Transdiagnostic behavioral and genetic contributors to repetitive negative thinking: A machine learning approach.
Machine learning ensemble method
Neuroticism
Polygenic risk score
Principal component analysis
Repetitive negative thinking
Journal
Journal of psychiatric research
ISSN: 1879-1379
Titre abrégé: J Psychiatr Res
Pays: England
ID NLM: 0376331
Informations de publication
Date de publication:
06 2023
06 2023
Historique:
received:
11
11
2022
revised:
25
04
2023
accepted:
03
05
2023
medline:
29
5
2023
pubmed:
14
5
2023
entrez:
13
5
2023
Statut:
ppublish
Résumé
Repetitive negative thinking (RNT) is a symptom that can negatively impact the treatment and course of common psychiatric disorders such as depression and anxiety. We aimed to characterize behavioral and genetic correlates of RNT to infer potential contributors to its genesis and maintenance. We applied a machine learning (ML) ensemble method to define the contribution of fear, interoceptive, reward, and cognitive variables to RNT, along with polygenic risk scores (PRS) for neuroticism, obsessive compulsive disorder (OCD), worry, insomnia, and headaches. We used the PRS and 20 principal components of the behavioral and cognitive variables to predict intensity of RNT. We employed the Tulsa-1000 study, a large database of deeply phenotyped individuals recruited between 2015 and 2018. PRS for neuroticism was the main predictor of RNT intensity (R This study is an exploratory approach that must be validated with a second, independent cohort. Furthermore, this is an association study, limiting causal inference. RNT is highly determined by genetic risk for neuroticism, a behavioral construct that confers risk to a variety of internalizing disorders, and by emotional processing and learning features, including interoceptive aversiveness. These results suggest that targeting emotional and interoceptive processing areas, which involve central autonomic network structures, could be useful in the modulation of RNT intensity.
Sections du résumé
BACKGROUND
Repetitive negative thinking (RNT) is a symptom that can negatively impact the treatment and course of common psychiatric disorders such as depression and anxiety. We aimed to characterize behavioral and genetic correlates of RNT to infer potential contributors to its genesis and maintenance.
METHODS
We applied a machine learning (ML) ensemble method to define the contribution of fear, interoceptive, reward, and cognitive variables to RNT, along with polygenic risk scores (PRS) for neuroticism, obsessive compulsive disorder (OCD), worry, insomnia, and headaches. We used the PRS and 20 principal components of the behavioral and cognitive variables to predict intensity of RNT. We employed the Tulsa-1000 study, a large database of deeply phenotyped individuals recruited between 2015 and 2018.
RESULTS
PRS for neuroticism was the main predictor of RNT intensity (R
LIMITATIONS
This study is an exploratory approach that must be validated with a second, independent cohort. Furthermore, this is an association study, limiting causal inference.
CONCLUSIONS
RNT is highly determined by genetic risk for neuroticism, a behavioral construct that confers risk to a variety of internalizing disorders, and by emotional processing and learning features, including interoceptive aversiveness. These results suggest that targeting emotional and interoceptive processing areas, which involve central autonomic network structures, could be useful in the modulation of RNT intensity.
Identifiants
pubmed: 37178517
pii: S0022-3956(23)00225-X
doi: 10.1016/j.jpsychires.2023.05.039
pii:
doi:
Banques de données
ClinicalTrials.gov
['NCT02450240']
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
207-213Informations de copyright
Copyright © 2023 Elsevier Ltd. All rights reserved.
Déclaration de conflit d'intérêts
Declaration of competing interest The authors have no competing interests to report.