Dataset Information

Developing and validating a prediction model of adolescent major depressive disorder in the offspring of depressed parents.

ABSTRACT:

Background

Parental depression is common and is a major risk factor for depression in adolescents. Early identification of adolescents at elevated risk of developing major depressive disorder (MDD) in this group could improve early access to preventive interventions.

Methods

Using longitudinal data from 337 adolescents at high familial risk of depression, we developed a risk prediction model for adolescent MDD. The model was externally validated in an independent cohort of 1,384 adolescents at high familial risk. We assessed predictors at baseline and MDD at follow-up (a median of 2-3 years later). We compared the risk prediction model to a simple comparison model based on screening for depressive symptoms. Decision curve analysis was used to identify which model-predicted risk score thresholds were associated with the greatest clinical benefit.

Results

The MDD risk prediction model discriminated between those adolescents who did and did not develop MDD in the development (C-statistic = .783, IQR (interquartile range) = .779, .778) and the validation samples (C-statistic = .722, IQR = -.694, .741). Calibration in the validation sample was good to excellent (calibration intercept = .011, C-slope = .851). The MDD risk prediction model was superior to the simple comparison model where discrimination was no better than chance (C-statistic = .544, IQR = .536, .572). Decision curve analysis found that the highest clinical utility was at the lowest risk score thresholds (0.01-0.05).

Conclusions

The developed risk prediction model successfully discriminated adolescents who developed MDD from those who did not. In practice, this model could be further developed with user involvement into a tool to target individuals for low-intensity, selective preventive intervention.

SUBMITTER: Stephens A

PROVIDER: S-EPMC10087673 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Developing and validating a prediction model of adolescent major depressive disorder in the offspring of depressed parents.

Stephens Alice A Allardyce Judith J Weavers Bryony B Lennon Jessica J Jones Rhys Bevan RB Powell Victoria V Eyre Olga O Potter Robert R Price Valentina Escott VE Osborn David D Thapar Anita A Collishaw Stephan S Thapar Ajay A Heron Jon J Rice Frances F

Journal of child psychology and psychiatry, and allied disciplines 20220912 3

<h4>Background</h4>Parental depression is common and is a major risk factor for depression in adolescents. Early identification of adolescents at elevated risk of developing major depressive disorder (MDD) in this group could improve early access to preventive interventions.<h4>Methods</h4>Using longitudinal data from 337 adolescents at high familial risk of depression, we developed a risk prediction model for adolescent MDD. The model was externally validated in an independent cohort of 1,384 a ...[more]

PMID: 36096685

Similar Datasets

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

Developing and validating a prediction model of adolescent major depressive disorder in the offspring of depressed parents.

Background

Methods

Results

Conclusions

Publications

Developing and validating a prediction model of adolescent major depressive disorder in the offspring of depressed parents.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets