Evidence map›Paper›PMID 30050090›Full record

ArticleScientific reports2018

Metabolomic and lipidomic profile in men with obstructive sleep apnoea: implications for diagnosis and biomarkers of cardiovascular risk.

Adriana Lebkuchen, Valdemir M Carvalho, Gabriela Venturini, Jéssica S Salgueiro, Lunara S Freitas, Alessandra Dellavance, Franco C Martins, Geraldo Lorenzi-Filho, Karina H M Cardozo, Luciano F Drager

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
40citing papers in PubMed, 1 pooled it
3.1field-weighted citation impact, top 8% 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

40 citing papers in PubMed, 1 synthesis or guideline pooled it, 64 citations in OpenAlex.

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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 4 institutions in 1 country.

Adriana LebkuchenUnidade de Hipertensão, Instituto do Coração, Faculdade de Medicina, Universidade de São Paulo, São Paulo, SP, Brazil. drikalebe@hotmail.com.
Valdemir M CarvalhoGrupo Fleury, São Paulo, SP, Brazil.
Gabriela VenturiniLaboratório de Genética e Cardiologia Molecular, Instituto do Coração, Faculdade de Medicina, Universidade de São Paulo, São Paulo, SP, Brazil.
Jéssica S SalgueiroGrupo Fleury, São Paulo, SP, Brazil.
Lunara S FreitasUnidade de Hipertensão, Instituto do Coração, Faculdade de Medicina, Universidade de São Paulo, São Paulo, SP, Brazil.
Alessandra DellavanceGrupo Fleury, São Paulo, SP, Brazil.
Franco C MartinsLaboratório do sono, Instituto do Coração, Faculdade de Medicina, Universidade de São Paulo, São Paulo, SP, Brazil.
Geraldo Lorenzi-FilhoLaboratório do sono, Instituto do Coração, Faculdade de Medicina, Universidade de São Paulo, São Paulo, SP, Brazil.
Karina H M CardozoGrupo Fleury, São Paulo, SP, Brazil.
Luciano F DragerUnidade de Hipertensão, Instituto do Coração, Faculdade de Medicina, Universidade de São Paulo, São Paulo, SP, Brazil. luciano.drager@incor.com.br.
Fleury S.A. (Brazil) · BRUniversidade de São Paulo · BRHospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo · BRInstituto do Sono · BR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The use of metabolomic and lipidomic strategies for selecting potential biomarkers for obstructive sleep apnoea (OSA) has been little explored. We examined adult male patients with OSA (defined by an apnoea-hypopnoea index ≥15 events/hour), as well as age-, gender-, and fat-composition-matched volunteers without OSA. All subjects were subjected to clinical evaluation, sleep questionnaires for detecting the risk of OSA (Berlin and NoSAS score), metabolomic analysis by gas chromatography coupled to mass spectrometry and lipidomic analysis with liquid chromatography followed by detection by MALDI-MS. This study included 37 patients with OSA and 16 controls. From the 6 metabolites and 22 lipids initially selected, those with the best association with OSA were glutamic acid, deoxy sugar and arachidonic acid (metabolites), and glycerophosphoethanolamines, sphingomyelin and lyso-phosphocholines (lipids). For the questionnaires, the NoSAS score performed best with screening for OSA (area under the curve [AUC] = 0.724, p = 0.003). The combination of the NoSAS score with metabolites or lipids resulted in an AUC for detecting OSA of 0.911 and 0.951, respectively. In conclusion, metabolomic and lipidomic strategies suggested potential early biomarkers in OSA that could also be helpful in screening for this sleep disorder beyond traditional questionnaires.

Indexed as

MetabolomeAdultBiomarkersBrazilCardiovascular DiseasesGas Chromatography-Mass SpectrometryHumansLipidsMaleMiddle AgedRisk AssessmentSleep Apnea, ObstructiveSpectrometry, Mass, Matrix-Assisted Laser Desorption-IonizationSurveys and QuestionnairesBiomarkersLipids

Identifiers

PMID30050090
PMCPMC6062521
OpenAlexW2883787580

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.