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Personalized relapse prediction in patients with major depressive disorder using digital biomarkers.

ABSTRACT: Major depressive disorder (MDD) is a chronic illness wherein relapses contribute to significant patient morbidity and mortality. Near-term prediction of relapses in MDD patients has the potential to improve outcomes by helping implement a 'predict and preempt' paradigm in clinical care. In this study, we developed a novel personalized (N-of-1) encoder-decoder anomaly detection-based framework of combining anomalies in multivariate actigraphy features (passive) as triggers to utilize an active concurrent self-reported symptomatology questionnaire (core symptoms of depression and anxiety) to predict near-term relapse in MDD. The framework was evaluated on two independent longitudinal observational trials, characterized by regular bimonthly (every other month) in-person clinical assessments, weekly self-reported symptom assessments, and continuous activity monitoring data with two different wearable sensors for ≥ 1 year or until the first relapse episode. This combined passive-active relapse prediction framework achieved a balanced accuracy of ≥ 71%, false alarm rate of ≤ 2.3 alarm/patient/year with a median relapse detection time of 2-3 weeks in advance of clinical onset in both studies. The study results suggest that the proposed personalized N-of-1 prediction framework is generalizable and can help predict a majority of MDD relapses in an actionable time frame with relatively low patient and provider burden.

SUBMITTER: Vairavan S

PROVIDER: S-EPMC10616277 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature

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Personalized relapse prediction in patients with major depressive disorder using digital biomarkers.

Vairavan Srinivasan S Rashidisabet Homa H Li Qingqin S QS Ness Seth S Morrison Randall L RL Soares Claudio N CN Uher Rudolf R Frey Benicio N BN Lam Raymond W RW Kennedy Sidney H SH Trivedi Madhukar M Drevets Wayne C WC Narayan Vaibhav A VA

Scientific reports 20231030 1

Major depressive disorder (MDD) is a chronic illness wherein relapses contribute to significant patient morbidity and mortality. Near-term prediction of relapses in MDD patients has the potential to improve outcomes by helping implement a 'predict and preempt' paradigm in clinical care. In this study, we developed a novel personalized (N-of-1) encoder-decoder anomaly detection-based framework of combining anomalies in multivariate actigraphy features (passive) as triggers to utilize an active co ...[more]

PMID: 37903878

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Project description:Importance:More than half of all patients with major depressive disorder (MDD) experience a relapse within 2 years after recovery. It is unclear how relapse affects brain morphologic features during the course of MDD. Objective:To use structural magnetic resonance imaging to identify morphologic brain changes associated with relapse in MDD. Design, Setting, and Participants:In this longitudinal case-control study, patients with acute MDD at baseline and healthy controls were recruited from the University of Münster Department of Psychiatry from March 21, 2010, to November 14, 2014, and were reassessed from November 11, 2012, to October 28, 2016. Depending on patients' course of illness during follow-up, they were subdivided into groups of patients with and without relapse. Whole-brain gray matter volume and cortical thickness of the anterior cingulate cortex, orbitofrontal cortex, middle frontal gyrus, and insula were assessed via 3-T magnetic resonance imaging at baseline and 2 years later. Main Outcomes and Measures:Gray matter was analyzed via group (no relapse, relapse, and healthy controls) by time (baseline and follow-up) analysis of covariance, controlling for age and total intracranial volume. Confounding factors of medication and depression severity were assessed. Results:This study included 37 patients with MDD and a relapse (19 women and 18 men; mean [SD] age, 37.0 [12.7] years), 23 patients with MDD and without relapse (13 women and 10 men; mean [SD] age, 32.5 [10.5] years), and 54 age- and sex-matched healthy controls (24 women and 30 men; mean [SD] age, 37.5 [8.7] years). A significant group-by-time interaction controlling for age and total intracranial volume revealed that patients with relapse showed a significant decline of insular volume (difference, -0.032; 95% CI, -0.063 to -0.002; P = .04) and dorsolateral prefrontal volume (difference, -0.079; 95% CI, -0.113 to -0.045; P < .001) from baseline to follow-up. In patients without relapse, gray matter volume in these regions did not change significantly (insula: difference, 0.027; 95% CI, -0.012 to 0.066; P = .17; and dorsolateral prefrontal volume: difference, 0.023; 95% CI, -0.020 to 0.066; P = .30). Volume changes were not correlated with psychiatric medication or with severity of depression at follow-up. Additional analysis of cortical thickness showed an increase in the anterior cingulate cortex (difference, 0.073 mm; 95% CI, 0.023-0.123 mm; P = .005) and orbitofrontal cortex (difference, 0.089 mm; 95% CI, 0.032-0.147 mm; P = .003) from baseline to follow-up in patients without relapse. Conclusion and Relevance:A distinct association of relapse in MDD with brain morphologic features was revealed using a longitudinal design. Relapse is associated with brain structures that are crucial for regulation of emotions and thus needs to be prevented. This study might be a step to guide future prognosis and maintenance treatment in patients with recurrent MDD.

Project description:Background: Major Depressive Disorder (MDD) is a moderately heritable disorder with a high lifetime prevalence. At present, laboratory blood tests to support MDD diagnosis are not available. Methods: We used a classifier approach on blood gene expression profiles of a unique set of non-medicated subjects (MDD patients and controls) to select genes of which expression is predictive for disease status. To reveal blood gene expression changes related to MDD disease, we applied a powerful ex vivo stimulus to the blood, i.e. incubation with lipopolysaccharide (LPS; 10 ng/ml blood). Results: Based on LPS-stimulated blood gene expression using whole-genome microarrays in 42 subjects (primary cohort; 21 MDD patients (mean age 42.3 years), 21 healthy controls (mean age 41.9 years)), we identified a set of genes (CAPRIN1, CLEC4A, KRT23, MLC1, PLSCR1, PROK2, ZBTB16) that serves as a molecular signature of MDD. These findings were validated for the primary cohort using an independent quantitative PCR method (P = 0.007). The difference between depressive patients and controls was confirmed (P = 0.019) in a replication cohort of 13 patients with MDD (mean age 42.8 years) and 14 controls (mean age 45.6 years). The MDD-signature score comprised of expression levels of 7 genes could discriminate depressive patients from controls with sensitivity of 76.9% and specificity of 71.8%. Conclusions: We show for the first time that molecular analysis of stimulated blood cells can be used as an endophenotype for MDD diagnosis, which is a milestone in establishing biomarkers for neuropsychiatric disorders with moderate heritability in general. Our results may provide a new entry point for following and predicting treatment outcome, as well as prediction of severity and recurrence of MDD. In total, 33 MDD patients and 34 healthy controls were analyzed using basal gene expression in whole blood, and gene expression from whole blood that was stimulated with LPS for 5-6 h, using microarrays. Patients were arbitrarily selected from all patients to serve as primary cohort (nMDD = 21 (MDD01-MDD21); nControls = 21 (Con01-Con21)), or replication cohort (nMDD = 12 (MDD22-MDD35); nControls = 13 (Con22-Con37)) using microarrays. This submission does not include Samples CON21_LPS or CON30_LPS.

Dataset Information

Personalized relapse prediction in patients with major depressive disorder using digital biomarkers.

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Personalized relapse prediction in patients with major depressive disorder using digital biomarkers.

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