanisms that underlie this increased susceptibility remain incompletely understood. Historically, female subjects were excluded from preclinical studies and clinical trials. Additionally, chronic stress paradigms used to study psychiatric pathology in animal models were developed for use in males. However, recent changes in National Institutes of Health policy encourage inclusion of female subjects, and considerable work has been performed in recent years to understand biological sex differences that may underlie differences in susceptibility to chronic stress-associated psychiatric conditions. Here, we review the utility as well as current challenges of using the framework of the National Institute of Mental Health's Research Domain Criteria as a transdiagnostic approach to study sex differences in rodent models of chronic stress including recent progress in the study of sex differences in the neurobehavioral domains of negative valence, positive valence, cognition, social processes, and arousal.

Dimensional Neuroimaging Endophenotypes: Neurobiological Representations of Disease Heterogeneity Through Machine Learning

Machine learning has been increasingly used to obtain individualized neuroimaging signatures for disease diagnosis, prognosis, and response to treatment in neuropsychiatric and neurodegenerative disorders. Therefore, it has contributed to a better understanding of disease heterogeneity by identifying disease subtypes with different brain phenotypic measures. In this review, we first present a systematic literature overview of studies using machine learning and multimodal magnetic resonance imaging to unravel disease heterogeneity in various neuropsychiatric and neurodegenerative disorders, including Alzheimer's disease, schizophrenia, major depressive disorder, autism spectrum disorder, and multiple sclerosis, as well as their potential in a transdiagnostic framework, where neuroanatomical and neurobiological commonalities were assessed across diagnostic boundaries. Subsequently, we summarize relevant machine learning methodologies and their clinical interpretability. We discuss the potential clinical implications of the current findings and envision future research avenues. Finally, we discuss an emerging paradigm called dimensional neuroimaging endophenotypes. Dimensional neuroimaging endophenotypes dissects the neurobiological heterogeneity of neuropsychiatric and neurodegenerative disorders into low-dimensional yet informative, quantitative brain phenotypic representations, serving as robust intermediate phenotypes (i.e., endophenotypes), presumably reflecting the interplay of underlying genetic, lifestyle, and environmental processes associated with disease etiology.

Allocation of Users of Mental Health Services to Needs-Based Care Clusters: An Italian Pilot Study

In Italy, despite strong community-based mental health services, needs assessment is unsatisfactory. Using the Mental Health Clustering Tool (MHCT) we adopted a multidimensional and non-diagnosis dependent approach to assign mental health services users with similar needs to groups corresponding to resources required for effective care. We tested the MHCT in nine Departments of Mental Health in four Italian regions. After a brief training, 318 professionals assessed 12,938 cases with a diagnosis of schizophrenia, depression, bipolar disorder and personality disorder through the MHCT. 53% of cases were 40-59 years, half were females, 51% had a diagnosis of schizophrenia, 48% of cases were clinically severe. Clusters included different levels of clinical severity and diagnostic groups. The largest cluster was 11 (ongoing recurrent psychosis), with 18.9% of the sample, followed by cluster 3 (non-psychotic disorders of moderate severity). The MHCT could capture a variety of problems of people with mental disorders beyond the traditional psychiatric assessment, therefore depicting service population from a different standpoint. Following a brief training, MHCT assessment proved to be feasible. The automatic allocation of cases made the attribution to clusters easy and acceptable by professionals. To what extent clustering provide a sound base for care planning will be the matter of further research.

Transdiagnostic Cognitive Behavioral Psychotherapy: Unified Protocol as an Example

The fact that there are many limitations of the current classification systems led to the emergence of transdiagnostic approaches (TA) that focus on the common psychopathological processes underlying disorders rather than categorical diagnoses. TA focuses on the underlying common psychopathological processes in the emergence and persistence of mental disorders. Thus, even if the disorders are categorically diagnosed differently, it can be determined how they overlap or separate with each other. TA aims to treat mental disorders using these aspects. TA has a flexible and modular structure that can be easily integrated into cognitive behavioral therapies. The rest of this review will focus on the Unified Protocol (UP), one of the most popular TA examples. The main purpose of the UP is to enable patients to recognize their feelings and give more adaptive reactions to their negative emotions. Accordingly, UP consists of eight modules. The modules can usually be completed in a total of 11 - 17 weeks. Each 50 to 60-minute individual session is held once a week. If necessary, changes can be made to the number or the frequency of sessions allocated to modules. The goals of each module of the UP and the treatment approaches towards these goals allow a very clearly defined approach. For this reason, there is a need for a detailed evaluation, conceptualization and treatment plan before the UP is put into practice. We hope that mental health professionals from Turkey will contribute to the developments in the UP.

Experiences with Family Planning amongst Persons with Mental Health Problems: A Nationwide Patient Survey

High rates of unintended pregnancies in patients with mental health problems reflect the unmet need for tailored family planning. This study aims to explore aspects of family planning that are especially challenging for patients experiencing health problems by obtaining the perspective of (former) patients and those with close relationships with the (former) patients. In August 2021, members of a Dutch national mental health panel, consisting of (former) patients and close ones, were invited to respond to a 34-question online survey that included questions on four domains: reproductive history, decision making, parenting, and sexuality. This study has revealed the severe and adverse impact of mental health problems across all of the four domains of reproductive health and family planning, which the questions specifically targeted. Based on these results, we recommend discussing family planning with all patients experiencing or at risk for mental health problems and their partners. These discussions should address a desire to have children, (involuntary) childlessness, uncertainties about parenting and sexuality, while remaining considerate of experienced taboos.

Identifying pleiotropic genes for major psychiatric disorders with GWAS summary statistics using multivariate adaptive association tests

BACKGROUND

Genome wide association studies (GWAS) have discovered a few of single nucleotide polymorphisms (SNPs) related to major psychiatric disorders. However, it is not completely clear which genes play a pleiotropic role in multiple disorders. The study aimed to identify the pleiotropic genes across five psychiatric disorders using multivariate adaptive association tests.

METHODS

Summary statistics of five psychiatric disorders were downloaded from Psychiatric Genomics Consortium. We applied linkage disequilibrium score regression (LDSC) to estimate genetic correlation and conducted tissue and cell type specificity analyses based on Multi-marker Analysis of GenoMic Annotation (MAGMA). Then, we identified the pleiotropic genes using MTaSPUsSet and aSPUs tests. We ultimately performed the functional analysis for pleiotropic genes.

RESULTS

We confirmed the significant genetic correlation and brain tissue and neuron specificity among five disorders. 100 pleiotropic genes were detected to be significantly associated with five psychiatric disorders, of which 55 were novel genes. These genes were functionally enriched in neuron differentiation and synaptic transmission.

LIMITATIONS

The effect direction of pleiotropic genes couldn't be distinguished due to without individual-level data.

CONCLUSION

We identified pleiotropic genes using multivariate adaptive association tests and explored their biological function. The findings may provide novel insight into the development and implementation of prevention and treatment as well as targeted drug discovery in practice.

Divergent presentation of anxiety in high-risk groups within the intellectual disability population

Background

Anxiety symptomatology is common in individuals with intellectual disability (ID). Symptomatology includes both traditional Diagnostic and Statistical Manual of Mental Disorders, 5th Edition (DSM-5) anxiety disorders and autism spectrum disorder (ASD)-related anxiety traits. Some genetic disorders such as Cornelia de Lange (CdLS) and fragile X syndromes (FXS) are at very high risk of anxiety and afford the opportunity to examine prevalence, profiles and associated person characteristics. However, prevalence and associated characteristics of anxiety in these high-risk groups remain poorly described and understood. The aim of the current study was to examine the prevalence and profile of DSM-5 and ASD-related anxiety symptomatology in individuals with CdLS and FXS and associated behavioural and cognitive characteristics.

Methods

Questionnaires and interviews assessing DSM-5 and ASD-related anxiety were conducted with caregivers of individuals with CdLS (n = 49) and FXS (n = 36).

Results

DSM-5 anxiety symptomatology was present in both groups with high co-morbidity across anxiety diagnoses. ASD-related anxiety was also prevalent with specific difficulties related to intolerance of uncertainty identified in both groups. Symptomatology was persistent over the lifespan for both groups. Anxiety type was partially associated with repetitive behaviour but not measures of overall ASD phenomenology in CdLS.

Conclusions

DSM-5 and ASD-related anxiety are common in these high-risk syndromes associated with ID. Prospective syndrome specific presentations and associations, which may implicate specific underlying mechanisms, are discussed. Clinicians should be aware of the risk and difficulties involved in assessment of anxiety in individuals with ID, including atypical types, to ensure these individuals do not “miss” diagnoses and support in general clinical practice.

Mental Health Risk Profiles and Related Substance Use During Coronavirus Pandemic Among College Students Who Use Substances

College students have shown elevated mental distress during the coronavirus disease of 2019 (COVID-19). The extent and persistence of mental distress as COVID-19 restrictions have continued is unclear. This study used latent profile analysis to identify student mental health risk subgroups and to evaluate subgroups in relation with substance use. A four-profile solution was supported with a sample of 930 college students (69.6% female, 58.1% White) from 11 US-based institutions. Students were characterized by slight mental health symptoms, mild mental health symptoms, moderate-to-severe mental health symptoms with mild psychosis/substance use, and severe mental health symptoms. The severe profile comprised more ethnoracial or sexual minorities and students impacted from COVID-19. Whereas the severe profile had more alcohol-related consequences, the slight profile had fewer cannabis-related consequences. COVID-19 has exacerbated college student risks for psychiatric disorders. Students of diverse backgrounds and more impacted by COVID-19 show disproportionately more mental distress and related substance use.

Improving Web-Based Treatment Intake for Multiple Mental and Substance Use Disorders by Text Mining and Machine Learning: Algorithm Development and Validation

BACKGROUND

Text mining and machine learning are increasingly used in mental health care practice and research, potentially saving time and effort in the diagnosis and monitoring of patients. Previous studies showed that mental disorders can be detected based on text, but they focused on screening for a single predefined disorder instead of multiple disorders simultaneously.

OBJECTIVE

The aim of this study is to develop a Dutch multi-class text-classification model to screen for a range of mental disorders to refer new patients to the most suitable treatment.

METHODS

On the basis of textual responses of patients (N=5863) to a questionnaire currently used for intake and referral, a 7-class classifier was developed to distinguish among anxiety, panic, posttraumatic stress, mood, eating, substance use, and somatic symptom disorders. A linear support vector machine was fitted using nested cross-validation grid search.

RESULTS

The highest classification rate was found for eating disorders (82%). The scores for panic (55%), posttraumatic stress (52%), mood (50%), somatic symptom (50%), anxiety (35%), and substance use disorders (33%) were lower, likely because of overlapping symptoms. The overall classification accuracy (49%) was reasonable for a 7-class classifier.

CONCLUSIONS

A classification model was developed that could screen text for multiple mental health disorders. The screener resulted in an additional outcome score that may serve as input for a formal diagnostic interview and referral. This may lead to a more efficient and standardized intake process.

Genetic variants in miRNAs differentially expressed during brain development and their relevance to psychiatric disorders susceptibility

OBJECTIVES

MicroRNAs (miRNAs) play an important regulatory role in the expression of genes involved in brain functions during development. Genetic variants in miRNA genes may impact their regulatory function and lead to psychiatric disorders. To evaluate the role of genetic variants in genes of miRNAs differentially expressed during neurodevelopment on autism spectrum disorder (ASD), attention deficit/hyperactivity disorder (ADHD), schizophrenia (SCZ), and major depressive disorder (MDD).

METHODS

The miRNAs were identified in the literature. Summary statistics from the most recent genome-wide association studies to date were used to evaluate the association between the selected polymorphisms and each disorder in a look-up approach. In a global analysis, we compared the standardised risk effect of variants in neurodevelopment-related miRNAs with those in the remaining miRNAs from miRBase.

RESULTS

The global analysis showed that variants in neurodevelopment-related miRNAs had higher risk effects compared to the other miRNAs for SCZ (p = 0.010) and ADHD (p = 0.001). MIR33B, MIR29B2, MIR29C, MIR137, and MIR135A1 were significantly associated with SCZ, while 55.9% of the miRNAs were at least nominally associated with one or more psychiatric disorders (p < 0.05).

CONCLUSIONS

Genetic variants in neurodevelopment-related miRNAs play an important role in the genetic susceptibility of psychiatric disorders, mainly SCZ and ADHD.

Glutathione S-Transferase Polymorphisms and Clinical Characteristics in Autism Spectrum Disorders

Background: Autism spectrum disorders (ASD) are a heterogeneous group of developmental disorders, with different levels of symptoms, functioning, and comorbidities. Recent findings suggested that oxidative stress and genetic variability in glutathione S-transferases (GSTs) might increase the risk of ASD development. We aimed to determine whether GST polymorphisms influence the severity of symptoms as well as the cognitive and adaptive abilities in children with ASD. Methods: The sample included 113 ASD cases. All participants were genotyped for GSTA1, GSTM1, GSTT1, and GSTP1 polymorphisms. The clinical characteristics were determined with Autism Diagnostic Interview-Revised (ADI-R) in all of the participants. In non-verbal participants, we explored the adaptive functioning using the Vineland Adaptive Behavior Scale II, while in verbal participants, we used the Wechsler Abbreviated Scale of Intelligence (WASI). Results: It was shown that the GSTA1 * CC genotype was a predictor of a lower non-verbal communication impairment as well as of a lower chance of having seizures during life. GSTM1-active genotype predicted a higher adaptive functioning. The predictive effect of GSTA1, GSTM1, and GSTT1 genotype was moderated by exposure during pregnancy (maternal smoking and medication). The GSTP1 * IleIle genotype was significantly associated to a better cognitive functioning in children with ASD. Conclusion: Besides the complex gene-environment interaction for the specific risk of developing ASD, there is also a possible complexity of interactions between genetic and environmental factors influencing the level of symptoms and impairment in people with ASD. Detoxification and antioxidant enzymes, such as GSTA1, might contribute to the core of this complexity.

Potential Transdiagnostic Lipid Mediators of Inflammatory Activity in Individuals With Serious Mental Illness

Mental disorders are heterogeneous and psychiatric comorbidities are common. Previous studies have suggested a link between inflammation and mental disorders. This link can manifest as increased levels of proinflammatory mediators in circulation and as signs of neuroinflammation. Furthermore, there is strong evidence that individuals suffering from psychiatric disorders have increased risk of developing metabolic comorbidities. Our group has previously shown that, in a cohort of low-functioning individuals with serious mental disorders, there is increased expression of genes associated with the NLRP3 inflammasome, a known sensor of metabolic perturbations, as well as increased levels of IL-1-family cytokines. In the current study, we set out to explore the interplay between disease-specific changes in lipid metabolism and known markers of inflammation. To this end, we performed mass spectrometry-based lipidomic analysis of plasma samples from low-functioning individuals with serious mental disorders (n = 39) and matched healthy controls (n = 39). By identifying non-spurious immune-lipid associations, we derived a partial correlation network of inflammatory markers and molecular lipids. We identified levels of lipids as being altered between individuals with serious mental disorders and controls, showing associations between lipids and inflammatory mediators, e.g., osteopontin and IL-1 receptor antagonist. These results indicate that, in low-functioning individuals with serious mental disorders, changes in specific lipids associate with immune mediators that are known to affect neuroinflammatory diseases.

Beyond the tip of the iceberg: A narrative review to identify research gaps on comorbid psychiatric disorders in adolescents with methamphetamine use disorder or chronic methamphetamine use

BACKGROUND

Methamphetamine use disorder (MUD) frequently begins in adolescence, often accompanied by other psychiatric or mental disorders. Up to now, no comprehensive review about MUD and comorbid disorders in adolescents is available. We thus aimed to review the literature on comorbid mental disorders and MUD in adolescents in order to identify future research topics. Method: A PubMed search was conducted in July 2019. Relevant comorbidities were defined as attention-deficit disorder with/without hyperactivity, anxiety disorders, depression, eating disorders, post-traumatic stress disorder, psychosis, borderline personality disorder, conduct disorder and antisocial personality disorder, as well as other substance use disorders. For each comorbidity, we summarized prevalence rates, findings on comorbidity mechanisms, and recommended treatment options, if applicable. Results: Few articles focused on MUD in adolescents. Prevalence rates differed largely between comorbid disorders, with tobacco use disorder, conduct disorder, post-traumatic stress disorder, anxiety disorders, and attention-deficit disorders being the most prevalent comorbidities while eating disorders were rare. Examined onset patterns and comorbidity mechanisms indicated three groups of comorbidities: preexisting disorders self-medicated with methamphetamine, disorders induced by chronic methamphetamine use, and disorders arising due to risk factors shared with MUD. Reviewed comorbidities were frequently associated with worse treatment outcomes. Conclusions: The limited evidence is in stark contrast to the presumably high prevalence and relevance of comorbid mental disorders in adolescents with MUD. Suggestions for future research topics, informed by adult findings, include genetic vulnerabilities, biological changes, and consequences of different use patterns. Surprisingly few MUD treatment programs explicitly integrate comorbid mental disorder modules.

Transdiagnostic approaches to mental health problems: Current status and future directions

Despite a longstanding and widespread influence of the diagnostic approach to mental ill health, there is an emerging and growing consensus that such psychiatric nosologies may no longer be fit for purpose in research and clinical practice. In their place, there is gathering support for a "transdiagnostic" approach that cuts across traditional diagnostic boundaries or, more radically, sets them aside altogether, to provide novel insights into how we might understand mental health difficulties. Removing the distinctions between proposed psychiatric taxa at the level of classification opens up new ways of classifying mental health problems, suggests alternative conceptualizations of the processes implicated in mental health, and provides a platform for novel ways of thinking about onset, maintenance, and clinical treatment and recovery from experiences of disabling mental distress. In this Introduction to a Special Section on Transdiagnostic Approaches to Psychopathology, we provide a narrative review of the transdiagnostic literature in order to situate the Special Section articles in context. We begin with a brief history of the diagnostic approach and outline several challenges it currently faces that arguably limit its applicability in current mental health science and practice. We then review several recent transdiagnostic approaches to classification, biopsychosocial processes, and clinical interventions, highlighting promising novel developments. Finally, we present some key challenges facing transdiagnostic science and make suggestions for a way forward. (PsycINFO Database Record (c) 2020 APA, all rights reserved).

The network approach to psychopathology: a review of the literature 2008-2018 and an agenda for future research

The network approach to psychopathology posits that mental disorders can be conceptualized and studied as causal systems of mutually reinforcing symptoms. This approach, first posited in 2008, has grown substantially over the past decade and is now a full-fledged area of psychiatric research. In this article, we provide an overview and critical analysis of 363 articles produced in the first decade of this research program, with a focus on key theoretical, methodological, and empirical contributions. In addition, we turn our attention to the next decade of the network approach and propose critical avenues for future research in each of these domains. We argue that this program of research will be best served by working toward two overarching aims: (a) the identification of robust empirical phenomena and (b) the development of formal theories that can explain those phenomena. We recommend specific steps forward within this broad framework and argue that these steps are necessary if the network approach is to develop into a progressive program of research capable of producing a cumulative body of knowledge about how specific mental disorders operate as causal systems.

The Relationship of Post-traumatic Stress Disorder to End-of-life Care Received by Dying Veterans: a Secondary Data Analysis

BACKGROUND

Post-traumatic stress disorder (PTSD) can be exacerbated by subsequent trauma, but it is unclear if symptoms are worsened by impending death. PTSD symptoms, including hyperarousal, negative mood and thoughts, and traumatic re-experiencing, can impact end-of-life symptoms, including pain, mood, and poor sleep. Thus, increased symptoms may lead to increased end-of-life healthcare utilization.

OBJECTIVES

To determine if veterans with PTSD have increased end-of-life healthcare utilization or medication use and to examine predictors of medication administration.

DESIGN

Secondary analysis of a stepped-wedge design implementation trial to improve end-of-life care for Veterans Affairs (VA) inpatients. Outcome variables were collected via direct chart review. Analyses included hierarchical, generalized estimating equation models, clustered by medical center.

SUBJECTS

Veterans, inpatient at one of six VA facilities, dying between 2005 and 2011.

MAIN MEASURES

Emergency room (ER) visits, hospitalizations, and medication administration in the last 7 days of life.

KEY RESULTS

Of 5341 veterans, 468 (8.76%) had PTSD. Of those, 21.4% (100/468) had major depression and 36.5% (171/468) had anxiety. Veterans with PTSD were younger (mean age 65.4 PTSD, 70.5 no PTSD, p < 0.0001) and had more VA hospitalizations and ER visits in the last 12 months of life (admissions: PTSD 2.8, no PTSD 2.4, p < 0.0001; ER visits: 3.2 vs 2.5, p < 0.0001). PTSD was associated with antipsychotic administration (OR 1.52, 95% CI 1.06-2.18). Major depression (333/5341, 6.2%) was associated with opioid administration (OR 1.348, 95% CI 1.129-1.609) and benzodiazepines (OR 1.489, 95% CI 1.141-1.943). Anxiety disorders (778/5341, 14.6%) were only associated with benzodiazepines (OR 1.598, 95% CI 1.194-2.138).

CONCLUSIONS

PTSD's association with increased end-of-life healthcare utilization and increased antipsychotic administration in the final days of life suggests increased symptom burden and potential for terminal delirium in individuals with PTSD. Understanding the burden of psychiatric illness and potential risks for delirium may facilitate the end-of-life care for these patients.

TRIAL REGISTRATION

NCT00234286.

Transdiagnostic Dimensions of Psychiatric Comorbidity in Individuals at Clinical High Risk for Psychosis: A Preliminary Study Informed by HiTOP

Background: Although psychiatric comorbidity is the norm among individuals at clinical high risk for psychotic disorders (CHR), research has yet to examine transdiagnostic dimensional models of comorbidity in this critical population. Methods: This study analyzed quantitative measures of eleven psychiatric syndromes in a group at CHR (n = 71) and a matched healthy comparison group (n = 73) to determine these syndromes' dimensional structure and relationships to cognition, functioning, and risk of conversion to psychotic disorders. Results: Relative to the comparison group, the CHR group was elevated on all eleven psychiatric syndromes. Exploratory factor analysis found three psychopathology dimensions: internalizing, negative symptoms, and positive symptoms. Depression cross-loaded onto the internalizing and negative symptom dimensions. Hypomania loaded positively on positive symptoms but negatively on negative symptoms. The negative symptom factor was associated with poorer cognition and functioning and a higher risk of conversion to psychosis. Conclusions: These dimensions align with internalizing, detachment, and thought disorder, three of the five spectra in higher-order models such as the Hierarchical Taxonomy of Psychopathology (HiTOP). In the CHR state, detachment appears to be particularly insidious and predictive of psychosis. Further research is required to distinguish depression and hypomania from attenuated psychotic symptoms in this population.

What's in a model? Network models as tools instead of representations of what psychiatric disorders really are

Network models block reductionism about psychiatric disorders only if models are interpreted in a realist manner - that is, taken to represent "what psychiatric disorders really are." A flexible and more instrumentalist view of models is needed to improve our understanding of the heterogeneity and multifactorial character of psychiatric disorders.

Childhood physical abuse and emotional neglect are specifically associated with adult mental disorders

Background: Childhood adversities and trauma (CAT) are associated with adult mental disorders. Nevertheless, although CAT of different domains mostly co-occurs, and co-morbidity is common, the associations between CAT and mental disorders, when taking these interrelations into account, are not well known.Aims: We aimed to study differential associations between the five core domains of CAT and current axis-I disorders, taking into consideration their interrelations.Methods: Four hundred and fifteen outpatients attending adult primary (n = 255) and psychiatric care (n = 160) were assessed with the Trauma and Distress Scale (TADS) and the Mini International Neuropsychiatric Interview (MINI). Associations between CAT core domains and diagnostic categories were examined by path analyses.Results: At least some infrequent experience of CAT (83.6%), mostly of neglect, and current mental disorders (49.4%), mostly depression, was frequent, as were co-morbidities and co-occurrence of CAT domains. Considering these interrelations in a path model of excellent fit, physical abuse predicted depressive, manic, psychotic and anxiety disorders, whereas emotional neglect predicted depressive, anxiety and substance misuse disorders.Conclusions: Of all five CAT core domains, physical abuse and emotional neglect had the strongest association with adult psychiatric disorders and might have transmitted earlier reported main effects of other CAT domains onto mental disorders.

Prediction, Understanding, and Medicine

What is medicine? One obvious answer in the context of the contemporary clinical tradition is that medicine is the process of curing sick people. However, this "curative thesis" is not satisfactory, even when "cure" is defined generously and even when exceptions such as cosmetic surgery are set aside. Historian of medicine Roy Porter argues that the position of medicine in society has had, and still has, little to do with its ability to make people better. Moreover, the efficacy of medicine for improving population health has been famously doubted by historians and epidemiologists. The curative thesis demands that we have mostly been stupid, duped, or staggeringly hopeful, given that medicine has not until recently offered more than a handful of effective cures. I suggest, in this article, that the core medical competence is neither to cure, nor to prevent, disease, but to understand and to predict it. I argue that this approach does a better job than the curative thesis at explaining why not all medicine is concerned with curative efforts and that it enjoys historical support from the ancient entanglement of prophecy and medicine and from the fact that medicine thrived for centuries with almost no effective cures and continues to thrive today in various forms that are mostly without curative efficacy. I suggest that this approach grounds a fairer approach to alternative, traditional, and other medical practices, as well as some fresh lessons for the development of mainstream medicine.

PSYCHIATRIC COMORBIDITY DOES NOT ONLY DEPEND ON DIAGNOSTIC THRESHOLDS: AN ILLUSTRATION WITH MAJOR DEPRESSIVE DISORDER AND GENERALIZED ANXIETY DISORDER

BACKGROUND

High rates of psychiatric comorbidity are subject of debate: To what extent do they depend on classification choices such as diagnostic thresholds? This paper investigates the influence of different thresholds on rates of comorbidity between major depressive disorder (MDD) and generalized anxiety disorder (GAD).

METHODS

Point prevalence of comorbidity between MDD and GAD was measured in 74,092 subjects from the general population (LifeLines) according to Diagnostic and Statistical Manual of Mental Disorders (DSM-IV-TR) criteria. Comorbidity rates were compared for different thresholds by varying the number of necessary criteria from ≥ 1 to all nine symptoms for MDD, and from ≥ 1 to all seven symptoms for GAD.

RESULTS

According to DSM thresholds, 0.86% had MDD only, 2.96% GAD only, and 1.14% both MDD and GAD (odds ratio (OR) 42.6). Lower thresholds for MDD led to higher rates of comorbidity (1.44% for ≥ 4 of nine MDD symptoms, OR 34.4), whereas lower thresholds for GAD hardly influenced comorbidity (1.16% for ≥ 3 of seven GAD symptoms, OR 38.8). Specific patterns in the distribution of symptoms within the population explained this finding: 37.3% of subjects with core criteria of MDD and GAD reported subthreshold MDD symptoms, whereas only 7.6% reported subthreshold GAD symptoms.

CONCLUSIONS

Lower thresholds for MDD increased comorbidity with GAD, but not vice versa, owing to specific symptom patterns in the population. Generally, comorbidity rates result from both empirical symptom distributions and classification choices and cannot be reduced to either of these exclusively. This insight invites further research into the formation of disease concepts that allow for reliable predictions and targeted therapeutic interventions.