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Identification of potential biomarkers in malnutrition children with severity by 1H-NMR-based metabolomics: a preliminary study in the Chinese population

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Abstract

Purpose

Child 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.

Methods

Fasted 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.

Results

Ten, 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.

Conclusion

Eight 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.

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Data availability

The data used during the current study are available from the corresponding author on reasonable request.

Abbreviations

3-HB:

3-Hydroxybutyrate

AST:

Aspartate aminotransferase

BMI:

Body mass index

CKMB:

Creatine kinase MB

Con:

Control

GPC:

Glycerophosphorylcholine

HAZ:

Length/height-for-age z score

LC–MS/MS:

Liquid chromatography–tandem mass spectrometry

LDH:

Lactate dehydrogenase

LDL:

Low-density lipoprotein

LMI:

Lean mass index

Mil:

Mild malnutrition

Mod:

Moderate malnutrition

NAG:

N-Acetyl-glycoprotein

NMR:

Nuclear magnetic resonance

OPLS-DA:

Orthogonal partial least-squares discrimination analysis

PCA:

Principal component analysis

PLS-DA:

Partial least-squares discrimination analysis

PUFAs:

Polyunsaturated fatty acids

SAM:

Severe acute malnutrition

Sev:

Severe malnutrition

SMOTE:

Synthetic minority over-sampling technique

T2DM:

Type II diabetes mellitus

TCA:

Tricarboxylic acid

VIP:

Variable importance in projection

VLDL:

Very low-density lipoprotein

WAZ:

Weight-for-age z score

WHZ:

Weight-for-height z score

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Funding

This study was supported by the National Natural Science Foundation of China (Grant nos. 82072015 and 82103859), the Natural Science Foundation of Fujian Province of China (no. 2022J01062), and Guiding project of the Natural Science Foundation of Fujian (no. 2019D010).

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Authors and Affiliations

  1. Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, 422 Siming South Road, Xiamen, 361005, Fujian, China

    Yajie Chang, Jinxia Wu, Quanquan Li, Guiping Shen & Jianghua Feng

  2. Department of Child Health, Women and Children’s Hospital, School of Medicine, Xiamen University, Xiamen, 361003, Fujian, China

    Jing Chen, Rong Huang & Yanyan Lin

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  1. Yajie Chang
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Contributions

GS, JF, JC, RH, and YL designed the research; YC and QL performed the NMR experiment; YC analyzed data and wrote the manuscript; JC, RH, JW, and YL collected and analyzed data; JF and GS provided critical revision for important intellectual content of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Guiping Shen.

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No conflicts of interest are declared for any of the authors.

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Chang, Y., Chen, J., Huang, R. et al. Identification of potential biomarkers in malnutrition children with severity by 1H-NMR-based metabolomics: a preliminary study in the Chinese population. Eur J Nutr 62, 3193–3205 (2023). https://doi.org/10.1007/s00394-023-03224-7

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