Almond Consumption for 8 Weeks Altered Host and Microbial Metabolism in Comparison to a Control Snack in Young Adults
Department of Nutrition and Exercise Physiology, University of Missouri, Columbia
Department of Molecular and Cell Biology, University of California, Merced
NIH West Coast Metabolomics Center, University of California, Davis
Department of Nutrition, University of California, Davis
Obesity and Metabolism Research Unit, USDA□Agricultural Research Service Western Human Nutrition Research Center, University of California, Davis
#Corresponding Author: Jaapna Dhillon, PhD, Department of Nutrition and Exercise Physiology School of Medicine, University of Missouri, Columbia 204 Gwynn Hall, Columbia, MO 65211, Email: jdhillon@missouri.eduABSTRACT
Almond consumption can improve cardiometabolic (CM) health. However, the mechanisms underlying those benefits are not well characterized. This study explored the effects of consuming a snack of almonds vs. crackers for 8 weeks on changes in metabolomic profiles in young adults (clinicaltrials.gov ID: NCT03084003). Participants (n=73, age: 18-19 years, BMI: 18-41 kg/m2) were randomly assigned to consume either almonds (2 oz/d, n=38) or an isocaloric control snack of graham crackers (325 kcal/d, n=35) daily for 8 weeks. Blood samples were collected at baseline prior to and at 4 and 8 weeks after the intervention. Metabolite abundances in the serum were quantified by hydrophilic interaction chromatography quadrupole (Q) time-of-flight (TOF) mass spectrometry (MS/MS), gas chromatography (GC) TOF MS, CSH-ESI (electrospray) QTOF MS/MS, and targeted analyses for free PUFAs, total fatty acids, oxylipins and endocannabinoids. Linear mixed model analyses with baseline-adjustment were conducted, and those results were used for enrichment and network analyses. Microbial community pathway predictions from 16S rRNA sequencing of fecal samples was done using PICRUST2. Almond consumption enriched unsaturated triglycerides, unsaturated phosphatidylcholines, saturated and unsaturated lysophosphatidylcholines, tricarboxylic acids, and tocopherol clusters (p<0.05). Targeted analyses reveal lower levels of omega-3 total fatty acids (TFAs) overall in the almond group compared to the cracker group (p<0.05). Microbial amino acid biosynthesis, and amino sugar and nucleotide sugar metabolism pathways were also differentially enriched at the end of the intervention (p<0.05). The study demonstrates the differential effects of almonds on host tocopherol, lipid, and TCA cycle metabolism with potential changes in microbial metabolism, which may interact with host metabolism to facilitate the CM benefits.
Article notes
Competing Interest Statement
RMO discloses grants and non-financial support from Almond Board of California for conducting the study. JD, OF, JWN have no conflicts of interest.
Clinical Trial
NCT03084003
Funding Statement
RMO discloses grants and non-financial support from Almond Board of California for conducting the study. JD, OF, JWN have no conflicts of interest.
INTRODUCTION
The complex interplay among the host genome, the gut microflora, and environmental (diet, lifestyle, socioeconomic determinants etc.) and behavioral factors modulates multiple metabolic pathways resulting in specific biological and physiological effects (1) that ultimately influence health and disease. Metabolomic approaches that integrate large swaths of metabolism provide an experimental advantage by mapping metabolites in such a fashion that putative mechanisms can be elucidated as demonstrated in the SuGAR Project (2–4).
To date, nutritional metabolomics has focused more on the identification of biomarkers for foods and diet-related disease states (5). Over the past decade, potential biomarkers or metabolite signatures have been identified for several foods including red meat (6), coffee (7), citrus fruits (8), cruciferous vegetables (9), almonds (10), and other plant foods (11). However, there is limited research on the elucidation of molecular mechanisms responsible for the effects of dietary interventions (5). The potential of identifying changes in metabolic pathways is improving with advancements in analytical chemistry techniques, metabolite databases, and computational tools.
Another important consideration in nutritional metabolomics is the gut microbiome-host interactions (12). A considerable number of metabolomic features that are influenced by dietary changes can also be modifiable by the gut microbiome. For example, the metabolomic signatures observed in human fecal donor samples were reproduced in the urine and feces of humanized mice (13). Changes to the diets fed to humanized mice altered those metabolomic signatures and mirrored changes in bacterial community composition (13).
Nut consumption is associated with reduced cardiovascular disease mortality (14). Moreover, interventional studies demonstrate favorable effects of nut consumption on clinical lipid profiles, blood glucose, and endothelial function (14). Nut studies employing metabolomics have identified potential urinary biomarkers and signatures of walnut (15) and pistachio (16) as well as urinary (17) and plasma (18) biomarkers of mixed nut consumption and erythrocyte signatures of almond consumption (10). These metabolites collectively include markers of fatty acid metabolism, ellagitannin-derived microbial metabolites, microbial-derived phenolic metabolites, and intermediate tryptophan/serotonin pathway metabolites. There are limited -omics data that map the changes in metabolic pathways following the chronic consumption of nuts. The high unsaturated fat content and relatively low available carbohydrate content of almonds has the potential to change carbohydrate and lipid metabolism.
We have previously demonstrated the effects of almond snacking for 8 weeks on cardiometabolic (19) and microbiome profiles (20) in young adults. The unique nutrient profile of almonds improved postprandial glucoregulation (19) and alpha-diversity of the gut microbiome (20) compared to cracker snacking at the end of the 8-week intervention. Here, we use untargeted and targeted metabolomics to capture the alterations in serum metabolites involved in metabolic pathways with 8 weeks of almond snacking.
METHODS
All procedures involving human subjects were approved by the University of California (UC) Merced Institutional Review Board. The study is registered on ClinicalTrials.gov (registration number: NCT03084003). The samples used in the present study were collected previously and presented separately here to provide the requisite focus on these results, which complement our previous studies (21, 22)
Participants
Seventy-three (41 women and 32 men) young adults (18–19 years old, BMI: 18-41 kg/m2) were recruited to participate in an 8-week randomized, controlled, parallel-arm intervention examining the effects of almond vs. cracker snacking on cardiometabolic, microbiome, and metabolomics outcomes. The eligibility criteria were as follows: (a) 18-19 years of age, (b) newly enrolled, 1st-year college students with no nut allergies, (c) non-smokers, and (d) no diagnosed cardiometabolic disorders. Participants were recruited via advertisements and those who met eligibility criteria provided written, informed consent before beginning the study.
Study design and protocol
The primary study design has been described previously (19). The sample size calculations for the primary study were based on glucoregulatory profiles at the end of the 8-week intervention (19). Participants were randomized into one of two study arms: (1) almonds and (2) crackers. Participants in the almond group (n=38) consumed 57 g/d (2 oz; 327 kcal; 14% carbohydrate (8g fiber), 74% fat, 13% protein) of whole, dry-roasted almonds. Participants in the cracker group (n=35) served as the isocaloric control consuming 5 sheets (77.5 g/d) of graham crackers (325 kcal; 74% carbohydrate (2.5 g fiber, 20% fat, 6% protein). The cracker group was asked to avoid all nuts, seeds, and nut-containing products. Anthropometric, biochemical, dietary and microbiome data were collected and analyzed as described previously (23). Serum samples were collected at baseline prior to starting the intervention and at 4 and 8 weeks as previously described (21) and stored at −80 °C.
Gas chromatography (GC) time-of-flight (TOF) mass spectrometry (MS) data acquisition and processing
Serum aliquots were thawed, extracted, trimethylsilyl-derivatized, and the metabolite abundances quantified by GCTOF MS as previously described (24). Derivatized samples were analyzed on an Agilent 7890 A gas chromatograph (Santa Clara, CA) equipped with a 30 m x 0.25 mm i.d., 25 μm Rtx5Sil-MS column with an additional 10 m integrated guard column (Restek, Bellefonte PA) (25, 26). Mass spectrometry was performed using a Leco Pegasus IV time-of-flight mass spectrometer (St. Joseph, MI). Thirteen internal standards, C8–C30 fatty acid methyl esters, were added to samples for retention time alignment markers, quality control purposes and quantification corrections.
Raw mass spectra were preprocessed using ChromaTOF vs. 4.0 and were further processed using the BinBase algorithm and database for metabolite identification (27, 28). Quantification of metabolites are reported as peak heights.
All samples were analyzed in one batch, and data quality and instrument performance were constantly monitored using quality controls comprised of pooled serum samples and injected every 10 samples.
Hydrophilic Interaction chromatography (HILIC) quadrupole (Q) time-of-flight (TOF) MS/MS data acquisition and processing
Serum aliquots were extracted as previously described (29). The bottom layer of the 2-phase solution was used for HILIC-MS. Extracts were analyzed on a Agilent 1290 Infinity II LC System (Santa Clara, CA) with a 150 mm long, 2.1 mm interdiameter (id), and 1.7 μm particles Waters Acquity UPLC BEH Amide column protected by a short guard column. Mass spectrometry was performed using a Sciex 6600 TTOF mass spectrometer (Framingham, MA) with resolution R=10,000 for positively charged polar compounds. Nineteen internal standards, optimized for HILIC-MS, were added to samples for retention time alignment markers, quality control purposes, and quantification corrections.
Raw data were processed using MS-DIAL (version 3.2) (30). Quantification of metabolites are reported as peak heights. All samples were analyzed in one batch, and data quality and instrument performance were constantly monitored using blanks and quality controls, which were comprised of pooled plasma samples (BioIVT) and injected every 10 samples.
Charged Surface Hybrid electrospray (CSH-ESI) QTOF MS/MS data acquisition and processing
Serum aliquots were extracted as previously described (29). The top layer of the 2-phase solution was used for lipidomic analyses. Extracts were analyzed on an Agilent 1290 Infinity II LC System (Santa Clara, CA) with a 100 mm long, 2.1 mm id, and 1.7 μm particles Waters Acquity UPLC CSH C18 column protected by a short guard column. Mass spectrometry was performed using an Agilent 6530 QTOF mass spectrometer with resolution R=10,000 for positively charged lipids and an Agilent 6530b QTOF mass spectrometer with resolution R=20,000 for negatively charged lipids. Twenty-four internal standards, optimized for lipidomics, were added to samples for retention time alignment markers, quality control purposes, and quantification corrections.
Raw data were processed using MS-DIAL (version 2.8) (30). Quantification of metabolites are reported as peak heights. All samples were analyzed in one batch, and data quality and instrument performance were constantly monitored using quality controls, which were comprised of pooled plasma samples (BioIVT) and injected every 10 samples.
Targeted analyses of total fatty acids
Serum aliquots were extracted, derivatized, and analyzed as previously described (31, 32). Extracts in 8:1 methanol/toluene were transformed into fatty acid methyl esters (FAMEs) by sequential incubation with methanolic sodium hydroxide and methanolic hydrochloric acid, isolated in hexane from neutralized solutions and quantified by GC-MS. Briefly, FAMEs were separated on a 30m x 0.25mm, 0.25 µm DB-225ms on a 6890 gas chromatogram interfaced with a 5973A mass selective detector (Agilent Technologies) and quantified against 6-to 8-point calibration curves. Data was acquired with Chemstation v E.02 and processed with MassHunter v. 3.0.2. Results were corrected for recoveries of perdeuterated palmitate introduced as a triglyceride prior to extraction.
Targeted analyses of free PUFAs, oxylipins, and endocannabinoids
Serum aliquots were extracted as previously described (33). Residues in isopropanol extracts were separated on a 2.1 mm x 150 mm, 1.7 µm BEH C18 column (Waters, Milford, MA) and detected by electrospray ionization with multi reaction monitoring on a API 6500 QTRAP (Sciex; Redwood City, CA). Metabolites were quantified against 7-to 9-point calibration curves of authentic standards and internal standard corrections using modifications as previously reported (2).
Chemical enrichment analysis
Chemical similarity enrichment analysis was conducted using ChemRICH, which is a software for metabolomic datasets that uses medical subject headings and Tanimoto substructure chemical similarity coefficients to cluster metabolites into non-overlapping chemical groups (35). The quantitative data set comprised of the baseline-adjusted overall snack effect p-values of all annotated metabolites. Statistically significant p-values for clusters of metabolites were obtained by self-contained Kolmogorov–Smirnov tests and adjusted for FDR.
Network analysis
Network analysis was used to explore differences between the almond and cracker groups within a biochemical and structural context. A biochemical and chemical similarity network was created for all measured metabolites with KEGG and PubChem CID identifiers using MetaMapR (36). Metabolites involved in biochemical transformations were connected based on product-precursor relationships defined in the KEGG RPAIR database. Metabolites sharing structural properties defined in PubChem Substructure Fingerprints (37) were connected at a Tanimoto similarity threshold ≥ 0.7. The quantitative data set comprised of the overall effect size (Hedge’s g for almond vs cracker groups) and baseline-adjusted snack effect p-values. In cases of significant time x group effects, the larger magnitude of effect size between week 4 or week 8 was presented. Since this was an exploratory analysis, the p-values were not FDR adjusted. The network was then visualized in Cytoscape 3.7.2 (38) using the yFiles organic layout and visual separation of clusters in the network was facilitated with the GLay community clustering algorithm (39). Significant metabolites which did not have KEGG identifiers are included as independent nodes with manually annotated edges in their respective pathway clusters.
Microbial community metabolic potential
The functional potential of the microbial community was predicted from 16S rRNA using Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt2) (40, 41), which uses hidden-state prediction algorithms to infer gene families (i.e. KEGG orthologs (KO) here) and MinPath for inference of pathway abundances. The gene abundances were analyzed by constructing a combined time x group factor and fitting the within-subject correlation in a linear model blocked on subject in the limma-voom pipeline in R (42, 43). Pre-planned contrasts were constructed from the model. KO enrichment of differentially expressed (p<0.05 corrected for multiple contrast testing) metabolic pathway genes for each contrast were conducted in MicrobiomeAnalyst (44) via hypergeometric tests to evaluate if specific metabolic pathways are represented more often than expected by chance. P-values for pathway enrichment were adjusted for FDR.
We identified the serum metabolites potentially derived from gut microbial metabolism using the Annotation of Metabolite Origins via Networks (AMON) tool (45). Participants gut microbiomes were profiled previously via 16S rRNA sequencing of stool samples (46). The input dataset submitted to AMON comprised of microbiome KO identifiers, KO list of the human genome, and KEGG compound IDs of the metabolomics data. The metabolites produced or metabolized by gut microbiome are annotated in the networks (Figure 1). The contribution of the microbial community metabolism to the serum metabolite abundances was evaluated using MIMOSA2 (47, 48).
RESULTS
Participant characteristics and findings from parent study
SERRF normalization reduced the cvRSD of QCs in the untargeted analyses
For GC-TOF MS, the cvRSD of QCs decreased from 23.7% (raw) to 12.7% (SERRF). For ESI (-) QTOF-MS, cvRSD of QCs decreased from 14% (raw) to 6.5% (SERRF). For ESI (+) QTOF-MS, cvRSD of QCs decreased from 17.9% (raw) to 4.5% (SERRF). For HILIC-MS, cvRSD of QCs decreased from 15% (raw) to 10% (SERRF).
Data quality of the targeted total fatty acid, oxylipin, endocannabinoid, and non-esterified PUFA analyses
Surrogate recoveries were between 28 - 108% for all oxylipin and endocannabinoid analytes and 54% for fatty acids. Analytical precision was assessed by duplicate analysis of a plasma pool control (UTAK) (n=20) and was excellent with 83% of oxylipin and endocannabinoid analytes with <30% RSD and 63% of fatty acids with <30% RSD for the UTAKs analyzed, respectively.
Top metabolites
A total of 129 untargeted metabolites had significant (FDR<0.05) overall time effect. The means of those untargeted metabolites that had baseline-adjusted snack and snack x time p-values <0.05 are shown in Table 1. Almond consumption resulted in overall greater levels of alpha-tocopherol, ecgonine, erucic acid, indole-3-carboxaldehyde, lysophosphatidylcholines (18:0), and oleamide, and lower levels of 3-hydroxyisovaleroylcarnitine and CE (16:0) than cracker consumption (baseline-adjusted snack effect, p<0.05).
Several metabolites depicted significant snack and time interactions (baseline-adjusted snack x time effect, p<0.05, Table 1). Contrasts depict that cracker consumption resulted in greater levels of isomaltose at week 4 compared to week 8 (p<0.05). Almond consumption resulted in greater levels of oxoproline, methyl O-D-galactopyranoside, and N2,N2-Dimethylguanosine at week 4 compared to week 8 (p<0.05).
The means of specific targeted metabolites that showed significant (p<0.05) baseline-adjusted snack and snack x time effects are shown in Table 2. Almond consumption resulted in overall lower levels of NEFA and TFA fractions of alpha-linolenic acid (ALA) and eicosapentaenoic acid (EPA), and eicosatetraenoic acid (ETA) n3 NEFA, and ALA metabolites, 9-HOTE, 15(16)-EpODE, and docosahexaenoic acid (DHEA) compared to cracker consumption (baseline-adjusted snack effect, p<0.05). The omega-6:omega-3 and DPA:EPA fatty acid ratios were higher, and Σ omega-3 and palmitoleic acid:palmitic acid ratio were lower in response to almond consumption compared to cracker consumption (baseline-adjusted snack effect, p<0.05).
Chemical similarity enrichment analysis of metabolites indicates greater changes in unsaturated triglycerides and lysophosphatidylcholine metabolism with almond consumption
ChemRICH analysis, which was conducted on baseline-adjusted metabolite values, mapped 625 of the identified (untargeted) metabolites to 58 non-overlapping chemical classes. Seven clusters were significantly different between the almond and cracker groups (FDR-adjusted p-value < 0.05) overall. Almond consumption enriched unsaturated triglycerides, unsaturated phosphatidylcholines, saturated and unsaturated lysophosphatidylcholines, sphingomyelins, and tricarboxylic acids clusters. The key metabolites in each of those clusters are depicted in Table 3. The tocopherol cluster was also influenced with almond consumption overall (FDR-adjusted p-value <0.05) with gamma-tocopherol significantly decreased (p<0.05) and alpha-tocopherol increased compared with cracker consumption (Table 3).
DISCUSSION
The metabolomics analyses in our study demonstrated shifts in host metabolism with almond consumption particularly that of tocopherol, lipids, and the TCA cycle with some differential time effects noted as well. The study also suggests that changes in microbial metabolism could potentially influence host metabolism.
Almond consumption for 8 weeks increased alpha-tocopherol and decreased gamma-tocopherol, which is supported by targeted studies (49). This could be due to the preferential uptake of alpha-tocopherol in the liver when dietary intake is increased and reduced retention of other tocopherol forms (50). Increased alpha-tocopherol is typically considered a biomarker for compliance with almond consumption. The increase in alpha-tocopherol was most prominent at week 4 and appeared to have plateaued at week 8 suggesting that levels may reach a saturable limit by week 4. Previous studies have demonstrated that in participants with normal plasma alpha-tocopherol concentrations of 25 µM, the plasma concentrations do not increase more than 2-3 fold even upon supplementation (50–52) further suggesting that levels of alpha-tocopherol are saturable. In a dose-dependent almond study, 10% of energy intake from almonds resulted in a 13.7 % increase, and 20% in 18.7 % increase in plasma alpha-tocopherol over 4 weeks (49). In our study, the decrease in the magnitude of alpha-tocopherol concentration change (over 8 weeks) as baseline concentrations increase also suggests the presence of a saturable pool (Supplemental Figure 2). Our dietary recall data also indicates an increase in alpha-tocopherol over 4 weeks with no further change beyond week 4 with almond consumption (21). Given the differential response in alpha- and gamma-tocopherol to almond consumption, we propose that the ratio may serve as a robust marker of dietary compliance when the intervention may be of a lesser quantity and/or duration to not reach saturable levels in circulation.
Almond consumption was also associated with greater levels of phosphatidylcholines, and its derivative LPC species, which have been reported to influence diverse cell types such as endothelial cells, adipocytes, hepatocytes, and immune cells (53). Due to the complexity of its metabolism, the role of LPCs in disease causality is controversial (53). In a recent review (53), while in vitro studies implicated LPCs in apoptosis and pro-inflammatory conditions, clinical studies demonstrated that circulating LPCs were inversely associated with cardiovascular diseases. Other studies showed lower levels of diverse LPC species with obesity (54) and negative associations with inflammatory markers and insulin resistance (54, 55) suggesting that LPCs may protect against metabolic disorders. Analyses conducted on a subset of participants in the current study demonstrated significant (p<0.05) negative correlations of LPCs with inflammatory markers. Moreover, almond consumption for 8 weeks elicited positive correlations (r=0.30-0.55) of IL-6 with various LPC species including LPC (20:1), LPC (18:0), and LPC (18:1), and IL-10 with LPC (22:4) whereas negative correlations (r=0.2-0.41) were identified in the cracker group (p<0.05 for group comparisons). IL-10 is an anti-inflammatory cytokine while IL-6 can be either inflammatory or anti-inflammatory (56). These relationships suggest that LPCs and inflammatory markers in relatively healthy adults are mediated by dietary conditions and that chronic almond consumption may have a role to play in immune health through the maintenance or increase in LPCs. However, other considerations for interpretation of LPC effects across diet studies should include examination of tissues and/or cell types, and comparisons between healthy and diseased states and/or saturated and unsaturated species.
Almond consumption generally increased unsaturated triglycerides over 8 weeks. More specifically, the almond group had higher levels of oleamide, which is a fatty acid amide of oleic acid. Surprisingly, the targeted analyses did not detect a difference in oleic acid TFA between groups even though it is the most predominant fatty acid in almonds. However, this inconsistency has been documented previously. For example, in a dose-dependent study, half-dose almonds (37 ± 2 g/d) increased oleic acid in NEFA and TAG fractions; however, the full dose (75 ± 3 g/d) did not demonstrate an increase in NEFA fraction (57). Our dietary data indicates greater oleic acid intake in the almond group compared to the cracker group. The bioaccessibility of lipids from whole almonds during mastication and digestion may limit the amount of fat absorbed through the gastrointestinal tract (58), hence contributing to the incongruency between dietary intake and serum NEFA levels. However, since serum NEFA levels are predominately regulated by adipose triglyceride hydrolysis (59), these results suggest that almond consumption did not alter adipose composition or the rates of adipose lipolysis. Alternatively, because increased oleic acid can stimulate complete fatty acid oxidation (60), the increased intake with almond consumption may have increased oxidation including the oxidation of oleic acid itself to mask the potential to detect an increase in circulating levels. The greater levels of acetylcarnitine particularly at week 4 in the almond group suggests that beta-oxidation of fatty acids was increased and substantiates the data in the literature that increased oleic acid promotes beta oxidations of FFA. Nonetheless, our results suggest that in a free-living study, serum oleic acid may not be a viable biomarker of compliance with almond consumption.
Almond consumption also lowered the palmitoleic acid: palmitic acid ratio. A greater ratio is considered a diagnostic marker for early onset non-alcoholic steatohepatitis (61) and associated with the presence of impaired glucose tolerance and T2D (62) suggesting that the lower ratio with almond consumption may be indicative of a protective effect against metabolic disorders.
The targeted analysis reveals lower levels of omega-3 total fatty acids (TFAs) in the almond group. More specifically, almond consumption had lower levels of ALA, EPA (measured in NEFA and TFA fractions), ALA oxylipins (9-HOTE and 15(16)-EpODE), and DHEA, the acylethanolamide of the omega-3 derivative of DHA. Interestingly, our 24-hour dietary recall data revealed greater intake of EPA in the almond group compared to the cracker group and no differences in ALA intake by group suggesting that the estimated dietary intake of these fatty acids are not indicative of the actual changes in circulating levels. These discrepancies could be due to the low reproducibility of food omega-3 fatty acids by ASA24 (63). Furthermore, the complex metabolism of omega-3 fatty acids involves a series of enzymatic desaturation and elongation processes, interconversions between FAs, and translocation between different cellular compartments (64) that cannot be captured by estimating dietary intake.
The increases in aconitic, citric, isocitric, and succinic acids suggests that almond consumption stimulated the TCA cycle. The TCA cycle is a central metabolic pathway where key byproducts of nutrient digestion such as glucose, fatty acids, and some amino acids converge for energy production (65). The increased activity in the TCA cycle could be due to the higher dietary intake of MUFAs and PUFAs in the almond group. Higher fat intake has been found to elevate TCA cycle intermediates in rats (66) suggesting that increased availability of unsaturated FAs may increase the substrates for TCA cycle activity and in essence, feed the system. While in a recent human study, baseline levels of specific TCA cycle metabolites including isocitrate were associated with greater relative risk of T2D, the Mediterranean diet, a diet high in unsaturated fats, appeared to alleviate this risk (67). As mentioned previously, almond consumption increased acetylcarnitine, which is significant here because it is involved in the movement of acetyl-CoA into the mitochondria during beta-oxidation, thereby providing the raw materials for TCA cycle (68). Thus, the increase in TCA cycle activity may be facilitated by increased acetylcarnitine. These data provide a mechanism by which increased dietary MUFA (oleic acid) (and potentially PUFAs) can improve glucose metabolism (via enhanced TCA cycle activity). In the same subjects studied here, almond consumption improved glucose tolerance (21).
Another carnitine derivative, 3-hydroxyisovaleroylcarnitine, which is a degradation byproduct of ketogenic amino acids, was lower in the almond group. This may suggest that biotin intake was increased. Increased circulating levels of 3-hydroxyisovaleroylcarnitine is associated with impaired leucine catabolism due to reduced activity of 3-methylcrotonyl-CoA carboxylase, which is a biotin-dependent enzyme in asymptomatic, marginally biotin deficient adults (69, 70). Although ASA24 data does not report biotin values, nuts are considered good sources of biotin and the dietary results suggest that the consumption of biotin-rich foods (red meat and eggs) was increased in the almond group.
We have previously documented increased alpha-diversity of the gut microbiome with almond consumption (22). The present analysis shows an enrichment of amino acid biosynthesis and carbohydrate metabolism such as amino sugar and nucleotide sugar metabolism in the almond group at week 8. Our analyses revealed differential snack effects on the microbial community potential of several MIMOSA2-predicted amino acids such as glutamate, glutamine, tryptophan, phenylalanine, and proline (Supplemental Table 2). However, the predicted microbial metabolism data are most useful when associated with the metabolomics data. The only compound that was positively predicted at the end of the intervention was N-acetyl-D-mannosamine (ManNAc), which is an acylaminosugar. The microbial community in the almond group at week 8 explained 14% of the variation in serum ManNAc. The analyses further suggest that sialic acid metabolism, which includes biosynthesis of N-acetylneuraminic acid (Neu5Ac) from ManNAc, and catabolism of Neu5Ac to ManNAc and subsequently to ManNAc-6-P, which ultimately enters glycolysis, are involved (71). Although serum ManNAc itself was not significantly different between the almond and cracker groups, we speculate that metabolomics and meta-transcriptomics of stool samples might reveal other interesting relationships associated with almond consumption.
Other potential products of microbial metabolism that were differentially influenced by diet included cyclo (Leu-Pro), indole-3-carboxaldehyde, phenylacetylglutamine, and pipecolic acid. Phenylacetylglutamine, which is a conjugate of glutamine and phenylacetate, the latter being derived from microbial metabolism of phenylalanine (72, 73), was greater in the almond group. Studies have documented the positive association of phenylacetylglutamine with alpha-diversity (72), which is supported by our study as well (data not shown). Pipecolic acid, which was lower in the almond group, could arise from food intake or produced by gut bacteria on lysine degradation (74). Although, cyclo (Leu-Pro) is a bacterium-derived dipeptide whose functional role isn’t well known, literature suggests that cyclodipeptides and their derivatives such as diketopiperazines, contribute to bacterial signaling systems (75–77). Almond consumption also resulted in greater indole-3-carboxaldehyde, which is a tryptophan metabolite that acts as a ligand for the aryl hydrocarbon receptor (AhR) in the intestinal immune cells that stimulate IL-22 production when activated. This activation is important for maintaining gut immunity (78), providing another potential benefit of almond consumption on gut health in addition to promoting alpha-diversity.
We used comprehensive analyses to study the serum metabolome of almond versus cracker consumers. The changes in microbial metabolism provide complementary insight into the metabolomics data. However, since we used prediction models for assessing microbial metabolism and community potential, the results should be interpreted with caution and in the context of the exploratory nature of the analyses. Moreover, more than half of the metabolites in our dataset could not be annotated owing to the nature of untargeted metabolomics, and biochemical relationships for most lipid mediators could not be defined due to the inherent limitations of KEGG database for lipidomic datasets. Nonetheless, these data provide significant insights on the effects of chronic almond consumption on metabolic pathways not previously explored. Future analyses will explore effects of factors such as BMI, sex, and metabolic risk on these outcomes.
Our results provide a deeper understanding of host TCA cycle and lipid metabolism with almond consumption in relatively healthy young adults. In addition, the findings also shed light into the interconnections between circulating metabolites and microbial metabolism in the context of an almond intervention. More generally, these findings provide further evidence for the potential impacts of dietary changes on host substrate metabolism and associated changes in gut microbe metabolism. Whether the changes in the gut microbe or metabolites influence host metabolism or vice versa remains to be elucidated but these data provide evidence for an association between gut microbe metabolism and host cellular metabolism.
Supporting information
Data Availability
The datasets generated for this study are available on request to the corresponding author.