Life course trajectories of affective symptoms and their early life predictors.


Journal

Longitudinal and life course studies : international journal
ISSN: 1757-9597
Titre abrégé: Longit Life Course Stud
Pays: England
ID NLM: 101513496

Informations de publication

Date de publication:
01 05 2022
Historique:
entrez: 3 8 2022
pubmed: 4 8 2022
medline: 6 8 2022
Statut: epublish

Résumé

Life course trajectories of affective symptoms (depression and anxiety) are heterogenous. However, few studies have investigated the role of early life risk factors in the development of these trajectories. The present study aimed to: (1) derive latent trajectories of affective symptoms over a period of more than 50 years (ages 13-69), and (2) examine early life risk factors for associations with specific life course trajectories of affective symptoms. Participants are from the MRC National Survey of Health and Development (NSHD) (n = 5,362). Affective symptoms were measured prospectively at ages 13, 15, 36, 43, 53, 60-64 and 69. A latent variable modelling framework was implemented to model longitudinal profiles of affective symptoms. Twenty-four prospectively measured early life predictors were tested for associations with different symptom profiles using multinomial logistic regression. Four life course profiles of affective symptoms were identified: (1) absence of symptoms (66.6% of the sample); (2) adolescent symptoms with good adult outcome (15.2%); (3) adult symptoms only (with no symptoms in adolescence and late life) (12.9%); (4) symptoms in adolescence and mid adulthood (5.2%). Of the 24 early life predictors observed, only four were associated with life course trajectories, with small effect sizes observed. People differ in their life course trajectories of anxiety and depression symptoms and that these differences are not largely influenced by early life factors tested in this study.

Sections du résumé

Background
Life course trajectories of affective symptoms (depression and anxiety) are heterogenous. However, few studies have investigated the role of early life risk factors in the development of these trajectories. The present study aimed to: (1) derive latent trajectories of affective symptoms over a period of more than 50 years (ages 13-69), and (2) examine early life risk factors for associations with specific life course trajectories of affective symptoms.
Method
Participants are from the MRC National Survey of Health and Development (NSHD) (n = 5,362). Affective symptoms were measured prospectively at ages 13, 15, 36, 43, 53, 60-64 and 69. A latent variable modelling framework was implemented to model longitudinal profiles of affective symptoms. Twenty-four prospectively measured early life predictors were tested for associations with different symptom profiles using multinomial logistic regression.
Results
Four life course profiles of affective symptoms were identified: (1) absence of symptoms (66.6% of the sample); (2) adolescent symptoms with good adult outcome (15.2%); (3) adult symptoms only (with no symptoms in adolescence and late life) (12.9%); (4) symptoms in adolescence and mid adulthood (5.2%). Of the 24 early life predictors observed, only four were associated with life course trajectories, with small effect sizes observed.
Conclusions
People differ in their life course trajectories of anxiety and depression symptoms and that these differences are not largely influenced by early life factors tested in this study.

Identifiants

pubmed: 35920619
doi: 10.1332/175795921X16487298020502
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

412-431

Subventions

Organisme : Medical Research Council
ID : MC_PC_20059
Pays : United Kingdom
Organisme : Medical Research Council
ID : MC_UU_00019/2
Pays : United Kingdom
Organisme : Medical Research Council
ID : MC_UU_00019/3
Pays : United Kingdom
Organisme : Medical Research Council
ID : MC_UU_00019/4
Pays : United Kingdom

Auteurs

Ellen J Thompson (EJ)

University of Sussex, UK.

George B Ploubidis (GB)

University College London, UK.

Marcus Richards (M)

University College London, UK.

Darya Gaysina (D)

University of Sussex, UK.

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Classifications MeSH