Evidence map›Paper›PMID 38443395›Full record

ArticleScientific reports2024

Association between BMI z-score and body composition indexes with blood pressure and grip strength in school-age children: a cross-sectional study.

Paola Vanessa Miranda-Alatriste, Eloisa Colin-Ramirez, Patricia Inda Icaza, Xóchitl Ponce-Martínez, Anette Shor Mochón, Natalia Martinsanchez Vázquez, Berenice García-Arreola, María de Los Ángeles Espinosa-Cuevas

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
2.0field-weighted citation impact, top 14% 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

6 citing papers in PubMed, 6 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Effect of 4 Weeks of High-Intensity Interval Training (HIIT) on VOJournal of functional morphology and kinesiology · 2025
    Article
  6. Review
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

8 authors at 3 institutions in 1 country.

Paola Vanessa Miranda-Alatriste *Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, 14080, Mexico City, Mexico.
Eloisa Colin-Ramirez *School of Sport Sciences, Universidad Anáhuac México, Av. Universidad Anáhuac 46, Lomas Anáhuac, 52786, Huixquilucan, Mexico. ecolin@anahuac.mx.
Patricia Inda IcazaUniversidad Anáhuac México, 52786, Huixquilucan, Mexico.
Xóchitl Ponce-MartínezUniversidad Iberoamericana Puebla, 72810, San Andrés Cholula, Mexico.
Anette Shor MochónUniversidad Anáhuac México, 52786, Huixquilucan, Mexico.
Natalia Martinsanchez VázquezUniversidad Anáhuac México, 52786, Huixquilucan, Mexico.
Berenice García-ArreolaUniversidad Anáhuac México, 52786, Huixquilucan, Mexico.
María de Los Ángeles Espinosa-CuevasInstituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, 14080, Mexico City, Mexico.
Universidad Anáhuac · MXInstituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán · MXIbero-American University Puebla · MX

Funding

Consejo Mexiquense de Ciencia y Tecnología 56
6 · The paper itself

Abstract

Childhood obesity is linked to diverse health outcomes, including elevated blood pressure (EBP). Emerging evidence showed that excess fat mass (FM) may have a deleterious impact on blood pressure even in normal-weight children. The primary objective of this study was to assess the association between body weight status by BMI z-score and body composition parameters by conventional bioelectrical impedance analysis (BIA) and bioelectrical impedance vector analysis (BIVA). Also, we aimed to explore the performance of BMI z-score, %FM, and FM index (FMI) in discriminating EBP in a sample of school-age Mexican children. Children were classified as having normal weight, overweight or obesity according to WHO criteria for BMI z-score. FMI was considered high when above 75th percentile, and fat free mass index (FFMI) was considered low when below 25th percentile of the reference population. Body composition was also classified according to the BIVA method and EBP was determined when systolic and/or diastolic blood pressure ≥ 90th percentile. BMI z-score groups were compared by Student´s t-test or the Mann-Whitney U test, or by the chi-square test or Fisher exact test. Receiving operating characteristic (ROC) analysis was performed. 61 children were included (52.5% boys, median age 9.8 (25th, 75th percentiles: 8.5, 11.0)) years. High FMI was observed in 32.3% of children with normal weight. Low FFMI was present in 93.5% of children with normal weight and 53.3% of those with overweight/obesity. According to BIVA, 58.1% and 43.3% of children with normal weight and overweight/obesity were classified as having cachexia. All the three adiposity indicators showed significant areas under the ROC curve (AURC) greater than 0.775 for EBP, with the largest one displayed for FM% (0.794). Hight FMI and low FFMI are common in children with normal weight. Identifying deficiency of FFM might be limited by using solely BMI indicators. Cachexia by BIVA was present in a high proportion of children with either normal weight or overweight/obesity. Both BMI z-score and FM (% and FMI) performed well at discriminating EBP, with a numerically greater AURC observed for FM%. Body composition in pediatric population is relevant for identifying body composition abnormalities at early age.

Indexed as

HypertensionPediatric ObesityBlood PressureBody CompositionBody Mass IndexCachexiaChildCross-Sectional StudiesFemaleHand StrengthHumansMaleOverweightAdiposityCardiovascular riskChildhood obesityElevated blood pressureLean mass

Identifiers

PMID38443395
PMCPMC10915149
OpenAlexW4392448566

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.