Decoding the Metabolome and Lipidome of Child Malnutrition by Mass Spectrometric Techniques: Present Status and Future Perspectives

Abstract

Child malnutrition (CM) is a global public health problem. It contributes to poor health in one in four children under five years worldwide and causes serious health problems in children, including stunted, wasted, and overweight growth. These serious public health issues lead to a higher chance of living in poverty in adulthood. Malnutrition is related with reduced economic productivity and increases the serious national and international burden. Currently, there is no meaningful therapeutic intervention of CM, and the use of different therapeutic foods has shown poor outcomes among supplemented malnourished children. The role of metabolites and lipids has been extensively recognized as early determinants of child health, but their contribution in CM and its pathobiology are poorly understood. This perspective provides a most recent update on these aspects. After briefly introducing the disciplines of metabolomics and lipidomics, we describe a mass spectrometry-based metabolic workflow for analysis of both metabolites and lipids and summarize several recent applications of metabolomics and lipidomics in CM. Finally, we discuss the future directions of the field toward the development of meaningful interventions for CM through metabolomics and lipidomics advances.

Figure 1.

Some key factors associated with child malnutrition and its consequences in children health.

Figure 2.

Model of precision medicine development through multi-omics approaches. Multi-omics includes genomics (study of gene), transcriptomics (study of gene expression), proteomics (study of protein), and metabolomics (study of small molecules metabolites or lipids). Systems biology followed by precision medicine incorporates comprehensive knowledge of multi-omics and external factors towards suitable therapy development.

Figure 3.

High-throughput metabolomics and lipidomics workflow using UHPLC-HRMS. (Step 1) study design, which includes selection of study subjects for example human or animal, sample types and replications. (Step 2) sample preparation includes most commonly used biphasic (metabolite and lipid) extraction method following modified Folch methods (chloroform/methanol/water = 2:1:1). (Step 3) data collection and data matrix generation. (Step 4) Statistical analysis and data visualization includes clustering analysis by multivariate score plot and heatmap analysis, fold change analysis, metabolic pathway, and biomarker discovery.