Evidence map›Paper›PMID 42218486›Full record

ArticleBMC medicine2026

Immune cell subsets linking insomnia trajectories and incident cognitive impairment: a prospective cohort study.

Liantian Chen, Jinjing Fu, Xueqing Jia, Liming Zhang, Xinli Jin, Shuhui Wu, Bin Zhu, Kaili Sun, Duoduo Fu, Yanyan Wang and 3 more

Abstract read
In one paragraph

Article in BMC medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Liantian ChenCenter for Clinical Big Data and Analytics Second Affiliated Hospital, Department of Big Data in Health Science School of Public Health, Zhejiang Key Laboratory of Intelligent Preventive Medicine, Zhejiang University School of Medicine, Hangzhou, 310058, Zhejiang, China.
Jinjing FuCenter for Clinical Big Data and Analytics Second Affiliated Hospital, Department of Big Data in Health Science School of Public Health, Zhejiang Key Laboratory of Intelligent Preventive Medicine, Zhejiang University School of Medicine, Hangzhou, 310058, Zhejiang, China.
Xueqing JiaCenter for Clinical Big Data and Analytics Second Affiliated Hospital, Department of Big Data in Health Science School of Public Health, Zhejiang Key Laboratory of Intelligent Preventive Medicine, Zhejiang University School of Medicine, Hangzhou, 310058, Zhejiang, China.
Liming ZhangCenter for Clinical Big Data and Analytics Second Affiliated Hospital, Department of Big Data in Health Science School of Public Health, Zhejiang Key Laboratory of Intelligent Preventive Medicine, Zhejiang University School of Medicine, Hangzhou, 310058, Zhejiang, China.
Xinli JinCenter for Clinical Big Data and Analytics Second Affiliated Hospital, Department of Big Data in Health Science School of Public Health, Zhejiang Key Laboratory of Intelligent Preventive Medicine, Zhejiang University School of Medicine, Hangzhou, 310058, Zhejiang, China.
Shuhui WuCenter for Clinical Big Data and Analytics Second Affiliated Hospital, Department of Big Data in Health Science School of Public Health, Zhejiang Key Laboratory of Intelligent Preventive Medicine, Zhejiang University School of Medicine, Hangzhou, 310058, Zhejiang, China.
Bin ZhuCenter for Clinical Big Data and Analytics Second Affiliated Hospital, Department of Big Data in Health Science School of Public Health, Zhejiang Key Laboratory of Intelligent Preventive Medicine, Zhejiang University School of Medicine, Hangzhou, 310058, Zhejiang, China.
Kaili SunCenter for Clinical Big Data and Analytics Second Affiliated Hospital, Department of Big Data in Health Science School of Public Health, Zhejiang Key Laboratory of Intelligent Preventive Medicine, Zhejiang University School of Medicine, Hangzhou, 310058, Zhejiang, China.
Duoduo FuDepartment of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA.
Yanyan WangNational Clinical Research Center for Geriatrics, West China School of Nursing, Healthcare Innovation Research Laboratory, West China Hospital, Sichuan University, Chengdu, 610041, China. kittyanwang520@scu.edu.cn.
Zuyun LiuCenter for Clinical Big Data and Analytics Second Affiliated Hospital, Department of Big Data in Health Science School of Public Health, Zhejiang Key Laboratory of Intelligent Preventive Medicine, Zhejiang University School of Medicine, Hangzhou, 310058, Zhejiang, China. Zuyun.liu@outlook.com.
Shimin LiAnalysis Center of Agrobiology and Environmental Sciences, Zhejiang University, Hangzhou, 310058, Zhejiang, China. Shiminli@zju.edu.cn.
Jingyun ZhangDepartment of Epidemiology and Health Statistics, School of Public Health, Hangzhou Medical College, 311399, Zhejiang, Hangzhou, China. jingyunzhang1102@outlook.com.

Funding

National Natural Science Foundation of China 72374180National Natural Science Foundation of China 82401856Research Center of Prevention and Treatment of Senescence Syndrome, School of Medicine Zhejiang University 2022010002
6 · The paper itself

Abstract

backgroundThis study aimed to examine the associations between insomnia trajectories and the risk of incident cognitive impairment, as well as to evaluate whether immune cell subsets mediate these associations.

methodsA total of 6,316 participants aged 50 years or older from the Health and Retirement Study (HRS) were included in this prospective cohort study. Insomnia symptoms, including severe insomnia, difficulty initiating sleep (DIS), difficulty maintaining sleep (DMS), early morning awakening (EMA), and nonrestorative sleep (NRS), were assessed using the Jenkins Sleep Questionnaire. Longitudinal insomnia trajectories were identified from three examination waves during 2010-2016 (waves 10, 12 and 13) using latent class trajectory modeling. Incident cognitive impairment was defined according to the Langa-Weir cognitive status classification during 2016-2022 (waves 13-16). Data on immune cell subsets were obtained through flow cytometry of peripheral blood samples collected in the 2016 HRS Venous Blood Substudy. Cox proportional hazards models and mediation analyses were performed to assess associations.

resultsTrajectories of DIS, EMA, and NRS were significantly associated with increased risk of incident cognitive impairment. Compared to participants with persistently good trajectories, those with worsening trajectories demonstrated significantly elevated risks of cognitive impairment, with hazard ratios of 1.37 (95% confidence interval [CI]: 1.16, 1.61) for DIS; 1.26 (95% CI: 1.07, 1.48) for EMA; and 1.16 (95% CI: 1.00, 1.35) for NRS. Mediation analysis revealed seven specific immune cell subsets as mediators of the associations of insomnia trajectories (DIS, EMA, and NRS) with the risk of incident cognitive impairment: CD4 + T cells (central memory, CM), CD4 + T cells (Naive), CD4 + T cells (Effector Memory, Tem), CD8 + T cells, CD8 + T cells (CM), CD8 + T cells (TemRA) and NK Cells (CD56LO), accounting for 1.16% to 8.21% of the total effect. Sex-stratified analysis revealed distinct mediation patterns: CD4 + T cells (Tem) and CD8 + T cells mediated the associations in males, whereas CD4 + T cells (CM), CD8 + T cells (CM) mediated the associations in females.

conclusionsThese findings reveal the associations of dynamic insomnia trajectories with cognitive health, and potential immune mechanisms, suggesting that maintaining healthy sleep patterns may mitigate neurodegenerative progression.

Indexed as

Cognitive DysfunctionSleep Initiation and Maintenance DisordersAgedFemaleHumansIncidenceMaleMiddle AgedProspective StudiesRisk FactorsCognitive impairmentCohort studyImmune cell subsetInsomniaMediation analysis

Identifiers

PMID42218486
PMCPMC13440101

What Socratic holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.