Evidence map›Paper›PMID 24842797›Full record

ArticleArchives of disease in childhood2014

Identifying the best body mass index metric to assess adiposity change in children.

Lisa Kakinami, Mélanie Henderson, Arnaud Chiolero, Tim J Cole, Gilles Paradis

Abstract read
In one paragraph

Article in Archives of disease in childhood, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
50citing papers in PubMed, 4 pooled it
–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

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

50 citing papers in PubMed, 4 syntheses or guidelines pooled it.

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  3. Drug interventions for the treatment of obesity in children and adolescents.The Cochrane database of systematic reviews · 2016
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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

5 authors.

Lisa KakinamiDepartment of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Canada.
Mélanie HendersonDivision of Endocrinology, Department of Pediatrics, CHU Sainte-Justine and Université de Montréal, Montreal, QC, Canada.
Arnaud ChioleroInstitute of Social and Preventive Medicine, Lausanne University Hospital, Lausanne, Switzerland.
Tim J ColePopulation, Policy and Practice Programme, University College London Institute of Child Health, London, UK.
Gilles ParadisDepartment of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Canada.

Funding

CIHR MOP-67121Medical Research Council MR/J004839/1
6 · The paper itself

Abstract

objectiveAlthough dual-energy X-ray absorptiometry (DEXA) is the preferred method to estimate adiposity, body mass index (BMI) is often used as a proxy. However, the ability of BMI to measure adiposity change among youth is poorly evidenced. This study explored which metrics of BMI change have the highest correlations with different metrics of DEXA change.

methodsData were from the Quebec Adipose and Lifestyle Investigation in Youth cohort, a prospective cohort of children (8-10 years at recruitment) from Québec, Canada (n=557). Height and weight were measured by trained nurses at baseline (2008) and follow-up (2010). Metrics of BMI change were raw (ΔBMIkg/m(2) ), adjusted for median BMI (ΔBMIpercentage) and age-sex-adjusted with the Centers for Disease Control and Prevention growth curves expressed as centiles (ΔBMIcentile) or z-scores (ΔBMIz-score). Metrics of DEXA change were raw (total fat mass; ΔFMkg), per cent (ΔFMpercentage), height-adjusted (fat mass index; ΔFMI) and age-sex-adjusted z-scores (ΔFMz-score). Spearman's rank correlations were derived.

resultsCorrelations ranged from modest (0.60) to strong (0.86). ΔFMkg correlated most highly with ΔBMIkg/m(2) (r = 0.86), ΔFMI with ΔBMIkg/m(2) and ΔBMIpercentage (r = 0.83-0.84), ΔFMz-score with ΔBMIz-score (r = 0.78), and ΔFMpercentage with ΔBMIpercentage (r = 0.68). Correlations with ΔBMIcentile were consistently among the lowest.

conclusionsIn 8-10-year-old children, absolute or per cent change in BMI is a good proxy for change in fat mass or FMI, and BMI z-score change is a good proxy for FM z-score change. However change in BMI centile and change in per cent fat mass perform less well and are not recommended.

Indexed as

Body Mass IndexAbsorptiometry, PhotonAdiposityAnthropometryCanadaChildCohort StudiesFemaleFollow-Up StudiesHumansMalePediatric ObesityProspective Studies

Identifiers

PMID24842797
PMCPMC4215345

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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.