Comparing feature selection and machine learning approaches for predicting CYP2D6 methylation from genetic variation

IntroductionPharmacogenetics currently supports clinical decision-making on the basis of a limited number of variants in a few genes and may benefit paediatric prescribing where there is a need for more precise dosing. Integrating genomic information such as methylation into pharmacogenetic models holds the potential to improve their accuracy and consequently prescribing decisions. Cytochrome P450 2D6 (CYP2D6) is a highly polymorphic gene conventionally associated with the metabolism of commonly used drugs and endogenous substrates. We thus sought to predict epigenetic loci from single nucleotide polymorphisms (SNPs) related to CYP2D6 in children from the GUSTO cohort.MethodsBuffy coat DNA methylation was quantified using the Illumina Infinium Methylation EPIC beadchip. CpG sites associated with CYP2D6 were used as outcome variables in Linear Regression, Elastic Net and XGBoost models. We compared feature selection of SNPs from GWAS mQTLs, GTEx eQTLs and SNPs within 2 MB of the CYP2D6 gene and the impact of adding demographic data. The samples were split into training (75%) sets and test (25%) sets for validation. In Elastic Net model and XGBoost models, optimal hyperparameter search was done using 10-fold cross validation. Root Mean Square Error and R-squared values were obtained to investigate each models’ performance. When GWAS was performed to determine SNPs associated with CpG sites, a total of 15 SNPs were identified where several SNPs appeared to influence multiple CpG sites.ResultsOverall, Elastic Net models of genetic features appeared to perform marginally better than heritability estimates and substantially better than Linear Regression and XGBoost models. The addition of nongenetic features appeared to improve performance for some but not all feature sets and probes. The best feature set and Machine Learning (ML) approach differed substantially between CpG sites and a number of top variables were identified for each model.DiscussionThe development of SNP-based prediction models for CYP2D6 CpG methylation in Singaporean children of varying ethnicities in this study has clinical application. With further validation, they may add to the set of tools available to improve precision medicine and pharmacogenetics-based dosing

Evaluating the Relative Predictive Validity of Measures of Self-Referential Processing for Depressive Symptom Severity

Supplementary Material

Psychotherapists’ perspective of the use of eHealth services to enhance positive mental health promotion

Objective Keyes’s two-continua model of mental health proposes that mental illness and positive mental health are two separate, correlated, unipolar dimensions. eHealth services have been used to deliver mental health care, though the focus remained largely on symptom reduction and management of negative aspects of mental health. The potential of eHealth services to promote positive mental well-being, however, has not been tapped sufficiently. The present study aims to explore psychotherapists’ perspective on the feasibility of eHealth services to enhance positive mental health promotion. Methods Seven focus group discussions were conducted among professionals ( n  = 38) who delivered psychotherapy to examine positive mental health in their practice. Responses related to the use of e-psychotherapy to promote mental well-being were extracted for use in a secondary analysis of data in this study. Thematic analysis of data via inductive approach was conducted to allow emergence of common themes. Results Three main themes related to psychotherapists’ perspective on the feasibility of eHealth intervention in enhancing positive mental health were identified: (1) use of eHealth to educate and improve positive mental health; (2) concerns on incorporating psychotherapy into online services; (3) other factors that affect uptake or effectiveness of eHealth intervention for positive mental health. Conclusions The study generally found support among psychotherapists for the feasibility of eHealth intervention in promoting positive mental health among clients. Potential difficulties in implementation and practicality concerns were discussed

Personalised Profiling in Mental Health: A CAT-based Approach for Maternal Well-being and Mood Disorders

Mood disorders are highly heterogeneous conditions that are a leading cause of disability around the world. This is particularly important in perinatal mental health, where there is an increased incidence during the perinatal period and significant impact on child outcomes. Psychological interventions such as internet-based Cognitive Behavioural Therapy (iCBT) have the potential to treat depression and anxiety and address underlying vulnerabilities, however it is limited in its ability to address individual vulnerabilities. We describe a framework where computerized adaptive testing (CAT), that has conventionally been applied in education, can be used to efficiently profile individual vulnerabilities. These responses as well as information from medical records and cognitive task information are incorporated into a recommender system for selecting iCBT modules that would be most likely to address individual vulnerabilities

Foundational Models for Personalised Mental Health

Mental health disorders are heterogenous in presentation and treatment response. For example, only one third of patients started on an antidepressant will achieve remission and each trial of medication can take several weeks. Additionally side effects and the development of chronic conditions such as diabetes or high cholesterol are common. We discuss the potential application of foundation models as developed from electronic medical records (EMRs), large language models (LLMs) and for pharmacogenetics drawing potential links and applications in mental health. In terms of EMRs, the concept of a patient representation has been used across applications such as disease prediction and personalised treatment. These approaches have been applied in mental health to label diseases such as depression and bipolar disorder as well as to predict suicide in risk assessment. We discuss a range of applications for LLMs, from supporting the preprocessing of EMRs for FEMRs, therapy support through transcription and assessment and patient monitoring, and psychoeducation. We discuss the potential applications of biomedical foundation models to precision medicine with pharmacogenetics. Finally, we touch on ways of integrating broad sources of data and outputs from various models

Utilising Computerised Adaptive Testing to Alleviate Respondent Burden in Maternal Stress Assessment

Maternal mental health plays a pivotal role in perinatal care, with far-reaching implications for both maternal well-being and child development. Amidst global challenges like the COVID-19 pandemic, the demand for effective mental health assessment has surged. In response, this study investigates the utility of Computerised Adaptive Testing (CAT) for profiling maternal stress. Using the Growing Up in Singapore Towards Healthy Outcomes (GUSTO) dataset, we focused on the Maternal Stress module, comprising the Perceived Stress Scale (PSS) and Parental Stress Index (PSI). We generated an item pool from both scales and employed the Graded Response Model (GRM) for calibration. A final item bank of 105 items was consolidated. Using the Concerto software, we devised a CAT questionnaire for test administration. We then used the 'Firestar' R package to simulate the CAT with various stopping criteria, revealing substantial question reduction (up to 84.8%) while still maintaining high correlations with true theta scores (up to 99.9%). Nonetheless, limitations include assumptions in simulations, item calibration, and a specific focus on maternal stress. This study underscores CAT's potential to streamline assessments, enhancing perinatal mental health evaluations, while signaling the need to further explore CAT’s potential in this domain

Evaluating the Relative Predictive Validity of Measures of Self-Referential Processing for Depressive Symptom Severity

The self-referential encoding task (SRET) has a number of implicit measures which are associated with various facets of depression, including depressive symptoms. While some measures have proven robust in predicting depressive symptoms, their effectiveness can vary depending on the methodology used. Hence, understanding the relative contributions of population differences, word lists and calculation methods to these associations with depression, is crucial for translating the SRET into a clinical screening tool. This study systematically investigated the predictive accuracy of various SRET measures across different samples, including one hospital population matched with healthy controls and two university student populations, exposed to differing word lists. Participants completed the standard SRET and its variations, including Likert scales and matrix formats. Both standard and novel SRET measures were calculated and compared for their relative and incremental contribution to their associations with depression, with mean squared error (MSE) used as the primary metric for measuring predictive accuracy. Results showed that most SRET measures significantly predicted depressive symptoms in clinical populations but not in healthy populations. Notably, models with task modifications, such as Matrix Endorsement Bias and Likert Endorsement Bias, achieved the lowest mean squared error (MSE), indicating better predictive accuracy compared to standard Endorsement Bias measures. These findings imply that task modifications and the use of longer word lists may enhance the effectiveness of screening methods in both clinical and research settings, potentially improving early detection and intervention for depression

Table_5_Transdiagnostic clustering of self-schema from self-referential judgements identifies subtypes of healthy personality and depression.DOCX

IntroductionThe heterogeneity of depressive and anxiety disorders complicates clinical management as it may account for differences in trajectory and treatment response. Self-schemas, which can be determined by Self-Referential Judgements (SRJs), are heterogeneous yet stable. SRJs have been used to characterize personality in the general population and shown to be prognostic in depressive and anxiety disorders.MethodsIn this study, we used SRJs from a Self-Referential Encoding Task (SRET) to identify clusters from a clinical sample of 119 patients recruited from the Institute of Mental Health presenting with depressive or anxiety symptoms and a non-clinical sample of 115 healthy adults. The generated clusters were examined in terms of most endorsed words, cross-sample correspondence, association with depressive symptoms and the Depressive Experiences Questionnaire and diagnostic category.ResultsWe identify a 5-cluster solution in each sample and a 7-cluster solution in the combined sample. When perturbed, metrics such as optimum cluster number, criterion value, likelihood, DBI and CHI remained stable and cluster centers appeared stable when using BIC or ICL as criteria. Top endorsed words in clusters were meaningful across theoretical frameworks from personality, psychodynamic concepts of relatedness and self-definition, and valence in self-referential processing. The clinical clusters were labeled “Neurotic” (C1), “Extraverted” (C2), “Anxious to please” (C3), “Self-critical” (C4), “Conscientious” (C5). The non-clinical clusters were labeled “Self-confident” (N1), “Low endorsement” (N2), “Non-neurotic” (N3), “Neurotic” (N4), “High endorsement” (N5). The combined clusters were labeled “Self-confident” (NC1), “Externalising” (NC2), “Neurotic” (NC3), “Secure” (NC4), “Low endorsement” (NC5), “High endorsement” (NC6), “Self-critical” (NC7). Cluster differences were observed in endorsement of positive and negative words, latency biases, recall biases, depressive symptoms, frequency of depressive disorders and self-criticism.DiscussionOverall, clusters endorsing more negative words tended to endorse fewer positive words, showed more negative biases in reaction time and negative recall bias, reported more severe depressive symptoms and a higher frequency of depressive disorders and more self-criticism in the clinical population. SRJ-based clustering represents a novel transdiagnostic framework for subgrouping patients with depressive and anxiety symptoms that may support the future translation of the science of self-referential processing, personality and psychodynamic concepts of self-definition to clinical applications.</p

Table_7_Transdiagnostic clustering of self-schema from self-referential judgements identifies subtypes of healthy personality and depression.DOCX

IntroductionThe heterogeneity of depressive and anxiety disorders complicates clinical management as it may account for differences in trajectory and treatment response. Self-schemas, which can be determined by Self-Referential Judgements (SRJs), are heterogeneous yet stable. SRJs have been used to characterize personality in the general population and shown to be prognostic in depressive and anxiety disorders.MethodsIn this study, we used SRJs from a Self-Referential Encoding Task (SRET) to identify clusters from a clinical sample of 119 patients recruited from the Institute of Mental Health presenting with depressive or anxiety symptoms and a non-clinical sample of 115 healthy adults. The generated clusters were examined in terms of most endorsed words, cross-sample correspondence, association with depressive symptoms and the Depressive Experiences Questionnaire and diagnostic category.ResultsWe identify a 5-cluster solution in each sample and a 7-cluster solution in the combined sample. When perturbed, metrics such as optimum cluster number, criterion value, likelihood, DBI and CHI remained stable and cluster centers appeared stable when using BIC or ICL as criteria. Top endorsed words in clusters were meaningful across theoretical frameworks from personality, psychodynamic concepts of relatedness and self-definition, and valence in self-referential processing. The clinical clusters were labeled “Neurotic” (C1), “Extraverted” (C2), “Anxious to please” (C3), “Self-critical” (C4), “Conscientious” (C5). The non-clinical clusters were labeled “Self-confident” (N1), “Low endorsement” (N2), “Non-neurotic” (N3), “Neurotic” (N4), “High endorsement” (N5). The combined clusters were labeled “Self-confident” (NC1), “Externalising” (NC2), “Neurotic” (NC3), “Secure” (NC4), “Low endorsement” (NC5), “High endorsement” (NC6), “Self-critical” (NC7). Cluster differences were observed in endorsement of positive and negative words, latency biases, recall biases, depressive symptoms, frequency of depressive disorders and self-criticism.DiscussionOverall, clusters endorsing more negative words tended to endorse fewer positive words, showed more negative biases in reaction time and negative recall bias, reported more severe depressive symptoms and a higher frequency of depressive disorders and more self-criticism in the clinical population. SRJ-based clustering represents a novel transdiagnostic framework for subgrouping patients with depressive and anxiety symptoms that may support the future translation of the science of self-referential processing, personality and psychodynamic concepts of self-definition to clinical applications.</p