Metabotyping and its application in targeted nutrition: an overview

Integration of metabolomics and machine learning for precise management and prevention of cardiometabolic risk in Asians

Rapid changes in dietary patterns have led to a rise in cardiometabolic diseases (CMDs) worldwide, highlighting the urgent need for effective dietary strategies to address the health issues. Compared to Caucasians, Asians are more susceptible to CMDs. Understanding the complex factors driving this increased susceptibility is essential for developing targeted interventions and preventive measures for Asian populations. Metabolomics plays a key role in identifying specific metabolic markers and pathways associated with CMDs, providing insights into disease mechanisms and helping to create individualized risk profiles. However, metabolomics faces several challenges, including difficulties in interpreting results across diverse ethnic groups, limitations in study design, variability in analytical platforms, and inconsistencies in data processing methods. Overcoming these challenges requires the adoption of advanced technologies, standardized approaches, and integration of multi-omics data to maximize the utility of metabolomics in clinical settings. As the volume and complexity of metabolomic data continue to increase, machine learning (ML) algorithms have become essential for effective data integration, interpretation, and knowledge extraction. Advanced ML techniques, such as deep learning and network analysis, can reveal hidden patterns, relationships, and metabolic pathways within large datasets, leading to deeper insights into biological systems and disease processes. By combining metabolomics and ML, we can facilitate early detection, enable personalized interventions, and support the development of targeted nutritional strategies, ultimately improving therapeutic outcomes and reducing the socioeconomic burden of CMDs in this region.

Metabolomics: Unveiling biological matrices in precision nutrition and health

Precision nutrition, an expanding field at the intersection of nutrition science and personalized medicine, is rapidly evolving with metabolomics integration. Metabolomics, facilitated by advanced technologies like mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy, facilitates comprehensive profiling of metabolites across diverse biological samples. From the perspective of health care systems, precision nutrition gains relevance due to the substantial impact of prevalent non-communicable diseases (NCDs) on societal well-being, which is directly linked with dietary habits and eating behavior. Furthermore, biomarker products derived from metabolomics have been utilized in Europe, the USA, and Brazil to understand metabolic dysregulations and tailor diets accordingly. Despite its burgeoning status, metabolomics holds great potential in revolutionizing nutritional science, particularly with the integration of artificial intelligence and machine learning, offering novel insights into personalized dietary interventions and disease prediction. This narrative review emphasizes the transformative impact of metabolomics in precision and delineates avenues for future research and application, paving the way for a more tailored and practical approach to nutrition management.

Circulatory levels of lysophosphatidylcholine species in obese adolescents: Findings from cross-sectional and prospective lipidomics analyses

BACKGROUND AND AIMS

Obesity has reached epidemic proportions, emphasizing the importance of reliable biomarkers for detecting early metabolic alterations and enabling early preventative interventions. However, our understanding of the molecular mechanisms and specific lipid species associated with childhood obesity remains limited. Therefore, the aim of this study was to investigate plasma lipidomic signatures as potential biomarkers for adolescent obesity.

METHODS AND RESULTS

A total of 103 individuals comprising overweight/obese (n = 46) and normal weight (n = 57) were randomly chosen from the baseline ORANGE (Obesity Reduction and Noncommunicable Disease Awareness through Group Education) cohort, having been followed up for a median of 7.1 years. Plasma lipidomic profiling was performed using the UHPLC-HRMS method. We used three different models adjusted for clinical covariates to analyze the data. Clustering methods were used to define metabotypes, which allowed for the stratification of subjects into subgroups with similar clinical and metabolic profiles. We observed that lysophosphatidylcholine (LPC) species like LPC.16.0, LPC.18.3, LPC.18.1, and LPC.20.3 were significantly (p < 0.05) associated with baseline and follow-up BMI in adolescent obesity. The association of LPC species with BMI remained consistently significant even after adjusting for potential confounders. Moreover, applying metabotyping using hierarchical clustering provided insights into the metabolic heterogeneity within the normal and obese groups, distinguishing metabolically healthy individuals from those with unhealthy metabolic profiles.

CONCLUSION

The specific LPC levels were found to be altered and increased in childhood obesity, particularly during the follow-up. These findings suggest that LPC species hold promise as potential biomarkers of obesity in adolescents, including healthy and unhealthy metabolic profiles.

Oxidative Stress in Military Missions-Impact and Management Strategies: A Narrative Analysis

This narrative review comprehensively examines the impact of oxidative stress on military personnel, highlighting the crucial role of physical exercise and tailored diets, particularly the ketogenic diet, in minimizing this stress. Through a meticulous analysis of the recent literature, the study emphasizes how regular physical exercise not only enhances cardiovascular, cognitive, and musculoskeletal health but is also essential in neutralizing the effects of oxidative stress, thereby improving endurance and performance during long-term missions. Furthermore, the implementation of the ketogenic diet provides an efficient and consistent energy source through ketone bodies, tailored to the specific energy requirements of military activities, and significantly contributes to the reduction in reactive oxygen species production, thus protecting against cellular deterioration under extreme stress. The study also underlines the importance of integrating advanced technologies, such as wearable devices and smart sensors that allow for the precise and real-time monitoring of oxidative stress and physiological responses, thus facilitating the customization of training and nutritional regimes. Observations from this review emphasize significant variability among individuals in responses to oxidative stress, highlighting the need for a personalized approach in formulating intervention strategies. It is crucial to develop and implement well-monitored, personalized supplementation protocols to ensure that each member of the military personnel receives a regimen tailored to their specific needs, thereby maximizing the effectiveness of measures to combat oxidative stress. This analysis makes a valuable contribution to the specialized literature, proposing a detailed framework for addressing oxidative stress in the armed forces and opening new directions for future research with the aim of optimizing clinical practices and improving the health and performance of military personnel under stress and specific challenges of the military field.

Revisiting multi-omics-based predictors of the plasma triglyceride response to an omega-3 fatty acid supplementation

Background

The aim of the present study was to identify the metabolomic signature of responders and non-responders to an omega-3 fatty acid (n-3 FA) supplementation, and to test the ability of a multi-omics classifier combining genomic, lipidomic, and metabolomic features to discriminate plasma triglyceride (TG) response phenotypes.

Methods

A total of 208 participants of the Fatty Acid Sensor (FAS). Study took 5 g per day of fish oil, providing 1.9-2.2 g eicosapentaenoic acid (EPA) and 1.1 g docosahexaenoic (DHA) daily over a 6-week period, and were further divided into two subgroups: responders and non-responders, according to the change in plasma TG levels after the supplementation. Changes in plasma levels of 6 short-chain fatty acids (SCFA) and 25 bile acids (BA) during the intervention were compared between subgroups using a linear mixed model, and the impact of SCFAs and BAs on the TG response was tested in a mediation analysis. Genotyping was conducted using the Illumina Human Omni-5 Quad BeadChip. Mass spectrometry was used to quantify plasma TG and cholesterol esters levels, as well as plasma SCFA and BA levels. A classifier was developed and tested within the DIABLO framework, which implements a partial least squares-discriminant analysis to multi-omics analysis. Different classifiers were developed by combining data from genomics, lipidomics, and metabolomics.

Results

Plasma levels of none of the SCFAs or BAs measured before and after the n-3 FA supplementation were significantly different between responders and non-responders. SCFAs but not BAs were marginally relevant in the classification of plasma TG responses. A classifier built by adding plasma SCFAs and lipidomic layers to genomic data was able to even the accuracy of 85% shown by the genomic predictor alone.

Conclusion

These results inform on the marginal relevance of SCFA and BA plasma levels as surrogate measures of gut microbiome in the assessment of the interindividual variability observed in the plasma TG response to an n-3 FA supplementation. Genomic data still represent the best predictor of plasma TG response, and the inclusion of metabolomic data added little to the ability to discriminate the plasma TG response phenotypes.

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.

Metabolomic biomarkers in autism: identification of complex dysregulations of cellular bioenergetics

Autism Spectrum Disorder (ASD or autism) is a phenotypically and etiologically heterogeneous condition. Identifying biomarkers of clinically significant metabolic subtypes of autism could improve understanding of its underlying pathophysiology and potentially lead to more targeted interventions. We hypothesized that the application of metabolite-based biomarker techniques using decision thresholds derived from quantitative measurements could identify autism-associated subpopulations. Metabolomic profiling was carried out in a case-control study of 499 autistic and 209 typically developing (TYP) children, ages 18-48 months, enrolled in the Children's Autism Metabolome Project (CAMP; ClinicalTrials.gov Identifier: NCT02548442). Fifty-four metabolites, associated with amino acid, organic acid, acylcarnitine and purine metabolism as well as microbiome-associated metabolites, were quantified using liquid chromatography-tandem mass spectrometry. Using quantitative thresholds, the concentrations of 4 metabolites and 149 ratios of metabolites were identified as biomarkers, each identifying subpopulations of 4.5-11% of the CAMP autistic population. A subset of 42 biomarkers could identify CAMP autistic individuals with 72% sensitivity and 90% specificity. Many participants were identified by several metabolic biomarkers. Using hierarchical clustering, 30 clusters of biomarkers were created based on participants' biomarker profiles. Metabolic changes associated with the clusters suggest that altered regulation of cellular metabolism, especially of mitochondrial bioenergetics, were common metabolic phenotypes in this cohort of autistic participants. Autism severity and cognitive and developmental impairment were associated with increased lactate, many lactate containing ratios, and the number of biomarker clusters a participant displayed. These studies provide evidence that metabolic phenotyping is feasible and that defined autistic subgroups can lead to enhanced understanding of the underlying pathophysiology and potentially suggest pathways for targeted metabolic treatments.

Targeting the metabolic profile of amino acids to identify the key metabolic characteristics in cerebral palsy

Background

Cerebral palsy (CP) is a neurodevelopmental disorder characterized by motor impairment. In this study, we aimed to describe the characteristics of amino acids (AA) in the plasma of children with CP and identify AA that could play a potential role in the auxiliary diagnosis and treatment of CP.

Methods

Using high performance liquid chromatography, we performed metabolomics analysis of AA in plasma from 62 CP children and 60 healthy controls. Univariate and multivariate analyses were then applied to characterize different AA. AA markers associated with CP were then identified by machine learning based on the Lasso regression model for the validation of intra-sample interactions. Next, we calculated a discriminant formula and generated a receiver operating characteristic (ROC) curve based on the marker combination in the discriminant diagnostic model.

Results

A total of 33 AA were detected in the plasma of CP children and controls. Compared with controls, 5, 7, and 10 different AA were identified in total participants, premature infants, and full-term infants, respectively. Of these, β-amino-isobutyric acid [p = 2.9*10(-4), Fold change (FC) = 0.76, Variable importance of protection (VIP) = 1.75], tryptophan [p = 5.4*10(-4), FC = 0.87, VIP = 2.22], and asparagine [p = 3.6*10(-3), FC = 0.82, VIP = 1.64], were significantly lower in the three groups of CP patients than that in controls. The combination of β-amino-isobutyric acid, tryptophan, and taurine, provided high levels of diagnostic classification and risk prediction efficacy for preterm children with an area under the curve (AUC) value of 0.8741 [95% confidence interval (CI): 0.7322-1.000]. The discriminant diagnostic formula for preterm infant with CP based on the potential marker combination was defined by p = 1/(1 + e-(8.295-0.3848* BAIBA-0.1120*Trp + 0.0108*Tau)).

Conclusion

Full-spectrum analysis of amino acid metabolomics revealed a distinct profile in CP, including reductions in the levels of β-amino-isobutyric acid, tryptophan, and taurine. Our findings shed new light on the pathogenesis and diagnosis of premature infants with CP.

Precision Nutrition Opportunities to Help Mitigate Nutrition and Health Challenges in Low- and Middle-Income Countries: An Expert Opinion Survey

Precision nutrition involves several data collection methods and tools that aim to better inform nutritional recommendations and improve dietary intake, nutritional status, and health outcomes. While the benefits of collecting precise data and designing well-informed interventions are vast, it is presently unclear whether precision nutrition is a relevant approach for tackling nutrition challenges facing populations in low- and middle-income countries (LMIC), considering infrastructure, affordability, and accessibility of approaches. The Swiss Food & Nutrition Valley (SFNV) Precision Nutrition for LMIC project working group assessed the relevance of precision nutrition for LMIC by first conducting an expert opinion survey and then hosting a workshop with nutrition leaders who live or work in LMIC. The experts were interviewed to discuss four topics: nutritional problems, current solutions, precision nutrition, and collaboration. Furthermore, the SFNV Precision Nutrition for LMIC Virtual Workshop gathered a wider group of nutrition leaders to further discuss precision nutrition relevance and opportunities. Our study revealed that precision public health nutrition, which has a clear focus on the stratification of at-risk groups, may offer relevant support for nutrition and health issues in LMIC. However, funding, affordability, resources, awareness, training, suitable tools, and safety are essential prerequisites for implementation and to equitably address nutrition challenges in low-resource communities.

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.

Self-organized metabotyping of obese individuals identifies clusters responding differently to bariatric surgery

Weight loss through bariatric surgery is efficient for treatment or prevention of obesity related diseases such as type 2 diabetes and cardiovascular disease. Long term weight loss response does, however, vary among patients undergoing surgery. Thus, it is difficult to identify predictive markers while most obese individuals have one or more comorbidities. To overcome such challenges, an in-depth multiple omics analyses including fasting peripheral plasma metabolome, fecal metagenome as well as liver, jejunum, and adipose tissue transcriptome were performed for 106 individuals undergoing bariatric surgery. Machine leaning was applied to explore the metabolic differences in individuals and evaluate if metabolism-based patients' stratification is related to their weight loss responses to bariatric surgery. Using Self-Organizing Maps (SOMs) to analyze the plasma metabolome, we identified five distinct metabotypes, which were differentially enriched for KEGG pathways related to immune functions, fatty acid metabolism, protein-signaling, and obesity pathogenesis. The gut metagenome of the most heavily medicated metabotypes, treated simultaneously for multiple cardiometabolic comorbidities, was significantly enriched in Prevotella and Lactobacillus species. This unbiased stratification into SOM-defined metabotypes identified signatures for each metabolic phenotype and we found that the different metabotypes respond differently to bariatric surgery in terms of weight loss after 12 months. An integrative framework that utilizes SOMs and omics integration was developed for stratifying a heterogeneous bariatric surgery cohort. The multiple omics datasets described in this study reveal that the metabotypes are characterized by a concrete metabolic status and different responses in weight loss and adipose tissue reduction over time. Our study thus opens a path to enable patient stratification and hereby allow for improved clinical treatments.

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.

New Advances in Metabolic Syndrome, from Prevention to Treatment: The Role of Diet and Food

The definition of metabolic syndrome (MetS) has undergone several changes over the years due to the difficulty in establishing universal criteria for it. Underlying the disorders related to MetS is almost invariably a pro-inflammatory state related to altered glucose metabolism, which could lead to elevated cardiovascular risk. Indeed, the complications closely related to MetS are cardiovascular diseases (CVDs) and type 2 diabetes (T2D). It has been observed that the predisposition to metabolic syndrome is modulated by complex interactions between human microbiota, genetic factors, and diet. This review provides a summary of the last decade of literature related to three principal aspects of MetS: (i) the syndrome's definition and classification, pathophysiology, and treatment approaches; (ii) prediction and diagnosis underlying the biomarkers identified by means of advanced methodologies (NMR, LC/GC-MS, and LC, LC-MS); and (iii) the role of foods and food components in prevention and/or treatment of MetS, demonstrating a possible role of specific foods intake in the development of MetS.

Implementation of Nutrigenetics and Nutrigenomics Research and Training Activities for Developing Precision Nutrition Strategies in Malaysia

Nutritional epidemiological studies show a triple burden of malnutrition with disparate prevalence across the coexisting ethnicities in Malaysia. To tackle malnutrition and related conditions in Malaysia, research in the new and evolving field of nutrigenetics and nutrigenomics is essential. As part of the Gene-Nutrient Interactions (GeNuIne) Collaboration, the Nutrigenetics and Nutrigenomics Research and Training Unit (N2RTU) aims to solve the malnutrition paradox. This review discusses and presents a conceptual framework that shows the pathway to implementing and strengthening precision nutrition strategies in Malaysia. The framework is divided into: (1) Research and (2) Training and Resource Development. The first arm collects data from genetics, genomics, transcriptomics, metabolomics, gut microbiome, and phenotypic and lifestyle factors to conduct nutrigenetic, nutrigenomic, and nutri-epigenetic studies. The second arm is focused on training and resource development to improve the capacity of the stakeholders (academia, healthcare professionals, policymakers, and the food industry) to utilise the findings generated by research in their respective fields. Finally, the N2RTU framework foresees its applications in artificial intelligence and the implementation of precision nutrition through the action of stakeholders.

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.

UPEC Colonic-Virulence and Urovirulence Are Blunted by Proanthocyanidins-Rich Cranberry Extract Microbial Metabolites in a Gut Model and a 3D Tissue-Engineered Urothelium

Uropathogenic Escherichia coli (UPEC) ecology-pathophysiology from the gut reservoir to its urothelium infection site is poorly understood, resulting in equivocal benefits in the use of cranberry as prophylaxis against urinary tract infections. To add further understanding from the previous findings on PAC antiadhesive properties against UPEC, we assessed in this study the effects of proanthocyanidins (PAC) rich cranberry extract microbial metabolites on UTI89 virulence and fitness in contrasting ecological UPEC's environments. For this purpose, we developed an original model combining a colonic fermentation system (SHIME) with a dialysis cassette device enclosing UPEC and a 3D tissue-engineered urothelium. Two healthy fecal donors inoculated the colons. Dialysis cassettes containing 7log10 CFU/mL UTI89 were immersed for 2h in the SHIME colons to assess the effect of untreated (7-day control diet)/treated (14-day PAC-rich extract) metabolomes on UPEC behavior. Engineered urothelium were then infected with dialysates containing UPEC for 6 h. This work demonstrated for the first time that in the control fecal microbiota condition without added PAC, the UPEC virulence genes were activated upstream the infection site, in the gut. However, PAC microbial-derived cranberry metabolites displayed a remarkable propensity to blunt activation of genes encoding toxin, adhesin/invasins in the gut and on the urothelium, in a donor-dependent manner. Variability in subjects' gut microbiota and ensuing contrasting cranberry PAC metabolism affects UPEC virulence and should be taken into consideration when designing cranberry efficacy clinical trials. IMPORTANCE Uropathogenic Escherichia coli (UPEC) are the primary cause of recurrent urinary tract infections (UTI). The poor understanding of UPEC ecology-pathophysiology from its reservoir-the gut, to its infection site-the urothelium, partly explains the inadequate and abusive use of antibiotics to treat UTI, which leads to a dramatic upsurge in antibiotic-resistance cases. In this context, we evaluated the effect of a cranberry proanthocyanidins (PAC)-rich extract on the UPEC survival and virulence in a bipartite model of a gut microbial environment and a 3D urothelium model. We demonstrated that PAC-rich cranberry extract microbial metabolites significantly blunt activation of UPEC virulence genes at an early stage in the gut reservoir. We also showed that altered virulence in the gut affects infectivity on the urothelium in a microbiota-dependent manner. Among the possible mechanisms, we surmise that specific microbial PAC metabolites may attenuate UPEC virulence, thereby explaining the preventative, yet contentious properties of cranberry against UTI.

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.

A double-blinded, randomized, parallel intervention to evaluate biomarker-based nutrition plans for weight loss: The PREVENTOMICS study

BACKGROUND & AIMS

Growing evidence suggests that biomarker-guided dietary interventions can optimize response to treatment. In this study, we evaluated the efficacy of the PREVENTOMCIS platform-which uses metabolomic and genetic information to classify individuals into different 'metabolic clusters' and create personalized dietary plans-for improving health outcomes in subjects with overweight or obesity.

METHODS

A 10-week parallel, double-blinded, randomized intervention was conducted in 100 adults (82 completers) aged 18-65 years, with body mass index ≥27 but <40 kg/m², who were allocated into either a personalized diet group (n = 49) or a control diet group (n = 51). About 60% of all food was provided free-of-charge. No specific instruction to restrict energy intake was given. The primary outcome was change in fat mass from baseline, evaluated by dual energy X-ray absorptiometry. Other endpoints included body weight, waist circumference, lipid profile, glucose homeostasis markers, inflammatory markers, blood pressure, physical activity, stress and eating behavior.

RESULTS

There were significant main effects of time (P < 0.01), but no group main effects, or time-by-group interactions, for the change in fat mass (personalized: -2.1 [95% CI -2.9, -1.4] kg; control: -2.0 [95% CI -2.7, -1.3] kg) and body weight (personalized: -3.1 [95% CI -4.1, -2.1] kg; control: -3.3 [95% CI -4.2, -2.4] kg). The difference between groups in fat mass change was -0.1 kg (95% CI -1.2, 0.9 kg, P = 0.77). Both diets resulted in significant improvements in insulin resistance and lipid profile, but there were no significant differences between groups.

CONCLUSION

Personalized dietary plans did not result in greater benefits over a generic, but generally healthy diet, in this 10-week clinical trial. Further studies are required to establish the soundness of different precision nutrition approaches, and translate this science into clinically relevant dietary advice to reduce the burden of obesity and its comorbidities.

CLINICAL TRIAL REGISTRY

ClinicalTrials.gov registry (NCT04590989).

Metabotyping for Precision Nutrition and Weight Management: Hype or Hope?

PURPOSE OF REVIEW

Precision nutrition requires a solid understanding of the factors that determine individual responses to dietary treatment. We review the current state of knowledge in identifying human metabotypes - based on circulating biomarkers - that can predict weight loss or other relevant physiological outcomes in response to diet treatment.

RECENT FINDINGS

Not many studies have been conducted in this area and the ones identified here are heterogeneous in design and methodology, and therefore difficult to synthesize and draw conclusions. The basis of the creation of metabotypes varies widely, from using thresholds for a single metabolite to using complex algorithms to generate multi-component constructs that include metabolite and genetic information. Furthermore, available studies are a mix of hypothesis-driven and hypothesis-generating studies, and most of them lack experimental testing in human trials. Although this field of research is still in its infancy, precision-based dietary intervention strategies focusing on the metabotype group level hold promise for designing more effective dietary treatments for obesity.

Metabotypes of flavan-3-ol colonic metabolites after cranberry intake: elucidation and statistical approaches

Purpose

Extensive inter-individual variability exists in the production of flavan-3-ol metabolites. Preliminary metabolic phenotypes (metabotypes) have been defined, but there is no consensus on the existence of metabotypes associated with the catabolism of catechins and proanthocyanidins. This study aims at elucidating the presence of different metabotypes in the urinary excretion of main flavan-3-ol colonic metabolites after consumption of cranberry products and at assessing the impact of the statistical technique used for metabotyping.

Methods

Data on urinary concentrations of phenyl-γ-valerolactones and 3-(hydroxyphenyl)propanoic acid derivatives from two human interventions has been used. Different multivariate statistics, principal component analysis (PCA), cluster analysis, and partial least square-discriminant analysis (PLS-DA), have been considered.

Results

Data pre-treatment plays a major role on resulting PCA models. Cluster analysis based on k-means and a final consensus algorithm lead to quantitative-based models, while the expectation–maximization algorithm and clustering according to principal component scores yield metabotypes characterized by quali-quantitative differences in the excretion of colonic metabolites. PLS-DA, together with univariate analyses, has served to validate the urinary metabotypes in the production of flavan-3-ol metabolites and to confirm the robustness of the methodological approach.

Conclusions

This work proposes a methodological workflow for metabotype definition and highlights the importance of data pre-treatment and clustering methods on the final outcomes for a given dataset. It represents an additional step toward the understanding of the inter-individual variability in flavan-3-ol metabolism.

Trial registration

The acute study was registered at clinicaltrials.gov as NCT02517775, August 7, 2015; the chronic study was registered at clinicaltrials.gov as NCT02764749, May 6, 2016.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00394-021-02692-z.

Towards precision cardiometabolic prevention: results from a machine learning, semi-supervised clustering approach in the nationwide population-based ORISCAV-LUX 2 study

Given the rapid increase in the incidence of cardiometabolic conditions, there is an urgent need for better approaches to prevent as many cases as possible and move from a one-size-fits-all approach to a precision cardiometabolic prevention strategy in the general population. We used data from ORISCAV-LUX 2, a nationwide, cross-sectional, population-based study. On the 1356 participants, we used a machine learning semi-supervised cluster method guided by body mass index (BMI) and glycated hemoglobin (HbA1c), and a set of 29 cardiometabolic variables, to identify subgroups of interest for cardiometabolic health. Cluster stability was assessed with the Jaccard similarity index. We have observed 4 clusters with a very high stability (ranging between 92 and 100%). Based on distinctive features that deviate from the overall population distribution, we have labeled Cluster 1 (N = 729, 53.76%) as "Healthy", Cluster 2 (N = 508, 37.46%) as "Family history-Overweight-High Cholesterol ", Cluster 3 (N = 91, 6.71%) as "Severe Obesity-Prediabetes-Inflammation" and Cluster 4 (N = 28, 2.06%) as "Diabetes-Hypertension-Poor CV Health". Our work provides an in-depth characterization and thus, a better understanding of cardiometabolic health in the general population. Our data suggest that such a clustering approach could now be used to define more targeted and tailored strategies for the prevention of cardiometabolic diseases at a population level. This study provides a first step towards precision cardiometabolic prevention and should be externally validated in other contexts.

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.

Advances in dietary pattern analysis in nutritional epidemiology

Background and Purpose

It used to be a common practice in the field of nutritional epidemiology to analyze separate nutrients, foods, or food groups. However, in reality, nutrients and foods are consumed in combination. The introduction of dietary patterns (DP) and their analysis has revolutionized this field, making it possible to take into account the synergistic effects of foods and to account for the complex interaction among nutrients and foods. Three approaches of DP analysis exist: (1) the hypothesis-based approach (based on prior knowledge regarding the current understanding of dietary components and their health relation), (2) the exploratory approach (solely relying on dietary intake data), and (3) the hybrid approach (a combination of both approaches). During the recent past, complementary approaches for DP analysis have emerged both conceptually and methodologically.

Method

We have summarized the recent developments that include incorporating the Treelet transformation method as a complementary exploratory approach in a narrative review.

Results

Uses, peculiarities, strengths, limitations, and scope of recent developments in DP analysis are outlined. Next, the narrative review gives an overview of the literature that takes into account potential relevant dietary-related factors, specifically the metabolome and the gut microbiome in DP analysis. Then the review deals with the aspect of data processing that is needed prior to DP analysis, particularly when dietary data arise from assessment methods other than the long-established food frequency questionnaire. Lastly, potential opportunities for upcoming DP analysis are summarized in the outlook.

Conclusion

Biological factors like the metabolome and the microbiome are crucial to understand diet-disease relationships. Therefore, the inclusion of these factors in DP analysis might provide deeper insights.

Approaches to Integrating Metabolomics and Multi-Omics Data: A Primer

Metabolomics deals with multiple and complex chemical reactions within living organisms and how these are influenced by external or internal perturbations. It lies at the heart of omics profiling technologies not only as the underlying biochemical layer that reflects information expressed by the genome, the transcriptome and the proteome, but also as the closest layer to the phenome. The combination of metabolomics data with the information available from genomics, transcriptomics, and proteomics offers unprecedented possibilities to enhance current understanding of biological functions, elucidate their underlying mechanisms and uncover hidden associations between omics variables. As a result, a vast array of computational tools have been developed to assist with integrative analysis of metabolomics data with different omics. Here, we review and propose five criteria-hypothesis, data types, strategies, study design and study focus- to classify statistical multi-omics data integration approaches into state-of-the-art classes under which all existing statistical methods fall. The purpose of this review is to look at various aspects that lead the choice of the statistical integrative analysis pipeline in terms of the different classes. We will draw particular attention to metabolomics and genomics data to assist those new to this field in the choice of the integrative analysis pipeline.

Personalized Nutrition and -Omics

With change in global concern toward food quality over food quantity, consumer concern and choice of healthy food has become a matter of prime importance. It gave rise to concept of “personalized or precision nutrition”. The theory behind personalization of nutrition is supported by multiple factors including advances in food analytics, nutrition based diseases and public health programs, increasing use of information technology in nutrition science, concept of gene-diet interaction and growing consumer capacity or concern by better and healthy foods. The advances in “omics” tools and related analytical techniques have resulted into tremendous scope of their application in nutrition science. As a consequence, a better understanding of underlying interaction between diet and individual is expected with addressing of key challenges for successful implementation of this science. In this chapter, the above aspects are discussed to get an insight into driving factors for increasing concern in personalized nutrition.

A Metabolomics Approach to Screening for Autism Risk in the Children's Autism Metabolome Project

Autism spectrum disorder (ASD) is biologically and behaviorally heterogeneous. Delayed diagnosis of ASD is common and problematic. The complexity of ASD and the low sensitivity of available screening tools are key factors in delayed diagnosis. Identification of biomarkers that reduce complexity through stratification into reliable subpopulations can assist in earlier diagnosis, provide insight into the biology of ASD, and potentially suggest targeted interventions. Quantitative metabolomic analysis was performed on plasma samples from 708 fasting children, aged 18 to 48 months, enrolled in the Children's Autism Metabolome Project (CAMP). The primary goal was to identify alterations in metabolism helpful in stratifying ASD subjects into subpopulations with shared metabolic phenotypes (i.e., metabotypes). Metabotypes associated with ASD were identified in a discovery set of 357 subjects. The reproducibility of the metabotypes was validated in an independent replication set of 351 CAMP subjects. Thirty-four candidate metabotypes that differentiated subsets of ASD from typically developing participants were identified with sensitivity of at least 5% and specificity greater than 95%. The 34 metabotypes formed six metabolic clusters based on ratios of either lactate or pyruvate, succinate, glycine, ornithine, 4-hydroxyproline, or α-ketoglutarate with other metabolites. Optimization of a subset of new and previously defined metabotypes into a screening battery resulted in 53% sensitivity (95% confidence interval [CI], 48%-57%) and 91% specificity (95% CI, 86%-94%). Thus, our metabolomic screening tool detects more than 50% of the autistic participants in the CAMP study. Further development of this metabolomic screening approach may facilitate earlier referral and diagnosis of ASD and, ultimately, more targeted treatments. LAY SUMMARY: Analysis of a selected set of metabolites in blood samples from children with autism and typically developing children identified reproducible differences in the metabolism of about half of the children with autism. Testing for these differences in blood samples can be used to help screen children as young as 18 months for risk of autism that, in turn, can facilitate earlier diagnoses. In addition, differences may lead to biological insights that produce more precise treatment options. We are exploring other blood-based molecules to determine if still a higher percentage of children with autism can be detected using this strategy. Autism Res 2020, 13: 1270-1285. © 2020 The Authors. Autism Research published by International Society for Autism Research published by Wiley Periodicals LLC.

Dried Blood Spot-Based Metabolomic Profiling in Adults with Cystic Fibrosis

Mucoviscidosis of the respiratory, gastrointestinal, and genitourinary tracts is the major pathology in patients with cystic fibrosis (CF), a lethal monogenic panethnic and multisystemic disease most commonly identified in Caucasians. Currently, the measurement of immuno reactive trypsinogen in dry blood spots (DBSs) is the gold-standard method for initial newborn screening for CF, followed by targeted CF transmembrane regulator (CFTR) mutation analysis, and ultimate confirmation with abnormally elevated sweat chloride. Previous metabolomics studies in patients with CF reported on different biomarkers such as breath 2-aminoacetophenone produced during acute and chronic infection in human tissues, including the lungs of CF patients. Herein, we used liquid and gas chromatography-mass spectrometry-based targeted metabolomics profiling to identify potentially reliable, sensitive, and specific biomarkers in DBSs collected from 69 young and adult people including CF patients (n = 39) and healthy control (n = 30). A distinctive metabolic profile including 26 significantly differentially expressed metabolites involving amino acids, glycolysis, mitochondrial and peroxisomal metabolism, and sorbitol pathways was identified. Specifically, the osmolyte (sorbitol) was remarkably downregulated in CF patients compared to healthy controls indicating perturbation in the sorbitol pathway, which may be responsible for the mucoviscidosis seen in patients with CF. The significance of our findings is supported by the clinical utility of inhaled mannitol and hypertonic saline in patients with CF. The systemic administration of sorbitol in such patients may confer additional benefits beyond the respiratory system, especially in those with misfolded CFTR proteins.

Association of Dietary Patterns and Type-2 Diabetes Mellitus in Metabolically Homogeneous Subgroups in the KORA FF4 Study

There is evidence that a change in lifestyle, especially physical activity and diet, can reduce the risk of developing type-2 diabetes mellitus (T2DM). However, the response to dietary changes varies among individuals due to differences in metabolic characteristics. Therefore, we investigated the association between dietary patterns and T2DM while taking into account these differences. For 1287 participants of the population-based KORA FF4 study (Cooperative Health Research in the Region of Augsburg), we identified three metabolically-homogenous subgroups (metabotypes) using 16 clinical markers. Based on usual dietary intake data, two diet quality scores, the Mediterranean Diet Score (MDS) and the Alternate Healthy Eating Index (AHEI), were calculated. We explored the associations between T2DM and diet quality scores. Multi-variable adjusted models, including metabotype subgroup, were fitted. In addition, analyses stratified by metabotype were carried out. We found significant interaction effects between metabotype and both diet quality scores (p < 0.05). In the analysis stratified by metabotype, significant negative associations between T2DM and both diet quality scores were detected only in the metabolically-unfavorable homogenous subgroup (Odds Ratio (OR) = 0.62, 95% confidence interval (CI) = 0.39-0.90 for AHEI and OR = 0.60, 95% CI = 0.40-0.96 for MDS). Prospective studies taking metabotype into account are needed to confirm our results, which allow for the tailoring of dietary recommendations in the prevention of T2DM.

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.

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.

Why interindividual variation in response to consumption of plant food bioactives matters for future personalised nutrition

Food phytochemicals are increasingly considered to play a key role in the cardiometabolic health effects of plant foods. However, the heterogeneity in responsiveness to their intake frequently observed in clinical trials can hinder the beneficial effects of these compounds in specific subpopulations. A range of factors, including genetic background, gut microbiota, age, sex and health status, could be involved in these interindividual variations; however, the current knowledge is limited and fragmented. The European network, European Cooperation in Science and Technology (COST)-POSITIVe, has analysed, in a systematic way, existing knowledge with the aim to better understand the factors responsible for the interindividual variation in response to the consumption of the major families of plant food bioactives, regarding their bioavailability and bioefficacy. If differences in bioavailability, likely reflecting differences in human subjects' genetics or in gut microbiota composition and functionality, are believed to underpin much of the interindividual variability, the key molecular determinants or microbial species remain to be identified. The systematic analysis of published studies conducted to assess the interindividual variation in biomarkers of cardiometabolic risk suggested some factors (such as adiposity and health status) as involved in between-subject variation. However, the contribution of these factors is not demonstrated consistently across the different compounds and biological outcomes and would deserve further investigations. The findings of the network clearly highlight that the human subjects' intervention studies published so far are not adequate to investigate the relevant determinants of the absorption/metabolism and biological responsiveness. They also emphasise the need for a new generation of intervention studies designed to capture this interindividual variation.

Insulin Resistance during normal child growth and development is associated with a distinct blood metabolic phenotype (Earlybird 72)

BACKGROUND

While insulin resistance (IR) is associated with specific metabolite signatures in adults, there have been few truly longitudinal studies in healthy children, either to confirm which abnormalities are present, or to determine whether they precede or result from IR. Therefore, we investigated the association of serum metabolites with IR in childhood in the Earlybird cohort.

METHODS

The Earlybird cohort is a well-characterized cohort of healthy children with annual measurements from age 5 to 16 years. For the first time, longitudinal association analyses between individual serum metabolites and homeostatic model assessment (HOMA) of insulin resistance (HOMA-IR) have been performed taking into account the effects of age, growth, puberty, adiposity, and physical activity.

RESULTS

IR was higher in girls than in boys and was associated with increasing body mass index (BMI). In longitudinal analysis IR was associated with reduced concentrations of branched-chain amino acids (BCAA), 2-ketobutyrate, citrate and 3-hydroxybutyrate, and higher concentrations of lactate and alanine. These findings demonstrate the widespread biochemical consequences of IR for intermediary metabolism, ketogenesis, and pyruvate oxidation during normal child growth and development.

CONCLUSIONS

Longitudinal analysis can differentiate metabolite signatures that precede or follow the development of greater levels of IR. In healthy normal weight children, higher levels of IR are associated with reduced levels of BCAA, ketogenesis, and fuel oxidation. In contrast, elevated lactate concentrations preceded the rise in IR. These changes reveal the metabolite signature of insulin action during normal growth, and they contrast with previous findings in obese children and adults that represent the consequences of IR and obesity.

Future prospects for dissecting inter-individual variability in the absorption, distribution and elimination of plant bioactives of relevance for cardiometabolic endpoints

PURPOSE

The health-promoting potential of food-derived plant bioactive compounds is evident but not always consistent across studies. Large inter-individual variability may originate from differences in digestion, absorption, distribution, metabolism and excretion (ADME). ADME can be modulated by age, sex, dietary habits, microbiome composition, genetic variation, drug exposure and many other factors. Within the recent COST Action POSITIVe, large-scale literature surveys were undertaken to identify the reasons and extent of inter-individual variability in ADME of selected plant bioactive compounds of importance to cardiometabolic health. The aim of the present review is to summarize the findings and suggest a framework for future studies designed to investigate the etiology of inter-individual variability in plant bioactive ADME and bioefficacy.

RESULTS

Few studies have reported individual data on the ADME of bioactive compounds and on determinants such as age, diet, lifestyle, health status and medication, thereby limiting a mechanistic understanding of the main drivers of variation in ADME processes observed across individuals. Metabolomics represent crucial techniques to decipher inter-individual variability and to stratify individuals according to metabotypes reflecting the intrinsic capacity to absorb and metabolize bioactive compounds.

CONCLUSION

A methodological framework was developed to decipher how the contribution from genetic variants or microbiome variants to ADME of bioactive compounds can be predicted. Future study design should include (1) a larger number of study participants, (2) individual and full profiling of all possible determinants of internal exposure, (3) the presentation of individual ADME data and (4) incorporation of omics platforms, such as genomics, microbiomics and metabolomics in ADME and efficacy studies.

Targeting the delivery of dietary plant bioactives to those who would benefit most: from science to practical applications

Background

A healthy diet and optimal lifestyle choices are amongst the most important actions for the prevention of cardiometabolic diseases. Despite this, it appears difficult to convince consumers to select more nutritious foods. Furthermore, the development and production of healthier foods do not always lead to economic profits for the agro-food sector. Most dietary recommendations for the general population represent a “one-size-fits-all approach” which does not necessarily ensure that everyone has adequate exposure to health-promoting constituents of foods. Indeed, we now know that individuals show a high variability in responses when exposed to specific nutrients, foods, or diets.

Purpose

This review aims to highlight our current understanding of inter-individual variability in response to dietary bioactives, based on the integration of findings of the COST Action POSITIVe. We also evaluate opportunities for translation of scientific knowledge on inter-individual variability in response to dietary bioactives, once it becomes available, into practical applications for stakeholders, such as the agro-food industry. The potential impact from such applications will form an important impetus for the food industry to develop and market new high quality and healthy foods for specific groups of consumers in the future. This may contribute to a decrease in the burden of diet-related chronic diseases.

Individual differences in ADME (Absorption, Digestion, Metabolism and Excretion) is believed to underpin much of the inter-individual variation in responses.

Recent developments in the area of food metabolome databases and fast improvements in innovative metabotyping technologies hold great promise for improved profiling of dietary intake, exposure to individual ingredients, foods and dietary patterns, as well as our ability to identify individual responsiveness.

The food industry needs well-defined population clusters or targets in order to be able to design “personalized products”.

There are indeed excellent industrial opportunities for foods that modulate gut microbiota, and thereby enable the delivery of food bioactive metabolites.

It is currently not clear whether knowledge on individual nutrient needs, based on genetic or metagenomic data, would affect long-term dietary and health behaviours.

Data to support the development of dietary recommendations may need to be generated by new n-of-1-based study designs in the future.

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.

Paving the Way to Precision Nutrition Through Metabolomics

Nutrition is an interdisciplinary science that studies the interactions of nutrients with the body in relation to maintenance of health and well-being. Nutrition is highly complex due to the underlying various internal and external factors that could model it. Thus, hacking this complexity requires more holistic and network-based strategies that could unveil these dynamic system interactions at both time and space scales. The ongoing omics era with its high-throughput molecular data generation is paving the way to embrace this complexity and is deeply reshaping the whole field of nutrition. Understanding the future paths of nutrition science is of importance from both translational and clinical perspectives. Basic nutrients which might include metabolites are important in nutrition science. Moreover, metabolites are key biological communication channels and represent an appealing functional readout at the interface of different major influential factors that define health and disease. Metabolomics is the technology that enables holistic and systematic analyses of metabolites in a biological system. Hence, given its intrinsic functionality, its tight connection to metabolism and its high clinical actionability potential, metabolomics is a very appealing technology for nutrition science. The ultimate goal is to deliver a tailored and clinically relevant nutritional recommendations and interventions to achieve precision nutrition. This work intends to present an update on the applications of metabolomics to personalize nutrition in translational and clinical settings. It also discusses the current conceptual shifts that are remodeling clinical nutrition practices in this Precision Medicine era. Finally, perspectives of clinical nutrition in the ever-growing, data-driven healthcare landscape are presented.

Amino Acid Dysregulation Metabotypes: Potential Biomarkers for Diagnosis and Individualized Treatment for Subtypes of Autism Spectrum Disorder

BACKGROUND

Autism spectrum disorder (ASD) is behaviorally and biologically heterogeneous and likely represents a series of conditions arising from different underlying genetic, metabolic, and environmental factors. There are currently no reliable diagnostic biomarkers for ASD. Based on evidence that dysregulation of branched-chain amino acids (BCAAs) may contribute to the behavioral characteristics of ASD, we tested whether dysregulation of amino acids (AAs) was a pervasive phenomenon in individuals with ASD. This is the first article to report results from the Children's Autism Metabolome Project (CAMP), a large-scale effort to define autism biomarkers based on metabolomic analyses of blood samples from young children.

METHODS

Dysregulation of AA metabolism was identified by comparing plasma metabolites from 516 children with ASD with those from 164 age-matched typically developing children recruited into the CAMP. ASD subjects were stratified into subpopulations based on shared metabolic phenotypes associated with BCAA dysregulation.

RESULTS

We identified groups of AAs with positive correlations that were, as a group, negatively correlated with BCAA levels in ASD. Imbalances between these two groups of AAs identified three ASD-associated amino acid dysregulation metabotypes. The combination of glutamine, glycine, and ornithine amino acid dysregulation metabotypes identified a dysregulation in AA/BCAA metabolism that is present in 16.7% of the CAMP subjects with ASD and is detectable with a specificity of 96.3% and a positive predictive value of 93.5% within the ASD subject cohort.

CONCLUSIONS

Identification and utilization of metabotypes of ASD can lead to actionable metabolic tests that support early diagnosis and stratification for targeted therapeutic interventions.

Wine-Derived Phenolic Metabolites in the Digestive and Brain Function

Wine, and specifically red wine, is a beverage with a great chemical complexity comprising a particular combination of phenolic compounds which are directly associated with its health-promoting properties. Wine polyphenols could induce changes in the composition of intestinal microbiota that would affect the production of physiologically active phenolic metabolites modifying the content and phenolic profile at the systemic level. In addition, in the human population, it seems that different “metabotypes”, or patterns of metabolizing wine polyphenols, exist, which would be reflected in the different biological fluids (i.e., plasma, urine and feces) and tissues of the human body. Moreover, wine polyphenols might change the composition of oral microbiota by an antimicrobial action and/or by inhibition of the adhesion of pathogens to oral cells, thus contributing to the maintenance of oral health. In turn, polyphenols and/or its metabolites could have a direct action on brain function, by positively affecting signaling routes involved in stress-induced neuronal response, as well as by preventing neuroticism-like disorders (i.e., anxiety and depression) through anti-inflammatory and epigenetic mechanisms. All of this would condition the positive effects on health derived from moderate wine consumption. This paper reviews all these topics, which are directly related with the effects of wine polyphenols at both digestive and brain level. Further progresses expected in the coming years in these fields are also discussed.

Assessing the respective contributions of dietary flavanol monomers and procyanidins in mediating cardiovascular effects in humans: randomized, controlled, double-masked intervention trial

Background

Flavanols are an important class of food bioactives that can improve vascular function even in healthy subjects. Cocoa flavanols (CFs) are composed principally of the monomer (-)-epicatechin (∼20%), with a degree of polymerisation (DP) of 1 (DP1), and oligomeric procyanidins (∼80%, DP2-10).

Objective

Our objective was to investigate the relative contribution of procyanidins and (-)-epicatechin to CF intake-related improvements in vascular function in healthy volunteers.

Design

In a randomized, controlled, double-masked, parallel-group dietary intervention trial, 45 healthy men (aged 18-35 y) consumed the following once daily for 1 mo: 1) a DP1-10 cocoa extract containing 130 mg (-)-epicatechin and 560 mg procyanidins, 2) a DP2-10 cocoa extract containing 20 mg (-)-epicatechin and 540 mg procyanidins, or 3) a control capsule, which was flavanol-free but had identical micro- and macronutrient composition.

Results

Consumption of DP1-10, but not of either DP2-10 or the control capsule, significantly increased flow-mediated vasodilation (primary endpoint) and the concentration of structurally related (-)-epicatechin metabolites (SREMs) in the circulatory system while decreasing pulse wave velocity and blood pressure. Total cholesterol significantly decreased after daily intake of both DP1-10 and DP2-10 as compared with the control.

Conclusions

CF-related improvements in vascular function are predominantly related to the intake of flavanol monomers and circulating SREMs in healthy humans but not to the more abundant procyanidins and gut microbiome-derived CF catabolites. Reduction in total cholesterol was linked to consumption of procyanidins but not necessarily to that of (-)-epicatechin. This trial was registered at clinicaltrials.gov as NCT02728466.

The Human Metabolism of Nuts Proanthocyanidins does not Reveal Urinary Metabolites Consistent with Distinctive Gut Microbiota Metabotypes

SCOPE

The stratification of individuals according to their gut microbiota metabotypes is crucial to understand the polyphenols health effects as reported for isoflavones and ellagitannins. To date, the existence of human gut microbiota metabotypes associated with proanthocyanidins (PAs) catabolism remains unclear.

METHODS & RESULTS

Sixty-eight healthy volunteers (40 adolescents and 28 adults) consumed a mixture of walnuts, almonds, and hazelnuts for 3 days, providing 163.65 ± 11.74 mg of PAs. Urine samples were analyzed by ultra-performance LC-ESI-quadrupole time-of-flight. Twenty-one isomers of conjugated valerolactones and valeric acids were identified, which derived from six valerolactone and valeric acid precursors after analysis of hydrolyzed urine. This combined approach allowed discrimination between the inter-individual variability related to phase-II enzymes polymorphisms and the metabolism of PAs by the gut microbiota. No associations of PAs metabolism with gender, age, BMI, or ellagitannin metabotypes were found. Different quantitative excretion was observed after multivariate analysis but not true gut microbiota metabotypes associated with PAs catabolism.

CONCLUSIONS

The metabolism of PAs does not reveal urinary metabolites consistent with distinctive gut microbiota metabotypes. The quantitative excretion of metabolites is inadequate to stratify individuals due to the strong influence of external factors (source, quantity, and time of the last intake of PAs, etc.).

Antioxidants in Personalized Nutrition and Exercise

The present review highlights the idea that antioxidant supplementation can be optimized when tailored to the precise antioxidant status of each individual. A novel methodologic approach involving personalized nutrition, the mechanisms by which antioxidant status regulates human metabolism and performance, and similarities between antioxidants and other nutritional supplements are described. The usefulness of higher-level phenotypes for data-driven personalized treatments is also explained. We conclude that personally tailored antioxidant interventions based on specific antioxidant inadequacies or deficiencies could result in improved exercise performance accompanied by consistent alterations in redox profile.

Nutrimetabolomics: An Integrative Action for Metabolomic Analyses in Human Nutritional Studies

The life sciences are currently being transformed by an unprecedented wave of developments in molecular analysis, which include important advances in instrumental analysis as well as biocomputing. In light of the central role played by metabolism in nutrition, metabolomics is rapidly being established as a key analytical tool in human nutritional studies. Consequently, an increasing number of nutritionists integrate metabolomics into their study designs. Within this dynamic landscape, the potential of nutritional metabolomics (nutrimetabolomics) to be translated into a science, which can impact on health policies, still needs to be realized. A key element to reach this goal is the ability of the research community to join, to collectively make the best use of the potential offered by nutritional metabolomics. This article, therefore, provides a methodological description of nutritional metabolomics that reflects on the state-of-the-art techniques used in the laboratories of the Food Biomarker Alliance (funded by the European Joint Programming Initiative "A Healthy Diet for a Healthy Life" (JPI HDHL)) as well as points of reflections to harmonize this field. It is not intended to be exhaustive but rather to present a pragmatic guidance on metabolomic methodologies, providing readers with useful "tips and tricks" along the analytical workflow.

Dietary Interventions to Modulate the Gut Microbiome-How Far Away Are We From Precision Medicine

The importance of the gut microbiome in human health and disease is fully acknowledged. A perturbation in the equilibrium among the different microbial populations living in the gut (dysbiosis) has been associated with the development of several types of diseases. Modulation of the gut microbiome through dietary intervention is an emerging therapeutic and preventive strategy for many conditions. Nevertheless, interpersonal differences in response to therapeutic treatments or dietary regimens are often observed during clinical trials, and recent research has suggested that subject-specific features of the gut microbiota may be responsible. In this review, we summarize recent findings in personalized nutrition, highlighting how individualized characterization of the microbiome may assist in designing ad hoc tailored dietary intervention for disease treatment and prevention. Moreover, we discuss the limitations and challenges encountered in integrating patient-specific microbial data into clinical practice.

Metabotypes Related to Meat and Vegetable Intake Reflect Microbial, Lipid and Amino Acid Metabolism in Healthy People

SCOPE

The objective of this study is to develop a new methodology to identify the relationship between dietary patterns and metabolites indicative of food intake and metabolism.

METHODS AND RESULTS

Plasma and urine samples from healthy Swiss subjects (n = 89) collected over two time points are analyzed for a panel of host-microbial metabolites using GC- and LC-MS. Dietary intake is evaluated using a validated food frequency questionnaire. Dietary pattern clusters and relationships with metabolites are determined using Non-Negative Matrix Factorization (NNMF) and Sparse Generalized Canonical Correlation Analysis (SGCCA). Use of NNMF allows detection of latent diet clusters in this population, which describes a high intake of meat or vegetables. SGCCA associates these clusters to i) diet-host microbial and lipid associated bile acid metabolism, and ii) essential amino acid metabolism.

CONCLUSION

This novel application of NNMF and SGCCA allows detection of distinct metabotypes for meat and vegetable dietary patterns in a heterogeneous population. As many of the metabolites associated with meat or vegetable intake are the result of host-microbiota interactions, the findings support a role for microbiota mediating the metabolic imprinting of different dietary choices.