Metabolic profiles from two different breakfast meals characterized by 1H NMR-based metabolomics

Metabolomic Analysis of Human Cirrhosis and Hepatocellular Carcinoma: A Pilot Study

BACKGROUND

Molecular changes in HCC development are largely unknown. As the liver plays a fundamental role in the body's metabolism, metabolic changes are to be expected.

AIMS

We aimed to identify metabolomic changes in HCC in comparison to liver cirrhosis (LC) patients, which could potentially serve as novel biomarkers for HCC diagnosis and prognosis.

METHODS

Metabolite expression from 38 HCC from the SORAMIC trial and 32 LC patients were analyzed by mass spectrometry. Metabolites with significant differences between LC and HCC at baseline were analyzed regarding expression over follow-up. In addition, association with overall survival was tested using univariate Cox proportional-hazard analysis.

RESULTS

41 metabolites showed differential expression between LC and HCC patients. 14 metabolites demonstrated significant changes in HCC patients during follow-up. Campesterol, lysophosphatidylcholine, octadecenoic and octadecadienoic acid, and furoylglycine showed a differential expression in the local ablation vs. palliative care group. High expression of eight metabolites (octadecenoic acid, 2-hydroxybutyrate, myo-inositol, isocitrate, erythronic acid, creatinine, pseudouridine, and erythrol) were associated with poor overall survival. The association between poor OS and octadecenoic acid and creatinine remained statistically significant even after adjusting for tumor burden and LC severity.

CONCLUSION

Our findings give promising insides into the metabolic changes during HCC carcinogenesis and provide candidate biomarkers for future studies. Campesterol and furoylglycine in particular were identified as possible biomarkers for HCC progression. Moreover, eight metabolites were detected as predictors for poor overall survival.

Assessing Adherence to Healthy Dietary Habits Through the Urinary Food Metabolome: Results From a European Two-Center Study

Background

Diet is one of the most important modifiable lifestyle factors in human health and in chronic disease prevention. Thus, accurate dietary assessment is essential for reliably evaluating adherence to healthy habits.

Objectives

The aim of this study was to identify urinary metabolites that could serve as robust biomarkers of diet quality, as assessed through the Alternative Healthy Eating Index (AHEI-2010).

Design

We set up two-center samples of 160 healthy volunteers, aged between 25 and 50, living as a couple or family, with repeated urine sampling and dietary assessment at baseline, and 6 and 12 months over a year. Urine samples were subjected to large-scale metabolomics analysis for comprehensive quantitative characterization of the food-related metabolome. Then, lasso regularized regression analysis and limma univariate analysis were applied to identify those metabolites associated with the AHEI-2010, and to investigate the reproducibility of these associations over time.

Results

Several polyphenol microbial metabolites were found to be positively associated with the AHEI-2010 score; urinary enterolactone glucuronide showed a reproducible association at the three study time points [false discovery rate (FDR): 0.016, 0.014, 0.016]. Furthermore, other associations were found between the AHEI-2010 and various metabolites related to the intake of coffee, red meat and fish, whereas other polyphenol phase II metabolites were associated with higher AHEI-2010 scores at one of the three time points investigated (FDR < 0.05 or β ≠ 0).

Conclusion

We have demonstrated that urinary metabolites, and particularly microbiota-derived metabolites, could serve as reliable indicators of adherence to healthy dietary habits.

Clinical Trail Registration

www.ClinicalTrials.gov, Identifier: NCT03169088.

Association of egg consumption, metabolic markers, and risk of cardiovascular diseases: A nested case-control study

Background

Few studies have assessed the role of individual plasma cholesterol levels in the association between egg consumption and the risk of cardiovascular diseases. This research aims to simultaneously explore the associations of self-reported egg consumption with plasma metabolic markers and these markers with the risk of cardiovascular disease (CVD).

Methods

Totally 4778 participants (3401 CVD cases subdivided into subtypes and 1377 controls) aged 30-79 were selected based on the China Kadoorie Biobank. Targeted nuclear magnetic resonance was used to quantify 225 metabolites in baseline plasma samples. Linear regression was conducted to assess associations between self-reported egg consumption and metabolic markers, which were further compared with associations between metabolic markers and CVD risk.

Results

Egg consumption was associated with 24 out of 225 markers, including positive associations for apolipoprotein A1, acetate, mean HDL diameter, and lipid profiles of very large and large HDL, and inverse associations for total cholesterol and cholesterol esters in small VLDL. Among these 24 markers, 14 were associated with CVD risk. In general, the associations of egg consumption with metabolic markers and of these markers with CVD risk showed opposite patterns.

Conclusions

In the Chinese population, egg consumption is associated with several metabolic markers, which may partially explain the protective effect of moderate egg consumption on CVD.

Funding

This work was supported by the National Natural Science Foundation of China (81973125, 81941018, 91846303, 91843302). The CKB baseline survey and the first re-survey were supported by a grant from the Kadoorie Charitable Foundation in Hong Kong. The long-term follow-up is supported by grants (2016YFC0900500, 2016YFC0900501, 2016YFC0900504, 2016YFC1303904) from the National Key R&D Program of China, National Natural Science Foundation of China (81390540, 81390541, 81390544), and Chinese Ministry of Science and Technology (2011BAI09B01). The funders had no role in the study design, data collection, data analysis and interpretation, writing of the report, or the decision to submit the article for publication.

Biomarkers of meat and seafood intake: an extensive literature review

Meat, including fish and shellfish, represents a valuable constituent of most balanced diets. Consumption of different types of meat and fish has been associated with both beneficial and adverse health effects. While white meats and fish are generally associated with positive health outcomes, red and especially processed meats have been associated with colorectal cancer and other diseases. The contribution of these foods to the development or prevention of chronic diseases is still not fully elucidated. One of the main problems is the difficulty in properly evaluating meat intake, as the existing self-reporting tools for dietary assessment may be imprecise and therefore affected by systematic and random errors. Dietary biomarkers measured in biological fluids have been proposed as possible objective measurements of the actual intake of specific foods and as a support for classical assessment methods. Good biomarkers for meat intake should reflect total dietary intake of meat, independent of source or processing and should be able to differentiate meat consumption from that of other protein-rich foods; alternatively, meat intake biomarkers should be specific to each of the different meat sources (e.g., red vs. white; fish, bird, or mammal) and/or cooking methods. In this paper, we present a systematic investigation of the scientific literature while providing a comprehensive overview of the possible biomarker(s) for the intake of different types of meat, including fish and shellfish, and processed and heated meats according to published guidelines for biomarker reviews (BFIrev). The most promising biomarkers are further validated for their usefulness for dietary assessment by published validation criteria.

A metabolite-based machine learning approach to diagnose Alzheimer-type dementia in blood: Results from the European Medical Information Framework for Alzheimer disease biomarker discovery cohort

Introduction

Machine learning (ML) may harbor the potential to capture the metabolic complexity in Alzheimer Disease (AD). Here we set out to test the performance of metabolites in blood to categorize AD when compared to CSF biomarkers.

Methods

This study analyzed samples from 242 cognitively normal (CN) people and 115 with AD-type dementia utilizing plasma metabolites (n = 883). Deep Learning (DL), Extreme Gradient Boosting (XGBoost) and Random Forest (RF) were used to differentiate AD from CN. These models were internally validated using Nested Cross Validation (NCV).

Results

On the test data, DL produced the AUC of 0.85 (0.80–0.89), XGBoost produced 0.88 (0.86–0.89) and RF produced 0.85 (0.83–0.87). By comparison, CSF measures of amyloid, p-tau and t-tau (together with age and gender) produced with XGBoost the AUC values of 0.78, 0.83 and 0.87, respectively.

Discussion

This study showed that plasma metabolites have the potential to match the AUC of well-established AD CSF biomarkers in a relatively small cohort. Further studies in independent cohorts are needed to validate whether this specific panel of blood metabolites can separate AD from controls, and how specific it is for AD as compared with other neurodegenerative disorders.

The combined use of 1 H and 2D NMR-based metabolomics and chemometrics for non-targeted screening of biomarkers and identification of reconstituted milk

BACKGROUND

The illegal undeclared addition of reconstituted milk powder to ultra-heat treated (UHT) milk to lower production costs is an example of economically motivated adulteration. This activity not only defrauds consumers but also places honest traders at a disadvantage, which could damage the reputation of milk producers and reduce the integrity of the markets. In this research, a non-targeted analytical strategy that combines proton (¹ H) nuclear magnetic resonance (NMR) spectroscopy with a chemometrics data mining tool was developed for the authentication of bovine UHT milk.

RESULTS

Unsupervised principal component analysis was used to distinguish UHT and tap-water-reconstituted powdered milk. Partial least squares-discriminant analysis (PLS-DA) with R² (Y) and Q² equal to 0.859 and 0.748, respectively, was used to differentiate UHT and reconstituted milk samples. Three compounds were selected as biomarkers to distinguish UHT and reconstituted milk and identified according to the standard NMR-spectra database. Finally, a PLS-DA model was established, according to the characteristic spectral bands, to identify UHT milk and reconstituted milk.

CONCLUSION

This procedure demonstrated the feasibility of using non-targeted NMR profiling combined with chemometric analysis to combat mislabeling and fraudulent practices in milk production. © 2019 Society of Chemical Industry.

NMR Spectroscopy for Metabolomics Research

Over the past two decades, nuclear magnetic resonance (NMR) has emerged as one of the three principal analytical techniques used in metabolomics (the other two being gas chromatography coupled to mass spectrometry (GC-MS) and liquid chromatography coupled with single-stage mass spectrometry (LC-MS)). The relative ease of sample preparation, the ability to quantify metabolite levels, the high level of experimental reproducibility, and the inherently nondestructive nature of NMR spectroscopy have made it the preferred platform for long-term or large-scale clinical metabolomic studies. These advantages, however, are often outweighed by the fact that most other analytical techniques, including both LC-MS and GC-MS, are inherently more sensitive than NMR, with lower limits of detection typically being 10 to 100 times better. This review is intended to introduce readers to the field of NMR-based metabolomics and to highlight both the advantages and disadvantages of NMR spectroscopy for metabolomic studies. It will also explore some of the unique strengths of NMR-based metabolomics, particularly with regard to isotope selection/detection, mixture deconvolution via 2D spectroscopy, automation, and the ability to noninvasively analyze native tissue specimens. Finally, this review will highlight a number of emerging NMR techniques and technologies that are being used to strengthen its utility and overcome its inherent limitations in metabolomic applications.

The 1H NMR serum metabolomics response to a two meal challenge: a cross-over dietary intervention study in healthy human volunteers

BACKGROUND

Metabolomics represents a powerful tool for exploring modulation of the human metabolome in response to food intake. However, the choice of multivariate statistical approach is not always evident, especially for complex experimental designs with repeated measurements per individual. Here we have investigated the serum metabolic responses to two breakfast meals: an egg and ham based breakfast and a cereal based breakfast using three different multivariate approaches based on the Projections to Latent Structures framework.

METHODS

In a cross over design, 24 healthy volunteers ate the egg and ham breakfast and cereal breakfast on four occasions each. Postprandial serum samples were subjected to metabolite profiling using ¹H nuclear magnetic resonance spectroscopy and metabolites were identified using 2D nuclear magnetic resonance spectroscopy. Metabolic profiles were analyzed using Orthogonal Projections to Latent Structures with Discriminant Analysis and Effect Projections and ANOVA-decomposed Projections to Latent Structures.

RESULTS

The Orthogonal Projections to Latent Structures with Discriminant Analysis model correctly classified 92 and 90% of the samples from the cereal breakfast and egg and ham breakfast, respectively, but confounded dietary effects with inter-personal variability. Orthogonal Projections to Latent Structures with Effect Projections removed inter-personal variability and performed perfect classification between breakfasts, however at the expense of comparing means of respective breakfasts instead of all samples. ANOVA-decomposed Projections to Latent Structures managed to remove inter-personal variability and predicted 99% of all individual samples correctly. Proline, tyrosine, and N-acetylated amino acids were found in higher concentration after consumption of the cereal breakfast while creatine, methanol, and isoleucine were found in higher concentration after the egg and ham breakfast.

CONCLUSIONS

Our results demonstrate that the choice of statistical method will influence the results and adequate methods need to be employed to manage sample dependency and repeated measurements in cross-over studies. In addition, ¹H nuclear magnetic resonance serum metabolomics could reproducibly characterize postprandial metabolic profiles and identify discriminatory metabolites largely reflecting dietary composition.

TRIAL REGISTRATION

Registered with ClinicalTrials.gov, identifier: NCT02039596 . Date of registration: January 17, 2014.

Recent Advances in the Application of Metabolomics for Nutrition and Health

Metabolomics is the study of small molecules called metabolites in biological samples. Application of metabolomics to nutrition research has expanded in recent years, with emerging literature supporting multiple applications. Key examples include applications of metabolomics in the identification and development of objective biomarkers of dietary intake, in developing personalized nutrition strategies, and in large-scale epidemiology studies to understand the link between diet and health. In this review, we provide an overview of the current applications and identify key challenges that need to be addressed for the further development of the field. Successful development of metabolomics for nutrition research has the potential to improve dietary assessment, help deliver personalized nutrition, and enhance our understanding of the link between diet and health.

Role of metabolomics in identification of biomarkers related to food intake

Dietary assessment methods including FFQ and food diaries are associated with many measurement errors including energy under-reporting and incorrect estimation of portion sizes. Such errors can lead to inconsistent results especially when investigating the relationship between food intake and disease causation. To improve the classification of a person's dietary intake and therefore clarify proposed links between diet and disease, reliable and accurate dietary assessment methods are essential. Dietary biomarkers have emerged as a complementary approach to the traditional methods, and in recent years, metabolomics has developed as a key technology for the identification of new dietary biomarkers. The objective of this review is to give an overview of the approaches used for the identification of biomarkers and potential use of the biomarkers. Over the years, a number of strategies have emerged for the discovery of dietary biomarkers including acute and medium term interventions and cross-sectional/cohort study approaches. Examples of the different approaches will be presented. Concomitant with the focus on single biomarkers of specific foods, there is an interest in the development of biomarker signatures for the identification of dietary patterns. In the present review, we present an overview of the techniques used in food intake biomarker discover, including the experimental approaches used and challenges faced in the field. While significant progress has been achieved in the field of dietary biomarkers in recent years, a number of challenges remain. Addressing these challenges will be key to ensure success in implementing use of dietary biomarkers.

High-Throughput Metabolomics by 1D NMR

Metabolomics deals with the whole ensemble of metabolites (the metabolome). As one of the -omic sciences, it relates to biology, physiology, pathology and medicine; but metabolites are chemical entities, small organic molecules or inorganic ions. Therefore, their proper identification and quantitation in complex biological matrices requires a solid chemical ground. With respect to for example, DNA, metabolites are much more prone to oxidation or enzymatic degradation: we can reconstruct large parts of a mammoth's genome from a small specimen, but we are unable to do the same with its metabolome, which was probably largely degraded a few hours after the animal's death. Thus, we need standard operating procedures, good chemical skills in sample preparation for storage and subsequent analysis, accurate analytical procedures, a broad knowledge of chemometrics and advanced statistical tools, and a good knowledge of at least one of the two metabolomic techniques, MS or NMR. All these skills are traditionally cultivated by chemists. Here we focus on metabolomics from the chemical standpoint and restrict ourselves to NMR. From the analytical point of view, NMR has pros and cons but does provide a peculiar holistic perspective that may speak for its future adoption as a population-wide health screening technique.

Metabolomic signature of the postprandial experience

BACKGROUND

Ingestion of a meal up to maximal tolerance induces unpleasant fullness sensation and changes in circulating metabolites. Our aim was to evaluate the relation between postprandial sensations and the metabolomic responses to a comfort meal.

METHODS

In 32 non-obese healthy men, homeostatic sensations (hunger/satiety, fullness), hedonic sensations (digestive well-being, mood), and the metabolomic profile in plasma (low-molecular weight metabolites and lipoprotein profiles) were measured before and 20 minutes after a comfort meal (warm ham and cheese sandwich and juice; total 300 mL; 425 kcal). Perception was measured on 10 cm scales and the metabolomic response by nuclear magnetic resonance spectroscopy.

KEY RESULTS

The comfort meal induced homeostatic sensations (satiety and fullness) associated with a positive hedonic reward (enhanced digestive well-being and mood) and a clear change in the metabolomic profile with a sharp discrimination between the pre and postprandial state by a non-supervised principal component analysis. The change in circulating metabolites correlated with the postprandial sensations: the increase in alanine correlated with the increase in fullness (R = 0.50; P = 0.004) and well-being (R = 0.50; P = 0.004); the increase in glucose correlated with the sensation of fullness (R = 0.40; P = 0.023) and enhanced mood (R = 0.41; P = 0.020).

CONCLUSION AND INFERENCES

Metabolomic changes in the response to a meal may provide an objective index of the postprandial experience, which may have clinical implications in the management of patients with poor meal tolerance or meal-related symptoms.

Nuclear Magnetic Resonance (NMR) Spectroscopy in Food Science: A Comprehensive Review

Nuclear magnetic resonance (NMR) spectroscopy is a robust method, which can rapidly analyze mixtures at the molecular level without requiring separation and/or purification steps, making it ideal for applications in food science. Despite its increasing popularity among food scientists, NMR is still an underutilized methodology in this area, mainly due to its high cost, relatively low sensitivity, and the lack of NMR expertise by many food scientists. The aim of this review is to help bridge the knowledge gap that may exist when attempting to apply NMR methodologies to the field of food science. We begin by covering the basic principles required to apply NMR to the study of foods and nutrients. A description of the discipline of chemometrics is provided, as the combination of NMR with multivariate statistical analysis is a powerful approach for addressing modern challenges in food science. Furthermore, a comprehensive overview of recent and key applications in the areas of compositional analysis, food authentication, quality control, and human nutrition is provided. In addition to standard NMR techniques, more sophisticated NMR applications are also presented, although limitations, gaps, and potentials are discussed. We hope this review will help scientists gain some of the knowledge required to apply the powerful methodology of NMR to the rich and diverse field of food science.

Recent Advances in Targeted and Untargeted Metabolomics by NMR and MS/NMR Methods

Metabolomics has made significant progress in multiple fronts in the last 18 months. This minireview aimed to give an overview of these advancements in the light of their contribution to targeted and untargeted metabolomics. New computational approaches have emerged to overcome the manual absolute quantitation step of metabolites in one-dimensional (1D) ¹H nuclear magnetic resonance (NMR) spectra. This provides more consistency between inter-laboratory comparisons. Integration of two-dimensional (2D) NMR metabolomics databases under a unified web server allowed for very accurate identification of the metabolites that have been catalogued in these databases. For the remaining uncatalogued and unknown metabolites, new cheminformatics approaches have been developed by combining NMR and mass spectrometry (MS). These hybrid MS/NMR approaches accelerated the identification of unknowns in untargeted studies, and now they are allowing for profiling ever larger number of metabolites in application studies.

Precision Nutrition: A Review of Personalized Nutritional Approaches for the Prevention and Management of Metabolic Syndrome

The translation of the growing increase of findings emerging from basic nutritional science into meaningful and clinically relevant dietary advices represents nowadays one of the main challenges of clinical nutrition. From nutrigenomics to deep phenotyping, many factors need to be taken into account in designing personalized and unbiased nutritional solutions for individuals or population sub-groups. Likewise, a concerted effort among basic, clinical scientists and health professionals will be needed to establish a comprehensive framework allowing the implementation of these new findings at the population level. In a world characterized by an overwhelming increase in the prevalence of obesity and associated metabolic disturbances, such as type 2 diabetes and cardiovascular diseases, tailored nutrition prescription represents a promising approach for both the prevention and management of metabolic syndrome. This review aims to discuss recent works in the field of precision nutrition analyzing most relevant aspects affecting an individual response to lifestyle/nutritional interventions. Latest advances in the analysis and monitoring of dietary habits, food behaviors, physical activity/exercise and deep phenotyping will be discussed, as well as the relevance of novel applications of nutrigenomics, metabolomics and microbiota profiling. Recent findings in the development of precision nutrition are highlighted. Finally, results from published studies providing examples of new avenues to successfully implement innovative precision nutrition approaches will be reviewed.