Dynamic risk for first onset of depressive disorders in adolescence: does change matter?


Journal

Psychological medicine
ISSN: 1469-8978
Titre abrégé: Psychol Med
Pays: England
ID NLM: 1254142

Informations de publication

Date de publication:
04 2023
Historique:
medline: 15 6 2023
pubmed: 23 11 2021
entrez: 22 11 2021
Statut: ppublish

Résumé

Risk factors for depressive disorders (DD) change substantially over time, but the prognostic value of these changes remains unclear. Two basic types of dynamic effects are possible. The 'Risk Escalation hypothesis' posits that worsening of risk levels predicts DD onset above average level of risk factors. Alternatively, the 'Chronic Risk hypothesis' posits that the average level rather than change predicts first-onset DD. We utilized data from the ADEPT project, a cohort of 496 girls (baseline age 13.5-15.5 years) from the community followed for 3 years. Participants underwent five waves of assessments for risk factors and diagnostic interviews for DD. For illustration purposes, we selected 16 well-established dynamic risk factors for adolescent depression, such as depressive and anxiety symptoms, personality traits, clinical traits, and social risk factors. We conducted Cox regression analyses with time-varying covariates to predict first DD onset. Consistently elevated risk factors (i.e. the mean of multiple waves), but not recent escalation, predicted first-onset DD, consistent with the Chronic Risk hypothesis. This hypothesis was supported across all 16 risk factors. Across a range of risk factors, girls who had first-onset DD generally did not experience a sharp increase in risk level shortly before the onset of disorder; rather, for years before onset, they exhibited elevated levels of risk. Our findings suggest that chronicity of risk should be a particular focus in screening high-risk populations to prevent the onset of DDs. In particular, regular monitoring of risk factors in school settings is highly informative.

Sections du résumé

BACKGROUND
Risk factors for depressive disorders (DD) change substantially over time, but the prognostic value of these changes remains unclear. Two basic types of dynamic effects are possible. The 'Risk Escalation hypothesis' posits that worsening of risk levels predicts DD onset above average level of risk factors. Alternatively, the 'Chronic Risk hypothesis' posits that the average level rather than change predicts first-onset DD.
METHODS
We utilized data from the ADEPT project, a cohort of 496 girls (baseline age 13.5-15.5 years) from the community followed for 3 years. Participants underwent five waves of assessments for risk factors and diagnostic interviews for DD. For illustration purposes, we selected 16 well-established dynamic risk factors for adolescent depression, such as depressive and anxiety symptoms, personality traits, clinical traits, and social risk factors. We conducted Cox regression analyses with time-varying covariates to predict first DD onset.
RESULTS
Consistently elevated risk factors (i.e. the mean of multiple waves), but not recent escalation, predicted first-onset DD, consistent with the Chronic Risk hypothesis. This hypothesis was supported across all 16 risk factors.
CONCLUSIONS
Across a range of risk factors, girls who had first-onset DD generally did not experience a sharp increase in risk level shortly before the onset of disorder; rather, for years before onset, they exhibited elevated levels of risk. Our findings suggest that chronicity of risk should be a particular focus in screening high-risk populations to prevent the onset of DDs. In particular, regular monitoring of risk factors in school settings is highly informative.

Identifiants

pubmed: 34802476
doi: 10.1017/S0033291721004190
pii: S0033291721004190
doi:

Types de publication

Journal Article Research Support, N.I.H., Extramural

Langues

eng

Sous-ensembles de citation

IM

Pagination

2352-2360

Subventions

Organisme : NIMH NIH HHS
ID : R01 MH093479
Pays : United States
Organisme : NIMH NIH HHS
ID : R56 MH117116
Pays : United States

Auteurs

Wenting Mu (W)

Department of Psychology, Tsinghua University, Beijing, China.

Kaiqiao Li (K)

Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA.

Yuan Tian (Y)

Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA.

Greg Perlman (G)

Department of Psychiatry, Stony Brook University, Stony Brook, NY, USA.

Giorgia Michelini (G)

Department of Psychiatry, Stony Brook University, Stony Brook, NY, USA.
Semel Institute for Neuroscience & Human Behavior, University of California Los Angeles, Los Angeles, CA, USA.

David Watson (D)

Department of Psychology, University of Notre Dame, Notre Dame, Indiana, USA.

Hans Ormel (H)

Department of Psychiatry, University Medical Center Groningen, Groningen, The Netherlands.

Daniel N Klein (DN)

Department of Psychology, Stony Brook University, Stony Brook, NY, USA.

Roman Kotov (R)

Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA.
Department of Psychiatry, Stony Brook University, Stony Brook, NY, USA.
Semel Institute for Neuroscience & Human Behavior, University of California Los Angeles, Los Angeles, CA, USA.

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