Mental Disorders: Exploring Their Many Faces

Classification frameworks such as the Diagnostic and Statistical Manual of Mental Disorders (DSM) play a crucial role in identifying psychiatric disorders. They do this by assessing individuals who exhibit a certain number of co-occurring symptoms from established lists. This method leads to what are known as “polythetic” diagnoses, where people sharing the same diagnosis may exhibit different symptoms, resulting in varied symptom profiles. The polythetic aspect of these diagnoses raises questions about the validity of the classification systems for mental disorders and even challenges the concept of mental disorders themselves.

A previous investigation by Fried & Nesse (2015) posited that “depression is not a consistent syndrome” due to findings that individuals diagnosed with major depressive disorder (MDD) demonstrated over 1,000 unique symptom combinations as assessed through a questionnaire. Such a sweeping conclusion may lack sufficient substantiation, as it oversimplifies the complexities of how depression manifests in different individuals.

Firstly, it is essential to differentiate between the instrument used to measure a phenomenon, such as a questionnaire, and the phenomenon itself. This misstep, referred to as “reification” (Hyman 2010), can lead to significant misunderstandings. To simplify: just as a scale measures weight but does not define the body’s mass, a questionnaire may not accurately capture the full spectrum of major depressive disorder. Furthermore, Nunes et al. (2020) argued that merely counting potential symptom combinations fails to adequately represent the heterogeneity of psychiatric disorders. The frequency with which these combinations occur is crucial in understanding the most common forms of a disorder, which should be the focus of both research and clinical practice. This was the central aim of the recent study by Spiller et al. (2024), which investigated symptom combination patterns across various mental disorders.

Comprehensive Study Methods Utilized in Mental Health Research

The researchers conducted this study utilizing a variety of data types to ensure robust findings.

Initially, Spiller et al. executed a computer simulation of a fictitious mental disorder, engaging 500 individuals in the study. The computer-generated scores represented responses to a hypothetical clinical instrument that evaluated five distinct symptoms. For a diagnosis, two out of the five symptoms were required, leading to a total of 32 possible combinations. The researchers carried out 100 computer simulations, consistently diagnosing approximately 50% of the 500 participants with the fictitious disorder in each cycle. Each simulation mirrored real-world conditions, including diverse scores for each symptom and varying interrelations among them.

In addition to simulations, the authors analyzed existing data from four extensive datasets from the USA, which included three from the Department of Veteran Affairs and one from the National Institute of Mental Health Data Archive. This data encompassed electronic medical records alongside self-report instruments utilized to derive four DSM diagnoses:

1. PTSD Checklist for DSM-5 (PCL-5; 20 items) for Post-Traumatic Stress Disorder (PTSD) in 41,543 individuals.
2. Patient Health Questionnaire (PHQ-9; 9 items) for Major Depressive Disorder (MDD) in 46,259.
3. Generalized Anxiety Disorder questionnaire (GAD-7; 7 items) for Generalized Anxiety Disorder (GAD) in 63,742.
4. Positive and Negative Syndrome Scale (PANSS; 7 items) for probable schizophrenia in 3,959.

Across both simulated and real-world datasets, the investigators meticulously calculated the frequency of occurrence for every symptom combination, providing a detailed analysis of how these combinations manifest in various populations.

Key Findings from Computer Simulations and Real-World Data

In the initial phase of the study based on computer simulations, the researchers discovered that not all symptom combinations exhibited equal likelihoods of expression. Instead, their findings revealed:

a highly skewed distribution of the probabilities associated with symptom combinations, indicating the presence of a few highly probable combinations while the majority remained less likely.

This indicates that only a select few symptom combinations are likely to manifest frequently, whereas most others are rare. This same pattern was evident in the analyses of real-world data in the subsequent phase of the research.

In all analyzed datasets, the majority of symptom combinations were infrequent. For example, in the depression dataset, a staggering 90.5% of symptom combinations—specifically, 201 out of 222 combinations—were reported by less than 1% of respondents. The prevalence of symptom combinations endorsed by fewer than 1% of participants was similarly high across other datasets: 99.8% for PTSD, 50% for GAD, and 41.7% for probable schizophrenia. Collectively, these findings suggest that, across all disorders, most potential theoretical symptom combinations are exceedingly rare.

When considering individual responses, a significant majority endorsed one of the ten most common combinations of symptoms: 70.4% of subjects with PTSD, 55.4% of those with MDD, 91.3% of individuals diagnosed with probable schizophrenia, and 84.9% of GAD patients. This indicates that most individuals tend to exhibit a limited number of the most prevalent symptom combinations.

Insights and Patterns Uncovered in Mental Disorder Research

In summary, the study’s findings illustrated that the current DSM classification system for assessing mental disorders indeed produced a range of symptom profiles. However, this heterogeneity in clinical presentations adhered to a recognizable pattern. As noted by the authors,

certain combinations of symptoms possess an exceedingly high probability of occurrence, whereas this probability diminishes significantly for most other potential combinations.

In other words, while major mental disorder diagnoses may present various manifestations, some of these manifestations are considerably more prevalent than others.

Major Strengths and Limitations of the Study on Mental Health

This study presents two notable strengths worth emphasizing. First, the researchers addressed the primary research question through a multifaceted approach, employing both computer simulations and the analysis of real-world data. This triangulation method—integrating evidence from diverse methodologies—enhances the strength and consistency of the study’s findings. Each study design carries its own susceptibility to specific biases. When similar outcomes are observed across varied study designs addressing the same research question, it increases our confidence in the overarching results, and conversely, it highlights the limitations present.

Moreover, the analysis of real-world data was conducted using large sample sizes that included comprehensive measurements of individual symptoms. This level of detail was critical for accurately deriving overall diagnoses and counting the prevalence of symptom combinations.

However, the authors also identified three significant limitations of their research:

• First, the methodology employed to count different symptom combinations did not account for the possibility that some combinations might share a significant number of symptoms. This is crucial, as merely counting combinations could create a misleading perception of extensive heterogeneity in clinical manifestations, which may not truly reflect clinical realities, especially if key symptoms overlap across different combinations.
• Second, the binary distinction between the presence and absence of specific symptoms risks oversimplifying the varying degrees of symptom expression.
• Lastly, the diagnoses of mental disorders were derived not from structured psychiatric interviews but rather by approximating DSM criteria through self-report questionnaires obtained from the datasets.

Additionally, it is important to note one final limitation: data from three of the four existing datasets (the larger ones) were collected from the electronic health records of the U.S. Department of Veterans Affairs. This may imply that the data primarily represent individuals with more severe disorders, as these patients are more likely to seek and receive care in specialized clinical settings. Consequently, manifestations of less severe disorders and their associated symptoms present in the general population may have been overlooked.

Practical Implications of Research Findings in Mental Health

The findings derived from this study by Spiller et al. compel us to acknowledge the inherent heterogeneity present in polythetic mental disorder diagnoses. This is a reality that clinicians confront in their daily practice: patients with the same diagnoses often exhibit markedly different, if not conflicting, symptoms. For instance, during a major depressive episode, while some patients may experience a pronounced decrease in appetite and sleep, others might actually encounter an increase in both.

Furthermore, it is essential to learn how to leverage this heterogeneity effectively. We could hypothesize that the various clinical manifestations may signify partially distinct underlying pathophysiological mechanisms, necessitating different treatment approaches. This concept embodies a personalized medicine approach, where patients are selected based on specific bio-clinical profiles and matched with treatments targeting particular disease mechanisms. For example, emerging research indicates that inflammation may serve as a pivotal disease mechanism for a subset of patients with depression. Ongoing clinical studies (Khandaker et al. 2018; Otte et al. 2020; Zwiep et al. 2022; Wessa et al. 2024) across several European nations are actively seeking to identify this subset of depressed patients based on combinations of biological parameters (such as blood concentrations of inflammatory markers or body mass index levels) alongside clinical features (including symptoms like anhedonia, fatigue, appetite, and sleep disturbances). The goal is to evaluate the efficacy of anti-inflammatory treatments as adjunctive therapies for these patients. However, this personalized approach remains a work in progress, as further research is required to thoroughly characterize the pathophysiology of diverse clinical manifestations, encompassing everything from environmental factors to molecular mechanisms.

The insights from Spiller et al. are encouraging: we can begin our investigation into heterogeneity not by grappling with an infinitely complex array of clinical manifestations but instead by focusing on the few prototypical symptom profiles that are more frequently observed.

Additionally, Spiller et al.’s findings encourage us to adopt a more pragmatic perspective regarding current diagnostic systems. While it is vital to recognize the historical significance of these systems in psychiatry, which have facilitated standardized communication among clinicians and researchers about mental disorders, it is equally important to acknowledge their limitations. Avoiding the “reification” error is crucial; we must view these systems as simplified tools employed to measure the intricate and multifaceted nature of mental disorders. These diagnostic tools are far from perfect and are not intended to serve as definitive answers. Instead, they must continue to evolve in tandem with our expanding understanding of the complex mechanisms underlying mental disorders.

Researcher Statement of Interests

Yuri is actively engaged in research focusing on the exploration of depression heterogeneity, but he was not involved in the study presented here or its peer-review evaluation.

Essential Links for Further Reading

Primary Research Paper

Spiller TR, Duek O, Helmer M, et al. (2024) Unveiling the Structure in Mental Disorder Presentations. JAMA Psychiatry. 2024;81(11):1101–1107. doi:10.1001/jamapsychiatry.2024.2047

Additional References and Insights

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Hyman SE. The diagnosis of mental disorders: the problem of reification. Annu Rev Clin Psychol. 2010;6:155-79. doi: 10.1146/annurev.clinpsy.3.022806.091532. PMID: 17716032.

Nunes A, Trappenberg T, Alda M. The definition and measurement of heterogeneity. Transl Psychiatry. 2020 Aug 24;10(1):299. doi: 10.1038/s41398-020-00986-0. PMID: 32839448; PMCID: PMC7445182.

Khandaker GM, Oltean BP, Kaser M, Dibben CRM, Ramana R, Jadon DR, Dantzer R, Coles AJ, Lewis G, Jones PB. Protocol for the insight study: a randomised controlled trial of single-dose tocilizumab in patients with depression and low-grade inflammation. BMJ Open. 2018 Sep 21;8(9):e025333. doi: 10.1136/bmjopen-2018-025333. PMID: 30244217; PMCID: PMC6157523.

Otte C, Chae WR, Nowacki J, Kaczmarczyk M, Piber D, Roepke S, Märschenz S, Lischewski S, Schmidt S, Ettrich B, Grabe HJ, Hegerl U, Hinkelmann K, Hofmann T, Janowitz D, Junghanns K, Kahl KG, Klein JP, Krueger THC, Leicht G, Prvulovic D, Reif A, Schoettle D, Strauss M, Westermair A, Friede T, Gold SM. Simvastatin add-on to escitalopram in patients with comorbid obesity and major depression (SIMCODE): study protocol of a multicentre, randomised, double-blind, placebo-controlled trial. BMJ Open. 2020 Dec 1;10(12):e040119. doi: 10.1136/bmjopen-2020-040119. PMID: 33262189; PMCID: PMC7709515.

Zwiep JC, Bet PM, Rhebergen D, Nurmohamed MT, Vinkers CH, Penninx BWJH, Milaneschi Y, Lamers F. Efficacy of celecoxib add-on treatment for immuno-metabolic depression: Protocol of the INFLAMED double-blind placebo-controlled randomized controlled trial. Brain Behav Immun Health. 2022 Dec 30;27:100585. doi: 10.1016/j.bbih.2022.100585. PMID: 36655056; PMCID: PMC9841030.

Wessa, C. Anti-inflammatory treatments for youth depression: promising but not yet implementable. The Mental Elf, 22 March 2024.