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

Pro-vegetarian dietary pattern and risk of breast cancer: a case-control study

BACKGROUND

There are a few conflicting results from studies assessing the association between plant-based diets, particularly pro-vegetarian dietary pattern (PDP), and breast cancer (BC) incidence. Therefore, this study aimed to investigate the association between PDP and BC odds in the Iranian population.

METHODS

This case-control study was conducted on 134 women with BC and 265 without cancer (control). Participants were selected from two referral hospitals in Tehran, Iran. Also, a validated food frequency questionnaire was used to collect food information. Logistic regression was used to assess the association between PDP and BC and the association between PDP and BC by menopausal status.

RESULTS

It was observed that in two models of logistic regression, the chance of BC was lower in the second and last tertile (T) than in the first tertile of PDP (model 1-T2: odds ratio (OR) = 0.39; 95% confidence interval (CI): 0.23-0.67; P = 0.001, and T3: OR = 0.43; 95% CI: 0.26-0.73; P = 0.002-model 2: T2: OR = 0.42; 95% CI: 0.24-0.74; P = 0.003, and T3: OR = 0.49; 95% CI: 0.27-0.88; P = 0.017). Also, according to menopausal status, the odds of developing BC in post-menopausal women in the second and last tertile of PDP was significantly lower than the first tertile in both logistic regression models.

CONCLUSIONS

The findings revealed that Iranian women who followed PDP had a lower chance of developing BC. Also, we found that a diet high in plant-based foods and low in animal products is beneficial for reducing BC odds, particularly for post-menopausal women.

Causality of Genetically Determined Metabolites on Chronic Kidney Disease: A Two-Sample Mendelian Randomization Study In Silico

Introduction: Chronic kidney disease (CKD) is associated with metabolic disorders. However, the evidence for the causality of circulating metabolites to promote or prevent CKD is still lacking. Methods: The two-sample Mendelian randomization (MR) analysis was conducted to evaluate the latent causal relationship between the genetically proxied 486 blood metabolites and CKD. Genome-wide association study (GWAS) data for exposures were derived from 7824 European GWAS on metabolite levels, which have been extensively utilized in the medical field to elucidate the mechanisms underlying disease onset and progression. The random inverse variance weighted (IVW) is the primary analysis for causality analysis while MR-Egger and weighted median as complementary analyses. For the further identification of metabolites, reverse MR and linkage disequilibrium score regression were performed for further evaluation. The drug target for N-acetylornithine was subsequently supplemented into the analysis, with MR and colocalization analysis being utilized. Key metabolic pathways were identified via MetaboAnalyst 4.0 (https://www.metaboanalyst.ca/) online website. Results: N-acetylornithine was identified as a reliable metabolite that increases the susceptibility to estimated glomerular filtration rate (eGFR) decrease (β = 0.047; 95% confidence interval: -0.068 to -0.026; PIVW = 1.5E-5). The "glyoxylate and dicarboxylate metabolism" pathway showed significant relevance to CKD development (P = 6E-4), whereas the "glycine, serine, and threonine metabolism" pathway was also recognized as associated with CKD by general practitioners (P = 7E-4). Colocalization analysis revealed a robust genetic link between N-acetylornithine and both CKD and eGFR, with 85.1% and 99.4% colocalization rates, respectively. IVW-MR analysis substantiated these findings with a significant positive association for CKD (odds ratio = 1.43, P = 4.7E-5) and a negative correlation with eGFR (b = -0.04, P = 1.13E-31). Conclusions: MR was utilized to explore the potential causal links between 61 genetic serum metabolites and CKD. N-acetylornithine and NAT8 were further explored as a potential therapeutic target for CKD treatment.

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.

Metabolic implications of amino acid metabolites in chronic kidney disease progression: a metabolomics analysis using OPLS-DA and MBRole2.0 database

BACKGROUND

As chronic kidney disease (CKD) progresses, metabolites undergo diverse transformations. Nevertheless, the impact of these metabolic changes on the etiology, progression, and prognosis of CKD remains uncertain. Our objective is to conduct a metabolomics analysis to scrutinize metabolites and identify significant metabolic pathways implicated in CKD progression, thereby pinpointing potential therapeutic targets for CKD management.

METHODS

We recruited 145 patients with CKD and determined their mGFR by measuring the plasma iohexol clearance, whereupon we partitioned them into four groups based on their mGFR values. Non-targeted metabolomics analysis was conducted using UPLC-MS/MS assays. Differential metabolites were identified via one-way ANOVA, PCA, PLS-DA, and OPLS-DA analyses employing the MetaboAnalyst 5.0 platform. Ultimately, we performed differential metabolite pathway enrichment analysis, using both the MetaboAnalyst 5.0 platform and the MBRole2.0 database.

RESULTS

According to the findings of the MBRole2.0 and MetaboAnalyst 5.0 enrichment analysis, six amino acid metabolism pathways were discovered to have significant roles in the progression of CKD, with the glycine, serine, and threonine metabolism pathway being the most prominent. The latter enriched 14 differential metabolites, of which six decreased while two increased concomitantly with renal function deterioration.

CONCLUSIONS

The metabolic analysis unveiled that glycine, serine, and threonine metabolism plays a pivotal role in the progression of CKD. Specifically, glycine was found to increase while serine decreased with the deterioration of CKD.

Advances in the management of chronic kidney disease

Chronic kidney disease (CKD) represents a global public health crisis, but awareness by patients and providers is poor. Defined as persistent abnormalities in kidney structure or function for more than three months, manifested as either low glomerular filtration rate or presence of a marker of kidney damage such as albuminuria, CKD can be identified through readily available blood and urine tests. Early recognition of CKD is crucial for harnessing major advances in staging, prognosis, and treatment. This review discusses the evidence behind the general principles of CKD management, such as blood pressure and glucose control, renin-angiotensin-aldosterone system blockade, statin therapy, and dietary management. It additionally describes individualized approaches to treatment based on risk of kidney failure and cause of CKD. Finally, it reviews novel classes of kidney protective agents including sodium-glucose cotransporter-2 inhibitors, glucagon-like peptide-1 receptor agonists, non-steroidal selective mineralocorticoid receptor antagonists, and endothelin receptor antagonists. Appropriate, widespread implementation of these highly effective therapies should improve the lives of people with CKD and decrease the worldwide incidence of kidney failure.

Nutritional metabolomics: Recent developments and future needs

Metabolomics has rapidly been adopted as one of the key methods in nutrition research. This review focuses on the recent developments and updates in the field, including the analytical methodologies that encompass improved instrument sensitivity, sampling techniques and data integration (multiomics). Metabolomics has advanced the discovery and validation of dietary biomarkers and their implementation in health research. Metabolomics has come to play an important role in the understanding of the role of small molecules resulting from the diet-microbiota interactions when gut microbiota research has shifted towards improving the understanding of the activity and functionality of gut microbiota rather than composition alone. Currently, metabolomics plays an emerging role in precision nutrition and the recent developments therein are discussed.

Causality Investigation between Gut Microbiota, Derived Metabolites, and Obstructive Sleep Apnea: A Bidirectional Mendelian Randomization Study

Various studies have highlighted the important associations between obstructive sleep apnea (OSA) and gut microbiota and related metabolites. Nevertheless, the establishment of causal relationships between these associations remains to be determined. Multiple mendelian randomization (MR) analyses were performed to genetically predict the causative impact of 196 gut microbiota and 83 metabolites on OSA. Two-sample MR was used to assess the potential association, and causality was evaluated using inverse variance weighted (IVW), MR-Egger, and weighted median (WM) methods. Multivariable MR (MVMR) was employed to ascertain the causal independence between gut microbiota and the metabolites linked to OSA. Additionally, Cochran's Q test, the MR Egger intercept test and the MR Steiger test were used for the sensitivity analyses. The analysis of the 196 gut microbiota revealed that genus_Ruminococcaceae (UCG009) (PIVW = 0.010) and genus_Subdoligranulum (PIVW = 0.041) were associated with an increased risk of OSA onset. Conversely, Family_Ruminococcaceae (PIVW = 0.030), genus_Coprococcus2 (PWM = 0.025), genus_Eggerthella (PIVW = 0.011), and genus_Eubacterium (xylanophilum_group) (PIVW = 0.001) were negatively related to the risk of OSA. Among the 83 metabolites evaluated, 3-dehydrocarnitine, epiandrosterone sulfate, and leucine were determined to be potential independent risk factors associated with OSA. Moreover, the reverse MR analysis demonstrated a suggestive association between OSA exposure and six microbiota taxa. This study offers compelling evidence regarding the potential beneficial or detrimental causative impact of the gut microbiota and its associated metabolites on OSA risk, thereby providing new insights into the mechanisms of gut microbiome-mediated OSA development.

Utilising the precision nutrition toolkit in the path towards precision medicine

The overall aim of precision nutrition is to replace the 'one size fits all' approach to dietary advice with recommendations that are more specific to the individual in order to improve the prevention or management of chronic disease. Interest in precision nutrition has grown with advancements in technologies such as genomics, proteomics, metabolomics and measurement of the gut microbiome. Precision nutrition initiatives have three major applications in precision medicine. First, they aim to provide more 'precision' dietary assessments through artificial intelligence, wearable devices or by employing omic technologies to characterise diet more precisely. Secondly, precision nutrition allows us to understand the underlying mechanisms of how diet influences disease risk and identify individuals who are more susceptible to disease due to gene-diet or microbiota-diet interactions. Third, precision nutrition can be used for 'personalised nutrition' advice where machine-learning algorithms can integrate data from omic profiles with other personal and clinical measures to improve disease risk. Proteomics and metabolomics especially provide the ability to discover new biomarkers of food or nutrient intake, proteomic or metabolomic signatures of diet and disease, and discover potential mechanisms of diet-disease interactions. Although there are several challenges that must be overcome to improve the reproducibility, cost-effectiveness and efficacy of these approaches, precision nutrition methodologies have great potential for nutrition research and clinical application.

Plant foods, healthy plant-based diets, and type 2 diabetes: a review of the evidence

Type 2 diabetes (T2D) is a metabolic chronic disease in which insulin resistance and insufficient insulin production lead to elevated blood glucose levels. The prevalence of T2D is growing worldwide, mainly due to obesity and the adoption of Western diets. Replacing animal foods with healthy plant foods is associated with a lower risk of T2D in prospective studies. In randomized controlled trials, the consumption of healthy plant foods in place of animal foods led to cardiometabolic improvements in patients with T2D or who were at high risk of the disease. Dietary patterns that limit or exclude animal foods and focus on healthy plant foods (eg, fruits, vegetables, whole grains, nuts, legumes), known as healthy, plant-based diets, are consistently associated with a lower risk of T2D in cohort studies. The aim of this review is to examine the differential effects of plant foods and animal foods on T2D risk and to describe the existing literature about the role of healthy, plant-based diets, particularly healthy vegan diets, in T2D prevention and management. The evidence from cohort studies and randomized controlled trials will be reported, in addition to the potential biological mechanisms that seem to be involved.

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).

Nutritional Epidemiology and Dietary Assessment for Patients With Kidney Disease: A Primer

Nutritional epidemiology seeks to understand nutritional determinants of disease in human populations using experimental and observational study designs. Though randomized controlled trials provide the strongest evidence of causality, the expense and difficulty of sustaining adherence to dietary interventions are substantial barriers to investigating dietary determinants of kidney disease. Therefore, nutritional epidemiology commonly employs observational study designs, particularly prospective cohort studies, to investigate long-term associations between dietary exposures and kidney disease. Due to the covarying nature and synergistic effects of dietary components, holistic characterizations of dietary exposures that simultaneously consider patterns of foods and nutrients regularly consumed are generally more relevant to disease etiology than single nutrients or foods. Dietary intakes have traditionally been self-reported and are subject to bias. Statistical methods including energy adjustment and regression calibration can reduce random and systematic measurement errors associated with self-reported diet. Novel approaches that assess diet more objectively are gaining popularity but have not yet fully replaced self-report and require refinement and validation in populations with chronic kidney disease. More accurate and frequent diet assessment in existing and future studies will yield evidence to better personalize dietary recommendations for the prevention and treatment of kidney disease.