Sleep Slow-Wave and Spindle Alterations in Young Smokers Correlated With the Severity of Cigarette Exposure.

Shuailin Ding, Ting Xue, Fang Dong, Yongxin Cheng, Yuxin Ma, Juan Wang, Dahua Yu, Kai Yuan

Abstract read

In one paragraph

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

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

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

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

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Shuailin DingSchool of Digital and Intelligent Industry, Inner Mongolia University of Science and Technology, Baotou, Inner Mongolia, China.

Ting XueSchool of Science, Inner Mongolia University of Science and Technology, Baotou, Inner Mongolia, China.ORCID https://orcid.org/0009-0009-8700-4787

Fang DongSchool of Digital and Intelligent Industry, Inner Mongolia University of Science and Technology, Baotou, Inner Mongolia, China.

Yongxin ChengSchool of Digital and Intelligent Industry, Inner Mongolia University of Science and Technology, Baotou, Inner Mongolia, China.

Yuxin MaSchool of Digital and Intelligent Industry, Inner Mongolia University of Science and Technology, Baotou, Inner Mongolia, China.

Juan WangSchool of Digital and Intelligent Industry, Inner Mongolia University of Science and Technology, Baotou, Inner Mongolia, China.

Dahua YuSchool of Automation and Electrical Engineering, Inner Mongolia University of Science and Technology, Baotou, Inner Mongolia, China.ORCID https://orcid.org/0000-0001-7850-7512

Kai YuanSchool of Automation and Electrical Engineering, Inner Mongolia University of Science and Technology, Baotou, Inner Mongolia, China.ORCID https://orcid.org/0000-0002-3098-1124

Funding

Chinese National Programs for Brain Science and Brain-like Intelligence Technology 2022ZD0214500Development Program for Young Talents of Science and Technology in Universities of Inner Mongolia NJYT24030Fundamental Research Funds for the Universities of Inner Mongolia 2023QNJS204Fundamental Research Funds for the Universities of Inner Mongolia 2023QNJS206Fundamental Research Funds for the Universities of Inner Mongolia 2024QNJS119National Natural Science Foundation of China 61971451National Natural Science Foundation of China 82260359National Natural Science Foundation of China 82371500National Natural Science Foundation of China U22A20303Natural Science Foundation of Inner Mongolia 2023QN08007Natural Science Foundation of Inner Mongolia 2025MS08027Natural Science Foundation of Inner Mongolia 2025MS08098STI2030-Major Project 2022ZD0207100

6 · The paper itself

Abstract

Smoking is closely associated with significant disruptions in sleep architecture. Although previous studies have reported altered self-reported sleep quality in adolescent smokers, relatively few studies using polysomnography have objectively examined critical sleep microstructural components (i.e., slow-wave activity and sleep spindle characteristics) in young smokers. This study investigated changes in these electroencephalographic indices in adolescent smokers and their associations with smoking-related characteristics. We recruited 20 young smokers and 16 non-smokers, collecting their overnight polysomnography data. The Fagerström Test for Nicotine Dependence assessed dependence levels, and the pack-year index quantified cumulative smoking exposure. We analysed slow waves (0.5-4.5 Hz) in N3 sleep and spindles (11-16 Hz) in N2 sleep, quantifying parameters via the YASA toolbox. Smokers showed impaired N3 slow-wave integrity (reduced amplitude and slopes) and enhanced N2 spindle activity (increased density, frequency and duration). Slow-wave amplitude reduction correlated negatively with smoking indices, whereas spindle enhancements correlated positively. Sleep slow waves and spindles may serve as biomarkers for sleep quality in young smokers, deepening understanding of the sleep-smoking relationship.

Indexed as

Cigarette SmokingSleep, Slow-WaveSmokingAdolescentElectroencephalographyFemaleHumansMalePolysomnographySleep StagesTobacco Use Disorderadolescent healthelectroencephalographicnicotine dependencesleep architecture disruptionyoung smokers

Identifiers

PMID41936568

DOI10.1111/adb.70149

PMCPMC13052274

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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.