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Assessment of Environmental and Genetic Factors and Mental Health Outcomes for Trajectories of Sleep Disturbance from Late Childhood to Early Adolescence

20 Pages Posted: 16 Jan 2025

See all articles by Jie Yan

Jie Yan

Government of the People's Republic of China - Army Medical University

Haolei Bai

Government of the People's Republic of China - Army Medical University

Yuqing Sun

Beijing Normal University (BNU) - State Key Laboratory of Cognitive Neuroscience and Learning

Meng Wang

Beijing Normal University (BNU) - State Key Laboratory of Cognitive Neuroscience and Learning

Qin Li

Hainan University

Yafang Pan

Government of the People's Republic of China - Army Medical University

Xiaodong Liu

Government of the People's Republic of China - Army Medical University

Yanping Li

Government of the People's Republic of China - Army Medical University

Zhongxiang Yao

Government of the People's Republic of China - Army Medical University

Yaojing Chen

Beijing Normal University (BNU) - State Key Laboratory of Cognitive Neuroscience and Learning

Zhanjun Zhang

Beijing Normal University (BNU) - State Key Laboratory of Cognitive Neuroscience and Learning

Zhi'an Hu

Government of the People's Republic of China - Department of Physiology and Pathophysiology

Chao He

Government of the People's Republic of China - Army Medical University

Bing Liu

Beijing Normal University (BNU) - State Key Laboratory of Cognitive Neuroscience and Learning

Xiaolong Zhang

Government of the People's Republic of China - Department of Physiology and Pathophysiology

More...

Abstract

Background: To evaluate the longitudinal associations of sleep disturbance trajectories with different risk factors and mental health outcomes from late childhood to early adolescence.

Methods: This cohort study used longitudinal data from the Adolescent Brain Cognitive Development study. Individuals were assessed for sleep disturbance annually and growth mixture modeling was employed to derive trajectories of sleep disturbance in 11509 individuals. Regression models examined the longitudinal associations of these trajectories with different risk factors and psychopathology syndromes.

Findings: A total of 11509 children (6029 [52.4%] boys) were categorized into 4 distinct trajectories of sleep disturbance: decreasing (292 [2.54%]), persistent (430 [3.74%]), increasing (470 [4.08%]), and stable low (10317 [89.64%]). Multinomial logistic regression models revealed that compared to stable low, adverse trajectories were distinctly associated with polygenic risk scores and changes in environmental factors from different domains. An increase in primary caregiver mental health problems showed the strongest association with increasing trajectory (OR 1.35, 95% CI 1.24 to 1.49, P < 0.001). Moreover, adverse trajectories showed changes in psychopathology syndromes consistent with themselves with stronger effects on internalizing than externalizing problems.

Interpretation: Adverse trajectories of sleep disturbance were distinctly associated with a variety of environmental and genetic risk factors and the development of psychopathology syndromes. These findings suggest that incorporation of environmental and genetic risk factors could help identify individuals with less favorable trajectory of sleep disturbance, who should be prioritized for early intervention to improve sleep health and mental health outcomes from late childhood to early adolescence.

Keywords: trajectory of sleep disturbance, environmental factors, genetics, mental health outcomes

Suggested Citation

Yan, Jie and Bai, Haolei and Sun, Yuqing and Wang, Meng and Li, Qin and Pan, Yafang and Liu, Xiaodong and Li, Yanping and Yao, Zhongxiang and Chen, Yaojing and Zhang, Zhanjun and Hu, Zhi'an and He, Chao and Liu, Bing and Zhang, Xiaolong, Assessment of Environmental and Genetic Factors and Mental Health Outcomes for Trajectories of Sleep Disturbance from Late Childhood to Early Adolescence. Available at SSRN: https://ssrn.com/abstract=5098082 or http://dx.doi.org/10.2139/ssrn.5098082

Jie Yan

Government of the People's Republic of China - Army Medical University ( email )

Chongqing, 400038
China

Haolei Bai

Government of the People's Republic of China - Army Medical University ( email )

Chongqing, 400038
China

Yuqing Sun

Beijing Normal University (BNU) - State Key Laboratory of Cognitive Neuroscience and Learning ( email )

Meng Wang

Beijing Normal University (BNU) - State Key Laboratory of Cognitive Neuroscience and Learning ( email )

Qin Li

Hainan University ( email )

No. 58, Renmin Avenue
570228, P.R.
Haikou, HainanProvince
China

Yafang Pan

Government of the People's Republic of China - Army Medical University ( email )

Chongqing, 400038
China

Xiaodong Liu

Government of the People's Republic of China - Army Medical University ( email )

Chongqing, 400038
China

Yanping Li

Government of the People's Republic of China - Army Medical University ( email )

Chongqing, 400038
China

Zhongxiang Yao

Government of the People's Republic of China - Army Medical University ( email )

Chongqing, 400038
China

Yaojing Chen

Beijing Normal University (BNU) - State Key Laboratory of Cognitive Neuroscience and Learning ( email )

Zhanjun Zhang

Beijing Normal University (BNU) - State Key Laboratory of Cognitive Neuroscience and Learning ( email )

Beijing
China

Zhi'an Hu

Government of the People's Republic of China - Department of Physiology and Pathophysiology ( email )

Chao He

Government of the People's Republic of China - Army Medical University ( email )

Chongqing, 400038
China

Bing Liu

Beijing Normal University (BNU) - State Key Laboratory of Cognitive Neuroscience and Learning ( email )

Xiaolong Zhang (Contact Author)

Government of the People's Republic of China - Department of Physiology and Pathophysiology ( email )

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