Predicting Depression Onset in Young People Based on Clinical, Cognitive, Environmental, and Neurobiological Data
Publikation: Beitrag in Fachzeitschrift › Forschungsartikel › Beigetragen › Begutachtung
Beitragende
- University of Melbourne
- Radboud University Nijmegen
- Universität Heidelberg
- Trinity College Dublin
- King's College London (KCL)
- Universität Mannheim
- Université Paris-Saclay
- University of Vermont
- University of Nottingham
- Charité – Universitätsmedizin Berlin
- Physikalisch-Technische Bundesanstalt
- École normale supérieure Paris-Saclay
- Assistance publique – Hôpitaux de Paris
- Christian-Albrechts-Universität zu Kiel (CAU)
- Fudan University
- Université de Bordeaux
- University of Toronto
- University of Montreal
- Georg-August-Universität Göttingen
- National Institutes of Health (NIH)
- Siemens AG
- Tampere University Hospital
Abstract
Background: Adolescent onset of depression is associated with long-lasting negative consequences. Identifying adolescents at risk for developing depression would enable the monitoring of risk factors and the development of early intervention strategies. Using machine learning to combine several risk factors from multiple modalities might allow prediction of depression onset at the individual level. Methods: A subsample of a multisite longitudinal study in adolescents, the IMAGEN study, was used to predict future (subthreshold) major depressive disorder onset in healthy adolescents. Based on 2-year and 5-year follow-up data, participants were grouped into the following: 1) those developing a diagnosis of major depressive disorder or subthreshold major depressive disorder and 2) healthy control subjects. Baseline measurements of 145 variables from different modalities (clinical, cognitive, environmental, and structural magnetic resonance imaging) at age 14 years were used as input to penalized logistic regression (with different levels of penalization) to predict depression onset in a training dataset (n = 407). The features contributing the highest to the prediction were validated in an independent hold-out sample (three independent IMAGEN sites; n = 137). Results: The area under the receiver operating characteristic curve for predicting depression onset ranged between 0.70 and 0.72 in the training dataset. Baseline severity of depressive symptoms, female sex, neuroticism, stressful life events, and surface area of the supramarginal gyrus contributed most to the predictive model and predicted onset of depression, with an area under the receiver operating characteristic curve between 0.68 and 0.72 in the independent validation sample. Conclusions: This study showed that depression onset in adolescents can be predicted based on a combination multimodal data of clinical characteristics, life events, personality traits, and brain structure variables.
Details
Originalsprache | Englisch |
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Seiten (von - bis) | 376-384 |
Seitenumfang | 9 |
Fachzeitschrift | Biological Psychiatry: Cognitive Neuroscience and Neuroimaging |
Jahrgang | 7 |
Ausgabenummer | 4 |
Publikationsstatus | Elektronische Veröffentlichung vor Drucklegung - 19 März 2022 |
Peer-Review-Status | Ja |
Externe IDs
PubMed | 33753312 |
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ORCID | /0000-0001-5398-5569/work/150329536 |
ORCID | /0000-0002-8493-6396/work/150330257 |
Schlagworte
ASJC Scopus Sachgebiete
Schlagwörter
- Adolescents, Depression, Machine learning, Major depressive disorder, Penalized logistic regression, Prediction