Serum Metabolomic Markers of Protein-Rich Foods and Incident CKD: Results From the Atherosclerosis Risk in Communities Study.

Rationale & Objective: While urine excretion of nitrogen estimates the total protein intake, biomarkers of specific dietary protein sources have been sparsely studied. Using untargeted metabolomics, this study aimed to identify serum metabolomic markers of 6 protein-rich foods and to examine whether dietary protein-related metabolites are associated with incident chronic kidney disease (CKD). Study Design: Prospective cohort study. Setting & Participants: A total of 3,726 participants from the Atherosclerosis Risk in Communities study without CKD at baseline. Exposures: Dietary intake of 6 protein-rich foods (fish, nuts, legumes, red and processed meat, eggs, and poultry), serum metabolites. Outcomes: Incident CKD (estimated glomerular filtration rate < 60 mL/min/1.73 m2 with ≥25% estimated glomerular filtration rate decline relative to visit 1, hospitalization or death related to CKD, or end-stage kidney disease). Analytical Approach: Multivariable linear regression models estimated cross-sectional associations between protein-rich foods and serum metabolites. C statistics assessed the ability of the metabolites to improve the discrimination of highest versus lower 3 quartiles of intake of protein-rich foods beyond covariates (demographics, clinical factors, health behaviors, and the intake of nonprotein food groups). Cox regression models identified prospective associations between protein-related metabolites and incident CKD. Results: Thirty significant associations were identified between protein-rich foods and serum metabolites (fish, n = 8; nuts, n = 5; legumes, n = 0; red and processed meat, n = 5; eggs, n = 3; and poultry, n = 9). Metabolites collectively and significantly improved the discrimination of high intake of protein-rich foods compared with covariates alone (difference in C statistics = 0.033, 0.051, 0.003, 0.024, and 0.025 for fish, nuts, red and processed meat, eggs, and poultry-related metabolites, respectively; P < 1.00 × 10-16 for all). Dietary intake of fish was positively associated with 1-docosahexaenoylglycerophosphocholine (22:6n3), which was inversely associated with incident CKD (HR, 0.82; 95% CI, 0.75-0.89; P = 7.81 × 10-6). Limitations: Residual confounding and sample-storage duration. Conclusions: We identified candidate biomarkers of fish, nuts, red and processed meat, eggs, and poultry. A fish-related metabolite, 1-docosahexaenoylglycerophosphocholine (22:6n3), was associated with a lower risk of CKD.

In this study, we aimed to identify associations between protein-rich foods (fish, nuts, legumes, red and processed meat, eggs, and poultry) and serum metabolites, which are small biological molecules involved in metabolism. Metabolites significantly associated with a protein-rich food individually and collectively improved the discrimination of the respective protein-rich food, suggesting that these metabolites should be prioritized in future diet biomarker research. We also studied associations between significant diet-related metabolites and incident kidney disease. One fish-related metabolite was associated with a lower kidney disease risk. This finding supports the recent nutritional guidelines recommending a Mediterranean diet, which includes fish as the main dietary protein source.

Metabolomic Association and Risk Prediction With Heart Failure in Older Adults.

BACKGROUND: Older adults have markedly increased risks of heart failure (HF), specifically HF with preserved ejection fraction (HFpEF). Identifying novel biomarkers can help in understanding HF pathogenesis and improve at-risk population identification. This study aimed to identify metabolites associated with incident HF, HFpEF, and HF with reduced ejection fraction and examine risk prediction in older adults. METHODS: Untargeted metabolomic profiling was performed in Black and White adults from the ARIC study (Atherosclerosis Risk in Communities) visit 5 (n=3719; mean age, 75 years). We applied Cox regressions to identify metabolites associated with incident HF and its subtypes. The metabolite risk score (MRS) was constructed and examined for associations with HF, echocardiographic measures, and HF risk prediction. Independent samples from visit 3 (n=1929; mean age, 58 years) were used for replication. RESULTS: Sixty metabolites (hazard ratios range, 0.79-1.49; false discovery rate, <0.05) were associated with incident HF after adjusting for clinical risk factors, eGFR, and NT-proBNP (N-terminal pro-B-type natriuretic peptide). Mannonate, a hydroxy acid, was replicated (hazard ratio, 1.36 [95% CI, 1.19-1.56]) with full adjustments. MRS was associated with an 80% increased risk of HF per SD increment, and the highest MRS quartile had 8.7× the risk of developing HFpEF than the lowest quartile. High MRS was also associated with unfavorable values of cardiac structure and function. Adding MRS over clinical risk factors and NT-proBNP improved 5-year HF risk prediction C statistics from 0.817 to 0.850 (∆C, 0.033 [95% CI, 0.017-0.047]). The association between MRS and incident HF was replicated after accounting for clinical risk factors (P<0.05). CONCLUSIONS: Novel metabolites associated with HF risk were identified, elucidating disease pathways, specifically HFpEF. An MRS was associated with HF risk and improved 5-year risk prediction in older adults, which may assist at at-risk population identification.

Cross-sectional analyses of metabolites across biological samples mediating dietary acid load and chronic kidney disease.

Chronic kidney disease (CKD) is a major public health burden, with dietary acid load (DAL) and gut microbiota playing crucial roles. As DAL can affect the host metabolome, potentially via the gut microbiota, we cross-sectionally investigated the interplay between DAL, host metabolome, gut microbiota, and early-stage CKD (TwinsUK, n = 1,453). DAL was positively associated with CKD stage G1-G2 (Beta (95% confidence interval) = 0.34 (0.007; 0.7), p = 0.046). After adjusting for covariates and multiple testing, we identified 15 serum, 14 urine, 8 stool, and 7 saliva metabolites, primarily lipids and amino acids, associated with both DAL and CKD progression. Of these, 8 serum, 2 urine, and one stool metabolites were found to mediate the DAL-CKD association. Furthermore, the stool metabolite 5-methylhexanoate (i7:0) correlated with 26 gut microbial species. Our findings emphasize the gut microbiota's therapeutic potential in countering DAL's impact on CKD through the host metabolome. Interventional and longitudinal studies are needed to establish causality.

Increased Plasma Branched Short-Chain Fatty Acids and Improved Glucose Homeostasis: The Microbiome and Insulin Longitudinal Evaluation Study (MILES).

Short-chain fatty acids (SCFAs) have been extensively studied for potential beneficial roles in glucose homeostasis and risk of diabetes; however, most of this research has focused on butyrate, acetate, and propionate. The effect on metabolism of branched SCFAs (BSCFAs; isobutyrate, isovalerate, and methylbutyrate) is largely unknown. In a cohort of 219 non-Hispanic White participants and 126 African American participants, we examined the association of BSCFA with dysglycemia (prediabetes and diabetes) and oral glucose tolerance test-based measures of glucose and insulin homeostasis, as well as with demographic, anthropometric, lifestyle, and lipid traits, and other SCFAs. We observed a bimodal distribution of BSCFAs, with 25 individuals having high levels (H-BSCFA group) and 320 individuals having lower levels (L-BSCFA group). The prevalence of dysglycemia was lower in the H-BSCFA group compared with the L-BSCFA group (16% vs. 49%; P = 0.0014). This association remained significant after adjustment for age, sex, race, BMI, and levels of other SCFAs. Consistent with the lower rate of dysglycemia, fasting and postprandial glucose levels were lower and the disposition index was higher in the H-BSCFA group. Additional findings in H-BSCFA versus L-BSCFA included lower fasting and postprandial C-peptide levels and lower insulin clearance without differences in insulin levels, insulin sensitivity, insulin secretion, or other variables examined, including diet and physical activity. As one of the first human studies associating higher BSCFA levels with lower odds of dysglycemia and improved glucose homeostasis, this study sets the stage for further investigation of BSCFA as a novel target for prevention or treatment of diabetes.

A Fecal Metabolite Signature of Impaired Fasting Glucose: Results From Two Independent Population-Based Cohorts.

Prediabetes is a metabolic condition associated with gut microbiome composition, although mechanisms remain elusive. We searched for fecal metabolites, a readout of gut microbiome function, associated with impaired fasting glucose (IFG) in 142 individuals with IFG and 1,105 healthy individuals from the UK Adult Twin Registry (TwinsUK). We used the Cooperative Health Research in the Region of Augsburg (KORA) cohort (318 IFG individuals, 689 healthy individuals) to replicate our findings. We linearly combined eight IFG-positively associated metabolites (1-methylxantine, nicotinate, glucuronate, uridine, cholesterol, serine, caffeine, and protoporphyrin IX) into an IFG-metabolite score, which was significantly associated with higher odds ratios (ORs) for IFG (TwinsUK: OR 3.9 [95% CI 3.02-5.02], P < 0.0001, KORA: OR 1.3 [95% CI 1.16-1.52], P < 0.0001) and incident type 2 diabetes (T2D; TwinsUK: hazard ratio 4 [95% CI 1.97-8], P = 0.0002). Although these are host-produced metabolites, we found that the gut microbiome is strongly associated with their fecal levels (area under the curve >70%). Abundances of Faecalibacillus intestinalis, Dorea formicigenerans, Ruminococcus torques, and Dorea sp. AF24-7LB were positively associated with IFG, and such associations were partially mediated by 1-methylxanthine and nicotinate (variance accounted for mean 14.4% [SD 5.1], P < 0.05). Our results suggest that the gut microbiome is linked to prediabetes not only via the production of microbial metabolites but also by affecting intestinal absorption/excretion of host-produced metabolites and xenobiotics, which are correlated with the risk of IFG. Fecal metabolites enable modeling of another mechanism of gut microbiome effect on prediabetes and T2D onset. ARTICLE HIGHLIGHTS: Prediabetes is a metabolic condition associated with gut microbiome composition, although mechanisms remain elusive. We investigated whether there is a fecal metabolite signature of impaired fasting glucose (IFG) and the possible underlying mechanisms of action. We identified a fecal metabolite signature of IFG associated with prevalent IFG in two independent cohorts and incident type 2 diabetes in a subanalysis. Although the signature consists of metabolites of nonmicrobial origin, it is strongly correlated with gut microbiome composition. Fecal metabolites enable modeling of another mechanism of gut microbiome effect on prediabetes by affecting intestinal absorption or excretion of host compounds and xenobiotics.

Genetic and gut microbiome determinants of SCFA circulating and fecal levels, postprandial responses and links to chronic and acute inflammation.

Short-chain fatty acids (SCFA) are involved in immune system and inflammatory responses. We comprehensively assessed the host genetic and gut microbial contribution to a panel of eight serum and stool SCFAs in two cohorts (TwinsUK, n = 2507; ZOE PREDICT-1, n = 328), examined their postprandial changes and explored their links with chronic and acute inflammatory responses in healthy individuals and trauma patients. We report low concordance between circulating and fecal SCFAs, significant postprandial changes in most circulating SCFAs, and a heritable genetic component (average h2: serum = 14%(SD = 14%); stool = 12%(SD = 6%)). Furthermore, we find that gut microbiome can accurately predict their fecal levels (AUC>0.71) while presenting weaker associations with serum. Finally, we report different correlation patterns with inflammatory markers depending on the type of inflammatory response (chronic or acute trauma). Our results illustrate the breadth of the physiological relevance of SCFAs on human inflammatory and metabolic responses highlighting the need for a deeper understanding of this important class of molecules.

Serum Metabolomic Markers of Dairy Consumption: Results from the Atherosclerosis Risk in Communities Study and the Bogalusa Heart Study.

BACKGROUND: Dairy consumption is related to chronic disease risk; however, the measurement of dairy consumption has largely relied upon self-report. Untargeted metabolomics allows for the identification of objective markers of dietary intake. OBJECTIVES: We aimed to identify associations between dietary dairy intake (total dairy, low-fat dairy, and high-fat dairy) and serum metabolites in 2 independent study populations of United States adults. METHODS: Dietary intake was assessed with food frequency questionnaires. Multivariable linear regression models were used to estimate cross-sectional associations between dietary intake of dairy and 360 serum metabolites analyzed in 2 subgroups of the Atherosclerosis Risk in Communities study (ARIC; n = 3776). Results from the 2 subgroups were meta-analyzed using fixed effects meta-analysis. Significant meta-analyzed associations in the ARIC study were then tested in the Bogalusa Heart Study (BHS; n = 785). RESULTS: In the ARIC study and BHS, the mean age was 54 and 48 years, 61% and 29% were Black, and the mean dairy intake was 1.7 and 1.3 servings/day, respectively. Twenty-nine significant associations between dietary intake of dairy and serum metabolites were identified in the ARIC study (total dairy, n = 14; low-fat dairy, n = 10; high-fat dairy, n = 5). Three associations were also significant in BHS: myristate (14:0) was associated with high-fat dairy, and pantothenate was associated with total dairy and low-fat dairy, but 23 of the 27 associations significant in the ARIC study and tested in BHS were not associated with dairy in BHS. CONCLUSIONS: We identified metabolomic associations with dietary intake of dairy, including 3 associations found in 2 independent cohort studies. These results suggest that myristate (14:0) and pantothenate (vitamin B5) are candidate biomarkers of dairy consumption.

The Combination of a Human Biomimetic Liver Microphysiology System with BIOLOGXsym, a Quantitative Systems Toxicology (QST) Modeling Platform for Macromolecules, Provides Mechanistic Understanding of Tocilizumab- and GGF2-Induced Liver Injury.

Biologics address a range of unmet clinical needs, but the occurrence of biologics-induced liver injury remains a major challenge. Development of cimaglermin alfa (GGF2) was terminated due to transient elevations in serum aminotransferases and total bilirubin. Tocilizumab has been reported to induce transient aminotransferase elevations, requiring frequent monitoring. To evaluate the clinical risk of biologics-induced liver injury, a novel quantitative systems toxicology modeling platform, BIOLOGXsym™, representing relevant liver biochemistry and the mechanistic effects of biologics on liver pathophysiology, was developed in conjunction with clinically relevant data from a human biomimetic liver microphysiology system. Phenotypic and mechanistic toxicity data and metabolomics analysis from the Liver Acinus Microphysiology System showed that tocilizumab and GGF2 increased high mobility group box 1, indicating hepatic injury and stress. Tocilizumab exposure was associated with increased oxidative stress and extracellular/tissue remodeling, and GGF2 decreased bile acid secretion. BIOLOGXsym simulations, leveraging the in vivo exposure predicted by physiologically-based pharmacokinetic modeling and mechanistic toxicity data from the Liver Acinus Microphysiology System, reproduced the clinically observed liver signals of tocilizumab and GGF2, demonstrating that mechanistic toxicity data from microphysiology systems can be successfully integrated into a quantitative systems toxicology model to identify liabilities of biologics-induced liver injury and provide mechanistic insights into observed liver safety signals.

Metabolomics of Dietary Intake of Total, Animal, and Plant Protein: Results from the Atherosclerosis Risk in Communities (ARIC) Study.

Background: Dietary consumption has traditionally been studied through food intake questionnaires. Metabolomics can be used to identify blood markers of dietary protein that may complement existing dietary assessment tools. Objectives: We aimed to identify associations between 3 dietary protein sources (total protein, animal protein, and plant protein) and serum metabolites using data from the Atherosclerosis Risk in Communities Study. Methods: Participants' dietary protein intake was derived from a food frequency questionnaire administered by an interviewer, and fasting serum samples were collected at study visit 1 (1987-1989). Untargeted metabolomic profiling was performed in 2 subgroups (subgroup 1: n = 1842; subgroup 2: n = 2072). Multivariable linear regression models were used to assess associations between 3 dietary protein sources and 360 metabolites, adjusting for demographic factors and other participant characteristics. Analyses were performed separately within each subgroup and meta-analyzed with fixed-effects models. Results: In this study of 3914 middle-aged adults, the mean (SD) age was 54 (6) y, 60% were women, and 61% were Black. We identified 41 metabolites significantly associated with dietary protein intake. Twenty-six metabolite associations overlapped between total protein and animal protein, such as pyroglutamine, creatine, 3-methylhistidine, and 3-carboxy-4-methyl-5-propyl-2-furanpropanoic acid. Plant protein was uniquely associated with 11 metabolites, such as tryptophan betaine, 4-vinylphenol sulfate, N-δ-acetylornithine, and pipecolate. Conclusions: The results of 17 of the 41 metabolites (41%) were consistent with those of previous nutritional metabolomic studies and specific protein-rich food items. We discovered 24 metabolites that had not been previously associated with dietary protein intake. These results enhance the validity of candidate markers of dietary protein intake and introduce novel metabolomic markers of dietary protein intake.

Metabolomic Profiles Associated With Blood Pressure Reduction in Response to the DASH and DASH-Sodium Dietary Interventions.

BACKGROUND: The DASH (Dietary Approaches to Stop Hypertension) diets reduced blood pressure (BP) in the DASH and DASH-Sodium trials, but the underlying mechanisms are unclear. We identified metabolites associated with systolic BP or diastolic BP (DBP) changes induced by dietary interventions (DASH versus control arms) in 2 randomized controlled feeding studies-the DASH and DASH-Sodium trials. METHODS: Metabolomic profiling was conducted in serum and urine samples collected at the end of diet interventions: DASH (n=219) and DASH-Sodium (n=395). Using multivariable linear regression models, associations were examined between metabolites and change in systolic BP and DBP. Tested for interactions between diet interventions and metabolites were the following comparisons: (1) DASH versus control diets in the DASH trial (serum), (2) DASH high-sodium versus control high-sodium diets in the DASH-Sodium trial (urine), and (3) DASH low-sodium versus control high-sodium diets in the DASH-Sodium trial (urine). RESULTS: Sixty-five significant interactions were identified (DASH trial [serum], 12; DASH high sodium [urine], 35; DASH low sodium [urine], 18) between metabolites and systolic BP or DBP. In the DASH trial, serum tryptophan betaine was associated with reductions in DBP in participants consuming the DASH diets but not control diets (P interaction, 0.023). In the DASH-Sodium trial, urine levels of N-methylglutamate and proline derivatives (eg, stachydrine, 3-hydroxystachydrine, N-methylproline, and N-methylhydroxyproline) were associated with reductions in systolic BP or DBP in participants consuming the DASH diets but not control diets (P interaction, <0.05 for all tests). CONCLUSIONS: We identified metabolites that were associated with BP lowering in response to dietary interventions. REGISTRATION: URL: https://www. CLINICALTRIALS: gov/ct2/show/NCT03403166; Unique identifier: NCT03403166 (DASH trial). URL: https://www. CLINICALTRIALS: gov/ct2/show/NCT00000608; Unique identifier: NCT00000608 (DASH-Sodium trial).

Whole-Genome Sequencing Analysis of Human Metabolome in Multi-Ethnic Populations.

Circulating metabolite levels may reflect the state of the human organism in health and disease, however, the genetic architecture of metabolites is not fully understood. We have performed a whole-genome sequencing association analysis of both common and rare variants in up to 11,840 multi-ethnic participants from five studies with up to 1666 circulating metabolites. We have discovered 1985 novel variant-metabolite associations, and validated 761 locus-metabolite associations reported previously. Seventy-nine novel variant-metabolite associations have been replicated, including three genetic loci located on the X chromosome that have demonstrated its involvement in metabolic regulation. Gene-based analysis have provided further support for seven metabolite-replicated loci pairs and their biologically plausible genes. Among those novel replicated variant-metabolite pairs, follow-up analyses have revealed that 26 metabolites have colocalized with 21 tissues, seven metabolite-disease outcome associations have been putatively causal, and 7 metabolites might be regulated by plasma protein levels. Our results have depicted the genetic contribution to circulating metabolite levels, providing additional insights into understanding human disease.

The secondary bile acid isoursodeoxycholate correlates with post-prandial lipemia, inflammation, and appetite and changes post-bariatric surgery.

Primary and secondary bile acids (BAs) influence metabolism and inflammation, and the gut microbiome modulates levels of BAs. We systematically explore the host genetic, gut microbial, and habitual dietary contribution to a panel of 19 serum and 15 stool BAs in two population-based cohorts (TwinsUK, n = 2,382; ZOE PREDICT-1, n = 327) and assess changes post-bariatric surgery and after nutritional interventions. We report that BAs have a moderately heritable genetic component, and the gut microbiome accurately predicts their levels in serum and stool. The secondary BA isoursodeoxycholate (isoUDCA) can be explained mostly by gut microbes (area under the receiver operating characteristic curve [AUC] = ∼80%) and associates with post-prandial lipemia and inflammation (GlycA). Furthermore, circulating isoUDCA decreases significantly 1 year after bariatric surgery (ß = -0.72, p = 1 × 10-5) and in response to fiber supplementation (ß = -0.37, p < 0.03) but not omega-3 supplementation. In healthy individuals, isoUDCA fasting levels correlate with pre-meal appetite (p < 1 × 10-4). Our findings indicate an important role for isoUDCA in lipid metabolism, appetite, and, potentially, cardiometabolic risk.

Metabolomic Markers of Ultra-Processed Food and Incident CKD.

BACKGROUND: High ultra-processed food consumption is associated with higher risk of CKD. However, there is no biomarker for ultra-processed food, and the mechanism through which ultra-processed food is associated with CKD is not clear. Metabolomics can provide objective biomarkers of ultra-processed food and provide important insights into the mechanisms by which ultra-processed food is associated with risk of incident CKD. Our objective was to identify serum metabolites associated with ultra-processed food consumption and investigate whether ultra-processed food-associated metabolites are prospectively associated with incident CKD. METHODS: We used data from 3751 Black and White men and women (aged 45-64 years) in the Atherosclerosis Risk in Communities study. Dietary intake was assessed using a semiquantitative 66-item food frequency questionnaire, and ultra-processed food was classified using the NOVA classification system. Multivariable linear regression models were used to identify the association between 359 metabolites and ultra-processed food consumption. Cox proportional hazards models were used to investigate the prospective association of ultra-processed food-associated metabolites with incident CKD. RESULTS: Twelve metabolites (saccharine, homostachydrine, stachydrine, N2, N2-dimethylguanosine, catechol sulfate, caffeine, 3-methyl-2-oxovalerate, theobromine, docosahexaenoate, glucose, mannose, and bradykinin) were significantly associated with ultra-processed food consumption after controlling for false discovery rate <0.05 and adjusting for sociodemographic factors, health behaviors, eGFR, and total energy intake. The 12 ultra-processed food-related metabolites significantly improved the prediction of ultra-processed food consumption (difference in C statistics: 0.069, P <1×10 -16 ). Higher levels of mannose, glucose, and N2, N2-dimethylguanosine were associated with higher risk of incident CKD after a median follow-up of 23 years. CONCLUSIONS: We identified 12 serum metabolites associated with ultra-processed food consumption and three of them were positively associated with incident CKD. Mannose and N2, N2-dimethylguanosine are novel markers of CKD that may explain observed associations between ultra-processed food and CKD. PODCAST: This article contains a podcast at https://dts.podtrac.com/redirect.mp3/www.asn-online.org/media/podcast/CJASN/2023_03_08_CJN08480722.mp3.

Butyrate-Producing Bacteria and Insulin Homeostasis: The Microbiome and Insulin Longitudinal Evaluation Study (MILES).

Gut microbiome studies have documented depletion of butyrate-producing taxa in type 2 diabetes. We analyzed associations between butyrate-producing taxa and detailed measures of insulin homeostasis, whose dysfunction underlies diabetes in 224 non-Hispanic Whites and 129 African Americans, all of whom completed an oral glucose tolerance test. Stool microbiome was assessed by whole-metagenome shotgun sequencing with taxonomic profiling. We examined associations among 36 butyrate-producing taxa (n = 7 genera and 29 species) and insulin sensitivity, insulin secretion, disposition index, insulin clearance, and prevalence of dysglycemia (prediabetes plus diabetes, 46% of cohort), adjusting for age, sex, BMI, and race. The genus Coprococcus was associated with higher insulin sensitivity (ß = 0.14; P = 0.002) and disposition index (ß = 0.12; P = 0.012) and a lower rate of dysglycemia (odds ratio [OR] 0.91; 95% CI 0.85-0.97; P = 0.0025). In contrast, Flavonifractor was associated with lower insulin sensitivity (ß = -0.13; P = 0.004) and disposition index (ß = -0.11; P = 0.04) and higher prevalence of dysglycemia (OR 1.22; 95% CI 1.08-1.38; P = 0.0013). Species-level analyses found 10 bacteria associated with beneficial directions of effects and two bacteria with adverse associations on insulin homeostasis and dysglycemia. Although most butyrate producers analyzed appear to be metabolically beneficial, this is not the case for all such bacteria, suggesting that microbiome-directed therapeutic measures to prevent or treat diabetes should be targeted to specific butyrate-producing taxa rather than all butyrate producers.

Serum metabolomic signatures of plant-based diets and incident chronic kidney disease.

BACKGROUND: Greater adherence to plant-based diets is associated with a lower risk of incident chronic kidney disease (CKD). Metabolomics can help identify blood biomarkers of plant-based diets and enhance understanding of underlying mechanisms. OBJECTIVES: Using untargeted metabolomics, we aimed to identify metabolites associated with 4 plant-based diet indices (PDIs) (overall PDI, provegetarian diet, healthful PDI, and unhealthful PDI) and incident CKD in 2 subgroups within the Atherosclerosis Risk in Communities study. METHODS: We calculated 4 PDIs based on participants' responses on an FFQ. We used multivariable linear regression to examine the association between 4 PDIs and 374 individual metabolites, adjusting for confounders. We used Cox proportional hazards regression to evaluate associations between PDI-related metabolites and incident CKD. Estimates were meta-analyzed across 2 subgroups (n1 = 1762; n2 = 1960). We calculated C-statistics to assess whether metabolites improved the prediction of those in the highest quintile compared to the lower 4 quintiles of PDIs, and whether PDI- and CKD-related metabolites predicted incident CKD beyond the CKD prediction model. RESULTS: We identified 82 significant PDI-metabolite associations (overall PDI = 27; provegetarian = 17; healthful PDI = 20; unhealthful PDI = 18); 11 metabolites overlapped across the overall PDI, provegetarian diet, and healthful PDI. The addition of metabolites improved prediction of those in the highest quintile as opposed to the lower 4 quintiles of PDIs compared with participant characteristics alone (range of differences in C-statistics = 0.026-0.104; P value ≤ 0.001 for all tests). Six PDI-related metabolites (glycerate, 1,5-anhydroglucitol, γ-glutamylalanine, γ-glutamylglutamate, γ-glutamylleucine, γ-glutamylvaline), involved in glycolysis, gluconeogenesis, pyruvate metabolism, and γ-glutamyl peptide metabolism, were significantly associated with incident CKD and improved prediction of incident CKD beyond the CKD prediction model (difference in C-statistics for 6 metabolites = 0.005; P value = 0.006). CONCLUSIONS: In a community-based study of US adults, we identified metabolites that were related to plant-based diets and predicted incident CKD. These metabolites highlight pathways through which plant-based diets are associated with incident CKD.

Plasma Metabolites Associated with a Protein-Rich Dietary Pattern: Results from the OmniHeart Trial.

SCOPE: Lack of biomarkers is a challenge for the accurate assessment of protein intake and interpretation of observational study data. The study aims to identify biomarkers of a protein-rich dietary pattern. METHODS AND RESULTS: The Optimal Macronutrient Intake Trial to Prevent Heart Disease (OmniHeart) trial is a randomized cross-over feeding study which tested three dietary patterns with varied macronutrient content (carbohydrate-rich; protein-rich with about half from plant sources; and unsaturated fat-rich). In 156 adults, differences in log-transformed plasma metabolite levels at the end of the protein- and carbohydrate-rich diet periods using paired t-tests is examined. Partial least-squares discriminant analysis is used to identify a set of metabolites which are influential in discriminating between the protein-rich versus carbohydrate-rich dietary patterns. Of 839 known metabolites, 102 metabolites differ significantly between the protein-rich and the carbohydrate-rich dietary patterns after Bonferroni correction, the majority of which are lipids (n = 35), amino acids (n = 27), and xenobiotics (n = 24). Metabolites which are the most influential in discriminating between the protein-rich and the carbohydrate-rich dietary patterns represent plant protein intake, food or beverage intake, and preparation methods. CONCLUSIONS: The study identifies many plasma metabolites associated with the protein-rich dietary pattern. If replicated, these metabolites may be used to assess level of adherence to a similar dietary pattern.

Serum Metabolites Associated with Healthy Diets in African Americans and European Americans.

BACKGROUND: High diet quality is associated with a lower risk of chronic diseases. Metabolomics can be used to identify objective biomarkers of diet quality. OBJECTIVES: We used metabolomics to identify serum metabolites associated with 4 diet indices and the components within these indices in 2 samples from African Americans and European Americans. METHODS: We studied cross-sectional associations between known metabolites and Healthy Eating Index (HEI)-2015, Alternative Healthy Eating Index (AHEI)-2010, the Dietary Approaches to Stop Hypertension Trial (DASH) diet, alternate Mediterranean diet (aMED), and their components using untargeted metabolomics in 2 samples (n1 = 1,806, n2 = 2,056) of the Atherosclerosis Risk in Communities study (aged 45-64 y at baseline). Dietary intakes were assessed using an FFQ. We used multivariable linear regression models to examine associations between diet indices and serum metabolites in each sample, adjusting for participant characteristics. Metabolites significantly associated with diet indices were meta-analyzed across 2 samples. C-statistics were calculated to examine if these candidate biomarkers improved prediction of individuals in the highest compared with lowest quintile of diet scores beyond participant characteristics. RESULTS: Seventeen unique metabolites (HEI: n = 6; AHEI: n = 5; DASH: n = 14; aMED: n = 2) were significantly associated with higher diet scores after Bonferroni correction in sample 1 and sample 2. Six of 17 significant metabolites [glycerate, N-methylproline, stachydrine, threonate, pyridoxate, 3-(4-hydroxyphenyl)lactate)] were associated with ≥1 dietary pattern. Candidate biomarkers of HEI, AHEI, and DASH distinguished individuals with highest compared with lowest quintile of diet scores beyond participant characteristics in samples 1 and 2 (P value for difference in C-statistics <0.02 for all 3 diet indices). Candidate biomarkers of aMED did not improve C-statistics beyond participant characteristics (P value = 0.930). CONCLUSIONS: A considerable overlap of metabolites associated with HEI, AHEI, DASH, and aMED reflects the similar food components and similar metabolic pathways involved in the metabolism of healthy diets in African Americans and European Americans.

Urine Metabolites Associated with the Dietary Approaches to Stop Hypertension (DASH) Diet: Results from the DASH-Sodium Trial.

SCOPE: Serum metabolomic markers of the Dietary Approaches to Stop Hypertension (DASH) diet are previously reported. In an independent study, the similarity of urine metabolomic markers are investigated. METHODS AND RESULTS: In the DASH-Sodium trial, participants are randomly assigned to the DASH diet or control diet, and received three sodium interventions (high, intermediate, low) within each randomized diet group in random order for 30 days each. Urine samples are collected at the end of each intervention period and analyzed for 938 metabolites. Two comparisons are conducted: 1) DASH-high sodium (n = 199) versus control-high sodium (n = 193), and 2) DASH-low sodium (n = 196) versus control-high sodium. Significant metabolites identified using multivariable linear regression are compared and the top 10 influential metabolites identified using partial least-squares discriminant analysis to the results from the DASH trial. Nine out of 10 predictive metabolites of the DASH-high sodium and DASH-low sodium diets are identical. Most candidate biomarkers from the DASH trial replicated. N-methylproline, chiro-inositol, stachydrine, and theobromine replicated as influential metabolites of DASH diets. CONCLUSIONS: Candidate biomarkers of the DASH diet identified in serum replicated in urine. Replicated influential metabolites are likely to be objective biomarkers of the DASH diet.