Plasma metabolome analysis identifies distinct human metabotypes in the postprandial state with different susceptibility to weight loss-mediated metabolic improvements

A single-blinded, randomized, parallel intervention to evaluate genetics and omics-based personalized nutrition in general population via an e-commerce tool: The PREVENTOMICS e-commerce study

BACKGROUND

Personalized nutrition (PN) has been proposed as a strategy to increase the effectiveness of dietary recommendations and ultimately improve health status.

OBJECTIVES

We aimed to assess whether including omics-based PN in an e-commerce tool improves dietary behavior and metabolic profile in general population.

METHODS

A 21-wk parallel, single-blinded, randomized intervention involved 193 adults assigned to a control group following Mediterranean diet recommendations (n = 57, completers = 36), PN (n = 70, completers = 45), or personalized plan (PP, n = 68, completers = 53) integrating a behavioral change program with PN recommendations. The intervention used metabolomics, proteomics, and genetic data to assist participants in creating personalized shopping lists in a simulated e-commerce retailer portal. The primary outcome was the Mediterranean diet adherence screener (MEDAS) score; secondary outcomes included biometric and metabolic markers and dietary habits.

RESULTS

Volunteers were categorized with a scoring system based on biomarkers of lipid, carbohydrate metabolism, inflammation, oxidative stress, and microbiota, and dietary recommendations delivered accordingly in the PN and PP groups. The intervention significantly increased MEDAS scores in all volunteers (control-3 points; 95% confidence interval [CI]: 2.2, 3.8; PN-2.7 points; 95% CI: 2.0, 3.3; and PP-2.8 points; 95% CI: 2.1, 3.4; q < 0.001). No significant differences were observed in dietary habits or health parameters between PN and control groups after adjustment for multiple comparisons. Nevertheless, personalized recommendations significantly (false discovery rate < 0.05) and selectively enhanced the scores calculated with biomarkers of carbohydrate metabolism (β: -0.37; 95% CI: -0.56, -0.18), oxidative stress (β: -0.37; 95% CI: -0.60, -0.15), microbiota (β: -0.38; 95% CI: -0.63, -0.15), and inflammation (β: -0.78; 95% CI: -1.24, -0.31) compared with control diet.

CONCLUSIONS

Integration of personalized strategies within an e-commerce-like tool did not enhance adherence to Mediterranean diet or improved health markers compared with general recommendations. The metabotyping approach showed promising results and more research is guaranteed to further promote its application in PN. This trial was registered at clinicaltrials.gov as NCT04641559 (https://clinicaltrials.gov/study/NCT04641559?cond=NCT04641559&rank=1).

Association Between the Postprandial Response to an Oral Glucose Tolerance Test and Anthropometric Changes After an 8-Week Low-Calorie Formula Diet - Results From the Lifestyle Intervention (LION) Study

SCOPE

Interindividual variations in postprandial metabolism and weight loss outcomes have been reported. The literature suggests links between postprandial metabolism and weight regulation. Therefore, the study aims to evaluate if postprandial glucose metabolism after a glucose load predicts anthropometric outcomes of a weight loss intervention.

METHODS AND RESULTS

Anthropometric data from adults with obesity (18-65 years, body mass index [BMI] 30.0-39.9 kg m-2) are collected pre- and post an 8-week formula-based weight loss intervention. An oral glucose tolerance test (OGTT) is performed at baseline, from which postprandial parameters are derived from glucose and insulin concentrations. Linear regression models explored associations between these parameters and anthropometric changes (∆) postintervention. A random forest model is applied to identify predictive parameters for anthropometric outcomes after intervention. Postprandial parameters after an OGTT of 158 participants (63.3% women, age 45 ± 12, BMI 34.9 ± 2.9 kg m-2) reveal nonsignificant associations with changes in anthropometric parameters after weight loss (p > 0.05). Baseline fat-free mass (FFM) and sex are primary predictors for ∆ FFM [kg].

CONCLUSION

Postprandial glucose metabolism after a glucose load does not predict anthropometric outcomes after short-term weight loss via a formula-based low-calorie diet in adults with obesity.

Quantifying the effect of nutritional interventions on metabolic resilience using personalized computational models

The manifestation of metabolic deteriorations that accompany overweight and obesity can differ greatly between individuals, giving rise to a highly heterogeneous population. This inter-individual variation can impede both the provision and assessment of nutritional interventions as multiple aspects of metabolic health should be considered at once. Here, we apply the Mixed Meal Model, a physiology-based computational model, to characterize an individual's metabolic health in silico. A population of 342 personalized models were generated using data for individuals with overweight and obesity from three independent intervention studies, demonstrating a strong relationship between the model-derived metric of insulin resistance (ρ = 0.67, p < 0.05) and the gold-standard hyperinsulinemic-euglycemic clamp. The model is also shown to quantify liver fat accumulation and β-cell functionality. Moreover, we show that personalized Mixed Meal Models can be used to evaluate the impact of a dietary intervention on multiple aspects of metabolic health at the individual level.

Postprandial glucose variability and clusters of sex hormones, liver enzymes, and cardiometabolic factors in a South African cohort of African ancestry

INTRODUCTION

This study aimed to, first, determine the clusters of sex hormones, liver enzymes, and cardiometabolic factors associated with postprandial glucose (PPG) and, second to evaluate the variation these clusters account for jointly and independently with polygenic risk scores (PRSs) in South Africans of African ancestry men and women.

RESEARCH DESIGN AND METHODS

PPG was calculated as the integrated area under the curve for glucose during the oral glucose tolerance test (OGTT) using the trapezoidal rule in 794 participants from the Middle-aged Soweto Cohort. Principal component analysis was used to cluster sex hormones, liver enzymes, and cardiometabolic factors, stratified by sex. Multivariable linear regression was used to assess the proportion of variance in PPG accounted for by principal components (PCs) and type 2 diabetes (T2D) PRS while adjusting for selected covariates in men and women.

RESULTS

The T2D PRS did not contribute to the PPG variability in both men and women. In men, the PCs' cluster of sex hormones, liver enzymes, and cardiometabolic explained 10.6% of the variance in PPG, with PC1 (peripheral fat), PC2 (liver enzymes and steroid hormones), and PC3 (lipids and peripheral fat) contributing significantly to PPG. In women, PC factors of sex hormones, cardiometabolic factors, and liver enzymes explained a similar amount of the variance in PPG (10.8%), with PC1 (central fat) and PC2 (lipids and liver enzymes) contributing significantly to PPG.

CONCLUSIONS

We demonstrated that inter-individual differences in PPG responses to an OGTT may be differentially explained by body fat distribution, serum lipids, liver enzymes, and steroid hormones in men and women.

Human Nutrition Research in the Data Era: Results of 11 Reports on the Effects of a Multiple-Micronutrient-Intervention Study

Large datasets have been used in molecular and genetic research for decades, but only a few studies have included nutrition and lifestyle factors. Our team conducted an n-of-1 intervention with 12 vitamins and five minerals in 9- to 13-year-old Brazilian children and teens with poor healthy-eating indices. A unique feature of the experimental design was the inclusion of a replication arm. Twenty-six types of data were acquired including clinical measures, whole-genome mapping, whole-exome sequencing, and proteomic and a variety of metabolomic measurements over two years. A goal of this study was to use these diverse data sets to discover previously undetected physiological effects associated with a poor diet that include a more complete micronutrient composition. We summarize the key findings of 11 reports from this study that (i) found that LDL and total cholesterol and fasting glucose decreased in the population after the intervention but with inter-individual variation; (ii) associated a polygenic risk score that predicted baseline vitamin B12 levels; (iii) identified metabotypes linking diet intake, genetic makeup, and metabolic physiology; (iv) found multiple biomarkers for nutrient and food groups; and (v) discovered metabolites and proteins that are associated with DNA damage. This summary also highlights the limitations and lessons in analyzing diverse omic data.

Precision Nutrition to Improve Risk Factors of Obesity and Type 2 Diabetes

PURPOSE OF REVIEW

Existing dietary and lifestyle interventions and recommendations, to improve the risk factors of obesity and type 2 diabetes with the target to mitigate this double global epidemic, have produced inconsistent results due to interpersonal variabilities in response to these conventional approaches, and inaccuracies in dietary assessment methods. Precision nutrition, an emerging strategy, tailors an individual's key characteristics such as diet, phenotype, genotype, metabolic biomarkers, and gut microbiome for personalized dietary recommendations to optimize dietary response and health. Precision nutrition is suggested to be an alternative and potentially more effective strategy to improve dietary intake and prevention of obesity and chronic diseases. The purpose of this narrative review is to synthesize the current research and examine the state of the science regarding the effect of precision nutrition in improving the risk factors of obesity and type 2 diabetes.

RECENT FINDINGS

The results of the research review indicate to a large extent significant evidence supporting the effectiveness of precision nutrition in improving the risk factors of obesity and type 2 diabetes. Deeper insights and further rigorous research into the diet-phenotype-genotype and interactions of other components of precision nutrition may enable this innovative approach to be adapted in health care and public health to the special needs of individuals. Precision nutrition provides the strategy to make individualized dietary recommendations by integrating genetic, phenotypic, nutritional, lifestyle, medical, social, and other pertinent characteristics about individuals, as a means to address the challenges of generalized dietary recommendations. The evidence presented in this review shows that precision nutrition markedly improves risk factors of obesity and type 2 diabetes, particularly behavior change.

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.

Perspective: Council for Responsible Nutrition Science in Session. Optimizing Health with Nutrition-Opportunities, Gaps, and the Future

Achieving optimal health is an aspirational goal for the population, yet the definition of health remains unclear. The role of nutrition in health has evolved beyond correcting malnutrition and specific deficiencies and has begun to focus more on achieving and maintaining 'optimal' health through nutrition. As such, the Council for Responsible Nutrition held its October 2022 Science in Session conference to advance this concept. Here, we summarize and discuss the findings of their Optimizing Health through Nutrition - Opportunities and Challenges workshop, including several gaps that need to be addressed to advance progress in the field. Defining and evaluating various indices of optimal health will require overcoming these key gaps. For example, there is a strong need to develop better biomarkers of nutrient status, including more accurate markers of food intake, as well as biomarkers of optimal health that account for maintaining resilience-the ability to recover from or respond to stressors without loss to physical and cognitive performance. In addition, there is a need to identify factors that drive individualized responses to nutrition, including genotype, metabotypes, and the gut microbiome, and to realize the opportunity of precision nutrition for optimal health. This review outlines hallmarks of resilience, provides current examples of nutritional factors to optimize cognitive and performance resilience, and gives an overview of various genetic, metabolic, and microbiome determinants of individualized responses.

Phenotypic flexibility in nutrition research to quantify human variability: building the bridge to personalised nutrition

Phenotypic flexibility is a methodology that accurately assesses health in terms of mechanistic understanding of the interrelationship of multiple metabolic and physiological processes. This starts from the perspective that a healthy person is better able to cope with changes in environmental stressors that affect homeostasis compared to people with a compromised health state. The term 'phenotypic flexibility' expresses the cumulative ability of overarching physiological processes to return to homeostatic levels after short-term perturbations. The concept of phenotypic flexibility to define biomarkers for nutrition-related health was introduced in 2009 in the area of health optimisation and prevention and delay of non-communicable disease. The core approach consists of the combination of imposing a challenge test to the body followed by time-resolved analysis of multiple biomarkers. This new approach may better facilitate nutritional health research in intervention studies since it may show effects on early derailed physiological markers and the biomarker response can be extended by perturbing the system, thereby making them more sensitive in detecting health effects from food and nutrition. At the same time, interindividual variation can also be extended and compressed by challenge tests, facilitating the bridge to personalised nutrition. This review will overview where the science is in this research arena and what the phenotypic flexibility potential is for the nutrition field.

Diet-induced Weight Loss and Phenotypic Flexibility Among Healthy Overweight Adults: A Randomized Trial

BACKGROUND

The capacity of an individual to respond to changes in food intake so that postprandial metabolic perturbations are resolved, and metabolism returns to its pre-prandial state, is called phenotypic flexibility. This ability may be a more important indicator of current health status than metabolic markers in a fasting state.

AIM

In this parallel randomized controlled trial study, an energy-restricted healthy diet and 2 dietary challenges were used to assess the effect of weight loss on phenotypic flexibility.

METHODS

Seventy-two volunteers with overweight and obesity underwent a 12-wk dietary intervention. The participants were randomized to a weight loss group (WLG) with 20% less energy intake or a weight-maintenance group (WMG). At weeks 1 and 12, participants were assessed for body composition by MRI. Concurrently, markers of metabolism and insulin sensitivity were obtained from the analysis of plasma metabolome during 2 different dietary challenges-an oral glucose tolerance test (OGTT) and a mixed-meal tolerance test.

RESULTS

Intended weight loss was achieved in the WLG (-5.6 kg, P < 0.0001) and induced a significant reduction in total and regional adipose tissue as well as ectopic fat in the liver. Amino acid-based markers of insulin action and resistance such as leucine and glutamate were reduced in the postprandial phase of the OGTT in the WLG by 11.5% and 28%, respectively, after body weight reduction. Weight loss correlated with the magnitude of changes in metabolic responses to dietary challenges. Large interindividual variation in metabolic responses to weight loss was observed.

CONCLUSION

Application of dietary challenges increased sensitivity to detect metabolic response to weight loss intervention. Large interindividual variation was observed across a wide range of measurements allowing the identification of distinct responses to the weight loss intervention and mechanistic insight into the metabolic response to weight loss.

Plasma metabolomic response to high-carbohydrate meals of differing glycaemic load in overweight women

BACKGROUND

Metabolomic dysregulation following a meal in overweight individuals with the Metabolic Syndrome (MetS) involves multiple pathways of nutrient storage and oxidation.

OBJECTIVE

The aim of the current study was to perform an acute cross-over intervention to examine the interactive actions of meal glycaemic load (GL) on the dynamic responses of the plasma metabolome in overweight females.

METHODS

Postmenopausal women [63 ± 1.23y; Healthy (n = 20) and MetS (n = 20)] ingested two differing high-carbohydrate test meals (73 g carbohydrate; 51% energy) composed of either low glycemic index (LGI) or high (HGI) foods in a randomised sequence. Plasma metabolome was analysed using liquid chromatography-mass spectrometry (LC-MS).

RESULTS

In the overweight women with MetS, there were suppressed postprandial responses for several amino acids (AAs), including phenylalanine, leucine, valine, and tryptophan, p < 0.05), irrespective of the meal type. Meal GL exerted a limited impact on the overall metabolomic response, although the postprandial levels of alanine were higher with the low GL meal and uric acid was greater following the high GL meal (p < 0.05).

CONCLUSIONS

MetS participants exhibited reduced differences in the concentrations of a small set of AAs and a limited group of metabolites implicated in energy metabolism following the meals. However, the manipulation of meal GL had minimal impact on the postprandial metabolome. This study suggests that the GL of a meal is not a major determinant of postprandial response, with a greater impact exerted by the metabolic health of the individual. Trial registration Australia New Zealand Clinical Trials Registry: ACTRN12615001108505 (21/10/2015).

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.

Metabolomic markers of glucose regulation after a lifestyle intervention in prediabetes

INTRODUCTION

Disentangling the specific factors that regulate glycemia from prediabetes to normoglycemia could improve type 2 diabetes prevention strategies. Metabolomics provides substantial insights into the biological understanding of environmental factors such as diet. This study aimed to identify metabolomic markers of regression to normoglycemia in the context of a lifestyle intervention (LSI) in individuals with prediabetes.

RESEARCH DESIGN AND METHODS

We conducted a single-arm intervention study with 24 weeks of follow-up. Eligible study participants had at least one prediabetes criteria according to the American Diabetes Association guidelines, and body mass index between 25 and 45 kg/m². LSI refers to a hypocaloric diet and >150 min of physical activity per week. Regression to normoglycemia (RNGR) was defined as achieving hemoglobin A1c (HbA1c) <5.5% in the final visit. Baseline and postintervention plasma metabolomic profiles were measured using liquid chromatography-tandem mass spectrometry. To select metabolites associated with RNGR, we conducted the least absolute shrinkage and selection operator-penalized regressions.

RESULTS

The final sample was composed of 82 study participants. Changes in three metabolites were significantly associated with regression to normoglycemia; N-acetyl-D-galactosamine (OR=0.54; 95% CI 0.32 to 0.82), putrescine (OR=0.90, 95% CI 0.81 to 0.98), and 7-methylguanine (OR=1.06; 95% CI 1.02 to 1.17), independent of HbA1c and weight loss. In addition, metabolomic perturbations due to LSI displayed enrichment of taurine and hypotaurine metabolism pathway (p=0.03) compatible with biomarkers of protein consumption, lower red meat and animal fats and higher seafood and vegetables.

CONCLUSIONS

Evidence from this study suggests that specific metabolomic markers have an influence on glucose regulation in individuals with prediabetes after 24 weeks of LSI independently of other treatment effects such as weight loss.

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.

Dynamic patterns of postprandial metabolic responses to three dietary challenges

Food intake triggers extensive changes in the blood metabolome. The kinetics of these changes depend on meal composition and on intrinsic, health-related characteristics of each individual, making the assessment of changes in the postprandial metabolome an opportunity to assess someone's metabolic status. To enable the usage of dietary challenges as diagnostic tools, profound knowledge about changes that occur in the postprandial period in healthy individuals is needed. In this study, we characterize the time-resolved changes in plasma levels of 634 metabolites in response to an oral glucose tolerance test (OGTT), an oral lipid tolerance test (OLTT), and a mixed meal (SLD) in healthy young males (n = 15). Metabolite levels for samples taken at different time points (20 per individual) during the challenges were available from targeted (132 metabolites) and non-targeted (502 metabolites) metabolomics. Almost half of the profiled metabolites (n = 308) showed a significant change in at least one challenge, thereof 111 metabolites responded exclusively to one particular challenge. Examples include azelate, which is linked to ω-oxidation and increased only in OLTT, and a fibrinogen cleavage peptide that has been linked to a higher risk of cardiovascular events in diabetes patients and increased only in OGTT, making its postprandial dynamics a potential target for risk management. A pool of 89 metabolites changed their plasma levels during all three challenges and represents the core postprandial response to food intake regardless of macronutrient composition. We used fuzzy c-means clustering to group these metabolites into eight clusters based on commonalities of their dynamic response patterns, with each cluster following one of four primary response patterns: (i) “decrease-increase” (valley-like) with fatty acids and acylcarnitines indicating the suppression of lipolysis, (ii) “increase-decrease” (mountain-like) including a cluster of conjugated bile acids and the glucose/insulin cluster, (iii) “steady decrease” with metabolites reflecting a carryover from meals prior to the study, and (iv) “mixed” decreasing after the glucose challenge and increasing otherwise. Despite the small number of subjects, the diversity of the challenges and the wealth of metabolomic data make this study an important step toward the characterization of postprandial responses and the identification of markers of metabolic processes regulated by food intake.

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.

Precision nutrition: Maintaining scientific integrity while realizing market potential

Precision Nutrition (PN) is an approach to developing comprehensive and dynamic nutritional recommendations based on individual variables, including genetics, microbiome, metabolic profile, health status, physical activity, dietary pattern, food environment as well as socioeconomic and psychosocial characteristics. PN can help answer the question “What should I eat to be healthy?”, recognizing that what is healthful for one individual may not be the same for another, and understanding that health and responses to diet change over time. The growth of the PN market has been driven by increasing consumer interest in individualized products and services coupled with advances in technology, analytics, and omic sciences. However, important concerns are evident regarding the adequacy of scientific substantiation supporting claims for current products and services. An additional limitation to accessing PN is the current cost of diagnostic tests and wearable devices. Despite these challenges, PN holds great promise as a tool to improve healthspan and reduce healthcare costs. Accelerating advancement in PN will require: (a) investment in multidisciplinary collaborations to enable the development of user-friendly tools applying technological advances in omics, sensors, artificial intelligence, big data management, and analytics; (b) engagement of healthcare professionals and payers to support equitable and broader adoption of PN as medicine shifts toward preventive and personalized approaches; and (c) system-wide collaboration between stakeholders to advocate for continued support for evidence-based PN, develop a regulatory framework to maintain consumer trust and engagement, and allow PN to reach its full potential.

Breath response following a nutritional challenge monitored by secondary electrospray ionization high-resolution mass spectrometry

On-line breath analysis using secondary electrospray ionization coupled to high-resolution mass spectrometry (SESI-HRMS) is a sensitive method for biomarker discovery. The strengths of this technology have already been demonstrated in the clinical environment. For the first time, this study demonstrates the application of SESI-HRMS in the field of nutritional science using a standardized nutritional intervention, consisting of a high-energy shake (950 kcal, 8% protein, 35% sugar and 57% fat). Eleven subjects underwent the intervention on three separate days and their exhaled breath was monitored up to six hours postprandially. In addition, sampling was performed during equivalent fasting conditions for selected subjects. To estimate the impact of inter- and intra-individual variability, analysis of variance simultaneous component analysis (ASCA) was conducted, revealing that the inter-individual variability accounted for 30 % of the data variation. To distinguish the effect of the intervention from fasting conditions, partial least squares discriminant analysis was performed. Candidate compound annotation was performed with pathway analysis and collision-induced dissociation (CID) experiments. Pathway analysis highlighted, among others, features associated with the metabolism of linoleate, butanoate and amino sugars. Tentative compounds annotated through CID measurements include fatty acids, amino acids, and amino acid derivatives, some of them likely derived from nutrients by the gut microbiome (e.g. propanoate, indoles), as well as organic acids from the Krebs cycle. Time-series clustering showed an overlap of observed kinetic trends with those reported previously in blood plasma.

Association between Habitual Diet and the Postprandial Glucose Response-An Enable Study

SCOPE

It is inconclusive which factors influence inter-individual variations of postprandial glucose response (PPGR). This study investigates whether the habitual diet is associated with PPGR.

METHODS AND RESULTS

Data from healthy adults (young adults with 18-25 years, middle-aged adults with 40-65 years, and older adults with 75-85 years) is collected at baseline and during an oral glucose tolerance test (OGTT) collected. Habitual diet is assessed by a food frequency questionnaire and two 24-h food lists. Associations between habitual diet and glucose incremental area under the curve (iAUC_min ) are examined by regression models. The intake of cereals and cereal products is negatively associated with glucose iAUC_min (p = 0.002) in the total cohort (N = 459, 50% women, 55 ± 21 years, BMI 26 ± 5 kg m^{- 2} ). Up to 9% of the variance in the glycemic response is explained by the respective dietary parameters identified in the models of the specific age groups.

CONCLUSION

There are age-specific diet-related effects on PPGR. The usual intake of cereals and cereal products seems to play a greater role in PPGR in more than one age group. Further research is needed, to establish how diet can be optimized based on age and PPGR.

Plasma Metabolic Signatures of Healthy Overweight Subjects Challenged With an Oral Glucose Tolerance Test

Insulin secretion following ingestion of a carbohydrate load affects a multitude of metabolic pathways that simultaneously change direction and quantity of interorgan fluxes of sugars, lipids and amino acids. In the present study, we aimed at identifying markers associated with differential responses to an OGTT a population of healthy adults. By use of three metabolite profiling platforms, we assessed these postprandial responses of a total of 202 metabolites in plasma of 72 healthy volunteers undergoing comprehensive phenotyping and of which half enrolled into a weight-loss program over a three-month period. A standard oral glucose tolerance test (OGTT) served as dietary challenge test to identify changes in postprandial metabolite profiles. Despite classified as healthy according to WHO criteria, two discrete clusters (A and B) were identified based on the postprandial glucose profiles with a balanced distribution of volunteers based on gender and other measures. Cluster A individuals displayed 26% higher postprandial glucose levels, delayed glucose clearance and increased fasting plasma concentrations of more than 20 known biomarkers of insulin resistance and diabetes previously identified in large cohort studies. The volunteers identified by canonical postprandial responses that form cluster A may be called pre-pre-diabetics and defined as “at risk” for development of insulin resistance. Moreover, postprandial changes in selected fatty acids and complex lipids, bile acids, amino acids, acylcarnitines and sugars like mannose revealed marked differences in the responses seen in cluster A and cluster B individuals that sustained over the entire challenge test period of 240 min. Almost all metabolites, including glucose and insulin, returned to baseline values at the end of the test (at 240 min), except a variety of amino acids and here those that have been linked to diabetes development. Analysis of the corresponding metabolite profile in a fasting blood sample may therefore allow for early identification of these subjects at risk for insulin resistance without the need to undergo an OGTT.

Modulation of Gut Microbiota and Metabolites by Berberine in Treating Mice With Disturbances in Glucose and Lipid Metabolism

Introduction: Glucose and lipid metabolism disturbances has become the third major disease after cancer and cardio-cerebrovascular diseases. Emerging evidence shows that berberine can effectively intervene glucose and lipid metabolism disturbances, but the underlying mechanisms of this remain unclear. To investigate this issue, we performed metagenomic and metabolomic analysis in a group of normal mice (the NC group), mice with disturbances in glucose and lipid metabolism (the MC group) and mice with disturbances in glucose and lipid metabolism after berberine intervention (the BER group).

Result: Firstly, analysis of the clinical indicators revealed that berberine significantly improved the blood glucose and blood lipid of the host. The fasting blood glucose level decreased by approximately 30% in the BER group after 8 weeks and the oral glucose tolerance test showed that the blood glucose level of the BER group was lower than that of the MC group at any time. Besides, berberine significantly reduced body weight, total plasma cholesterol and triglyceride. Secondly, compared to the NC group, we found dramatically decreased microbial richness and diversity in the MC group and BER group. Thirdly, LDA effect size suggested that berberine significantly altered the overall gut microbiota structure and enriched many bacteria, including Akkermansia (p < 0.01), Eubacterium (p < 0.01) and Ruminococcus (p < 0.01). Fourthly, the metabolomic analysis suggested that there were significant differences in the metabolomics signature of each group. For example, isoleucine (p < 0.01), phenylalanine (p < 0.05), and arbutin (p < 0.05) significantly increased in the MC group, and berberine intervention significantly reduced them. The arbutin content in the BER group was even lower than that in the NC group. Fifthly, by combined analysis of metagenomics and metabolomics, we observed that there were significantly negative correlations between the reduced faecal metabolites (e.g., arbutin) in the BER group and the enriched gut microbiota (e.g., Eubacterium and Ruminococcus) (p < 0.05). Finally, the correlation analysis between gut microbiota and clinical indices indicated that the bacteria (e.g., Eubacterium) enriched in the BER group were negatively associated with the above-mentioned clinical indices (p < 0.05).

Conclusion: Overall, our results describe that the changes of gut microbiota and metabolites are associated with berberine improving glucose and lipid metabolism disturbances.

Metabolomics of Type 1 and Type 2 Diabetes: Insights into Risk Prediction and Mechanisms

PURPOSE OF REVIEW

Metabolomics enables rapid interrogation of widespread metabolic processes making it well suited for studying diabetes. Here, we review the current status of metabolomic investigation in diabetes, highlighting its applications for improving risk prediction and mechanistic understanding.

RECENT FINDINGS

Findings of metabolite associations with type 2 diabetes risk have confirmed experimental observations (e.g., branched-chain amino acids) and also pinpointed novel pathways of diabetes risk (e.g., dimethylguanidino valeric acid). In type 1 diabetes, abnormal metabolite patterns are observed prior to the development of autoantibodies and hyperglycemia. Diabetes complications display specific metabolite signatures that are distinct from the metabolic derangements of diabetes and differ across vascular beds. Lastly, metabolites respond acutely to pharmacologic treatment, providing opportunities to understand inter-individual treatment responses. Metabolomic studies have elucidated biological mechanisms underlying diabetes development, complications, and therapeutic response. While not yet ready for clinical translation, metabolomics is a powerful and promising precision medicine tool.

Investigating the Postprandial Metabolome after Challenge Tests to Assess Metabolic Flexibility and Dysregulations Associated with Cardiometabolic Diseases

This review focuses on the added value provided by a research strategy applying metabolomics analyses to assess phenotypic flexibility in response to different nutritional challenge tests in the framework of metabolic clinical studies. We discuss findings related to the Oral Glucose Tolerance Test (OGTT) and to mixed meals with varying fat contents and food matrix complexities. Overall, the use of challenge tests combined with metabolomics revealed subtle metabolic dysregulations exacerbated during the postprandial period when comparing healthy and at cardiometabolic risk subjects. In healthy subjects, consistent postprandial metabolic shifts driven by insulin action were reported (e.g., a switch from lipid to glucose oxidation for energy fueling) with similarities between OGTT and mixed meals, especially during the first hours following meal ingestion while differences appeared in a wider timeframe. In populations with expected reduced phenotypic flexibility, often associated with increased cardiometabolic risk, a blunted response on most key postprandial pathways was reported. We also discuss the most suitable statistical tools to analyze the dynamic alterations of the postprandial metabolome while accounting for complexity in study designs and data structure. Overall, the in-depth characterization of the postprandial metabolism and associated phenotypic flexibility appears highly promising for a better understanding of the onset of cardiometabolic diseases.

A Scoping Review of the Application of Metabolomics in Nutrition Research: The Literature Survey 2000-2019

Nutrimetabolomics is an emerging field in nutrition research, and it is expected to play a significant role in deciphering the interaction between diet and health. Through the development of omics technology over the last two decades, the definition of food and nutrition has changed from sources of energy and major/micro-nutrients to an essential exposure factor that determines health risks. Furthermore, this new approach has enabled nutrition research to identify dietary biomarkers and to deepen the understanding of metabolic dynamics and the impacts on health risks. However, so far, candidate markers identified by metabolomics have not been clinically applied and more efforts should be made to validate those. To help nutrition researchers better understand the potential of its application, this scoping review outlined the historical transition, recent focuses, and future prospects of the new realm, based on trends in the number of human research articles from the early stage of 2000 to the present of 2019 by searching the Medical Literature Analysis and Retrieval System Online (MEDLINE). Among them, objective dietary assessment, metabolic profiling, and health risk prediction were positioned as three of the principal applications. The continued growth will enable nutrimetabolomics research to contribute to personalized nutrition in the future.

Measuring Postprandial Metabolic Flexibility to Assess Metabolic Health and Disease

Metabolic abnormalities substantially increase the risk of noncommunicable diseases, which are among the leading causes of mortality globally. Mitigating and preventing these adverse consequences remains challenging due to a limited understanding of metabolic health. Metabolic flexibility, a key tenet of metabolic health, encompasses the responsiveness of interrelated pathways to maintain energy homeostasis throughout daily physiologic challenges, such as the response to meal challenges. One critical underlying research gap concerns the measurement of postprandial metabolic flexibility, which remains incompletely understood. We concisely review the methodology for assessment of postprandial metabolic flexibility in recent human studies. We identify 3 commonalities of study design, specifically the nature of the challenge, nature of the response measured, and approach to data analysis. Primary interventions were acute short-term nutrition challenges, including single- and multiple-macronutrient tolerance tests. Postmeal challenge responses were measured via laboratory assays and instrumentation, based on a diverse set of metabolic flexibility indicators [e.g., energy expenditure (whole-body indirect calorimetry), glucose and insulin kinetics, metabolomics, transcriptomics]. Common standard approaches have been diabetes-centric with single-macronutrient challenges (oral-glucose-tolerance test) to characterize the postprandial response based on glucose and insulin metabolism; or broad measurements of energy expenditure with calculated macronutrient oxidation via indirect calorimetry. Recent methodological advances have included the use of multiple-macronutrient meal challenges that are more representative of physiologic meals consumed by free-living humans, combinatorial approaches for assays and instruments, evaluation of other metabolic flexibility indicators via precision health, systems biology, and temporal perspectives. Omics studies have identified potential novel indicators of metabolic flexibility, which provide greater granularity to prior evidence from canonical approaches. In summary, recent findings indicate the potential for an expanded understanding of postprandial metabolic flexibility, based on nonclassical measurements and methodology, which could represent novel dynamic indicators of metabolic diseases.

Molecular Aspects of Lifestyle and Environmental Effects in Patients With Diabetes: JACC Focus Seminar

Diabetes is characterized as an integrated condition of dysregulated metabolism across multiple tissues, with well-established consequences on the cardiovascular system. Recent advances in precision phenotyping in biofluids and tissues in large human observational and interventional studies have afforded a unique opportunity to translate seminal findings in models and cellular systems to patients at risk for diabetes and its complications. Specifically, techniques to assay metabolites, proteins, and transcripts, alongside more recent assessment of the gut microbiome, underscore the complexity of diabetes in patients, suggesting avenues for precision phenotyping of risk, response to intervention, and potentially novel therapies. In addition, the influence of external factors and inputs (eg, activity, diet, medical therapies) on each domain of molecular characterization has gained prominence toward better understanding their role in prevention. Here, the authors provide a broad overview of the role of several of these molecular domains in human translational investigation in diabetes.

Advantages of Studying the Metabolome in Response to Mixed-Macronutrient Challenges and Suggestions for Future Research Designs

Evaluating the postprandial response to a dietary challenge containing all macronutrients-carbohydrates, lipids, and protein-may provide stronger insights of metabolic health than a fasted measurement. Metabolomic profiling deepens the understanding of the homeostatic and adaptive response to a dietary challenge by classifying multiple metabolic pathways and biomarkers. A total of 26 articles were identified that measure the human blood metabolome or lipidome response to a mixed-macronutrient challenge. Most studies were cross-sectional, exploring the baseline and postprandial response to the dietary challenge. Large variations in study designs were reported, including the macronutrient and caloric composition of the challenge and the delivery of the challenge as a liquid shake or a solid meal. Most studies utilized a targeted metabolomics platform, assessing only a particular metabolic pathway, however, several studies utilized global metabolomics and lipidomics assays demonstrating the expansive postprandial response of the metabolome. The postprandial response of individual amino acids was largely dependent on the amino acid composition of the test meal, with the exception of alanine and proline, 2 nonessential amino acids. Long-chain fatty acids and unsaturated long-chain acylcarnitines rapidly decreased in response to the dietary challenges, representing the switch from fat to carbohydrate oxidation. Studies were reviewed that assessed the metabolome response in the context of obesity and metabolic diseases, providing insight on how weight status and disease influence the ability to cope with a nutrient load and return to homeostasis. Results demonstrate that the flexibility to respond to a substrate load is influenced by obesity and metabolic disease and flexibility alterations will be evident in downstream metabolites of fat, carbohydrate, and protein metabolism. In response, we propose suggestions for standardization between studies with the potential of creating a study exploring the postprandial response to a multitude of challenges with a variety of macronutrients.

Nutrition to Optimise Human Health-How to Obtain Physiological Substantiation?

Demonstrating in an unambiguous manner that a diet, let alone a single product, 'optimizes' health, presents an enormous challenge. The least complicated is when the starting situation is clearly suboptimal, like with nutritional deficiencies, malnutrition, unfavourable lifestyle, or due to disease or ageing. Here, desired improvements and intervention strategies may to some extent be clear. However, even then situations require approaches that take into account interactions between nutrients and other factors, complex dose-effect relationships etc. More challenging is to substantiate that a diet or a specific product optimizes health in the general population, which comes down to achieve perceived, 'non-medical' or future health benefits in predominantly healthy persons. Presumed underlying mechanisms involve effects of non-nutritional components with subtle and slowly occurring physiological effects that may be difficult to translate into measurable outcomes. Most promising strategies combine classical physiological concepts with those of 'multi-omics' and systems biology. Resilience-the ability to maintain or regain homeostasis in response to stressors-is often used as proxy for a particular health domain. Next to this, quantifying health requires personalized strategies, measurements preferably carried out remotely, real-time and in a normal living environment, and experimental designs other than randomized controlled trials (RCTs), for example N-of-1 trials.

Serum Metabolites Responding in a Dose-Dependent Manner to the Intake of a High-Fat Meal in Normal Weight Healthy Men Are Associated with Obesity

Although the composition of the human blood metabolome is influenced both by the health status of the organism and its dietary behavior, the interaction between these two factors has been poorly characterized. This study makes use of a previously published randomized controlled crossover acute intervention to investigate whether the blood metabolome of 15 healthy normal weight (NW) and 17 obese (OB) men having ingested three doses (500, 1000, 1500 kcal) of a high-fat (HF) meal can be used to identify metabolites differentiating these two groups. Among the 1024 features showing a postprandial response, measured between 0 h and 6 h, in the NW group, 135 were dose-dependent. Among these 135 features, 52 had fasting values that were significantly different between NW and OB men, and, strikingly, they were all significantly higher in OB men. A subset of the 52 features was identified as amino acids (e.g., branched-chain amino acids) and amino acid derivatives. As the fasting concentration of most of these metabolites has already been associated with metabolic dysfunction, we propose that challenging normal weight healthy subjects with increasing caloric doses of test meals might allow for the identification of new fasting markers associated with obesity.

Gut microbiota-derived metabolites as central regulators in metabolic disorders

Metabolic disorders represent a growing worldwide health challenge due to their dramatically increasing prevalence. The gut microbiota is a crucial actor that can interact with the host by the production of a diverse reservoir of metabolites, from exogenous dietary substrates or endogenous host compounds. Metabolic disorders are associated with alterations in the composition and function of the gut microbiota. Specific classes of microbiota-derived metabolites, notably bile acids, short-chain fatty acids, branched-chain amino acids, trimethylamine N-oxide, tryptophan and indole derivatives, have been implicated in the pathogenesis of metabolic disorders. This review aims to define the key classes of microbiota-derived metabolites that are altered in metabolic diseases and their role in pathogenesis. They represent potential biomarkers for early diagnosis and prognosis as well as promising targets for the development of novel therapeutic tools for metabolic disorders.

Distinct Postprandial Bile Acids Responses to a High-Calorie Diet in Men Volunteers Underscore Metabolically Healthy and Unhealthy Phenotypes

Bile acids (BAs) regulate dietary lipid hydrolysis and absorption in the proximal intestine. Several studies have highlighted a determinant role of circulating levels and/or metabolism of BAs in the pathogenesis of major cardiometabolic diseases. Whether changes in BA profiles are causative or are consequence of these diseases remains to be determined. Healthy male volunteers (n = 71) underwent a postprandial exploration following consumption of a hypercaloric high fat typical Western meal providing 1200 kcal. We investigated variations of circulating levels of 28 BA species, together with BA synthesis marker 7α-hydroxy-4-cholesten-3-one (C4) over an approximately diurnal 12 h period. Analysis of BA variations during the postprandial time course revealed two major phenotypes with opposite fluctuations, i.e., circulating levels of each individual species of unconjugated BAs were reduced after meal consumption whereas those of tauro- and glyco-conjugated BAs were increased. By an unbiased classification strategy based on absolute postprandial changes in BA species levels, we classified subjects into three distinct clusters; the two extreme clusters being characterized by the smallest absolute changes in either unconjugated-BAs or conjugated-BAs. Finally, we demonstrated that our clustering based on postprandial changes in BA profiles was associated with specific clinical and biochemical features, including postprandial triglyceride levels, BMI or waist circumference. Altogether, our study reveals that postprandial profiles/patterns of BAs in response to a hypercaloric high fat challenge is associated with healthy or unhealthy metabolic phenotypes that may help in the early identification of subjects at risk of developing metabolic disorders.

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.

The role of postprandial very-low-density lipoprotein in the development of atrial remodeling in metabolic syndrome

Background

Negatively charged very-low-density lipoprotein (VLDL-χ) in metabolic syndrome (MetS) patients exerts cytotoxic effects on endothelial cells and atrial myocytes. Atrial cardiomyopathy, manifested by atrial remodeling with a dilated diameter, contributes to atrial fibrillation pathogenesis and predicts atrial fibrillation development. The correlation of VLDL-χ with atrial remodeling is unknown. This study investigated the association between VLDL-χ and remodeling of left atrium.

Methods

Consecutively, 87 MetS and 80 non-MetS individuals between 23 and 74 years old (50.6% men) without overt cardiovascular diseases were included in the prospective cohort study. Blood samples were collected while fasting and postprandially (at 0.5, 1, 2, and 4 h after a unified meal). VLDL was isolated by ultracentrifugation; the percentile concentration of VLDL-χ (%) was determined by ultra-performance liquid chromatography. The correlations of left atrium diameter (LAD) with variables including VLDL-χ, LDL-C, HDL-C, triglycerides, glucose, and blood pressure, were analyzed by multiple linear regression models. A hierarchical linear model was conducted to test the independencies of each variable’s correlation with LAD.

Results

The mean LAD was 3.4 ± 0.5 cm in non-MetS subjects and 3.9 ± 0.5 cm in MetS patients (P < 0.01). None of the fasting lipid profiles were associated with LAD. VLDL-χ, BMI, waist circumference, hip circumference, and blood pressure were positively correlated with LAD (all P < 0.05) after adjustment for age and sex. Significant interactions between VLDL-χ and blood pressure, waist circumference, and hip circumference were observed. When adjusted for obesity- and blood pressure-related variables, 2-h postprandial VLDL-χ (mean 1.30 ± 0.61%) showed a positive correlation with LAD in MetS patients. Each 1% VLDL-χ increase was estimated to increase LAD by 0.23 cm.

Conclusions

Postprandial VLDL-χ is associated with atrial remodeling particularly in the MetS group. VLDL-χ is a novel biomarker and may be a therapeutic target for atrial cardiomyopathy in MetS patients.

Trial registration

ISRCTN 69295295. Retrospectively registered 9 June 2020.

From lifespan to healthspan: the role of nutrition in healthy ageing

Across the globe, there has been a marked increase in longevity, but significant inequalities remain. These are exacerbated by inadequate access to proper nutrition and health care services and to reliable information to make the decisions related to nutrition and health care. Many in economically developing as well as developed societies are plagued with the double-burden of energy excess and undernutrition. This has resulted in mental and physical deterioration, increased non-communicable disease rates, lost productivity, increased medical costs and reduced quality of life. While adequate nutrition is fundamental to good health at all stages of the life course, the impact of diet on prolonging good quality of life during ageing remains unclear. For progress to continue, there is need for new and/or innovative approaches to promoting health as individuals age, as well as qualitative and quantitative biomarkers and other accepted tools that can measure improvements in physiological integrity throughout life. A framework for progress has been proposed by the World Health Organization in their Global Strategy and Action Plan on Ageing and Health. Here, we focused on the impact of nutrition within this framework, which takes a broad, person-centred emphasis on healthy ageing, stressing the need to better understand each individual's intrinsic capacity, their functional abilities at various life stages, and the impact of their mental, and physical health, as well as the environments they inhabit.

Postprandial NMR-Based Metabolic Exchanges Reflect Impaired Phenotypic Flexibility across Splanchnic Organs in the Obese Yucatan Mini-Pig

The postprandial period represents one of the most challenging phenomena in whole-body metabolism, and it can be used as a unique window to evaluate the phenotypic flexibility of an individual in response to a given meal, which can be done by measuring the resilience of the metabolome. However, this exploration of the metabolism has never been applied to the arteriovenous (AV) exploration of organs metabolism. Here, we applied an AV metabolomics strategy to evaluate the postprandial flexibility across the liver and the intestine of mini-pigs subjected to a high fat–high sucrose (HFHS) diet for 2 months. We identified for the first time a postprandial signature associated to the insulin resistance and obesity outcomes, and we showed that the splanchnic postprandial metabolome was considerably affected by the meal and the obesity condition. Most of the changes induced by obesity were observed in the exchanges across the liver, where the metabolism was reorganized to maintain whole body glucose homeostasis by routing glucose formed de novo from a large variety of substrates into glycogen. Furthermore, metabolites related to lipid handling and energy metabolism showed a blunted postprandial response in the obese animals across organs. Finally, some of our results reflect a loss of flexibility in response to the HFHS meal challenge in unsuspected metabolic pathways that must be further explored as potential new events involved in early obesity and the onset of insulin resistance.

Identification of Milk and Cheese Intake Biomarkers in Healthy Adults Reveals High Interindividual Variability of Lewis System-Related Oligosaccharides

BACKGROUND

The use of biomarkers of food intake (BFIs) in blood and urine has shown great promise for assessing dietary intake and complementing traditional dietary assessment tools whose use is prone to misreporting.

OBJECTIVE

Untargeted LC-MS metabolomics was applied to identify candidate BFIs for assessing the intake of milk and cheese and to explore the metabolic response to the ingestion of these foods.

METHODS

A randomized controlled crossover study was conducted in healthy adults [5 women, 6 men; age: 23.6 ± 5.0 y; BMI (kg/m2): 22.1 ± 1.7].  After a single isocaloric intake of milk (600 mL), cheese (100 g), or soy-based drink (600 mL), serum and urine samples were collected postprandially up to 6 h and after fasting after 24 h. Untargeted metabolomics was conducted using LC-MS. Discriminant metabolites were selected in serum by multivariate statistical analysis, and their mass distribution and postprandial kinetics were compared.

RESULTS

Serum metabolites discriminant for cheese intake had a significantly lower mass distribution than metabolites characterizing milk intake (P = 4.1 × 10-4). Candidate BFIs for milk or cheese included saccharides, a hydroxy acid, amino acids, amino acid derivatives, and dipeptides. Two serum oligosaccharides, blood group H disaccharide (BGH) and Lewis A trisaccharide (LeA), specifically reflected milk intake but with high interindividual variability. The 2 oligosaccharides showed related but opposing trends: subjects showing an increase in either oligosaccharide did not show any increase in the other oligosaccharide. This result was confirmed in urine.

CONCLUSIONS

New candidate BFIs for milk or cheese could be identified in healthy adults, most of which were related to protein metabolism. The increase in serum of LeA and BGH after cow-milk intake in adults calls for further investigations considering the beneficial health effects on newborns of such oligosaccharides in maternal milk. The trial is registered at clinicaltrials.gov as NCT02705560.

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.

Profiling of Endogenous and Gut Microbial Metabolites to Indicate Metabotype-Specific Dietary Responses: A Systematic Review

Upon dietary exposure, the endogenous metabolism responds to the diet-derived nutrients and bioactive compounds, such as phytochemicals. However, the responses vary remarkably due to the interplay with other dietary components, lifestyle exposures, and intrinsic factors, which lead to differences in endogenous regulatory metabolism. These physiological processes are evidenced as a signature profile composed of various metabolites constituting metabolic phenotypes, or metabotypes. The metabolic profiling of biological samples following dietary intake hence would provide information about diet-that is, as the intake biomarkers and the ongoing physiological reactions triggered by this intake-thereby enable evaluation of the metabolic basis required to distinguish the different metabotypes. The capacity of nontargeted metabolomics to also encompass the unprecedented metabolite species has enabled the profiling of multiple metabolites and the corresponding metabotypes with a single analysis, decoding the complex interplay between diet, other relevant factors, and health. In this systematic review, we screened 345 articles published in English in January 2007-July 2018, which applied the metabolomics approach to profile the changes of endogenous metabolites in the blood related to dietary interventions, either derived by metabolism of gut microbiota or the human host. We excluded all the compounds that were directly derived from diet, and also the dietary interventions focusing on supplementation with individual compounds. After the removal of less relevant studies and assessment of eligibility, 49 articles were included in this review. First, we mention the contribution of individual factors, either modifiable or nonmodifiable factors, in shaping metabolic profile. Then, how different aspects of the diet would affect the metabolic profiles are disentangled. Next, the classes of endogenous metabolites altered following included dietary interventions are listed. We also discuss the current challenges in the field, along with future research opportunities.

Biomarkers of appetite: is there a potential role for metabolomics?

Knowing the biological signals associated with appetite control is crucial for understanding the regulation of food intake. Biomarkers of appetite have been defined as physiological measures that relate to subjective appetite ratings, measured food intake, or both. Several metabolites including amino acids, lipids and glucose were proposed as key molecules associated with appetite control over 60 years ago, and along with bile acids are all among possible appetite biomarker candidates. Additional metabolites that have been associated with appetite include endocannabinoids, lactate, cortisol and β-hydroxybutyrate. However, although appetite is a complex integrative process, studies often investigated a limited number of markers in isolation. Metabolomics involves the study of small molecules or metabolites present in biological samples such as urine or blood, and may present a powerful approach to further the understanding of appetite control. Using multiple analytical techniques allows the characterisation of molecules, such as carbohydrates, lipids, amino acids, bile acids and fatty acids. Metabolomics has proven successful in identifying markers of consumption of certain foods and biomarkers implicated in several diseases. However, it has been underexploited in appetite control or obesity. The aim of the present narrative review is to: (1) provide an overview of existing metabolites that have been identified in human biofluids and associated with appetite control; and (2) discuss the potential of metabolomics to deepen understanding of appetite control in humans.

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.

Metabolomics evaluation of repeated administration of potassium iodide on adult male rats

The long-lasting consequence of a new iodine thyroid blocking strategy (ITB) to be used in case of nuclear accident is evaluated in male Wistar rats using a metabolomics approach applied 30 days after ITB completion. The design used 1 mg/kg/day of KI over 8 days. Thyroid hormones remained unchanged, but there was a metabolic shift measured mainly in thyroid then in plasma and urine. In the thyroid, tyrosine metabolism associated to catecholamine metabolism was more clearly impacted than thyroid hormones pathway. It was accompanied by a peripheral metabolic shift including metabolic regulators, branched-chain amino acids, oxidant stress and inflammation-associated response. Our results suggested that iodide intake can impact gut microbiota metabolism, which was related to host metabolic regulations including in the thyroid. As there were no clear clinical signs of dysfunction or toxicity, we concluded that the measured metabolomics response to the new ITB strategy, especially in thyroid, is unlikely to reveal a pathological condition but a shift towards a new adaptive homeostatic state, called 'allostatic regulation'. The question now is whether or not the shift is permanent and if so at what cost for long-term health. We anticipate our data as a start point for further regulatory toxicity studies.

Perspective: Guiding Principles for the Implementation of Personalized Nutrition Approaches That Benefit Health and Function

Personalized nutrition (PN) approaches have been shown to help drive behavior change and positively influence health outcomes. This has led to an increase in the development of commercially available PN programs, which utilize various forms of individual-level information to provide services and products for consumers. The lack of a well-accepted definition of PN or an established set of guiding principles for the implementation of PN creates barriers for establishing credibility and efficacy. To address these points, the North American Branch of the International Life Sciences Institute convened a multidisciplinary panel. In this article, a definition for PN is proposed: "Personalized nutrition uses individual-specific information, founded in evidence-based science, to promote dietary behavior change that may result in measurable health benefits." In addition, 10 guiding principles for PN approaches are proposed: 1) define potential users and beneficiaries; 2) use validated diagnostic methods and measures; 3) maintain data quality and relevance; 4) derive data-driven recommendations from validated models and algorithms; 5) design PN studies around validated individual health or function needs and outcomes; 6) provide rigorous scientific evidence for an effect on health or function; 7) deliver user-friendly tools; 8) for healthy individuals, align with population-based recommendations; 9) communicate transparently about potential effects; and 10) protect individual data privacy and act responsibly. These principles are intended to establish a basis for responsible approaches to the evidence-based research and practice of PN and serve as an invitation for further public dialog. Several challenges were identified for PN to continue gaining acceptance, including defining the health-disease continuum, identification of biomarkers, changing regulatory landscapes, accessibility, and measuring success. Although PN approaches hold promise for public health in the future, further research is needed on the accuracy of dietary intake measurement, utilization and standardization of systems approaches, and application and communication of evidence.

Current and Future Nutritional Strategies to Modulate Inflammatory Dynamics in Metabolic Disorders

Obesity, type 2 diabetes, and other metabolic disorders have a large impact on global health, especially in Western countries. An important hallmark of metabolic disorders is chronic low-grade inflammation. A key player in chronic low-grade inflammation is dysmetabolism, which is defined as the inability to keep homeostasis resulting in loss of lipid control, oxidative stress, inflammation, and insulin resistance. Although often not yet detectable in the circulation, chronic low-grade inflammation can be present in one or multiple organs. The response to a metabolic challenge containing lipids may magnify dysfunctionalities at the tissue level, causing an overflow of inflammatory markers into the circulation and hence allow detection of early low-grade inflammation. Here, we summarize the evidence of successful application of metabolic challenge tests in type 2 diabetes, metabolic syndrome, obesity, and unhealthy aging. We also review how metabolic challenge tests have been successfully applied to evaluate nutritional intervention effects, including an "anti-inflammatory" mixture, dark chocolate, whole grain wheat and overfeeding. Additionally, we elaborate on future strategies to (re)gain inflammatory flexibility. Through epigenetic and metabolic regulation, the inflammatory response may be trained by regular mild and metabolic triggers, which can be understood from the perspective of trained immunity, hormesis and pro-resolution. New strategies to optimize dynamics of inflammation may become available.

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.

Postprandial Metabolic Effects of Fiber Mixes Revealed by in vivo Stable Isotope Labeling in Humans

Food supplementation with a fiber mix of guar gum and chickpea flour represents a promising approach to reduce the risk of type 2 diabetes mellitus (T2DM) by attenuating postprandial glycemia. To investigate the effects on postprandial metabolic fluxes of glucose-derived metabolites in response to this fiber mix, a randomized, cross-over study was designed. Twelve healthy, male subjects consumed three different flatbreads either supplemented with 2% guar gum or 4% guar gum and 15% chickpea flour or without supplementation (control). The flatbreads were enriched with ~2% of ¹³C-labeled wheat flour. Blood was collected at 16 intervals over a period of 360 min after bread intake and plasma samples were analyzed by GC-MS based metabolite profiling combined with stable isotope-assisted metabolomics. Although metabolite levels of the downstream metabolites of glucose, specifically lactate and alanine, were not altered in response to the fiber mix, supplementation of 4% guar gum was shown to significantly delay and reduce the exogenous formation of these metabolites. Metabolic modeling and computation of appearance rates revealed that the effects induced by the fiber mix were strongest for glucose and attenuated downstream of glucose. Further investigations to explore the potential of fiber mix supplementation to counteract the development of metabolic diseases are warranted.

Roux-en-Y Gastric Bypass Surgery Induces Distinct but Frequently Transient Effects on Acylcarnitine, Bile Acid and Phospholipid Levels

Roux-en-Y gastric bypass (RYGB) is an effective method to achieve sustained weight loss, but the mechanisms responsible for RYGB effects have not yet been fully characterized. In this study, we profiled the concentrations of 143 lipid metabolites in dry blood spots (DBS) of RYGB patients. DBS from obese patients (BMI range 35⁻44 kg/m²) were collected 7 days before, 15 and 90 days after the surgery. LC-MS/MS was used to quantify acylcarnitines, phosphatidylcholines, sphingomyelins and bile acids. RYGB caused a rapid increase in acylcarnitine levels that proved to be only transient, contrasting with the sustained decrease in phosphatidylcholines and increase of sphingomyelins and bile acids. A PLS-DA analysis revealed a 3-component model (R² = 0.9, Q² = 0.74) with key metabolites responsible for the overall metabolite differences. These included the BCAA-derived acylcarnitines and sphingomyelins with 16 and 18 carbons. We found important correlations between the levels of BCAA-derived acylcarnitines and specific sphingomyelins with plasma cholesterol and triacylglycerol concentrations. Along with the marked weight loss and clinical improvements, RYGB induced specific alterations in plasma acylcarnitines, bile acid and phospholipid levels. This calls for more studies on RYGB effects aiming to elucidate the metabolic adaptations that follow this procedure.