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

Impact of Dietary Intake and Cardiorespiratory Fitness on Glycemic Variability in Adolescents: An Observational Study

Background

Cardiorespiratory fitness (CRF), estimated by maximum oxygen consumption (VO2 max) during exercise, is worsening among adolescents and associated with a decline in metabolic health into adulthood. Glycemic patterns may provide a mechanism between CRF and health.

Objectives

This study assessed the feasibility of measuring glycemic patterns using continuous glucose monitoring (CGM) in adolescents, aged 14-22 y, to estimate the relationship between VO2 max and glucose patterns.

Methods

Healthy adolescents (n = 30) were recruited for a treadmill VO2 max test and to complete the following activities for 7-10 d: 1) wear a Dexcom G6 CGM, 2) complete ≥3 24-h dietary recalls, and 3) complete 1 at-home oral glucose tolerance test (OGTT, 75 g glucose). Glycemic patterns were extracted as mean glucose, the coefficient of variance, the mean amplitude of glycemic excursions, and the mean of daily differences. The 2-h glucose responses to the OGTT and individual meals were extracted. Statistical analyses evaluated the relationship between VO2 max and 1) overall glycemic patterns and 2) the maximum glucose level and AUC response to OGTT and meals, stratified by sex.

Results

Participant feasibility demonstrated that 90% completed CGM data (n = 27), 87% ≥7 d of CGM data (n = 26), 97% attempted OGTT (n = 29), and 93% completed ≥3 dietary recalls (n = 28). Most participants had normal BMI (70%) with an even distribution of sex (44% male). Males exhibited an inverse relationship between VO2 max and overall mean glucose (ß= -7.7, P = 0.04). Males demonstrated an inverse relationship between VO2 max and 1) maximum glucose (ß = -29, P = 0.006) and AUC (ß = -2702, P = 0.001) in response to the OGTT and 2) AUC (ß = -1293, P = 0.03) in response to meals. No association was observed between VO2 max and glucose patterns in females.

Conclusions

A sex-specific relationship between VO2 max and glycemic patterns was observed, suggesting a unique metabolic capacity during late adolescence by sex.This trial was registered at clinicaltrials.gov as NCT05845827.

Postprandial Metabolomic Profiling: Insights into Macronutrient-Specific Metabolic Responses in Healthy Individuals

BACKGROUND/OBJECTIVES

Understanding the metabolic responses to different macronutrients is crucial for assessing their impacts on health. This study aims to investigate the postprandial metabolomic profiles of healthy individuals following the consumption of glucose, protein, and lipids.

METHODS

Twenty-three healthy, normal-weight adults participated in the study, randomly assigned to consume 300 kcal from glucose, protein, or lipids after an overnight fast. Blood samples were collected at baseline and at 1, 2, and 3 h post-ingestion. An untargeted metabolomic approach using mass spectrometry was employed to analyze plasma metabolites.

RESULTS

In total, 21, 59, and 156 dysregulated metabolites were identified after glucose, protein, and lipid intake, respectively. Notably, 3'-O-methylguanosine levels decreased significantly after glucose consumption while remaining stable during lipid intake before increasing at 2 h. Common metabolites shared between glucose and lipid groups included 3'-O-methylguanosine, 3-oxotetradecanoic acid, poly-g-D-glutamate, and triglyceride (TG) (15:0/18:4/18:1).

CONCLUSIONS

The findings highlight distinct metabolic responses to macronutrient intake, emphasizing the role of specific metabolites in regulating postprandial metabolism. These insights contribute to understanding how dietary components influence metabolic health and insulin sensitivity.

Serum concentrations of lipids, ketones and acylcarnitines during the postprandial and fasting state: the Postprandial Metabolism (PoMet) study in healthy young adults

To improve the interpretation and utilisation of blood lipids, ketones and acylcarnitine concentrations as biomarkers in clinical assessments, more information is needed on their dynamic alterations in response to dietary intake and fasting. The aim of this intervention study was to characterise the changes in serum lipid, ketone and acylcarnitine concentrations 24 h after a standardised breakfast meal. Thirty-four healthy subjects (eighteen males and sixteen females) aged 20-30 years were served a breakfast meal (∼500 kcal, 36 E% fat, 46 E% carbohydrates, 16 E% protein, 2E% fibre), after which they consumed only water for 24 h. Blood samples were drawn before and at thirteen standardised timepoints after the meal. Metabolite concentrations were plotted as a function of time since the completion of the breakfast meal. Results demonstrated that concentrations of HDL-cholesterol and LDL-cholesterol decreased until ∼2 h (-4 % for both), while TAG concentrations peaked at 3 h (+27 %). Acetoacetate and β-hydroxybutyrate were highest 24 h after the meal (+433 and +633 %, respectively). Acetylcarnitine, butyrylcarnitine, hexanoylcarnitine, octanoylcarnitine, decanoylcarnitine and dodecanoylcarnitine reached the lowest values at 60 min (decreases ranging from -47 to -70 %), before increasing and peaking at 24 h after the meal (increases ranging from +86 to +120 %). Our findings suggest that distinguishing between fasting and non-fasting blood samples falls short of capturing the dynamics in lipid, ketone, carnitine and acylcarnitine concentrations. To enhance the utility of serum acylcarnitine analyses, we strongly recommend accounting for the specific time since the last meal at the time of blood sampling.

Fat burning capacity in a mixed macronutrient meal protocol does not reflect metabolic flexibility in women who are overweight or obese

Introduction

Metabolic flexibility, the ability to switch from glucose to fat as a fuel source, is considered a marker of metabolic health. Higher fat oxidation is often associated with greater flexibility and insulin sensitivity, while lower fat oxidation is linked to metabolic inflexibility and insulin resistance. However, our study challenges the universal validity of this relationship, uncovering a more nuanced understanding of the complex interplay between fuel source switching and fat oxidation, especially in the presence of insulin resistance.

Methods

In an 8-week controlled feeding intervention, overweight to obese women with insulin resistance (as defined by McAuley's index) were randomized to consume either a diet based on the Dietary Guidelines for Americans 2010 (DGA) or a 'Typical' American Diet (TAD), n = 22 each. Participants were given a high-fat mixed macronutrient challenge test (MMCT) (60% fat, 28% carbohydrates, and 12% protein) at weeks 0, 2, and 8. Plasma lipids, metabolome, and lipidome were measured at 0, 0.5, 3, and 6h postprandial (PP); substrate oxidation measures were also recorded at 0,1 3, and 6h PP. Metabolic flexibility was evaluated as the change in fat oxidation from fasting to PP. Mixed model and multivariate analyses were used to evaluate the effect of diet on these outcomes, and to identify variables of interest to metabolic flexibility.

Results

Intervention diets (DGA and TAD) did not differentially affect substrate oxidation or metabolic flexibility, and equivalence tests indicated that groups could be combined for subsequent analyses. Participants were classified into three groups based on the % of consumed MMCT fat was oxidized in the 6h post meal period at weeks 0, 2 and 8. Low fat burners (LB, n = 6, burned <30% of fat in MMCT) and high fat burners (HB, n = 7, burned > 40% of fat in MMCT) at all weeks. Compared to LB, HB group had higher fat mass, total mass, lean mass, BMI, lower HDLc and lower RER (p < 0.05), but not different % body fat or % lean mass. During week 0, at 1h PP, LB had an increase in % fat oxidation change from 0h compared to HB (p<0.05), suggesting higher metabolic flexibility. This difference disappeared later in the PP phase, and we did not detect this beyond week 0. Partial least squares discriminant analysis (PLSDA (regular and repeated measures (sPLSDA)) models identified that LB group, in the late PP phase, was associated with higher rates of disappearance of acylcarnitines (AC) and lysophosphatidylcholines (LPC) from plasma (Q2: 0.20, R2X: 0.177, R2Y: 0.716).

Conclusion

In women with insulin resistance, a high fat burning capacity does not imply high metabolic flexibility, and not all women with insulin resistance are metabolically inflexible. LPCs and ACs are promising biomarkers of metabolic flexibility.

Human metabolic chambers reveal a coordinated metabolic-physiologic response to nutrition

The emerging field of precision nutrition is based on the notion that inter-individual responses across diets of different calorie-macronutrient content may contribute to inter-individual differences in metabolism, adiposity, and weight gain. Free-living diet studies have been traditionally challenged by difficulties in controlling adherence to prescribed calories and macronutrient content and rarely allow a period of metabolic stability prior to metabolic measures (to minimize influences of weight changes). In this context, key physiologic measures central to precision nutrition responses may be most precisely quantified via whole room indirect calorimetry over 24-h, in which precise control of activity and nutrition can be achieved. In addition, these studies represent unique "N of 1" human crossover metabolic-physiologic experiments during which specific molecular pathways central to nutrient metabolism may be discerned. Here, we quantified 263 circulating metabolites during a ≈40-day inpatient admission in which up to 94 participants underwent seven monitored 24-h nutritional interventions of differing macronutrient composition in a whole-room indirect calorimeter to capture precision metabolic responses. Broadly, we observed heterogenous responses in metabolites across dietary chambers, with the exception of carnitines which tracked with 24-h respiratory quotient. We identified excursions in shared metabolic species (e.g., carnitines, glycerophospholipids, amino acids) that mapped onto gold-standard calorimetric measures of substrate oxidation preference and lipid availability. These findings support a coordinated metabolic-physiologic response to nutrition, highlighting the relevance of these controlled settings to uncover biological pathways of energy utilization during precision nutrition studies.

Time-resolved concentrations of serum amino acids, one-carbon metabolites and B-vitamin biomarkers during the postprandial and fasting state: the Postprandial Metabolism in Healthy Young Adults (PoMet) Study

Metabolomics has been utilised in epidemiological studies to investigate biomarkers of nutritional status and metabolism in relation to non-communicable diseases. However, little is known about the effect of prandial status on several biomarker concentrations. Therefore, the aim of this intervention study was to investigate the effect of a standardised breakfast meal followed by food abstinence for 24 h on serum concentrations of amino acids, one-carbon metabolites and B-vitamin biomarkers. Thirty-four healthy subjects (eighteen males and sixteen females) aged 20-30 years were served a breakfast meal (∼500 kcal) after which they consumed only water for 24 h. Blood samples were drawn before and at thirteen standardised timepoints after the meal. Circulating concentrations of most amino acids and metabolites linked to one-carbon metabolism peaked within the first 3 h after the meal. The branched-chain amino acids steadily increased from 6 or 8 hours after the meal, while proline decreased in the same period. Homocysteine and cysteine concentrations immediately decreased after the meal but steadily increased from 3 and 4 hours until 24 h. FMN and riboflavin fluctuated immediately after the meal but increased from 6 h, while folate increased immediately after the meal and remained elevated during the 24 h. Our findings indicate that accurate reporting of time since last meal is crucial when investigating concentrations of certain amino acids and one-carbon metabolites. Our results suggest a need for caution when interpretating studies, which utilise such biomarkers, but do not strictly control for time since the last meal.

Systematic Review and Quantitative Data Synthesis of Peripheral Blood Mononuclear Cell Transcriptomics Reveals Consensus Gene Expression Changes in Response to a High Fat Meal

SCOPE

Metabolic flexibility is essential for a healthy response to a high fat meal, and is assessed by measuring postprandial changes in blood markers including peripheral blood mononuclear cells (PBMCs; lymphocytes and monocytes). However, there is no clear consensus on postprandial gene expression and protein changes in these cells.

METHOD AND RESULTS

The study systematically reviews the literature reporting transcriptional and proteomic changes in PBMCs after consumption of a high fat meal. After re-analysis of the raw data to ensure equivalence between studies, ≈85 genes are significantly changed (defined as in the same direction in ≥3 studies) with about half involved in four processes: inflammation/oxidative stress, GTP metabolism, apoptosis, and lipid localization/transport. For meals consisting predominantly of unsaturated fatty acids (UFA), notable additional processes are phosphorylation and glucocorticoid response. For saturated fatty acids (SFA), genes related to migration/angiogenesis and platelet aggregation are also changed.

CONCLUSION

Despite differences in study design, common gene changes are identified in PBMCs following a high fat meal. These common genes and processes will facilitate definition of the postprandial transcriptome as part of the overall postcibalome, linking all molecules and processes that change in the blood after a meal.

Exploratory analyses on the effect of time since last meal on concentrations of amino acids, lipids, one-carbon metabolites, and vitamins in the Hordaland Health Study

PURPOSE

Dietary intake may have pronounced effects on circulating biomarker concentrations. Therefore, the aim was to provide a descriptive overview of serum metabolite concentrations in relation to time since last meal, focusing on amino acids, lipids, one-carbon metabolites, and biomarkers of vitamin status.

METHODS

We used baseline data from the observational community-based Hordaland Health Study, including 2960 participants aged 46-49 years and 2874 participants aged 70-74 years. A single blood draw was taken from each participant, and time since last meal varied. Estimated marginal geometric mean metabolite concentrations were plotted as a function of time since last meal, up to 7 h, adjusted for age, sex, and BMI.

RESULTS

We observed a common pattern for nearly all amino acids and one-carbon metabolites with highest concentrations during the first 3 h after dietary intake. Homocysteine and cysteine were lowest the 1st hour after a meal, while no patterns were observed for glutamate and glutamic acid. The concentrations of phylloquinone and triglycerides were highest 1 h after dietary intake. Thiamine and thiamine monophosphate concentrations were highest, while flavin mononucleotide concentrations were lowest within the first 2 h after a meal. No clear patterns emerged for the other fat-soluble vitamins, blood lipids, or B-vitamin biomarkers.

CONCLUSION

Our findings suggest that distinguishing between "fasting" and "non-fasting" blood samples may be inadequate, and a more granular approach is warranted. This may have implications for how to account for dietary intake when blood sampling in both clinical and research settings.

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.

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.

Postprandial Plasma Lipidomics Reveal Specific Alteration of Hepatic-derived Diacylglycerols in Nonalcoholic Fatty Liver Disease

BACKGROUND & AIMS

Hepatic energy metabolism is a dynamic process modulated by multiple stimuli. In nonalcoholic fatty liver disease (NAFLD), human studies typically focus on the static fasting state. We hypothesized that unique postprandial alterations in hepatic lipid metabolism are present in NAFLD.

METHODS

In a prospective clinical study, 37 patients with NAFLD and 10 healthy control subjects ingested a standardized liquid meal with pre- and postprandial blood sampling. Postprandial plasma lipid kinetics were characterized at the molecular lipid species level by untargeted lipidomics, cluster analysis, and lipid particle isolation, then confirmed in a mouse model.

RESULTS

There was a specific increase of multiple plasma diacylglycerol (DAG) species at 4 hours postprandially in patients with NAFLD but not in controls. This was replicated in a nonalcoholic steatohepatitis mouse model, where postprandial DAGs increased in plasma and concomitantly decreased in the liver. The increase in plasma DAGs appears early in the disease course, is dissociated from NAFLD severity and obesity, and correlates with postprandial insulin levels. Immunocapture isolation of very low density lipoprotein in human samples and stable isotope tracer studies in mice revealed that elevated postprandial plasma DAGs reflect hepatic secretion of endogenous, rather than meal-derived lipids.

CONCLUSIONS

We identified a selective insulin-related increase in hepatic secretion of endogenously derived DAGs after a mixed meal as a unique feature of NAFLD. DAGs are known to be lipotoxic and associated with atherosclerosis. Although it is still unknown whether the increased exposure to hepatic DAGs contributes to extrahepatic manifestations and cardiovascular risk in NAFLD, our study highlights the importance of extending NAFLD research beyond the fasting state.

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.

Genomics of Postprandial Lipidomics in the Genetics of Lipid-Lowering Drugs and Diet Network Study

Postprandial lipemia (PPL) is an important risk factor for cardiovascular disease. Inter-individual variation in the dietary response to a meal is known to be influenced by genetic factors, yet genes that dictate variation in postprandial lipids are not completely characterized. Genetic studies of the plasma lipidome can help to better understand postprandial metabolism by isolating lipid molecular species which are more closely related to the genome. We measured the plasma lipidome at fasting and 6 h after a standardized high-fat meal in 668 participants from the Genetics of Lipid-Lowering Drugs and Diet Network study (GOLDN) using ultra-performance liquid chromatography coupled to (quadrupole) time-of-flight mass spectrometry. A total of 413 unique lipids were identified. Heritable and responsive lipid species were examined for association with single-nucleotide polymorphisms (SNPs) genotyped on the Affymetrix 6.0 array. The most statistically significant SNP findings were replicated in the Amish Heredity and Phenotype Intervention (HAPI) Heart Study. We further followed up findings from GOLDN with a regional analysis of cytosine-phosphate-guanine (CpGs) sites measured on the Illumina HumanMethylation450 array. A total of 132 lipids were both responsive to the meal challenge and heritable in the GOLDN study. After correction for multiple testing of 132 lipids (α = 5 × 10-8/132 = 4 × 10-10), no SNP was statistically significantly associated with any lipid response. Four SNPs in the region of a known lipid locus (fatty acid desaturase 1 and 2/FADS1 and FADS2) on chromosome 11 had p < 8.0 × 10-7 for arachidonic acid FA(20:4). Those SNPs replicated in HAPI Heart with p < 3.3 × 10-3. CpGs around the FADS1/2 region were associated with arachidonic acid and the relationship of one SNP was partially mediated by a CpG (p = 0.005). Both SNPs and CpGs from the fatty acid desaturase region on chromosome 11 contribute jointly and independently to the diet response to a high-fat meal.