Evidence map›Paper›PMID 25969698›Full record

ArticleDiabetology & metabolic syndrome2015

Comparison between CDC and WHO BMI z-score and their relation with metabolic risk markers in Northern Portuguese obese adolescents.

Henrique Nascimento, Cristina Catarino, Denisa Mendonça, Pedro Oliveira, Ana Inês Alves, Ana Filipa Medeiros, Petronila Rocha Pereira, Carla Rêgo, Helena Ferreira Mansilha, Luísa Aires and 4 more

Open access · goldAbstract read
In one paragraph

Article in Diabetology & metabolic syndrome, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.7field-weighted citation impact, top 15% 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

3 citing papers in PubMed, 13 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

14 authors at 5 institutions in 1 country.

Henrique NascimentoBiological Science Department, Faculty of Pharmacy, University of Porto, Porto, Portugal ; Instituto de Biologia Molecular e Celular (Institute for Molecular and Cell Biology), Universidade do Porto, Porto, Portugal ; Instituto de Investigação e Inovação em Saúde (Institute for Research and Innovation in Health), Universidade do Porto, Porto, Portugal.
Cristina CatarinoBiological Science Department, Faculty of Pharmacy, University of Porto, Porto, Portugal ; Instituto de Biologia Molecular e Celular (Institute for Molecular and Cell Biology), Universidade do Porto, Porto, Portugal ; Instituto de Investigação e Inovação em Saúde (Institute for Research and Innovation in Health), Universidade do Porto, Porto, Portugal.
Denisa MendonçaPopulation Study Department, Biomedical Sciences Institute Abel Salazar (ICBAS), University of Porto, Porto, Portugal.
Pedro OliveiraPopulation Study Department, Biomedical Sciences Institute Abel Salazar (ICBAS), University of Porto, Porto, Portugal.
Ana Inês AlvesInstituto de Biologia Molecular e Celular (Institute for Molecular and Cell Biology), Universidade do Porto, Porto, Portugal ; Instituto de Investigação e Inovação em Saúde (Institute for Research and Innovation in Health), Universidade do Porto, Porto, Portugal ; Research Centre in Physical Activity, Health and Leisure (CIAFEL), Faculty of Sports, University of Porto, Porto, Portugal.
Ana Filipa MedeirosResearch Centre in Physical Activity, Health and Leisure (CIAFEL), Faculty of Sports, University of Porto, Porto, Portugal.
Petronila Rocha PereiraHealth Science Research Centre, University of Beira Interior, Covilhã, Portugal.
Carla RêgoChildren and Adolescent Centre, CUF Hospital; Center for Health Technology and Services Research (CINTESIS), Faculty of Medicine, University of Porto, Porto, Portugal.
Helena Ferreira MansilhaChildhood and Adolescence Department/Paediatric Service of Porto Hospital Centre, Porto, Portugal.
Luísa AiresResearch Centre in Physical Activity, Health and Leisure (CIAFEL), Faculty of Sports, University of Porto, Porto, Portugal ; University Institute of Maia (ISMAI), S. Pedro Avioso, Portugal.
Jorge MotaResearch Centre in Physical Activity, Health and Leisure (CIAFEL), Faculty of Sports, University of Porto, Porto, Portugal.
Alexandre QuintanilhaInstituto de Biologia Molecular e Celular (Institute for Molecular and Cell Biology), Universidade do Porto, Porto, Portugal ; Instituto de Investigação e Inovação em Saúde (Institute for Research and Innovation in Health), Universidade do Porto, Porto, Portugal ; Biomedical Sciences Institute Abel Salazar (ICBAS), University of Porto, Porto, Portugal.
Alice Santos-SilvaBiological Science Department, Faculty of Pharmacy, University of Porto, Porto, Portugal ; Instituto de Biologia Molecular e Celular (Institute for Molecular and Cell Biology), Universidade do Porto, Porto, Portugal ; Instituto de Investigação e Inovação em Saúde (Institute for Research and Innovation in Health), Universidade do Porto, Porto, Portugal.
Luís BeloBiological Science Department, Faculty of Pharmacy, University of Porto, Porto, Portugal ; Instituto de Biologia Molecular e Celular (Institute for Molecular and Cell Biology), Universidade do Porto, Porto, Portugal ; Instituto de Investigação e Inovação em Saúde (Institute for Research and Innovation in Health), Universidade do Porto, Porto, Portugal.
Universidade do Porto · PTi3S - Instituto de Investigação e Inovação em Saúde, Universidade do Porto · PTCentre for Health Technology and Services Research · PTCUF Porto Hospital · PTUniversity of Beira Interior · PT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundGrowth-curves are an important tool for evaluating the anthropometric development in pediatrics. The different growth-curves available are based in different populations, what leads to different cut-offs. Pediatric obesity tracks into adulthood and is associated with increased cardiovascular risk. The accurate assessment of a child nutritional status using growth-curves can indicate individuals that are either obese or in risk of becoming obese, allowing an early intervention. Moreover, the association between the data obtained from growth-curves with specific metabolic risk factors further highlights the importance of these charts. This study aimed to evaluate the associations between body mass index z-score (BMIzsc), determined using the growth-curves from the Centre for Disease Control and Prevention (CDC) and from the World Health Organization (WHO), with cardiovascular risk factors, represented here by metabolic syndrome (MS) and insulin resistance (IR) related parameters. The study involved 246 obese adolescents (10-18 years, 122 females). MS was defined according to the International Diabetes Federation. IR was considered for HOMA-IR greater than 2.5.

findingsNo difference between both BMIzsc in identifying MS was noticeable by a ROC analysis. For both indexes the area-under-the-curve increased for older groups, particularly for males. CDC-BMIzsc was the best predictor of MS by logistic regression when all population was considered, however MS was better predicted by WHO-BMIzsc for females and by CDC-BMIzsc for males. Younger girls and older boys were in increased risk for MS. Similar results were obtained for IR.

conclusionsA significant difference between the two BMIzsc regarding their association with MS and IR was not clear, being these associations weaker in younger individuals.

Indexed as

BMI z-scoreCDCInsulin resistanceMetabolic syndromeWHO

Identifiers

PMID25969698
PMCPMC4427917
OpenAlexW2146514671

What OpenQuestion holds

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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.