Evidence map›Paper›PMID 37550595›Full record

ArticleEuropean journal of nutrition2023

Identification of potential biomarkers in malnutrition children with severity by

Yajie Chang, Jing Chen, Rong Huang, Jinxia Wu, Yanyan Lin, Quanquan Li, Guiping Shen, Jianghua Feng

Abstract read
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In one paragraph

Article in European journal of nutrition, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.5field-weighted citation impact, top 38% 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

1 citing paper in PubMed, 3 citations in OpenAlex.

  1. 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 1 institution in 1 country.

Yajie ChangDepartment of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, 422 Siming South Road, Xiamen, 361005, Fujian, China.
Jing ChenDepartment of Child Health, Women and Children's Hospital, School of Medicine, Xiamen University, Xiamen, 361003, Fujian, China.
Rong HuangDepartment of Child Health, Women and Children's Hospital, School of Medicine, Xiamen University, Xiamen, 361003, Fujian, China.
Jinxia WuDepartment of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, 422 Siming South Road, Xiamen, 361005, Fujian, China.
Yanyan LinDepartment of Child Health, Women and Children's Hospital, School of Medicine, Xiamen University, Xiamen, 361003, Fujian, China.
Quanquan LiDepartment of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, 422 Siming South Road, Xiamen, 361005, Fujian, China.
Guiping ShenDepartment of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, 422 Siming South Road, Xiamen, 361005, Fujian, China. gpshen@xmu.edu.cn.ORCID http://orcid.org/0000-0002-0779-1859
Jianghua FengDepartment of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, 422 Siming South Road, Xiamen, 361005, Fujian, China.
Xiamen University · CN

Funding

Guiding project of the Natural Science Foundation of Fujian No. 2019D010National Natural Science Foundation of China No. 82072015National Natural Science Foundation of China No. 82103859Natural Science Foundation of Fujian Province No. 2022J01062
6 · The paper itself

Abstract

purposeChild malnutrition is a global public health problem, but the underlying pathophysiologic mechanisms with severity remain poorly understood, and the potential biomarkers served to the clinical diagnosis are still not available. This study aimed to identify the serum metabolic characteristics of malnourished children with severity.

methodsFasted overnight serum samples were collected following clinical standard procedures among 275 malnourished and 199 healthy children from the Women and Children's Hospital, Xiamen University Child Health Department from July 2020 to May 2022. Nuclear magnetic resonance (NMR)-based metabolomics strategy was applied to identify the potential serum biomarkers of malnutrition from 275 malnourished children aged 4 to 84 months with mild (Mil, 199 cases), moderate (Mod, 101 cases), and severe (Sev, 7 cases) malnutrition.

resultsTen, fifteen, and fifteen differential metabolites were identified from the Mil, Mod, and Sev malnutrition groups, respectively. Eight common metabolites, including increased acetoacetate, acetone, ethanol, succinate, 3-hydroxybutyrate, and decreased alanine, methionine, and N-acetyl-glycoprotein, could be the potential biomarkers for malnourished children. The altered metabolic pathways were mainly related to energy metabolism and amino acid metabolism via the network-based pathway enrichment.

conclusionEight potential biomarkers, including acetoacetate, acetone, ethanol, succinate, 3-hydroxybutyrate, alanine, methionine, and N-acetyl-glycoprotein, could characterize the child malnutrition. Child malnutrition-induced abnormal energy metabolism, impaired nutrition utilization and the reduced nutrient availability, and more metabolic disturbance will appear with the severity. Our results are valuable for further studies on the etiology and pathogenesis of malnutrition for clinical intervention and improvement.

Indexed as

Child Nutrition DisordersMalnutrition3-Hydroxybutyric AcidAcetoacetatesAcetoneAlanineBiomarkersChildEast Asian PeopleEthanolGlycoproteinsHumansMagnetic Resonance SpectroscopyMetabolomicsMethionineProton Magnetic Resonance Spectroscopy3-Hydroxybutyric AcidAcetoacetatesacetoacetic acidAcetoneAlanineBiomarkersEthanolGlycoproteinsMethionineSuccinatesChild malnutritionCorrelationMetabolic networkMetabolomicsNuclear magnetic resonance

Identifiers

PMID37550595
OpenAlexW4385622798

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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.