Evidence map›Paper›PMID 25945355›Full record

ArticleJournal of diabetes research2015

A new approach to define and diagnose cardiometabolic disorder in children.

Lars Bo Andersen, Jeppe Bo Lauersen, Jan Christian Brønd, Sigmund Alfred Anderssen, Luis B Sardinha, Jostein Steene-Johannessen, Robert G McMurray, Mauro V G Barros, Susi Kriemler, Niels Christian Møller and 5 more

Open access · goldAbstract read
In one paragraph

Article in Journal of diabetes research, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
46citing papers in PubMed, 1 pooled it
13.6field-weighted citation impact, top 1% 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

46 citing papers in PubMed, 1 synthesis or guideline pooled it, 114 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

15 authors at 8 institutions in 7 countries.

Lars Bo AndersenCenter of Research in Childhood Health, Department of Sport Sciences and Clinical Biomechanics, University of Southern Denmark, Campusvej 55, 5230 Odense M, Denmark ; Department of Sports Medicine, Norwegian School of Sport Sciences, Sognsveien 220, 0806 Oslo, Norway.ORCID 0000-0002-9800-029X
Jeppe Bo LauersenDepartment of Sports Medicine, Norwegian School of Sport Sciences, Sognsveien 220, 0806 Oslo, Norway.
Jan Christian BrøndCenter of Research in Childhood Health, Department of Sport Sciences and Clinical Biomechanics, University of Southern Denmark, Campusvej 55, 5230 Odense M, Denmark.
Sigmund Alfred AnderssenDepartment of Sports Medicine, Norwegian School of Sport Sciences, Sognsveien 220, 0806 Oslo, Norway.
Luis B SardinhaExercise and Health Laboratory, CIPER, Fac Motricidade Humana, Universidade de Lisboa, Estrada Dacosth, Cruz-Quebrada, 1499 Lisbon, Portugal.
Jostein Steene-JohannessenDepartment of Sports Medicine, Norwegian School of Sport Sciences, Sognsveien 220, 0806 Oslo, Norway.
Robert G McMurrayDepartment of Exercise and Sport Science, University of North Carolina, 025 Fetzer Gym, CB No. 8700, Chapel Hill, NC 27599-8700, USA.
Mauro V G BarrosSchool of Physical Education, University of Pernambuco, Campus Universitario HUOC-ESEF, Arnobio Marques 310, Santo Amaro, 50.100-130 Recife, PE, Brazil.
Susi KriemlerEpidemiology, Biostatistics and Prevention Institute, University of Zürich, Hirschengraben 84, 8001 Zürich, Switzerland.
Niels Christian MøllerCenter of Research in Childhood Health, Department of Sport Sciences and Clinical Biomechanics, University of Southern Denmark, Campusvej 55, 5230 Odense M, Denmark.
Anna BuggeCenter of Research in Childhood Health, Department of Sport Sciences and Clinical Biomechanics, University of Southern Denmark, Campusvej 55, 5230 Odense M, Denmark.
Peter Lund KristensenCenter of Research in Childhood Health, Department of Sport Sciences and Clinical Biomechanics, University of Southern Denmark, Campusvej 55, 5230 Odense M, Denmark.
Mathias Ried-LarsenCenter of Research in Childhood Health, Department of Sport Sciences and Clinical Biomechanics, University of Southern Denmark, Campusvej 55, 5230 Odense M, Denmark ; The Centre of Inflammation and Metabolism and Trygfondens Center for Aktiv Sundhed, Department of Infectious Diseases and CMRC, Rigshospitalet, Faculty of Health Sciences, University of Copenhagen, Tagensvej 20, 2100 Copenhagen, Denmark.
Anders GrøntvedCenter of Research in Childhood Health, Department of Sport Sciences and Clinical Biomechanics, University of Southern Denmark, Campusvej 55, 5230 Odense M, Denmark.
Ulf EkelundDepartment of Sports Medicine, Norwegian School of Sport Sciences, Sognsveien 220, 0806 Oslo, Norway ; MRC Epidemiology Unit, Institute of Metabolic Science, Addenbrooke's Hospital, University of Cambridge, Hills Road, Cambridge CB2 0QQ, UK.
University of Southern Denmark · DKNorwegian School of Sport Sciences · NOUniversidade de Pernambuco · BRUniversity of Cambridge · GBUniversity of Copenhagen · DKUniversity of Lisbon · PTUniversity of North Carolina at Chapel Hill · USUniversity of Zurich · CH

Funding

Medical Research Council MC_U106179473Medical Research Council MC_UU_12015/3
6 · The paper itself

Abstract

The aim of the study was to test the performance of a new definition of metabolic syndrome (MetS), which better describes metabolic dysfunction in children. Methods. 15,794 youths aged 6-18 years participated. Mean z-score for CVD risk factors was calculated. Sensitivity analyses were performed to evaluate which parameters best described the metabolic dysfunction by analysing the score against independent variables not included in the score. Results. More youth had clustering of CVD risk factors (>6.2%) compared to the number selected by existing MetS definitions (International Diabetes Federation (IDF) < 1%). Waist circumference and BMI were interchangeable, but using insulin resistance homeostasis model assessment (HOMA) instead of fasting glucose increased the score. The continuous MetS score was increased when cardiorespiratory fitness (CRF) and leptin were included. A mean z-score of 0.40-0.85 indicated borderline and above 0.85 indicated clustering of risk factors. A noninvasive risk score based on adiposity and CRF showed sensitivity and specificity of 0.85 and an area under the curve of 0.92 against IDF definition of MetS. Conclusions. Diagnosis for MetS in youth can be improved by using continuous variables for risk factors and by including CRF and leptin.

Indexed as

AdolescentChildCross-Sectional StudiesFemaleHumansInsulin ResistanceMaleMetabolic SyndromeRisk FactorsWaist Circumference

Identifiers

PMID25945355
PMCPMC4402570
OpenAlexW1971904580

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

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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.