Evidence map›Paper›PMID 42143113›Full record

ArticleScientific reports2026

Prospective association between circadian syndrome and incident chronic lung disease in the CHARLS and ELSA cohorts.

Boheng Liu, Shurui Wu, Yan Xu, Jipeng Jiang, Yang Liu

Abstract read
In one paragraph

Article in Scientific reports, 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

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

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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

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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

5 authors.

Boheng Liu *Postgraduate School, Medical School of Chinese PLA, Haidian, Beijing, People's Republic of China.
Shurui Wu *Postgraduate School, Medical School of Chinese PLA, Haidian, Beijing, People's Republic of China.
Yan XuPostgraduate School, Medical School of Chinese PLA, Haidian, Beijing, People's Republic of China.
Jipeng JiangDepartment of Thoracic Surgery, The First Medical Center of Chinese PLA General Hospital, Haidian, Beijing, People's Republic of China. mentu1992@126.com.
Yang LiuDepartment of Thoracic Surgery, The First Medical Center of Chinese PLA General Hospital, Haidian, Beijing, People's Republic of China. sunny301x@sina.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Circadian syndrome (CircS) involves the integration of systemic circadian disruption with cardiometabolic risk factors, but its longitudinal effect on incident chronic lung disease (CLD) remains unknown. This prospective dual-cohort study analyzed data from the China Health and Retirement Longitudinal Study (CHARLS, n = 7,553) and the English Longitudinal Study of Ageing (ELSA, n = 4,957). CircS was defined by the clustering of at least 4 out of 7 circadian-metabolic components. Multivariate Cox proportional hazards models estimated hazard ratios (HRs) for incident CLD over a seven-year follow-up. In the discovery cohort (CHARLS), CircS was independently associated with an increased risk of incident CLD (HR = 1.16, 95% CI = 1.01-1.33). This was robustly confirmed in the validation cohort (ELSA) with a stronger effect size (HR = 1.53, 95% CI = 1.20-1.95). Furthermore, subgroup analyses revealed that this risk was significantly amplified by alcohol consumption in the British population (P < 0.05), suggesting an interactive effect between circadian misalignment and lifestyle stressors. In conclusion, CircS serves as a robust, independent predictor of respiratory decline. Evaluating circadian integrity offers a reliable indicator of respiratory vulnerability, and holistic chronomedicine approaches targeting sleep hygiene and moderate alcohol consumption may help mitigate the global CLD burden.

Indexed as

Chronobiology DisordersCircadian RhythmLung DiseasesAgedChinaChronic DiseaseFemaleHumansIncidenceLongitudinal StudiesMaleMiddle AgedProportional Hazards ModelsProspective StudiesRisk FactorsChronic lung diseaseChronomedicineCircadian syndromeCohort studyEpidemiologySleep quality

Identifiers

PMID42143113
PMCPMC13376350

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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 OpenQuestion graph.