A metabolome and microbiome wide association study of healthy eating index points to the mechanisms linking dietary pattern and metabolic status

Dietary selective effects manifest in the human gut microbiota from species composition to strain genetic makeup

Diet significantly influences the human gut microbiota, a key player in health. We analyzed shotgun metagenomic sequencing data from healthy individuals with long-term dietary patterns-vegan, flexitarian, or omnivore-and included detailed dietary surveys and blood biomarkers. Dietary patterns notably affected the bacterial community composition by altering the relative abundances of certain species but had a minimal impact on microbial functional repertoires. However, diet influenced microbial functionality at the strain level, with diet type linked to strain genetic variations. We also found molecular signatures of selective pressure in species enriched by specific diets. Notably, species enriched in omnivores exhibited stronger positive selection, such as multiple iron-regulating genes in the meat-favoring bacterium Odoribacter splanchnicus, an effect that was also validated in independent cohorts. Our findings offer insights into how diet shapes species and genetic diversity in the human gut microbiota.

Dietary Sugar and Saturated Fat Consumption Associated with the Gastrointestinal Microbiome during Pregnancy

BACKGROUND

Growing evidence supports changes in the gastrointestinal microbiome over the course of pregnancy may have an impact on the short- and long-term health of both the mother and the child.

OBJECTIVE

Our objective was to explore the association of diet quality, as measured by the Healthy Eating Index (HEI), with the composition and gene ontology (GO) representation of microbial function in the maternal gastrointestinal microbiome during pregnancy.

METHODS

We conducted a retrospective, observational analysis of n = 185 pregnant participants in the Pregnancy Eating Attributes Study. Maternal dietary intake was assessed in the first trimester using the automated self-administered 24-h recall method, from which the HEI 2015 was calculated. Rectal swabs were obtained in the second trimester and sequenced using the NovaSeq 6000 system shotgun platform. We used unsupervised clustering to identify microbial enterotypes representative of maternal taxa and GO functional term composition. Multivariable linear models were used to identify associations between taxa, functional terms, and food components while controlling for relevant covariates. Multinomial regression was then used to predict enterotype membership based on a participant's HEI food component score.

RESULTS

Those in the high diet quality tertile had a lower early pregnancy BMI [mean (M) = 23.48 kg/m2, SD = 3.38] compared with the middle (M = 27.35, SD = 6.01) and low (M = 27.49, SD = 6.99) diet quality tertiles (P < 0.01). There were no statistically significant associations between the HEI components or the total HEI score and the 4 alpha diversity measures. Differences in taxa and GO term enterotypes were found in participants with, but not limited to, a higher saturated fat component score (β = 1.35, P = 0.01), added sugar HEI component (β = 0.07, P < 0.001), and higher total dairy score (β = 1.58, P = 0.01).

CONCLUSIONS

Specific dietary components are associated with microbial composition and function in the second trimester of pregnancy. These findings provide a foundation for future testable hypotheses.

Longitudinal Plasma Metabolome Patterns and Relation to Kidney Function and Proteinuria in Pediatric CKD

Key Points

Longitudinal untargeted metabolomics. Children with CKD have a circulating metabolome that changes over time.

Background

Understanding plasma metabolome patterns in relation to changing kidney function in pediatric CKD is important for continued research for identifying novel biomarkers, characterizing biochemical pathophysiology, and developing targeted interventions. There are a limited number of studies of longitudinal metabolomics and virtually none in pediatric CKD.

Methods

The CKD in Children study is a multi-institutional, prospective cohort that enrolled children aged 6 months to 16 years with eGFR 30–90 ml/min per 1.73 m2. Untargeted metabolomics profiling was performed on plasma samples from the baseline, 2-, and 4-year study visits. There were technologic updates in the metabolomic profiling platform used between the baseline and follow-up assays. Statistical approaches were adopted to avoid direct comparison of baseline and follow-up measurements. To identify metabolite associations with eGFR or urine protein-creatinine ratio (UPCR) among all three time points, we applied linear mixed-effects (LME) models. To identify metabolites associated with time, we applied LME models to the 2- and 4-year follow-up data. We applied linear regression analysis to examine associations between change in metabolite level over time (∆level) and change in eGFR (∆eGFR) and UPCR (∆UPCR). We reported significance on the basis of both the false discovery rate (FDR) <0.05 and P < 0.05.

Results

There were 1156 person-visits (N : baseline=626, 2-year=254, 4-year=276) included. There were 622 metabolites with standardized measurements at all three time points. In LME modeling, 406 and 343 metabolites associated with eGFR and UPCR at FDR <0.05, respectively. Among 530 follow-up person-visits, 158 metabolites showed differences over time at FDR <0.05. For participants with complete data at both follow-up visits (n =123), we report 35 metabolites with ∆level–∆eGFR associations significant at FDR <0.05. There were no metabolites with significant ∆level–∆UPCR associations at FDR <0.05. We report 16 metabolites with ∆level–∆UPCR associations at P < 0.05 and associations with UPCR in LME modeling at FDR <0.05.

Conclusions

We characterized longitudinal plasma metabolomic patterns associated with eGFR and UPCR in a large pediatric CKD population. Many of these metabolite signals have been associated with CKD progression, etiology, and proteinuria in previous CKD Biomarkers Consortium studies. There were also novel metabolite associations with eGFR and proteinuria detected.

Relationships and Mendelian Randomization of Gut Microbe-Derived Metabolites with Metabolic Syndrome Traits in the METSIM Cohort

The role of gut microbe-derived metabolites in the development of metabolic syndrome (MetS) remains unclear. This study aimed to evaluate the associations of gut microbe-derived metabolites and MetS traits in the cross-sectional Metabolic Syndrome In Men (METSIM) study. The sample included 10,194 randomly related men (age 57.65 ± 7.12 years) from Eastern Finland. Levels of 35 metabolites were tested for associations with 13 MetS traits using lasso and stepwise regression. Significant associations were observed between multiple MetS traits and 32 metabolites, three of which exhibited particularly robust associations. N-acetyltryptophan was positively associated with Homeostatic Model Assessment for Insulin Resistant (HOMA-IR) (β = 0.02, p = 0.033), body mass index (BMI) (β = 0.025, p = 1.3 × 10-16), low-density lipoprotein cholesterol (LDL-C) (β = 0.034, p = 5.8 × 10-10), triglyceride (0.087, p = 1.3 × 10-16), systolic (β = 0.012, p = 2.5 × 10-6) and diastolic blood pressure (β = 0.011, p = 3.4 × 10-6). In addition, 3-(4-hydroxyphenyl) lactate yielded the strongest positive associations among all metabolites, for example, with HOMA-IR (β = 0.23, p = 4.4 × 10-33), and BMI (β = 0.097, p = 5.1 × 10-52). By comparison, 3-aminoisobutyrate was inversely associated with HOMA-IR (β = -0.19, p = 3.8 × 10-51) and triglycerides (β = -0.12, p = 5.9 × 10-36). Mendelian randomization analyses did not provide evidence that the observed associations with these three metabolites represented causal relationships. We identified significant associations between several gut microbiota-derived metabolites and MetS traits, consistent with the notion that gut microbes influence metabolic homeostasis, beyond traditional risk factors.

Circulating Metabolomic Associations with Neurocognitive Outcomes in Pediatric CKD

BACKGROUND

Children with CKD are at risk for impaired neurocognitive functioning. We investigated metabolomic associations with neurocognition in children with CKD.

METHODS

We leveraged data from the Chronic Kidney Disease in Children (CKiD) study and the Neurocognitive Assessment and Magnetic Resonance Imaging Analysis of Children and Young Adults with Chronic Kidney Disease (NiCK) study. CKiD is a multi-institutional cohort that enrolled children aged 6 months to 16 years with eGFR 30-90 ml/min per 1.73 m 2 ( n =569). NiCK is a single-center cross-sectional study of participants aged 8-25 years with eGFR<90 ml/min per 1.73 m 2 ( n =60) and matched healthy controls ( n =67). Untargeted metabolomic quantification was performed on plasma (CKiD, 622 metabolites) and serum (NiCK, 825 metabolites) samples. Four neurocognitive domains were assessed: intelligence, attention regulation, working memory, and parent ratings of executive function. Repeat assessments were performed in CKiD at 2-year intervals. Linear regression and linear mixed-effects regression analyses adjusting for age, sex, delivery history, hypertension, proteinuria, CKD duration, and glomerular versus nonglomerular diagnosis were used to identify metabolites associated with neurocognitive z-scores. Analyses were performed with and without adjustment for eGFR.

RESULTS

There were multiple metabolite associations with neurocognition observed in at least two of the analytic samples (CKiD baseline, CKiD follow-up, and NiCK CKD). Most of these metabolites were significantly elevated in children with CKD compared with healthy controls in NiCK. Notable signals included associations with parental ratings of executive function: phenylacetylglutamine, indoleacetylglutamine, and trimethylamine N-oxide-and with intelligence: γ -glutamyl amino acids and aconitate.

CONCLUSIONS

Several metabolites were associated with neurocognitive dysfunction in pediatric CKD, implicating gut microbiome-derived substances, mitochondrial dysfunction, and altered energy metabolism, circulating toxins, and redox homeostasis.

PODCAST

This article contains a podcast at https://dts.podtrac.com/redirect.mp3/www.asn-online.org/media/podcast/CJASN/2023_11_17_CJN0000000000000318.mp3.

Diet Quality and the Fecal Microbiota in Adults in the American Gut Project

BACKGROUND

The Dietary Guidelines for Americans advises on dietary intake to meet nutritional needs, promote health, and prevent diseases. Diet affects the intestinal microbiota and is increasingly linked to health. It is vital to investigate the relationships between diet quality and the microbiota to better understand the impact of nutrition on human health.

OBJECTIVES

This study aimed to investigate the differences in fecal microbiota composition in adults from the American Gut Project based on their adherence to the Dietary Guidelines for Americans.

METHODS

This study was a cross-sectional analysis of the 16S sequencing and food frequency data of a subset of adults (n = 432; age = 18-60 y; 65% female, 89% white) participating in the crowdsourced American Gut Project. The Healthy Eating Index-2015 assessed the compliance with Dietary Guideline recommendations. The cohort was divided into tertiles based on Healthy Eating Index-2015 scores, and differences in taxonomic abundances and diversity were compared between high and low scorers.

RESULTS

The mean Total Score for low-scoring adults (58.1 ± 5.4) was comparable with the reported score of the average American adult (56.7). High scorers for the Total Score and components related to vegetables, grains, and dairy had greater alpha diversity than low scorers. High scorers in the fatty acid component had a lower alpha diversity than low scorers (95% CI: 0.35, 1.85). A positive log-fold difference in abundance of plant carbohydrate-metabolizing taxa in the families Lachnospiraceae and Ruminococcaceae was observed in high-scoring tertiles for Total Score, vegetable, fruit, and grain components (Benjamini-Hochberg; q < 0.05).

CONCLUSIONS

Adults with greater compliance to the Dietary Guidelines demonstrated higher diversity in their fecal microbiota and greater abundance of bacteria capable of metabolizing complex carbohydrates, providing evidence on how Dietary Guidelines support the gut microbiota.

Can Ordering Groceries Online Support Diet Quality in Adults Who Live in Low Food Access and Low-Income Environments?

During the COVID-19 pandemic, U.S. food assistance programs allowed the use of program benefits to order groceries online. We examined relationships between the food environment, food assistance, online grocery ordering, and diet quality among adults from one low-income, low food access community in Northeastern Connecticut during the pandemic. Via online survey, adults (n = 276) reported their perceived home and store food environments, food assistance participation, whether they ordered groceries online, and consumption frequency and liking of foods/beverages to calculate diet quality indices. Those who ordered groceries online (44.6%) were more likely to participate in food assistance programs and report greater diet quality. Perceived healthiness of store and home food environments was variable, with the ease of obtaining and selecting unhealthy foods in the neighborhood significantly greater than healthy foods. Healthier perceived home food environments were associated with significantly higher diet qualities, especially among individuals who participated in multiple food assistance programs. Ordering groceries online interacted with multiple measures of the food environment to influence diet quality. Generally, the poorest diet quality was observed among individuals who perceived their store and home food environments as least healthy and who did not order groceries online. Thus, ordering groceries online may support higher diet quality among adults who can use their food assistance for purchasing groceries online and who live in low-income, low-access food environments.

Holistic Integration of Omics Tools for Precision Nutrition in Health and Disease

The combination of multiple omics approaches has emerged as an innovative holistic scope to provide a more comprehensive view of the molecular and physiological events underlying human diseases (including obesity, dyslipidemias, fatty liver, insulin resistance, and inflammation), as well as for elucidating unique and specific metabolic phenotypes. These omics technologies include genomics (polymorphisms and other structural genetic variants), epigenomics (DNA methylation, histone modifications, long non-coding RNA, telomere length), metagenomics (gut microbiota composition, enterotypes), transcriptomics (RNA expression patterns), proteomics (protein quantities), and metabolomics (metabolite profiles), as well as interactions with dietary/nutritional factors. Although more evidence is still necessary, it is expected that the incorporation of integrative omics could be useful not only for risk prediction and early diagnosis but also for guiding tailored dietary treatments and prognosis schemes. Some challenges include ethical and regulatory issues, the lack of robust and reproducible results due to methodological aspects, the high cost of omics methodologies, and high-dimensional data analyses and interpretation. In this review, we provide examples of system biology studies using multi-omics methodologies to unravel novel insights into the mechanisms and pathways connecting the genotype to clinically relevant traits and therapy outcomes for precision nutrition applications in health and disease.

Lactobacillus plantarum Ameliorates High-Carbohydrate Diet-Induced Hepatic Lipid Accumulation and Oxidative Stress by Upregulating Uridine Synthesis

The overconsumption of carbohydrates induces oxidative stress and lipid accumulation in the liver, which can be alleviated by modulation of intestinal microbiota; however, the underlying mechanism remains unclear. Here, we demonstrated that a strain affiliated with Lactobacillus plantarum (designed as MR1) efficiently attenuated lipid deposition, oxidative stress, as well as inflammatory response, which are caused by high-carbohydrate diet (HC) in fish with poor utilization ability of carbohydrates. Serum untargeted metabolome analysis indicated that pyrimidine metabolism was the significantly changed pathway among the groups. In addition, the content of serum uridine was significantly decreased in the HC group compared with the control group, while it increased by supplementation with L. plantarum MR1. Further analysis showed that addition of L. plantarum MR1 reshaped the composition of gut microbiota and increased the content of intestinal acetate. In vitro experiment showed that sodium acetate could induce the synthesis of uridine in hepatocytes. Furthermore, we proved that uridine could directly ameliorate oxidative stress and decrease liver lipid accumulation in the hepatocytes. In conclusion, this study indicated that probiotic L. plantarum MR1 ameliorated high-carbohydrate diet-induced hepatic lipid accumulation and oxidative stress by increasing the circulating uridine, suggesting that intestinal microbiota can regulate the metabolism of nucleotides to maintain host physiological homeostasis.

Obesity influences composition of salivary and fecal microbiota and impacts the interactions between bacterial taxa

Obesity is an increasing global health concern and is associated with a broad range of morbidities. The gut microbiota are increasingly recognized as important contributors to obesity and cardiometabolic health. This study aimed to characterize oral and gut microbial communities, and evaluate host: microbiota interactions between clinical obesity classifications. We performed 16S rRNA sequencing on fecal and salivary samples, global metabolomics profiling on plasma and stool samples, and dietary profiling in 135 healthy individuals. We grouped individuals by obesity status, based on body mass index (BMI), including lean (BMI 18-124.9), overweight (BMI 25-29.9), or obese (BMI ≥30). We analyzed differences in microbiome composition, community inter-relationships, and predicted microbial function by obesity status. We found that salivary bacterial communities of lean and obese individuals were compositionally and phylogenetically distinct. An increase in obesity status was positively associated with strong correlations between bacterial taxa, particularly with bacterial groups implicated in metabolic disorders including Fretibacterium, and Tannerella. Consumption of sweeteners, especially xylitol, significantly influenced compositional and phylogenetic diversities of salivary and fecal bacterial communities. In addition, obesity groups exhibited differences in predicted bacterial metabolic activity, which was correlated with host's metabolite concentrations. Overall, obesity was associated with distinct changes in bacterial community dynamics, particularly in saliva. Consideration of microbiome community structure and inclusion of salivary samples may improve our ability to understand pathways linking microbiota to obesity and cardiometabolic disease.

Using Machine Learning to Identify Metabolomic Signatures of Pediatric Chronic Kidney Disease Etiology

BACKGROUND

Untargeted plasma metabolomic profiling combined with machine learning (ML) may lead to discovery of metabolic profiles that inform our understanding of pediatric CKD causes. We sought to identify metabolomic signatures in pediatric CKD based on diagnosis: FSGS, obstructive uropathy (OU), aplasia/dysplasia/hypoplasia (A/D/H), and reflux nephropathy (RN).

METHODS

Untargeted metabolomic quantification (GC-MS/LC-MS, Metabolon) was performed on plasma from 702 Chronic Kidney Disease in Children study participants (n: FSGS=63, OU=122, A/D/H=109, and RN=86). Lasso regression was used for feature selection, adjusting for clinical covariates. Four methods were then applied to stratify significance: logistic regression, support vector machine, random forest, and extreme gradient boosting. ML training was performed on 80% total cohort subsets and validated on 20% holdout subsets. Important features were selected based on being significant in at least two of the four modeling approaches. We additionally performed pathway enrichment analysis to identify metabolic subpathways associated with CKD cause.

RESULTS

ML models were evaluated on holdout subsets with receiver-operator and precision-recall area-under-the-curve, F1 score, and Matthews correlation coefficient. ML models outperformed no-skill prediction. Metabolomic profiles were identified based on cause. FSGS was associated with the sphingomyelin-ceramide axis. FSGS was also associated with individual plasmalogen metabolites and the subpathway. OU was associated with gut microbiome-derived histidine metabolites.

CONCLUSION

ML models identified metabolomic signatures based on CKD cause. Using ML techniques in conjunction with traditional biostatistics, we demonstrated that sphingomyelin-ceramide and plasmalogen dysmetabolism are associated with FSGS and that gut microbiome-derived histidine metabolites are associated with OU.