Untargeted ¹H NMR-Based Metabolomics Analysis of Urine and Serum Profiles after Consumption of Lentils, Chickpeas, and Beans: An Extended Meal Study To Discover Dietary Biomarkers of Pulses

Abstract

High legume intake has been shown to have beneficial effects on the health of humans. The use of nutritional biomarkers, as a complement to self-reported questionnaires, could assist in evaluating dietary intake and downstream effects on human health. The aim of this study was to investigate potential biomarkers of the consumption of pulses (i.e., white beans, chickpeas, and lentils) by using untargeted NMR-based metabolomics. Meals rich in pulses were consumed by a total of 11 participants in a randomized crossover study and multilevel partial least-squares regression was employed for paired comparisons. Metabolomics analysis indicated that trigonelline, 3-methylhistidine, dimethylglycine, trimethylamine, and lysine were potential, though not highly specific, biomarkers of pulse intake. Furthermore, monitoring of these metabolites for a period of 48 h after intake revealed a range of different excretion patterns among pulses. Following the consumption of pulses, a metabolomic profiling revealed that the concentration ratios of trigonelline, choline, lysine, and histidine were similar to those found in urine. In conclusion, this study identified potential urinary biomarkers of exposure to dietary pulses and provided valuable information about the time-response effect of these putative biomarkers.

Diet‐Related Metabolites Associated with Cognitive Decline Revealed by Untargeted Metabolomics in a Prospective Cohort

“…This unidentified ion (Figure S2, Supporting Information ) was not in the signature but had a univariate odds ratio for CD of 0.90 (for 1SD‐increase in intensity). Paraxanthine ( m / z 181.0720, RT 7.44 min, level 1), the major metabolite of caffeine, and trigonelline ( m / z 138.0535, RT 0.93 min, level 1), previously proposed as a biomarker of coffee intake, but also of legumes, were modestly correlated with coffee intake ( r = 0.26 and r = 0.18) and showed no association with CD (univariate OR for 1SD‐increase = 0.98 and 1.03, respectively). In conclusion, atractyligenin glucuronide, cyclo(leucyl‐prolyl), and m / z 96.0444 were the best markers of coffee intake and were all associated with a reduced risk of CD while caffeine, paraxanthine, and trigonelline reflected coffee intake much less accurately and were inconsistently associated with CD.…”

Discovery of Intake Biomarkers of Lentils, Chickpeas, and White Beans by Untargeted LC–MS Metabolomics in Serum and Urine

“…The study was registered at the ISRCTN registry with the code ISRCTN17200423. Details of this randomized, controlled, crossover study had been reported previously [11] and were listed in the Supporting Information. The clinical intervention was approved by the Bioethical Committee of the University of Barcelona (ref: IRB00003099) and followed Helsinki Declaration guidelines.…”

“…[5] To date, only a limited number of foods have been associated with validated BFIs, but pulses are not among them, since only a few studies dealing with this topic have been recently published. [6][7][8][9][10][11] The first of them was focused on the quantification of kaempferol in urine after bean consumption. [6] However, this compound is present in a wide range of vegetables.…”

“…[9] More recently our research group published two human studies applying untargeted NMR experiments, one in an observational framework [10] while the other was a postprandial controlled intervention study. [11] The first study showed that pulse consumers had higher urinary excretions of different metabolites related to choline, protein, and energy metabolism, [10] whereas the results of the second study suggested trigonelline, 3-methylhistidine, dimethylglycine, TMA, glutamine, choline, lysine, and histidine as candidate biomarkers of pulse intake. [11] However, as the authors pointed out, these metabolites are not highly specific to pulse consumption.…”

“…This research was developed within the same context of the NMR study mentioned before. [11] In addition to analyzing the nutrikinetic profiles in urine and serum, in this study we aimed to go one step further and develop a complementary experiment with pooled urinary samples. This was focused on a comparison of the data provided by the nutrikinetic profile with the 6 and 24 h pooled urine samples to check the overlapping information.…”