Non-targeted metabolomics of cooked cowpea (Vigna unguiculata) and pigeon pea (Cajanus cajan) from Ghana using two distinct and complementary analytical platforms

Urine and Dried Blood Spots From Children and Pregnant Women Reveal Phytochemicals, Amino Acids, and Carnitine Metabolites as Cowpea Consumption Biomarkers

SCOPE

Legumes consumption has been proven to promote health across the lifespan; cowpeas have demonstrated efficacy in combating childhood malnutrition and growth faltering, with an estimated malnutrition prevalence of 35.6% of children in Ghana. This cowpea feeding study aimed to identify a suite of metabolic consumption biomarkers in children and adults.

METHODS AND RESULTS

Urine and dried blood spots (DBS) from 24 children (9-21 months) and 21 pregnant women (>18 years) in Northern Ghana are collected before and after dose-escalated consumption of four cowpea varieties for 15 days. Untargeted metabolomics identified significant increases in amino acids, phytochemicals, and lipids. The carnitine metabolism pathway is represented by 137 urine and 43 DBS metabolites, with significant changes to tiglylcarnitine and acetylcarnitine. Additional noteworthy candidate biomarkers are mansouramycin C, N-acetylalliin, proline betaine, N2, N5-diacetylornithine, S-methylcysteine, S-methylcysteine sulfoxide, and cis-urocanate. S-methylcysteine and S-methylcysteine sulfoxide are targeted and quantified in urine.

CONCLUSION

This feeding study for cowpea biomarkers supports the utility of a suite of key metabolites classified as amino acids, lipids, and phytochemicals for dietary legume and cowpea-specific food exposures of global health importance.

Legume Fingerprinting through Lipid Composition: Utilizing GC/MS with Multivariate Statistics

This study presents a tentative analysis of the lipid composition of 47 legume samples, encompassing species such as Phaseolus spp., Vicia spp., Pisum spp., and Lathyrus spp. Lipid extraction and GC/MS (gas chromatography with mass spectrometric detection) analysis were conducted, followed by multivariate statistical methods for data interpretation. Hierarchical Cluster Analysis (HCA) revealed two major clusters, distinguishing beans and snap beans (Phaseolus spp.) from faba beans (Vicia faba), peas (Pisum sativum), and grass peas (Lathyrus sativus). Principal Component Analysis (PCA) yielded 2D and 3D score plots, effectively discriminating legume species. Linear Discriminant Analysis (LDA) achieved a 100% accurate classification of the training set and a 90% accuracy of the test set. The lipid-based fingerprinting elucidated compounds crucial for discrimination. Both PCA and LDA biplots highlighted squalene and fatty acid methyl esters (FAMEs) of 9,12,15-octadecatrienoic acid (C18:3) and 5,11,14,17-eicosatetraenoic acid (C20:4) as influential in the clustering of beans and snap beans. Unique compounds, including 13-docosenoic acid (C22:1) and γ-tocopherol, O-methyl-, characterized grass pea samples. Faba bean samples were discriminated by FAMEs of heneicosanoic acid (C21:0) and oxiraneoctanoic acid, 3-octyl- (C18-ox). However, C18-ox was also found in pea samples, but in significantly lower amounts. This research demonstrates the efficacy of lipid analysis coupled with multivariate statistics for accurate differentiation and classification of legumes, according to their botanical origins.