Evidence map›Paper›PMID 34086802›Full record

Trial reportPloS one2021

Predictors for carotid and femoral artery intima-media thickness in a non-diabetic sleep clinic cohort.

Christopher Lambeth, Rita Perri, Sharon Lee, Manisha Verma, Nicole Campbell-Rogers, George Larcos, Karen Byth, Kristina Kairaitis, Terence Amis, John Wheatley

Open access · goldAbstract readClinical Trial
In one paragraph

Trial report in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.1field-weighted citation impact, top 55% of its field
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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

1 citing paper in PubMed, 1 citations in OpenAlex.

  1. Trial
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

10 authors at 2 institutions in 1 country.

Christopher LambethLudwig Engel Centre for Respiratory Research, The Westmead Institute for Medical Research, Westmead, Sydney, New South Wales, Australia.ORCID 0000-0002-6427-0275
Rita PerriLudwig Engel Centre for Respiratory Research, The Westmead Institute for Medical Research, Westmead, Sydney, New South Wales, Australia.
Sharon LeeLudwig Engel Centre for Respiratory Research, The Westmead Institute for Medical Research, Westmead, Sydney, New South Wales, Australia.
Manisha VermaLudwig Engel Centre for Respiratory Research, The Westmead Institute for Medical Research, Westmead, Sydney, New South Wales, Australia.
Nicole Campbell-RogersDepartment of Nuclear Medicine and Ultrasound, Westmead Hospital, Westmead, Sydney, New South Wales, Australia.
George LarcosDepartment of Nuclear Medicine and Ultrasound, Westmead Hospital, Westmead, Sydney, New South Wales, Australia.
Karen BythResearch and Education Network, Western Sydney Local Health District, Westmead Hospital, Westmead, Sydney, New South Wales, Australia.ORCID 0000-0002-1253-6393
Kristina KairaitisLudwig Engel Centre for Respiratory Research, The Westmead Institute for Medical Research, Westmead, Sydney, New South Wales, Australia.ORCID 0000-0002-8808-3183
Terence AmisLudwig Engel Centre for Respiratory Research, The Westmead Institute for Medical Research, Westmead, Sydney, New South Wales, Australia.
John WheatleyLudwig Engel Centre for Respiratory Research, The Westmead Institute for Medical Research, Westmead, Sydney, New South Wales, Australia.
Westmead Hospital · AUThe University of Sydney · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionThe impact of sleep disordered breathing (SDB) on arterial intima-media thickness (IMT), a surrogate measure for cardiovascular disease, remains uncertain, in part because of the potential for non-SDB vascular risk factor interactions. In the present study, we determined predictors for common carotid (CCA) and femoral (CFA) artery IMT in an adult, sleep clinic cohort where non-SDB vascular risk factors (particularly diabetes) were eliminated or controlled.

methodsWe recruited 296 participants for polysomnography (standard SDB severity metrics) and CCA/CFA ultrasound examinations, followed by a 12 month vascular risk factor minimisation (RFM) and continuous positive pressure (CPAP) intervention for participants with a range of SDB severity (RFM Sub-Group, n = 157; apnea hyponea index [AHI]: 14.7 (7.2-33.2), median [IQR]). Univariable and multivariable linear regression models determined independent predictors for IMT. Linear mixed effects modelling determined independent predictors for IMT change across the intervention study. P<0.05 was considered significant.

resultsAge, systolic blood pressure and waist:hip ratio were identified as non-SDB predictive factors for CCA IMT and age, weight and total cholesterol:HDL ratio for CFA IMT. No SDB severity metric emerged as an independent predictor for either CCA or CFA IMT, except in the RFM Sub-Group, where a 2-fold increase in AHI predicted a 2.4% increase in CFA IMT. Across the intervention study, CCA IMT decreased in those who lost weight, but there was no CPAP use interaction. CFA IMT, however, decreased by 12.9% (95%CI 6.8, 18.7%, p = 0.001) in those participants who both lost weight and used CPAP > = 4hours/night.

conclusionWe conclude that SDB severity has little impact on CCA IMT values when non-SDB vascular risk factors are minimised or not present. This is the first study, however, to suggest a potential linkage between SDB severity and CFA IMT values.

trial registrationAustralian New Zealand Clinical Trials Registry, ACTRN12611000250932 and ACTRN12620000694910.

Indexed as

AdultBlood PressureBody Mass IndexCarotid ArteriesFemaleFemoral ArteryHumansMaleSleep Apnea SyndromesTunica Intima

Identifiers

PMID34086802
PMCPMC8177540
OpenAlexW3165020100

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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 OpenQuestion graph.