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Identification of transdiagnostic psychiatric disorder subtypes using unsupervised learning.

ABSTRACT: Psychiatric disorders show heterogeneous symptoms and trajectories, with current nosology not accurately reflecting their molecular etiology and the variability and symptomatic overlap within and between diagnostic classes. This heterogeneity impedes timely and targeted treatment. Our study aimed to identify psychiatric patient clusters that share clinical and genetic features and may profit from similar therapies. We used high-dimensional data clustering on deep clinical data to identify transdiagnostic groups in a discovery sample (N = 1250) of healthy controls and patients diagnosed with depression, bipolar disorder, schizophrenia, schizoaffective disorder, and other psychiatric disorders. We observed five diagnostically mixed clusters and ordered them based on severity. The least impaired cluster 0, containing most healthy controls, showed general well-being. Clusters 1-3 differed predominantly regarding levels of maltreatment, depression, daily functioning, and parental bonding. Cluster 4 contained most patients diagnosed with psychotic disorders and exhibited the highest severity in many dimensions, including medication load. Depressed patients were present in all clusters, indicating that we captured different disease stages or subtypes. We replicated all but the smallest cluster 1 in an independent sample (N = 622). Next, we analyzed genetic differences between clusters using polygenic scores (PGS) and the psychiatric family history. These genetic variables differed mainly between clusters 0 and 4 (prediction area under the receiver operating characteristic curve (AUC) = 81%; significant PGS: cross-disorder psychiatric risk, schizophrenia, and educational attainment). Our results confirm that psychiatric disorders consist of heterogeneous subtypes sharing molecular factors and symptoms. The identification of transdiagnostic clusters advances our understanding of the heterogeneity of psychiatric disorders and may support the development of personalized treatments.

SUBMITTER: Pelin H

PROVIDER: S-EPMC8429672 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundAlzheimer's disease (AD) is a highly heterogeneous disease with diverse trajectories and outcomes observed in clinical populations. Understanding this heterogeneity can enable better treatment, prognosis and disease management. Studies to date have mainly used imaging or cognition data and have been limited in terms of data breadth and sample size. Here we examine the clinical heterogeneity of Alzheimer's disease patients using electronic health records (EHR) to identify and characterise disease subgroups using multiple clustering methods, identifying clusters which are clinically actionable.MethodsWe identified AD patients in primary care EHR from the Clinical Practice Research Datalink (CPRD) using a previously validated rule-based phenotyping algorithm. We extracted and included a range of comorbidities, symptoms and demographic features as patient features. We evaluated four different clustering methods (k-means, kernel k-means, affinity propagation and latent class analysis) to cluster Alzheimer's disease patients. We compared clusters on clinically relevant outcomes and evaluated each method using measures of cluster structure, stability, efficiency of outcome prediction and replicability in external data sets.ResultsWe identified 7,913 AD patients, with a mean age of 82 and 66.2% female. We included 21 features in our analysis. We observed 5, 2, 5 and 6 clusters in k-means, kernel k-means, affinity propagation and latent class analysis respectively. K-means was found to produce the most consistent results based on four evaluative measures. We discovered a consistent cluster found in three of the four methods composed of predominantly female, younger disease onset (43% between ages 42-73) diagnosed with depression and anxiety, with a quicker rate of progression compared to the average across other clusters.ConclusionEach clustering approach produced substantially different clusters and K-Means performed the best out of the four methods based on the four evaluative criteria. However, the consistent appearance of one particular cluster across three of the four methods potentially suggests the presence of a distinct disease subtype that merits further exploration. Our study underlines the variability of the results obtained from different clustering approaches and the importance of systematically evaluating different approaches for identifying disease subtypes in complex EHR.

Project description:BackgroundEarlier age of pubertal maturation in females is associated with increased risk for mental health problems in adolescence, compared with on-time or later maturation. However, most investigations of pubertal timing and mental health consider risk for individual disorders and fail to account for comorbidity. A latent-modeling approach using a large, nationally representative sample could better explain the transdiagnostic nature of the consequences of early-onset puberty.MethodsData on age of menarche and mental disorders were drawn from a population-representative sample of adolescents (n=4925), ages 13-17. Confirmatory factor analysis was used to fit four latent disorder categories: distress, eating, and externalizing, and fear disorders. Timing of menarche included those with earlier (age≤10, age 11) and later age of onset (age 13, 14+), relative to those with average timing of menarche (age 12). Associations between timing of menarche and latent disorders were estimated in a structural equation model (SEM), adjusted for age, income, race, parent marital status, BMI, and childhood adversity.ResultsThe measurement model evidenced acceptable fit (CFI=0.91; RMSEA=0.02). Onset of menarche before age 11 was significantly associated with distress disorders (coefficient=0.096; p<0.0001), fear disorders (coefficient=0.09; p<0.0001), and externalizing disorders (coefficient=0.039; p=0.049) as compared to on-time or late menarche. No residual associations of early menarche with individual disorders over and above the latent disorders were observed.ConclusionThe latent modeling approach illuminated meaningful transdiagnostic psychiatric associations with early timing of menarche. Biological processes initiated at puberty can influence cognitive and affective processes as well as social relationships for adolescents. Under developmentally normative conditions, these changes may be adaptive. However, for those out of sync with their peers, researchers and clinicians should recognize the potential for these processes to influence liability to a broad array of psychopathological consequences in adolescence.

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Identification of transdiagnostic psychiatric disorder subtypes using unsupervised learning.

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