Evidence map›Paper›PMID 36213296›Full record

ArticleFrontiers in endocrinology2022

Cross-sectional associations between cardiorespiratory fitness and NMR-derived metabolic biomarkers in children - the PANIC study.

Eero A Haapala, Marja H Leppänen, Maarit Lehti, Niina Lintu, Tuomo Tompuri, Anna Viitasalo, Ursula Schwab, Timo A Lakka

Open access · goldAbstract read
In one paragraph

Article in Frontiers in endocrinology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
0.9field-weighted citation impact, top 21% 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, 1 synthesis or guideline pooled it, 4 citations in OpenAlex.

  1. Pooled it
  2. Review
  3. Article
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 4 institutions in 1 country.

Eero A HaapalaFaculty of Sport and Health Sciences, University of Jyväskylä, Jyväskylä, Finland.
Marja H LeppänenFaculty of Sport and Health Sciences, University of Jyväskylä, Jyväskylä, Finland.
Maarit LehtiFaculty of Sport and Health Sciences, University of Jyväskylä, Jyväskylä, Finland.
Niina LintuInstitute of Biomedicine, School of Medicine, University of Eastern Finland, Kuopio, Finland.
Tuomo TompuriInstitute of Biomedicine, School of Medicine, University of Eastern Finland, Kuopio, Finland.
Anna ViitasaloInstitute of Biomedicine, School of Medicine, University of Eastern Finland, Kuopio, Finland.
Ursula SchwabInstitute of Public Health and Clinical Nutrition, School of Medicine, University of Eastern Finland, Kuopio, Finland.
Timo A LakkaInstitute of Biomedicine, School of Medicine, University of Eastern Finland, Kuopio, Finland.
University of Eastern Finland · FIFolkhälsans Forskningscentrum · FIKuopio University Hospital · FIUniversity of Jyväskylä · FI

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: Cardiorespiratory fitness has been inversely associated with cardiovascular risk across the lifespan. Some studies in adults suggest that higher cardiorespiratory fitness is associated with cardioprotective metabolite profile, but the evidence in children is lacking. Therefore, we investigated the cross-sectional association of cardiorespiratory fitness with serum nuclear magnetic resonance derived metabolic biomarkers in children. Methods: A population sample of 450 children aged 6-8 years was examined. Cardiorespiratory fitness was assessed by a maximal exercise test on a cycle ergometer and quantified as maximal power output normalised for lean body mass assessed by dual-energy X-ray absorbtiometry. Serum metabolites were assessed using a high throughput nuclear magnetic resonance platform. The data were analysed using linear regression analyses adjusted for age and sex and subsequently for body fat percentage (BF%) assessed by DXA. Results: Cardiorespiratory fitness was directly associated with high density lipoprotein (HDL) cholesterol (β=0.138, 95% CI=0.042 to 0.135, p=0.005), average HDL particle diameter (β=0.102, 95% CI=0.004 to 0.199, p=0.041), and the concentrations of extra-large HDL particles (β=0.103, 95% CI=0.006 to 0.201, p=0.038), large HDL particles (β=0.122, 95% CI=0.025 to 0.220, p=0.014), and medium HDL particles (β=0.143, 95% CI=0.047 to 0.239, p=0.004) after adjustment for age and sex. Higher cardiorespiratory fitness was also associated with higher concentrations of ApoA1 (β=0.145, 95% CI=0.047 to 0.242, p=0.003), glutamine (β=0.161, 95% CI=0.064 to 0.257, p=0.001), and phenylalanine (β=0.187, 95% CI=0.091 to 0.283, p<0.001). However, only the direct associations of cardiorespiratory fitness with the concentrations of HDL cholesterol (β=0.114, 95% CI=0.018 to 0.210, p=0.021), medium HDL particles (β=0.126, 95% CI=0.030 to 0.223, p=0.010), ApoA1 (β=0.126, 95% CI=0.030 to 0.223, p=0.011), glutamine (β=0.147, 95% CI=0.050 to 0.224, p=0.003), and phenylalanine (β=0.217, 95% CI=0.122 to 0.311, p<0.001) remained statistically significant after further adjustment for BF%. Conclusions: Higher cardiorespiratory fitness was associated with a cardioprotective biomarker profile in children. Most associations were independent of BF% suggesting that the differences in serum metabolites between children are driven by cardiorespiratory fitness and not adiposity.

Indexed as

Cardiorespiratory FitnessAdultBiomarkersChildCholesterol, HDLCross-Sectional StudiesExerciseGlutamineHumansMagnetic Resonance SpectroscopyPhenylalanineBiomarkersCholesterol, HDLGlutaminePhenylalanineaerobic fitnessfitnesslipoproteinsmetabolismmetabolomicsobesitypediatrics

Identifiers

PMID36213296
PMCPMC9538338
OpenAlexW4297009305

What OpenQuestion holds

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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.