Association of the habitual dietary intake with the fatty liver index and effect modification by metabotypes in the population-based KORA-Fit study

BACKGROUND

Non-alcoholic fatty liver disease (NAFLD) is an emerging threat for public health with diet being a major risk factor in disease development and progression. However, the effects of habitual food consumption on fatty liver are still inconclusive as well as the proposed role of the individuals' metabolic profiles. Therefore, the aim of our study is to examine the associations between diet and NAFLD with an emphasis on the influence of specific metabotypes in the general population.

METHODS

A total of 689 participants (304 men and 385 women) of the KORA-Fit (S4) survey, a follow-up study of the population-based KORA cohort study running in the Region of Augsburg, Germany, were included in this analysis. Dietary information was derived from repeated 24-h food lists and a food frequency questionnaire. The intake of energy and energy-providing nutrients were calculated using the national food composition database. The presence of fatty liver was quantified by the fatty liver index (FLI), and metabotypes were calculated using K-means clustering. Multivariable linear regression models were used for the analysis of habitual food groups and FLI; for the evaluation of macronutrients, energy substitution models were applied.

RESULTS

A higher consumption of nuts and whole grains, and a better diet quality (according to Alternate Healthy Eating Index and Mediterranean Diet Score) were associated with lower FLI values, while the intake of soft drinks, meat, fish and eggs were associated with a higher FLI. The isocaloric substitution of carbohydrates with polyunsaturated fatty acids was associated with a decreased FLI, while substitution with monounsaturated fatty acids and protein showed increased FLI. Statistically significant interactions with the metabotype were observed for most food groups.

CONCLUSION

The consumption of plant-based food groups, including nuts and whole grains, and diet quality, were associated with lower FLI values, whereas the intake of soft drinks and products of animal origin (meat, fish, eggs) were associated with a higher FLI. The observed statistically significant interactions with the metabotype for most food groups could help to develop targeted prevention strategies on a population-based level if confirmed in independent prospective studies.

Distinct patterns of personalised dietary advice delivered by a metabotype framework similarly improve dietary quality and metabolic health parameters: secondary analysis of a randomised controlled trial

Background

In a 12-week randomised controlled trial, personalised nutrition delivered using a metabotype framework improved dietary intake, metabolic health parameters and the metabolomic profile compared to population-level dietary advice. The objective of the present work was to investigate the patterns of dietary advice delivered during the intervention and the alterations in dietary intake and metabolic and metabolomic profiles to obtain further insights into the effectiveness of the metabotype framework.

Methods

Forty-nine individuals were randomised into the intervention group and subsequently classified into metabotypes using four biomarkers (triacylglycerol, HDL-C, total cholesterol, glucose). These individuals received personalised dietary advice from decision tree algorithms containing metabotypes and individual characteristics. In a secondary analysis of the data, patterns of dietary advice were identified by clustering individuals according to the dietary messages received and clusters were compared for changes in dietary intake and metabolic health parameters. Correlations between changes in blood clinical chemistry and changes in metabolite levels were investigated.

Results

Two clusters of individuals with distinct patterns of dietary advice were identified. Cluster 1 had the highest percentage of messages delivered to increase the intake of beans and pulses and milk and dairy products. Cluster 2 had the highest percentage of messages delivered to limit the intake of foods high in added sugar, high-fat foods and alcohol. Following the intervention, both patterns improved dietary quality assessed by the Alternate Mediterranean Diet Score and the Alternative Healthy Eating Index, nutrient intakes, blood pressure, triacylglycerol and LDL-C (p ≤ 0.05). Several correlations were identified between changes in total cholesterol, LDL-C, triacylglycerol, insulin and HOMA-IR and changes in metabolites levels, including mostly lipids (sphingomyelins, lysophosphatidylcholines, glycerophosphocholines and fatty acid carnitines).

Conclusion

The findings indicate that the metabotype framework effectively personalises and delivers dietary advice to improve dietary quality and metabolic health.

Clinical trial registration

isrctn.com, identifier ISRCTN15305840.

Associations between dietary intake and glucose tolerance in clinical and metabolomics-based metabotypes

BACKGROUND

Metabotyping is a novel concept to group metabolically similar individuals. Different metabotypes may respond differently to dietary interventions; hence, metabotyping may become an important future tool in precision nutrition strategies. However, it is not known if metabotyping based on comprehensive omic data provides more useful identification of metabotypes compared to metabotyping based on only a few clinically relevant metabolites.

AIM

This study aimed to investigate if associations between habitual dietary intake and glucose tolerance depend on metabotypes identified from standard clinical variables or comprehensive nuclear magnetic resonance (NMR) metabolomics.

METHODS

We used cross-sectional data from participants recruited through advertisements aimed at people at risk of type 2 diabetes mellitus (n = 203). Glucose tolerance was assessed with a 2-h oral glucose tolerance test (OGTT), and habitual dietary intake was recorded with a food frequency questionnaire. Lipoprotein subclasses and various metabolites were quantified with NMR spectroscopy, and plasma carotenoids were quantified using high-performance liquid chromatography. We divided participants into favorable and unfavorable clinical metabotypes based on established cutoffs for HbA1c and fasting and 2-h OGTT glucose. Favorable and unfavorable NMR metabotypes were created using k-means clustering of NMR metabolites.

RESULTS

While the clinical metabotypes were separated by glycemic variables, the NMR metabotypes were mainly separated by variables related to lipoproteins. A high intake of vegetables was associated with a better glucose tolerance in the unfavorable, but not the favorable clinical metabotype (interaction, p = 0.01). This interaction was confirmed using plasma concentrations of lutein and zeaxanthin, objective biomarkers of vegetable intake. Although non-significantly, the association between glucose tolerance and fiber intake depended on the clinical metabotypes, while the association between glucose tolerance and intake of saturated fatty acids and dietary fat sources depended on the NMR metabotypes.

CONCLUSION

Metabotyping may be a useful tool to tailor dietary interventions that will benefit specific groups of individuals. The variables that are used to create metabotypes will affect the association between dietary intake and disease risk.

Metabotyping: a tool for identifying subgroups for tailored nutrition advice

Diet-related diseases are the leading cause of death globally and strategies to tailor effective nutrition advice are required. Personalised nutrition advice is increasingly recognised as more effective than population-level advice to improve dietary intake and health outcomes. A potential tool to deliver personalised nutrition advice is metabotyping which groups individuals into homogeneous subgroups (metabotypes) using metabolic profiles. In summary, metabotyping has been successfully employed in human nutrition research to identify subgroups of individuals with differential responses to dietary challenges and interventions and diet–disease associations. The suitability of metabotyping to identify clinically relevant subgroups is corroborated by other fields such as diabetes research where metabolic profiling has been intensely used to identify subgroups of patients that display patterns of disease progression and complications. However, there is a paucity of studies examining the efficacy of the approach to improve dietary intake and health parameters. While the application of metabotypes to tailor and deliver nutrition advice is very promising, further evidence from randomised controlled trials is necessary for further development and acceptance of the approach.

Machine Learning in Nutrition Research

Data currently generated in the field of nutrition are becoming increasingly complex and high-dimensional, bringing with them new methods of data analysis. The characteristics of machine learning (ML) make it suitable for such analysis and thus lend itself as an alternative tool to deal with data of this nature. ML has already been applied in important problem areas in nutrition, such as obesity, metabolic health, and malnutrition. Despite this, experts in nutrition are often without an understanding of ML, which limits its application and therefore potential to solve currently open questions. The current article aims to bridge this knowledge gap by supplying nutrition researchers with a resource to facilitate the use of ML in their research. ML is first explained and distinguished from existing solutions, with key examples of applications in the nutrition literature provided. Two case studies of domains in which ML is particularly applicable, precision nutrition and metabolomics, are then presented. Finally, a framework is outlined to guide interested researchers in integrating ML into their work. By acting as a resource to which researchers can refer, we hope to support the integration of ML in the field of nutrition to facilitate modern research.

Metabolomics profiles of premenopausal women are different based on O-desmethylangolensin metabotype

Urinary O-desmethylangolensin (ODMA) concentrations provide a functional gut microbiome marker of dietary isoflavone daidzein metabolism to ODMA. Individuals who do not have gut microbial environments that produce ODMA have less favourable cardiometabolic and cancer risk profiles. Urinary metabolomics profiles were evaluated in relation to ODMA metabotypes within and between individuals over time. Secondary analysis of data was conducted from the BEAN2 trial, which was a cross-over study of premenopausal women consuming 6 months on a high and a low soya diet, each separated by a 1-month washout period. In all of the 672 samples in the study, sixty-six of the eighty-four women had the same ODMA metabotype at seven or all eight time points. Two or four urine samples per woman were selected based on temporal metabotypes in order to compare within and across individuals. Metabolomics assays for primary metabolism and biogenic amines were conducted in sixty urine samples from twenty women. Partial least-squares discriminant analysis was used to compare metabolomics profiles. For the same ODMA metabotype across different time points, no profile differences were detected. For changes in metabotype within individuals and across individuals with different metabotypes, distinct metabolomes emerged. Influential metabolites (variables importance in projection score > 2) included several phenolic compounds, carnitine and derivatives, fatty acid and amino acid metabolites and some medications. Based on the distinct metabolomes of producers v. non-producers, the ODMA metabotype may be a marker of gut microbiome functionality broadly involved in nutrient and bioactive metabolism and should be evaluated for relevance to precision nutrition initiatives.

A Clustering Approach to Meal-Based Analysis of Dietary Intakes Applied to Population and Individual Data

BACKGROUND

Examination of meal intakes can elucidate the role of individual meals or meal patterns in health not evident by examining nutrient and food intakes. To date, meal-based research has been limited to focus on population rather than individual intakes, without considering portions or nutrient content when characterizing meals.

OBJECTIVES

We aimed to characterize meals commonly consumed, incorporating portions and nutritional content, and to determine the accuracy of nutrient intake estimates using these meals at both population and individual levels.

METHODS

The 2008-2010 Irish National Adult Nutrition Survey (NANS) data were used. A total of 1500 participants, with a mean ± SD age of 44.5 ± 17.0 y and BMI of 27.1 ± 5.0 kg/m2, recorded their intake using a 4-d weighed food diary. Food groups were identified using k-means clustering. Partitioning around the medoids clustering was used to categorize similar meals into groups (generic meals) based on their Nutrient Rich Foods Index (NRF9.3) score and the food groups that they contained. The nutrient content for each generic meal was defined as the mean content of the grouped meals. Seven standard portion sizes were defined for each generic meal. Mean daily nutrient intakes were estimated using the original and the generic data.

RESULTS

The 27,336 meals consumed were aggregated to 63 generic meals. Effect sizes from the comparisons of mean daily nutrient intakes (from the original compared with generic meals) were negligible or small, with P values ranging from <0.001 to 0.941. When participants were classified according to nutrient-based guidelines (high, adequate, or low), the proportion of individuals who were classified into the same category ranged from 55.3% to 91.5%.

CONCLUSIONS

A generic meal-based method can estimate nutrient intakes based on meal rather than food intake at the sample population and individual levels. Future work will focus on incorporating this concept into a meal-based dietary intake assessment tool.

Evaluation of the metabotype concept after intervention with oral glucose tolerance test and dietary fiber-enriched food: An enable study

BACKGROUND AND AIMS

Evidence suggests that people react differently to the same diet due to inter-individual differences. However, few studies have investigated variation in response to dietary interventions based on individuals' baseline metabolic characteristics. This study aims to examine the differential reaction of metabotype subgroups to an OGTT and a dietary fiber intervention.

METHODS AND RESULTS

We assigned 356 healthy participants of an OGTT sub-study and a 12-week dietary fiber intervention sub-study within the enable cluster to three metabotype subgroups previously identified in the KORA F4 study population. To explore the association between plasma glucose level and metabotype subgroups, we used linear mixed models adjusted for age, sex, and physical activity. Individuals in different metabotype subgroups showed differential responses to OGTT. Compared to the healthy metabotype (metabotype 1), participants in intermediate metabotype (metabotype 2) and unfavorable metabotype (metabotype 3) had significantly higher plasma glucose concentrations at 120 min after glucose bolus (β = 7.881, p = 0.005; β = 32.79, p < 0.001, respectively). Additionally, the linear regression model showed that the Area under the curve (AUC) of plasma glucose concentrations was significantly different across the metabotype subgroups. The associations between metabotype subgroups and metabolic parameters among fiber intervention participants remained insignificant in the multivariate-adjusted linear model. However, the metabotype 3 had the highest mean reduction in insulin, cholesterol parameters (TC, LDLc, and non-HDLc), and systolic and diastolic blood pressure at the end of the intervention period.

CONCLUSION

This study supports the use of the metabotype concept to identify metabolically similar subgroups and to develop targeted dietary interventions at the metabotype subgroup level for the primary prevention of diet-related diseases.

Optimized Metabotype Definition Based on a Limited Number of Standard Clinical Parameters in the Population-Based KORA Study

The aim of metabotyping is to categorize individuals into metabolically similar groups. Earlier studies that explored metabotyping used numerous parameters, which made it less transferable to apply. Therefore, this study aimed to identify metabotypes based on a set of standard laboratory parameters that are regularly determined in clinical practice. K-means cluster analysis was used to group 3001 adults from the KORA F4 cohort into three clusters. We identified the clustering parameters through variable importance methods, without including any specific disease endpoint. Several unique combinations of selected parameters were used to create different metabotype models. Metabotype models were then described and evaluated, based on various metabolic parameters and on the incidence of cardiometabolic diseases. As a result, two optimal models were identified: a model composed of five parameters, which were fasting glucose, HDLc, non-HDLc, uric acid, and BMI (the metabolic disease model) for clustering; and a model that included four parameters, which were fasting glucose, HDLc, non-HDLc, and triglycerides (the cardiovascular disease model). These identified metabotypes are based on a few common parameters that are measured in everyday clinical practice. These metabotypes are cost-effective, and can be easily applied on a large scale in order to identify specific risk groups that can benefit most from measures to prevent cardiometabolic diseases, such as dietary recommendations and lifestyle interventions.

Comprehensive Human Milk Patterns Are Related to Infant Growth and Allergy in the CHMP Study

SCOPE

The aim of the present study is to identify human milk pattern using multi-omics datasets and to explore association between patterns, infant growth, and allergy using data from the Chinese Human Milk Project (CHMP) study.

METHODS AND RESULTS

Three patterns are identified from integrative analysis of proteome, lipidome, and glycome profiles of 143 mature human milk samples. Factor 1 is positively associated with 128 proteins, phospholipids, and human milk oligosaccharides (HMOs) including lacto N-neohexaose (LNnH) and lacto-N-difucohexaose II (LNDFH II); factor 2 is negatively associated with as₁ -casein, phospholipids while positively associates with HMOs including LNnH, lactosialyl tetrasaccharide c (LSTc), and 2'-fucosyllactose (2'FL); factor 3 is positively associated with lysophospholipids while negatively associates with 27 proteins, triglycerides with two saturated fatty acids, 6'-sialyllactose (6'SL) and 2'FL. In general, factor 1 and factor 2 are associated with slower while factor 3 is associated with faster growth rate (p < 0.044). One unit higher in loadings of factor 2 is associated with 34% lower risk of allergies (p ≤ 0.017). Associations are not significant after adjustment for city except for factor 1.

CONCLUSIONS

Three possible human milk patterns with varying degree of stability are identified. Future work is needed to understand these patterns in terms of generalization, biologic mechanisms, and genotype influences.

Classifying Individuals Into a Dietary Pattern Based on Metabolomic Data

SCOPE

The objectives are to develop a metabolomic-based model capable of classifying individuals into dietary patterns and to investigate the reproducibility of the model.

METHODS AND RESULTS

K-means cluster analysis is employed to derive dietary patterns using metabolomic data. Differences across the dietary patterns are examined using nutrient biomarkers. The model is used to assign individuals to a dietary pattern in an independent cohort, A-DIET Confirm (n = 175) at four time points. The stability of participants to a dietary pattern is assessed. Four dietary patterns are derived: moderately unhealthy, convenience, moderately healthy, and prudent. The moderately unhealthy and convenience patterns has lower adherence to the alternative healthy eating index (AHEI) and the alternative mediterranean diet score (AMDS) compared to the moderately healthy and prudent patterns (AHEI = 24.5 and 22.9 vs 26.7 and 28.4, p < 0.001). The dietary patterns are replicated in A-DIET Confirm, with good reproducibility across four time points. The stability of participants' dietary pattern membership ranged from 25.0% to 61.5%.

CONCLUSION

The multivariate model classifies individuals into dietary patterns based on metabolomic data. In an independent cohort, the model classifies individuals into dietary patterns at multiple time points furthering the potential of such an approach for nutrition research.

Optimisation of a metabotype approach to deliver targeted dietary advice

BACKGROUND

Targeted nutrition is defined as dietary advice tailored at a group level. Groups known as metabotypes can be identified based on individual metabolic profiles. Metabotypes have been associated with differential responses to diet, which support their use to deliver dietary advice. We aimed to optimise a metabotype approach to deliver targeted dietary advice by encompassing more specific recommendations on nutrient and food intakes and dietary behaviours.

METHODS

Participants (n = 207) were classified into three metabotypes based on four biomarkers (triacylglycerol, total cholesterol, HDL-cholesterol and glucose) and using a k-means cluster model. Participants in metabotype-1 had the highest average HDL-cholesterol, in metabotype-2 the lowest triacylglycerol and total cholesterol, and in metabotype-3 the highest triacylglycerol and total cholesterol. For each participant, dietary advice was assigned using decision trees for both metabotype (group level) and personalised (individual level) approaches. Agreement between methods was compared at the message level and the metabotype approach was optimised to incorporate messages exclusively assigned by the personalised approach and current dietary guidelines. The optimised metabotype approach was subsequently compared with individualised advice manually compiled.

RESULTS

The metabotype approach comprised advice for improving the intake of saturated fat (69% of participants), fibre (66%) and salt (18%), while the personalised approach assigned advice for improving the intake of folate (63%), fibre (63%), saturated fat (61%), calcium (34%), monounsaturated fat (24%) and salt (14%). Following the optimisation of the metabotype approach, the most frequent messages assigned to address intake of key nutrients were to increase the intake of fruit and vegetables, beans and pulses, dark green vegetables, and oily fish, to limit processed meats and high-fat food products and to choose fibre-rich carbohydrates, low-fat dairy and lean meats (60-69%). An average agreement of 82.8% between metabotype and manual approaches was revealed, with excellent agreements in metabotype-1 (94.4%) and metabotype-3 (92.3%).

CONCLUSIONS

The optimised metabotype approach proved capable of delivering targeted dietary advice for healthy adults, being highly comparable with individualised advice. The next step is to ascertain whether the optimised metabotype approach is effective in changing diet quality.

Perspective: Metabotyping-A Potential Personalized Nutrition Strategy for Precision Prevention of Cardiometabolic Disease

Diet is an important, modifiable lifestyle factor of cardiometabolic disease risk, and an improved diet can delay or even prevent the onset of disease. Recent evidence suggests that individuals could benefit from diets adapted to their genotype and phenotype: that is, personalized nutrition. A novel strategy is to tailor diets for groups of individuals according to their metabolic phenotypes (metabotypes). Randomized controlled trials evaluating metabotype-specific responses and nonresponses are urgently needed to bridge the current gap of knowledge with regard to the efficacy of personalized strategies in nutrition. In this Perspective, we discuss the concept of metabotyping, review the current literature on metabotyping in the context of cardiometabolic disease prevention, and suggest potential strategies for metabotype-based nutritional advice for future work. We also discuss potential determinants of metabotypes, including gut microbiota, and highlight the use of metabolomics to define effective markers for cardiometabolic disease-related metabotypes. Moreover, we hypothesize that people at high risk for cardiometabolic diseases have distinct metabotypes and that individuals grouped into specific metabotypes may respond differently to the same diet, which is being tested in a project of the Joint Programming Initiative: A Healthy Diet for a Healthy Life.

Stratifying nutritional restriction in cancer therapy: Next stop, personalized medicine

Dietary interventions combined with cancer drugs represent a clinically valid polytherapy. In particular nutrient restriction (NR) in the form of varied fasting or caloric restriction regimens holds great clinical promise, conceptually due to the voracious anabolic appetite of cancer cells. This metabolic dependency is driven by a strong selective pressure to increasingly acquire biomass of a proliferating tumor and can be therapeutically exploited as vulnerability. A host of preclinical data suggest that NR can potentiate the efficacy of, or alleviate resistance to, cancer drugs. However, complicating clinical implementation are the many variables involved, such as host biology, cancer stage and type, oncogenic mutation landscape, tumor heterogeneity, variations in treatment modalities, and patient compliance to NR protocols. This calls for systematic preclinical screens and co-clinical studies to predict effective combinations of NR with cancer drugs and to allow for patient stratification regarding responsiveness to polytherapy. Such screen-and-stratify pipelines should consider tumor heterogeneity as well as the role of immune effectors in the tumor microenvironment and may lead to biomarker discovery advancing the oncology field toward personalized options with improved translatability to clinical settings. This opinion-based review provides a critical overview of recent literature investigating NR for cancer treatment, pinpoints limitations of current studies, and suggests standardizations and refinements for future studies and trials. The proposed measures aim to increase the translational value of preclinical data and effectively harness the vast potential of NR as adjuvant for cancer therapy.

A gene-diet interaction-based score predicts response to dietary fat in the Women's Health Initiative

BACKGROUND

Although diet response prediction for cardiometabolic risk factors (CRFs) has been demonstrated using single genetic variants and main-effect genetic risk scores, little investigation has gone into the development of genome-wide diet response scores.

OBJECTIVE

We sought to leverage the multistudy setup of the Women's Health Initiative cohort to generate and test genetic scores for the response of 6 CRFs (BMI, systolic blood pressure, LDL cholesterol, HDL cholesterol, triglycerides, and fasting glucose) to dietary fat.

METHODS

A genome-wide interaction study was undertaken for each CRF in women (n ∼ 9000) not participating in the dietary modification (DM) trial, which focused on the reduction of dietary fat. Genetic scores based on these analyses were developed using a pruning-and-thresholding approach and tested for the prediction of 1-y CRF changes as well as long-term chronic disease development in DM trial participants (n ∼ 5000).

RESULTS

Only 1 of these genetic scores, for LDL cholesterol, predicted changes in the associated CRF. This 1760-variant score explained 3.7% (95% CI: 0.09, 11.9) of the variance in 1-y LDL cholesterol changes in the intervention arm but was unassociated with changes in the control arm. In contrast, a main-effect genetic risk score for LDL cholesterol was not useful for predicting dietary fat response. Further investigation of this score with respect to downstream disease outcomes revealed suggestive differential associations across DM trial arms, especially with respect to coronary heart disease and stroke subtypes.

CONCLUSIONS

These results lay the foundation for the combination of many genome-wide gene-diet interactions for diet response prediction while highlighting the need for further research and larger samples in order to achieve robust biomarkers for use in personalized nutrition.

Evaluation of the Metabotype Concept Identified in an Irish Population in the German KORA Cohort Study

SCOPE

Previous work identified three metabolically homogeneous subgroups of individuals ("metabotypes") using k-means cluster analysis based on fasting serum levels of triacylglycerol, total cholesterol, HDL cholesterol, and glucose. The aim is to reproduce these findings and describe metabotype groups by dietary habits and by incident disease occurrence.

METHODS AND RESULTS

1744 participants from the KORA F4 study and 2221 participants from the KORA FF4 study are assigned to the three metabotype clusters previously identified by minimizing the Euclidean distances. In both KORA studies, the assignment of participants results in three metabolically distinct clusters, with cluster 3 representing the group of participants with the most unfavorable metabolic characteristics. Individuals of cluster 3 are further characterized by the highest incident disease occurrence during follow-up; they also reveal the most unfavorable diet with significantly lowest intakes of vegetables, dairy products, and fibers, and highest intakes of total, red, and processed meat.

CONCLUSION

The three metabotypes originally identified in an Irish population are successfully reproduced. In addition to this validation approach, the observed differences in disease incidence across metabotypes represent an important new finding that strongly supports the metabotyping approach as a tool for risk stratification.

The Relationship between Fish Intake and Urinary Trimethylamine-N-Oxide

SCOPE

Fish intake is reported to be associated with certain health benefits; however, accurate assessment of fish intake is still problematic. The objective of this study is to identify fish intake biomarkers and examine relationships with health parameters in a free-living population.

METHODS AND RESULTS

In the NutriTech study, ten participants randomized into the fish group consume increasing quantities of fish for 3 days per week for 3 weeks. Urine is analyzed by NMR spectroscopy. Trimethylamine-N-oxide (TMAO), dimethylamine, and dimethyl sulfone are identified and display significant dose-response with intake (p < 0.05). Fish consumption yields a greater increase in urinary TMAO compared to red meat. Biomarker-derived fish intake is calculated in the National Adult Nutrition Survey cross-sectional study. However, the correlation between fish intake and TMAO (r = 0.148, p < 0.01) and that between fish intake and calculated fish intake (r = 0.142, p < 0.01) are poor. In addition, TMAO shows significantly positive correlation with serum insulin and insulin resistance in males and the relationship is more pronounced for males with high dietary fat intake.

CONCLUSION

Urinary TMAO displays a strong dose-response relationship with fish intake; however, use of TMAO alone is insufficient to determine fish intake in a free-living population.

The Role of the Gut Microbiome in Predicting Response to Diet and the Development of Precision Nutrition Models. Part II: Results

The gut microbiota is increasingly implicated in the health and metabolism of its human host. The host's diet is a major component influencing the composition and function of the gut microbiota, and mounting evidence suggests that the composition and function of the gut microbiota influence the host's metabolic response to diet. This effect of the gut microbiota on personalized dietary response is a growing focus of precision nutrition research and may inform the effort to tailor dietary advice to the individual. Because the gut microbiota has been shown to be malleable to some extent, it may also allow for therapeutic alterations of the gut microbiota in order to alter response to certain dietary components. This article is the second in a 2-part review of the current research in the field of precision nutrition incorporating the gut microbiota into studies investigating interindividual variability in response to diet. Part I reviews the methods used by researchers to design and carry out such studies as well as analyze the results subsequently obtained. Part II reviews the findings of these studies and discusses the gaps in our current knowledge and directions for future research. The studies reviewed provide the current understanding in this field of research and a foundation from which we may build, utilizing and expanding upon the methods and results they present to inform future studies.

Metabotyping and its role in nutrition research

Personalised nutrition is at its simplest form the delivery of dietary advice at an individual level. Incorporating response to different diets has resulted in the concept of precision nutrition. Harnessing the metabolic phenotype to identify subgroups of individuals that respond differentially to dietary interventions is becoming a reality. More specifically, the classification of individuals in subgroups according to their metabolic profile is defined as metabotyping and this approach has been employed to successfully identify differential response to dietary interventions. Furthermore, the approach has been expanded to develop a framework for the delivery of targeted nutrition. The present review examines the application of the metabotype approach in nutrition research with a focus on developing personalised nutrition. Application of metabotyping in longitudinal studies demonstrates that metabotypes can be associated with cardiometabolic risk factors and diet-related diseases while application in interventions can identify metabotypes with differential responses. In general, there is strong evidence that metabolic phenotyping is a promising strategy to identify groups at risk and to potentially improve health promotion at a population level. Future work should verify if targeted nutrition can change behaviours and have an impact on health outcomes.

Modifying effect of metabotype on diet-diabetes associations

Purpose

Inter-individual metabolic differences may be a reason for previously inconsistent results in diet–diabetes associations. We aimed to investigate associations between dietary intake and diabetes for metabolically homogeneous subgroups (‘metabotypes’) in a large cross-sectional study.

Methods

We used data of 1517 adults aged 38–87 years from the German population-based KORA FF4 study (2013/2014). Dietary intake was estimated based on the combination of a food frequency questionnaire and multiple 24-h food lists. Glucose tolerance status was classified based on an oral glucose tolerance test in participants without a previous diabetes diagnosis using American Diabetes Association criteria. Logistic regression was applied to examine the associations between dietary intake and diabetes for two distinct metabotypes, which were identified based on 16 biochemical and anthropometric parameters.

Results

A low intake of fruits and a high intake of total meat, processed meat and sugar-sweetened beverages (SSB) were significantly associated with diabetes in the total study population. Stratified by metabotype, associations with diabetes remained significant for intake of total meat (OR 1.67, 95% CI 1.04–2.67) and processed meat (OR 2.23, 95% CI 1.24–4.04) in the metabotypes with rather favorable metabolic characteristics, and for intake of fruits (OR 0.83, 95% CI 0.68–0.99) and SSB (OR:1.21, 95% CI 1.09–1.35) in the more unfavorable metabotype. However, only the association between SSB intake and diabetes differed significantly by metabotype (p value for interaction = 0.01).

Conclusions

Our findings suggest an influence of metabolic characteristics on diet–diabetes associations, which may help to explain inconsistent previous results. The causality of the observed associations needs to be confirmed in prospective and intervention studies.

Electronic supplementary material

The online version of this article (10.1007/s00394-019-01988-5) contains supplementary material, which is available to authorized users.

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.

Are All Breast-fed Infants Equal? Clustering Metabolomics Data to Identify Predictive Risk Clusters for Childhood Obesity

OBJECTIVES

Fetal and early life represent a period of developmental plasticity during which metabolic pathways are modified by environmental and nutritional cues. Little is known on the pathways underlying this multifactorial complex. We explored whether 6 months old breast-fed infants could be clustered into metabolically similar groups and that those metabotypes could be used to predict later obesity risk.

METHODS

Plasma samples were obtained from 183 breast-fed infants aged 6 months participating in the European multicenter Childhood Obesity Project study. We measured amino acids along with polar lipid concentrations (acylcarnitines, lysophosphatidylcholines, phosphatidylcholines, sphingomyelins). We determined the metabotypes using a Bayesian agglomerative clustering method and investigated the properties of these clusters with respect to clinical, programming, and metabolic factors up to 6 years of age.

RESULTS

We identified 20 metabolite clusters comprising 1 to 39 children. Phosphatidylcholines predominantly influenced the clustering process. In the largest clusters (n ≥ 14), large differences existed for birth length (unadjusted P < 0.0001) and length and weight at 6 months (unadjusted P < 0.0001 and P = 0.012, respectively). Infants tended to cluster together by country (unadjusted P < 0.001). The body mass index (BMI) z score at 6 years of age tended to differ (unadjusted P = 0.07).

CONCLUSIONS

Our exploratory study provided evidence that breast-fed infants are not metabolically homogeneous and that variation in metabolic profiles among infants may provide insight into later development and health. This work highlights the potential of metabotypes for identifying inter-individual differences that may form the basis for developing personalized early preventive strategies.

Identification of Comprehensive Metabotypes Associated with Cardiometabolic Diseases in the Population-Based KORA Study

SCOPE

"Metabotyping" describes the grouping of metabolically similar individuals. We aimed to identify valid metabotypes in a large cohort for targeted dietary intervention, for example, for disease prevention.

METHODS AND RESULTS

We grouped 1729 adults aged 32-77 years of the German population-based KORA F4 study (2006-2008) using k-means cluster analysis based on 34 biochemical and anthropometric parameters. We identified three metabolically distinct clusters showing significantly different biochemical parameter concentrations. Cardiometabolic disease status was determined at baseline in the F4 study and at the 7 year follow-up termed FF4 (2013/2014) to compare disease prevalence and incidence between clusters. Cluster 3 showed the most unfavorable marker profile with the highest prevalence of cardiometabolic diseases. Also, disease incidence was higher in cluster 3 compared to clusters 2 and 1, respectively, for hypertension (41.2%/25.3%/18.2%), type 2 diabetes (28.3%/5.1%/2.0%), hyperuricemia/gout (10.8%/2.3%/0.7%), dyslipidemia (19.2%/18.3%/5.6%), all metabolic (54.5%/36.8%/19.7%), and all cardiovascular (6.3%/5.5%/2.3%) diseases together.

CONCLUSION

Cluster analysis based on an extensive set of biochemical and anthropometric parameters allows the identification of comprehensive metabotypes that were distinctly different in cardiometabolic disease occurrence. As a next step, targeted dietary strategies should be developed with the goal of preventing diseases, especially in cluster 3.

Non-targeted metabolomic biomarkers and metabotypes of type 2 diabetes: A cross-sectional study of PREDIMED trial participants

AIM

To characterize the urinary metabolomic fingerprint and multi-metabolite signature associated with type 2 diabetes (T2D), and to classify the population into metabotypes related to T2D.

METHODS

A metabolomics analysis using the ¹H-NMR-based, non-targeted metabolomic approach was conducted to determine the urinary metabolomic fingerprint of T2D compared with non-T2D participants in the PREDIMED trial. The discriminant metabolite fingerprint was subjected to logistic regression analysis and ROC analyses to establish and to assess the multi-metabolite signature of T2D prevalence, respectively. Metabotypes associated with T2D were identified using the k-means algorithm.

RESULTS

A total of 33 metabolites were significantly different (P<0.05) between T2D and non-T2D participants. The multi-metabolite signature of T2D comprised high levels of methylsuccinate, alanine, dimethylglycine and guanidoacetate, and reduced levels of glutamine, methylguanidine, 3-hydroxymandelate and hippurate, and had a 96.4% AUC, which was higher than the metabolites on their own and glucose. Amino-acid and carbohydrate metabolism were the main metabolic alterations in T2D, and various metabotypes were identified in the studied population. Among T2D participants, those with a metabotype of higher levels of phenylalanine, phenylacetylglutamine, p-cresol and acetoacetate had significantly higher levels of plasma glucose.

CONCLUSION

The multi-metabolite signature of T2D highlights the altered metabolic fingerprint associated mainly with amino-acid, carbohydrate and microbiota metabolism. Metabotypes identified in this patient population could be related to higher risk of long-term cardiovascular events and therefore require further studies. Metabolomics is a useful tool for elucidating the metabolic complexity and interindividual variation in T2D towards the development of stratified precision nutrition and medicine. Trial registration at www.controlled-trials.com: ISRCTN35739639.

Metabotyping for the development of tailored dietary advice solutions in a European population: the Food4Me study

Traditionally, personalised nutrition was delivered at an individual level. However, the concept of delivering tailored dietary advice at a group level through the identification of metabotypes or groups of metabolically similar individuals has emerged. Although this approach to personalised nutrition looks promising, further work is needed to examine this concept across a wider population group. Therefore, the objectives of this study are to: (1) identify metabotypes in a European population and (2) develop targeted dietary advice solutions for these metabotypes. Using data from the Food4Me study (n 1607), k-means cluster analysis revealed the presence of three metabolically distinct clusters based on twenty-seven metabolic markers including cholesterol, individual fatty acids and carotenoids. Cluster 2 was identified as a metabolically healthy metabotype as these individuals had the highest Omega-3 Index (6·56 (sd 1·29) %), carotenoids (2·15 (sd 0·71) µm) and lowest total saturated fat levels. On the basis of its fatty acid profile, cluster 1 was characterised as a metabolically unhealthy cluster. Targeted dietary advice solutions were developed per cluster using a decision tree approach. Testing of the approach was performed by comparison with the personalised dietary advice, delivered by nutritionists to Food4Me study participants (n 180). Excellent agreement was observed between the targeted and individualised approaches with an average match of 82 % at the level of delivery of the same dietary message. Future work should ascertain whether this proposed method could be utilised in a healthcare setting, for the rapid and efficient delivery of tailored dietary advice solutions.

Processed red meat contribution to dietary patterns and the associated cardio-metabolic outcomes

Evidence suggests that processed red meat consumption is a risk factor for CVD and type 2 diabetes (T2D). This analysis investigates the association between dietary patterns, their processed red meat contributions, and association with blood biomarkers of CVD and T2D, in 786 Irish adults (18–90 years) using cross-sectional data from a 2011 national food consumption survey. All meat-containing foods consumed were assigned to four food groups (n 502) on the basis of whether they contained red or white meat and whether they were processed or unprocessed. The remaining foods (n 2050) were assigned to twenty-nine food groups. Two-step and k-means cluster analyses were applied to derive dietary patterns. Nutrient intakes, plasma fatty acids and biomarkers of CVD and T2D were assessed. A total of four dietary patterns were derived. In comparison with the pattern with lower contributions from processed red meat, the dietary pattern with greater processed red meat intakes presented a poorer Alternate Healthy Eating Index (21·2 (sd 7·7)), a greater proportion of smokers (29 %) and lower plasma EPA (1·34 (sd 0·72) %) and DHA (2·21 (sd 0·84) %) levels (P<0·001). There were no differences in classical biomarkers of CVD and T2D, including serum cholesterol and insulin, across dietary patterns. This suggests that the consideration of processed red meat consumption as a risk factor for CVD and T2D may need to be re-assessed.

Personalised Interventions-A Precision Approach for the Next Generation of Dietary Intervention Studies

Diet is a key modifiable risk factor for non-communicable diseases. However, we currently are not benefitting from the full potential of its protective effects. This is due to a number of reasons, including high individual variability in response to certain diets. It is now well acknowledged that in order to gain the full benefit of dietary regimes it is essential to take into account individual responses. With this in mind, the present review examines the concept of precision nutrition and the performance of n-of-1 studies, and discusses the development of certain approaches that will be critical for development of the concepts.

Metabotyping and its application in targeted nutrition: an overview

Metabolic diversity leads to differences in nutrient requirements and responses to diet and medication between individuals. Using the concept of metabotyping – that is, grouping metabolically similar individuals – tailored and more efficient recommendations may be achieved. The aim of this study was to review the current literature on metabotyping and to explore its potential for better targeted dietary intervention in subjects with and without metabolic diseases. A comprehensive literature search was performed in PubMed, Google and Google Scholar to find relevant articles on metabotyping in humans including healthy individuals, population-based samples and patients with chronic metabolic diseases. A total of thirty-four research articles on human studies were identified, which established more homogeneous subgroups of individuals using statistical methods for analysing metabolic data. Differences between studies were found with respect to the samples/populations studied, the clustering variables used, the statistical methods applied and the metabotypes defined. According to the number and type of the selected clustering variables, the definitions of metabotypes differed substantially; they ranged between general fasting metabotypes, more specific fasting parameter subgroups like plasma lipoprotein or fatty acid clusters and response groups to defined meal challenges or dietary interventions. This demonstrates that the term ‘metabotype’ has a subjective usage, calling for a formalised definition. In conclusion, this literature review shows that metabotyping can help identify subgroups of individuals responding differently to defined nutritional interventions. Targeted recommendations may be given at such metabotype group levels. Future studies should develop and validate definitions of generally valid metabotypes by exploiting the increasingly available metabolomics data sets.

Use of metabotyping for optimal nutrition

In recent years there has been general agreement that dietary advice needs to be tailored to the individual and that we need to move from a one size fits all approach. Evidence has emerged that personalising dietary advice results in improved dietary behaviours. Concomitant with this there has been an increase in the application of developing technologies such as metabolomics to nutrition studies. The concept of the metabotype has emerged and set to play a key role in the development and delivery or personalised nutrition. The term metabotype refers to a group of individuals with similar metabolic profiles. This review gives an overview of the potential role of this approach in delivering optimal nutrition advice.

Contemporary issues regarding nutrition in cardiovascular rehabilitation

In this article, we discuss certain contemporary and controversial topics in cardiovascular (CV) nutrition including recent data regarding the health benefits of the Mediterranean diet, the role of saturated fatty acids, red meat and the microbiome in CV disease and the current role of personalized CV nutrition. Findings from the PREDIMED study now demonstrate the health benefits of the Mediterranean diet even in the absence of heart disease. The study highlighted that even small, sustained and easily implementable changes to diet can provide significant health benefits even in Mediterranean regions. Likewise, observational data in secondary prevention show that increased adherence to the Mediterranean diet is associated with good long-term clinical outcomes among subjects with stable coronary heart disease. The role of saturated fats in the development of CV disease remains controversial, although data suggest that these fats are associated with modestly increased risk of CV events. In contrast, the obesity epidemic currently driving the CV risk worldwide is in large part due to excess consumption of refined carbohydrates. Furthermore, a growing body of evidence suggests that the intestinal microbiome is highly sensitive to lifestyle choices and may play a pivotal role in modulating CV disease development. For example, recent evidence linking processed and unprocessed meats to increased CV risk pointed to the gut microbial metabolite trimethylamine N-oxide as a potential culprit. Finally, given the high interindividual variability in response to interventions including diet, personalized nutrition has potential to play a major role in tailoring diets based on genetic make-up to maximize health benefits. This approach is still in its infancy but is highly promising.

The use of mass spectrometry for analysing metabolite biomarkers in epidemiology: methodological and statistical considerations for application to large numbers of biological samples

Data quality is critical for epidemiology, and as scientific understanding expands, the range of data available for epidemiological studies and the types of tools used for measurement have also expanded. It is essential for the epidemiologist to have a grasp of the issues involved with different measurement tools. One tool that is increasingly being used for measuring biomarkers in epidemiological cohorts is mass spectrometry (MS), because of the high specificity and sensitivity of MS-based methods and the expanding range of biomarkers that can be measured. Further, the ability of MS to quantify many biomarkers simultaneously is advantageously compared to single biomarker methods. However, as with all methods used to measure biomarkers, there are a number of pitfalls to consider which may have an impact on results when used in epidemiology. In this review we discuss the use of MS for biomarker analyses, focusing on metabolites and their application and potential issues related to large-scale epidemiology studies, the use of MS "omics" approaches for biomarker discovery and how MS-based results can be used for increasing biological knowledge gained from epidemiological studies. Better understanding of the possibilities and possible problems related to MS-based measurements will help the epidemiologist in their discussions with analytical chemists and lead to the use of the most appropriate statistical tools for these data.

Plasma fatty acid patterns reflect dietary habits and metabolic health: A cross-sectional study

SCOPE

Using pattern analysis, we investigated the relationship between plasma fatty acid patterns, dietary intake, and biomarkers of metabolic health using data from the Irish National Adult Nutrition Survey.

METHODS AND RESULTS

Plasma fatty acid patterns were derived from 26 plasma fatty acids using k-means cluster analysis. Four clusters were identified, each with a distinct fatty acid profile. Cluster 1 included high proportions of linoleic acid (LA) and low proportions of stearic acid (SA); cluster 2 was higher in n-3 polyunsaturated fatty acids and SA; the profile of cluster 3 was higher in very-long-chain saturated fatty acid (VLCSFA) and lower in α-linolenic acid (ALA) (cluster 3); while cluster 4 was higher in fatty acids related to de novo lipogenesis and 20:3n-6 and lower in LA (cluster 4). In general, cluster 4 was associated with adverse metabolic profile and higher metabolic risk (p < 0.033). Clusters 2 and 3 were associated with healthier and protective phenotypes (p < 0.033).

CONCLUSION

Distinct fatty acid patterns were identified which were related to demographics, dietary habits, and metabolic profile. A pattern higher in VLCSFA and lower in ALA was associated with healthier metabolic outcome.

Can metabotyping help deliver the promise of personalised nutrition?

Over a decade since the completion of the human genome sequence, the promise of personalised nutrition available to all has yet to become a reality. While the definition was originally very gene-focused, in recent years, a model of personalised nutrition has emerged with the incorporation of dietary, phenotypic and genotypic information at various levels. Developing on from the idea of personalised nutrition, the concept of targeted nutrition has evolved which refers to the delivery of tailored dietary advice at a group level rather than at an individual level. Central to this concept is metabotyping or metabolic phenotyping, which is the ability to group similar individuals together based on their metabolic or phenotypic profiles. Applications of the metabotyping concept extend from the nutrition to the medical literature. While there are many examples of the metabotype approach, there is a dearth in the literature with regard to the development of tailored interventions for groups of individuals. This review will first explore the effectiveness of personalised nutrition in motivating behaviour change and secondly, examine potential novel ways for the delivery of personalised advice at a population level through a metabotyping approach. Based on recent findings from our work, we will demonstrate a novel strategy for the delivery of tailored dietary advice at a group level using this concept. In general, there is a strong emerging evidence to support the effectiveness of personalised nutrition; future work should ascertain if targeted nutrition can motivate behaviour change in a similar manner.