Identification of differential responses to an oral glucose tolerance test in healthy adults

Light stimulation of mitochondria reduces blood glucose levels

Mitochondria regulate metabolism, but solar light influences its rate. Photobiomodulation (PBM) with red light (670 nm) increases mitochondrial membrane potentials and adenosine triphosphate production and may increase glucose demand. Here we show, with a glucose tolerance test, that PBM of normal subjects significantly reduces blood sugar levels. A 15 min exposure to 670 nm light reduced the degree of blood glucose elevation following glucose intake by 27.7%, integrated over 2 h after the glucose challenge. Maximum glucose spiking was reduced by 7.5%. Consequently, PBM with 670 nm light can be used to reduce blood glucose spikes following meals. This intervention may reduce damaging fluctuations of blood glucose on the body.

Exploration of differential responses to FODMAPs and gluten in people with irritable bowel syndrome- a double-blind randomized cross-over challenge study

INTRODUCTION

There is large variation in response to diet in irritable bowel syndrome (IBS) and determinants for differential response are poorly understood.

OBJECTIVES

Our aim was to investigate differential clinical and molecular responses to provocation with fermentable oligo-, di-, monosaccharides, and polyols (FODMAPs) and gluten in individuals with IBS.

METHODS

Data were used from a crossover study with week-long interventions with either FODMAPs, gluten or placebo. The study also included a rapid provocation test. Molecular data consisted of fecal microbiota, short chain fatty acids, and untargeted plasma metabolomics. IBS symptoms were evaluated with the IBS severity scoring system. IBS symptoms were modelled against molecular and baseline questionnaire data, using Random Forest (RF; regression and clustering), Parallel Factor Analysis (PARAFAC), and univariate methods.

RESULTS

Regression and classification RF models were in general of low predictive power (Q2 ≤ 0.22, classification rate < 0.73). Out of 864 clustering models, only 2 had significant associations to clusters (0.69 < CR < 0.73, p < 0.05), but with no associations to baseline clinical measures. Similarly, PARAFAC revealed no clear association between metabolome data and IBS symptoms.

CONCLUSION

Differential IBS responses to FODMAPs or gluten exposures could not be explained from clinical and molecular data despite extensive exploration with different data analytical approaches. The trial is registered at www.

CLINICALTRIALS

gov as NCT03653689 31/08/2018.

Silymarin Reduced Insulin Resistance in Non-Diabetic Women with Obesity

Silymarin has ameliorated obesity, type 2 diabetes (T2DM), and insulin resistance (IR) in combination with standard therapy, diet, or exercise in recent studies. Obesity and IR are the main risk factors for developing T2DM and other metabolic disorders. Today, there is a need for new strategies to target IR in patients with these metabolic diseases. In the present longitudinal study, a group of non-diabetic insulin-resistant women with type 1 and type 2 obesity were given silymarin for 12 weeks, with no change in habitual diet and physical activity. We used the Homeostatic Model Assessment for Insulin Resistance Index (HOMA-IR) to determine IR at baseline and after silymarin treatment (t = 12 weeks). We obtained five timepoint oral glucose tolerance tests, and other biochemical and clinical parameters were analyzed before and after treatment. Treatment with silymarin alone significantly reduced mean fasting plasma glucose (FPG) and HOMA-IR levels at 12 weeks compared to baseline values (p < 0.05). Mean fasting plasma insulin (FPI), total cholesterol, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), triglycerides (Tg), indirect bilirubin, and C-reactive protein (CRP) levels decreased compared to baseline values, although changes were non-significant. The overall results suggest that silymarin may offer a therapeutic alternative to improve IR in non-diabetic individuals with obesity. Further clinical trials are needed in this type of patient to strengthen the results of this study.

The Effect of Acute Hyperglycaemia Induced by Oral Glucose Load on Heart Rate Variability and Skin Microvascular Reactivity in Young Adults

We aimed to elucidate the effects of acute hyperglycaemia, induced by an oral glucose tolerance test (OGTT), on the autonomic nervous system (ANS) and skin microvascular reactivity at the time point of peak plasma glucose concentration (cglc) in 20 young, healthy participants. We assessed their heart rate variability (HRV) as a measure of the ANS activity and the parameters of post-occlusive reactive hyperaemia (PORH) to estimate skin microvascular reactivity as measured by laser Doppler (LD) fluxmetry. The tests were repeated 30 min after a standard OGTT (75 g glucose dissolved in 250 mL water) and, in a separate control experiment, after drinking the same amount of water. Participants had their cglc and serum insulin measured at three consecutive time-points according to the testing protocol. The low-frequency (LF) spectral power, the LF to high-frequency (LF/HF) ratio, and the diastolic blood pressure increased significantly more after water than after OGTT, and there was a trend of the peak LD flux of PORH decreasing more after OGTT than after water. Significant correlations between some PORH and all the HRV parameters and cglc increase after OGTT were found, implying diminished vascular reactivity evoked by hyperglycaemia in healthy subjects with lower glucose tolerance.

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.

Differential Responders to a Mixed Meal Tolerance Test Associated with Type 2 Diabetes Risk Factors and Gut Microbiota-Data from the MEDGI-Carb Randomized Controlled Trial

The global prevalence of type 2 diabetes mellitus (T2DM) has surged in recent decades, and the identification of differential glycemic responders can aid tailored treatment for the prevention of prediabetes and T2DM. A mixed meal tolerance test (MMTT) based on regular foods offers the potential to uncover differential responders in dynamical postprandial events. We aimed to fit a simple mathematical model on dynamic postprandial glucose data from repeated MMTTs among participants with elevated T2DM risk to identify response clusters and investigate their association with T2DM risk factors and gut microbiota. Data were used from a 12-week multi-center dietary intervention trial involving high-risk T2DM adults, comparing high- versus low-glycemic index foods within a Mediterranean diet context (MEDGICarb). Model-based analysis of MMTTs from 155 participants (81 females and 74 males) revealed two distinct plasma glucose response clusters that were associated with baseline gut microbiota. Cluster A, inversely associated with HbA1c and waist circumference and directly with insulin sensitivity, exhibited a contrasting profile to cluster B. Findings imply that a standardized breakfast MMTT using regular foods could effectively distinguish non-diabetic individuals at varying risk levels for T2DM using a simple mechanistic model.

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.

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

BACKGROUND

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

AIM

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

METHODS

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

RESULTS

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

CONCLUSION

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

Metabotyping: a tool for identifying subgroups for tailored nutrition advice

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

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

BACKGROUND AND AIMS

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

METHODS AND RESULTS

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

CONCLUSION

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

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

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

Determination of factors associated with serum cholesterol response to dairy fat consumption in overweight adults: Secondary analysis from an RCT

Elevated intakes of saturated fatty acids (SFA) can adversely affect serum cholesterol levels. Dairy fat contains ~60% SFA, prompting healthy eating guidelines to recommend low-fat dairy. Physiological, and environmental factors influence inter-individual variance in response to food consumption. Evidence exploring the dairy matrix has differing effects of dairy fat consumption on serum cholesterol levels when consumed in the form of cheese. The extent of this variability and determinants of response to dairy fat are currently unknown. The objective of this study was to determine factors associated with lipid metabolism response to a dairy fat intervention, with a focus on serum cholesterol. A 6-week randomized parallel intervention trial was carried out in healthy volunteers (≥50 years, BMI ≥25 kg/m²). Participants (n = 104) consumed ~40 g dairy fat daily in addition to their usual diet, in 1 of 3 forms: butter, cheese, or reduced-fat cheese and butter. For this analysis, “response” was based on the percentage (%) change in serum total cholesterol (TC), low-density lipoprotein cholesterol (LDL-c), and high-density lipoprotein cholesterol (HDL-c) from pre- to post-intervention. Participants were divided into tertiles for each lipid response. The upper and lower tertiles were used to categorize participants as “responders” and “non-responders.” For TC and LDL-c, response was classified as a decrease, whereas “response” was defined as an increase for HDL-c. Clinical response was also considered, by calculating pre- and post-intervention prevalence of those meeting target levels of cholesterol recommendations. Participants demonstrating the largest % decrease (Tertile 1; “responders”) in TC had significantly higher levels of TC and HDL-c, at baseline, and lower levels of triglycerides (TAGs) compared to those in tertile 3 (i.e., TC non-responders). Those with the largest % decrease in LDL-c (Tertile 1: LDL-c responders) had higher baseline levels of LDL-c and lower levels of TAGs. Multiple regression analysis revealed that the % change in TC and LDL-c was associated with baseline TC, TAG, body weight and high-sensitivity C-reactive protein (hsCRP; P < 0.05). Previous work has demonstrated the dairy food matrix affects lipid response to dairy consumption. This study suggests that phenotypic differences may also influence response to dairy fat in overweight individuals.

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.

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.

A Narrative Review of Human Clinical Trials on the Impact of Phenolic-Rich Plant Extracts on Prediabetes and Its Subgroups

Phenolic-rich plant extracts have been demonstrated to improve glycemic control in individuals with prediabetes. However, there is increasing evidence that people with prediabetes are not a homogeneous group but exhibit different glycemic profiles leading to the existence of prediabetes subgroups. Prediabetes subgroups have been identified as: isolated impaired fasting glucose (IFG), isolated impaired glucose tolerance (IGT), and combined impaired fasting glucose and glucose intolerance (IFG/IGT). The present review investigates human clinical trials examining the hypoglycemic potential of phenolic-rich plant extracts in prediabetes and prediabetes subgroups. Artemisia princeps Pampanini, soy (Glycine max (L.) Merrill) leaf and Citrus junos Tanaka peel have been demonstrated to improve fasting glycemia and thus may be more useful for individuals with IFG with increasing hepatic insulin resistance. In contrast, white mulberry (Morus alba Linn.) leaf, persimmon (Diospyros kaki) leaf and Acacia. Mearnsii bark were shown to improve postprandial glycemia and hence may be preferably beneficial for individuals with IGT with increasing muscle insulin resistance. Elaeis guineensis leaf was observed to improve both fasting and postprandial glycemic measures depending on the dose. Current evidence remains scarce regarding the impact of the plant extracts on glycemic control in prediabetes subgroups and therefore warrants further study.

Increased oral processing and a slower eating rate increase glycaemic, insulin and satiety responses to a mixed meal tolerance test

PURPOSE

Variations in specific oral processing behaviours may contribute to differences in glucose, insulin and satiety responses to a standardised test meal. This study tested how natural variations in oral processing between slower and faster eaters contribute to differences in post-prandial glucose (PP glucose), insulin response (PP insulin) and post-meal satiety for a standardised test meal.

METHODS

Thirty-three participants with higher risk for type 2 diabetes consumed a standardised test-meal while being video recorded to derive specific oral processing behaviours. Plasma glucose, insulin and satiety measures were collected at baseline, during and post meal. Participants were split into slower and faster eaters using median split based on their eating rates and individual bolus properties were analysed at the point of swallow.

RESULTS

There were large variations in eating rate (p < 0.001). While there was no significant difference in PP glucose response (p > 0.05), slower eaters showed significantly higher PP insulin between 45 and 60 min (p < 0.001). Slower eaters had longer oro-sensory exposure and increased bolus saliva uptake which was associated with higher PP glucose iAUC. Faster eating rate and larger bolus particle size at swallow correlated with lower PP glucose iAUC. A slower eating rate with greater chews per bite significantly increased insulin iAUC. Faster eaters also consistently rated their hunger and desire to eat higher than slower eaters (p < 0.05).

CONCLUSIONS

Natural variations in eating rate and the associated oral processing contributed to differences in PP glucose, PP insulin and satiety responses. Encouraging increased chewing and longer oral-exposure time during consumption, may promote early glucose absorption and greater insulin and satiety responses, and help support euglycaemia.

TRIAL REGISTRATION

ClinicalTrials.gov identifier: NCT04522063.

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.

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.

Acute-phase glycoprotein profile responses to different oral macronutrient challenges: Influence of sex, functional hyperandrogenism and obesity

Acute-phase glycoprotein ¹H-NMR spectroscopy profiles serve as surrogate markers of chronic inflammation in metabolic disorders such as obesity, diabetes and polycystic ovary syndrome (PCOS). The latter is associated with increased height-to-width (H/W) ratios of GlycA and GlycB after fasting, but not to glycoprotein areas, regardless of obesity. We studied the responses to separate glucose, lipid and protein oral challenges of five glycoprotein variables (GlycA, GlycB, and GlycF areas and the GlycA and GlycB H/W ratios) in 17 women with PCOS, 17 control women, and 19 healthy men. Glucose and protein ingestion resulted into decreases in all glycoprotein variables, whereas lipid ingestion increased GlycA, GlycF and induced minimal changes in GlycB and GlycB H/W. We found no effects of obesity or group of subjects on postprandial glycoprotein variables regardless of the macronutrient being ingested. However, a statistically significant interaction indicated that obesity blunted the decrease in some of these variables in control women and men, whereas obese women with PCOS showed larger changes when compared with their non-obese counterparts. In conclusion, acute-phase glycoprotein profiles indicate an anti-inflammatory response during postprandial phase that is less pronounced after lipid ingestion, and is counteracted by the chronic inflammatory background associated with obesity and PCOS.

Wearable Technology to Quantify the Nutritional Intake of Adults: Validation Study

Background

Wearable and mobile sensor technologies can be useful tools in precision nutrition research and practice, but few are reliable for obtaining accurate and precise measurements of diet and nutrition.

Objective

This study aimed to assess the ability of wearable technology to monitor the nutritional intake of adult participants. This paper describes the development of a reference method to validate the wristband’s estimation of daily nutritional intake of 25 free-living study participants and to evaluate the accuracy (kcal/day) and practical utility of the technology.

Methods

Participants were asked to use a nutrition tracking wristband and an accompanying mobile app consistently for two 14-day test periods. A reference method was developed to validate the estimation of daily nutritional intake of participants by the wristband. The research team collaborated with a university dining facility to prepare and serve calibrated study meals and record the energy and macronutrient intake of each participant. A continuous glucose monitoring system was used to measure adherence with dietary reporting protocols, but these findings are not reported. Bland-Altman tests were used to compare the reference and test method outputs (kcal/day).

Results

A total of 304 input cases were collected of daily dietary intake of participants (kcal/day) measured by both reference and test methods. The Bland-Altman analysis had a mean bias of −105 kcal/day (SD 660), with 95% limits of agreement between −1400 and 1189. The regression equation of the plot was Y=−0.3401X+1963, which was significant (P<.001), indicating a tendency for the wristband to overestimate for lower calorie intake and underestimate for higher intake. Researchers observed transient signal loss from the sensor technology of the wristband to be a major source of error in computing dietary intake among participants.

Conclusions

This study documents high variability in the accuracy and utility of a wristband sensor to track nutritional intake, highlighting the need for reliable, effective measurement tools to facilitate accurate, precision-based technologies for personal dietary guidance and intervention.

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

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

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

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

Exploring the Diversity of Sugar Compounds in Healthy, Prediabetic, and Diabetic Volunteers

SCOPE

Diabetes is thought to primarily represent a disturbance of carbohydrate metabolism; however, population studies employing metabolomics have mainly identified plasma amino acids and lipids, or their products, as biomarkers. In this pilot study, the aim is to analyze a wide spectrum of sugar compounds in the fasting state and during an oral glucose tolerance test (OGTT) in healthy, prediabetic, and type 2 diabetic volunteers.

METHODS AND RESULTS

The three volunteer groups underwent a standard OGTT. Plasma samples obtained in the fasting state, 30 and 90 min after the OGTT, are subjected to a semitargeted GC-MS (gas chromatography-mass spectrometry) sugar profiling. Overall, 40 sugars are detected in plasma, of which some are yet unknown to change during an OGTT. Several sugars (e.g., trehalose) reveal significant differences between the volunteer groups both in fasting plasma and in distinct time courses after the OGTT. This suggests an endogenous production from orally absorbed glucose and/or an insulin-dependent production/removal from plasma.

CONCLUSION

It is demonstrated that more sugars than expected can be found in human plasma. Since some of these show characteristic differences depending on health status, it may be worthwhile to assess their usability as biomarkers for diagnosing early-stage insulin resistance and type 2 diabetes.

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.

An Acute, Placebo-Controlled, Single-Blind, Crossover, Dose-Response, Exploratory Study to Assess the Effects of New Zealand Pine Bark Extract (Enzogenol®) on Glycaemic Responses in Healthy Participants

An acute, placebo-controlled, single-blind, crossover, dose-response, exploratory study was designed to investigate the hypoglycaemic effects of New Zealand pine bark extract (Enzogenol®). Twenty-five healthy participants categorised into having a monophasic or complex (biphasic or triphasic) glucose curve shape at the control visit consumed a placebo and Enzogenol® (50 and 400 mg) on three separate occasions before an oral glucose tolerance test (OGTT). In the monophasic group, 50 and 400 mg of Enzogenol® significantly reduced the mean glucose incremental area under the curve (iAUC) compared to control 241.3 ± 20.2 vs. 335.4 ± 34.0 mmol/L·min, p = 0.034 and 249.3 ± 25.4 vs. 353.6 ± 31.5 mmol/L·min, p = 0.012, respectively. The 400 mg dose further reduced the percentage increment of postprandial glucose (%PG) 31.4% ± 7.9% vs. 47.5% ± 8.6%, p = 0.010, glucose peak 7.9 ± 0.3 vs. 8.9 ± 0.3 mmol/L, p = 0.025 and 2h-OGTT postprandial glucose (2hPG) 6.1 ± 0.3 vs. 6.7 ± 0.3 mmol/L, p = 0.027. Glucose iAUC was not significantly different in the complex group, except for reductions in %PG 28.7% ± 8.2% vs. 43.4% ± 5.9%, p = 0.012 after 50 mg dose and 27.7% ± 5.4% vs. 47.3% ± 7.2%, p = 0.025 after 400 mg dose. The results suggest that Enzogenol® may have hypoglycaemic effects in healthy participants, especially those exhibiting monophasic shapes.

TLR2 and TLR4 Surface and Gene Expression in White Blood Cells after Fasting and Oral Glucose, Lipid and Protein Challenges: Influence of Obesity and Sex Hormones

We studied if macronutrients of the diet have different effects on leukocyte activation, and if these effects are influenced by sex hormones or obesity. We analyzed leukocyte cell surface and gene expression of toll-like receptors 2 and 4 (TLR2 and TLR4) during fasting and after macronutrient loads in women with polycystic ovary syndrome and female and male controls. Fasting TLR2 surface expression in neutrophils was higher in men than in women. Obese subjects presented higher TLR2 gene expression than nonobese individuals, particularly in men. In contrast, surface TLR4 expression was lower in men and in obese individuals. Postprandial cell-surface expression decreased similarly after all macronutrient loads. Neutrophil TLR2 decreased only in obese subjects whereas TLR4 showed a greater decrease in nonobese individuals. However, TLR2 gene expression increased after glucose ingestion and decreased during the lipid load, while TLR4 was induced in response to lipids and mostly to glucose. Postprandial TLR gene expression was not influenced by group of subjects or obesity. Both cell-surface and gene postprandial expression inversely correlated with their fasting levels. These responses suggest a transient compensatory response aiming to prevent postprandial inflammation. However, obesity and sex hormones showed opposite influences on surface expression of TLR2 and TLR4, but not on their gene expression, pointing to regulatory posttranscriptional mechanisms.

Personalised nutrition - phenotypic and genetic variation in response to dietary intervention

Personalised nutrition allows individual differences in dietary, lifestyle, anthropometry, phenotype and/or genomic profile to be used to direct specific dietary advice. For personalised nutrition advice to be effective both sides need to be considered; firstly, that factors influencing variation in response to dietary intervention are identified and appropriate advice can be derived and secondly; that these are then used effectively in the provision of nutrition advice, resulting in a positive dietary and/or lifestyle behaviour change. There is considerable evidence demonstrating genetic and phenotypic influence on the biological response to the consumption of nutrients and bioactives. However, findings are often mixed, with studies often investigating at the level of a single nutrient/bioactive and/or a single genetic/phenotypic variation, meaning the derivation of specific advice at a dietary level in an individual/group of individuals can be complex. Similarly, the impact of using this information to derive personalised advice is also mixed, with some studies demonstrating no effectiveness and others showing a significant impact. The present paper will outline examples of phenotypic and genetic variation influencing response to nutritional interventions, and will consider how they could be used in the provision of personalised nutrition.

A Unified Conceptual Framework for Metabolic Phenotyping in Diagnosis and Prognosis

Understanding metabotype (multicomponent metabolic characteristics) variation can help to generate new diagnostic and prognostic biomarkers, as well as models, with potential to impact on patient management. We present a suite of conceptual approaches for the generation, analysis, and understanding of metabotypes from body fluids and tissues. We describe and exemplify four fundamental approaches to the generation and utilization of metabotype data via multiparametric measurement of (i) metabolite levels, (ii) metabolic trajectories, (iii) metabolic entropies, and (iv) metabolic networks and correlations in space and time. This conceptual framework can underpin metabotyping in the scenario of personalized medicine, with the aim of improving clinical outcomes for patients, but the framework will have value and utility in areas of metabolic profiling well beyond this exemplar.

Factors influencing the cardiometabolic response to (poly)phenols and phytosterols: a review of the COST Action POSITIVe activities

Purpose

Evidence exists regarding the beneficial effects of diets rich in plant-based foods regarding the prevention of cardiometabolic diseases. These plant-based foods are an exclusive and abundant source of a variety of biologically active phytochemicals, including polyphenols, carotenoids, glucosinolates and phytosterols, with known health-promoting effects through a wide range of biological activities, such as improvements in endothelial function, platelet function, blood pressure, blood lipid profile and insulin sensitivity. We know that an individual’s physical/genetic makeup may influence their response to a dietary intervention, and thereby may influence the benefit/risk associated with consumption of a particular dietary constituent. This inter-individual variation in responsiveness has also been described for dietary plant bioactives but has not been explored in depth. To address this issue, the European scientific experts involved in the COST Action POSITIVe systematically analyzed data from published studies to assess the inter-individual variation in selected clinical biomarkers associated with cardiometabolic risk, in response to the consumption of plant-based bioactives (poly)phenols and phytosterols. The present review summarizes the main findings resulting from the meta-analyses already completed.

Results

Meta-analyses of randomized controlled trials conducted within POSITIVe suggest that age, sex, ethnicity, pathophysiological status and medication may be responsible for the heterogeneity in the biological responsiveness to (poly)phenol and phytosterol consumption and could lead to inconclusive results in some clinical trials aiming to demonstrate the health effects of specific dietary bioactive compounds. However, the contribution of these factors is not yet demonstrated consistently across all polyphenolic groups and cardiometabolic outcomes, partly due to the heterogeneity in trial designs, low granularity of data reporting, variety of food vectors and target populations, suggesting the need to implement more stringent reporting practices in the future studies. Studies investigating the effects of genetic background or gut microbiome on variability were limited and should be considered in future studies.

Conclusion

Understanding why some bioactive plant compounds work effectively in some individuals but not, or less, in others is crucial for a full consideration of these compounds in future strategies of personalized nutrition for a better prevention of cardiometabolic disease. However, there is also still a need for the development of a substantial evidence-base to develop health strategies, food products or lifestyle solutions that embrace this variability.

A balanced diet, rich in plant-based foods is known for the prevention of obesity, diabetes, and cardiovascular disease risk.

(Poly)phenols and phytosterols displayed a range of biological effects of relevance to contribute to the cardiometabolic health benefits of plant foods

However, inter-individual variability in response to plant food bioactive consumption exists, and there is a need to understand the causes of this variation.

Analysis of published RCTs examining impact of consumption of (poly)phenols and phytosterols on cardiometabolic risk factors demonstrated that a number of factors including age, sex, adiposity and health status could contribute to the effect demonstrated within these studies.

Genome and microbiome studies will help identify what may be causing this variation.

More studies, specifically designed to investigate individual variation are needed to fully understand the factors responsible for and the impact of this variation

Once fully understood, such variation should be used in directing personalised nutrition advice.

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.

Clusters of Glycemic Response to Oral Glucose Tolerance Tests Explain Multivariate Metabolic and Anthropometric Outcomes of Bariatric Surgery in Obese Patients

Glycemic responses to bariatric surgery are highly heterogeneous among patients and defining response types remains challenging. Recently developed data-driven clustering methods have uncovered subtle pathophysiologically informative patterns among patients without diabetes. This study aimed to explain responses among patients with and without diabetes to bariatric surgery with clusters of glucose concentration during oral glucose tolerance tests (OGTTs). We assessed 30 parameters at baseline and at four subsequent follow-up visits over one year on 154 participants in the Bialystok Bariatric Surgery Study. We applied latent trajectory classification to OGTTs and multinomial regression and generalized linear mixed models to explain differential responses among clusters. OGTT trajectories created four clusters representing increasing dysglycemias that were discordant from standard diabetes diagnosis criteria. The baseline OGTT cluster increased the predictive power of regression models by over 31% and aided in correctly predicting more than 83% of diabetes remissions. Principal component analysis showed that the glucose homeostasis response primarily occurred as improved insulin sensitivity concomitant with improved the OGTT cluster. In sum, OGTT clustering explained multiple, correlated responses to metabolic surgery. The OGTT is an intuitive and easy-to-implement index of improvement that stratifies patients into response types, a vital first step in personalizing diabetic care in obese subjects.

Modifying effect of metabotype on diet-diabetes associations

Purpose

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

Methods

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

Results

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

Conclusions

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

Electronic supplementary material

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

Paving the Way to Precision Nutrition Through Metabolomics

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

Evolving data analysis of an Oral Lipid Tolerance Test toward the standard for the Oral Glucose Tolerance Test: Cross species modeling effects of AZD7687 on plasma triacylglycerol

We have developed a novel mechanistic pharmacokinetic-pharmacodynamic (PK/PD) model to describe the time course of plasma triglyceride (TAG) after Oral Lipid Tolerance Test (OLTT) and the effects of AZD7687, an inhibitor of diacylglycerol acyltransferase 1 (DGAT1), in humans, rats, and mice. Pharmacokinetic and plasma TAG data were obtained both in animals and in two phase I OLTT studies. In the PK/PD model, the introduction of exogenous TAG is represented by a first order process. The endogenous production and removal of TAG from plasma are described with a turnover model. AZD7687 inhibits the contribution of exogenous TAG into circulation. One or two compartment models with first order absorption was used to describe the PK of AZD7687 for the different species. Nonlinear mixed effect modeling was used to fit the model to the data. The effects of AZD7687 on the plasma TAG time course during an OLTT as well as interindividual variability were well described by the model in all three species. Meal fat content or data from single vs repeated dosing did not affect model parameter estimates. Body mass index was found to be a significant covariate on the plasma TAG baseline. The system parameters of the model will facilitate analysis for other compounds and provide tools to bring the standard of OLTT data analysis closer to the analyses of Oral Glucose Tolerance Test data maximizing knowledge gain.

Postprandial inflammatory responses after oral glucose, lipid and protein challenges: Influence of obesity, sex and polycystic ovary syndrome

BACKGROUND & AIMS

Most evidence linking the polycystic ovary syndrome (PCOS) with chronic low-grade inflammation has been obtained in the fasting state. We have studied the postprandial inflammatory response to oral glucose, lipid and protein challenges and the possible influences of obesity, sex and PCOS on these responses.

METHODS

On alternate days, we submitted 17 women with PCOS (9 non-obese, 8 obese), 17 control women (9 non-obese, 8 obese) and 19 control men (10 non-obese, 9 obese) to isocaloric (300 Kcal) oral macronutrient loads. We assayed serum for TNF-α, IL-6, IL-18, IL-10, pentraxin-3 and galectin-3 concentrations and leukocytes for expression of TNF, IL6, IL10 and their receptors TNFRSF1B, IL6R and IL10RA.

RESULTS

Circulating IL-6 levels decreased after glucose and protein ingestion but slightly increased after oral lipid intake. Leukocyte IL6 expression did not change after the ingestion of any macronutrient yet IL6R expression increased during all macronutrient challenges, the largest increase being observed after glucose ingestion. Serum TNF-α similarly decreased during either macronutrient load, whereas TNF expression increased after macronutrient ingestion, the highest increase observed after oral glucose. TNFRSF1B expression also increased after glucose intake but not after lipid or protein ingestion. No global effect of obesity or group on postprandial circulating IL-6, TNF-α, or IL6, IL6R, TNF and TNFRSF1B expression was found. Circulating IL-18 concentrations decreased during all oral challenges, whereas in case of galectin-3 and pentraxin-3 only the protein load caused a reduction in its concentrations. Of the genes studied here, IL10 showed the largest increase in expression throughout all the postprandial curves, particularly after glucose. Obesity blunted the increase in IL10 expression. IL10RA expression decreased after glucose ingestion but remained unchanged during lipid and protein loads.

CONCLUSIONS

Glucose ingestion, as opposed to lipid and protein intake, results into the largest increase in leukocyte gene expression of inflammatory mediators. The expression of the anti-inflammatory cytokine IL10 was the largest observed here, suggesting a compensatory mechanisms against postprandial inflammation that may be blunted in obesity. However, these responses did not translate into the circulating concentrations of these inflammatory mediators during the immediate postprandial phase.

Recent Advances in the Application of Metabolomics for Nutrition and Health

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

Acute Glucose Load, Inflammation, Oxidative Stress, Nonenzymatic Glycation, and Screening for Gestational Diabetes

AIMS:

To investigate if oral glucose tolerance test (OGTT) associates with changes in maternal symptoms (ie, flushing, sweating), blood nonenzymatic advanced glycation end products (AGE), acute-phase reactive inflammatory markers, and oxidative stress.

METHODS:

Prospective case-control study of patients screened for gestational diabetes mellitus (GDM). One hundred nonfasting, second-trimester consecutive pregnant women allocated to either 50 g OGTT or water. Five women who had a 3-hour fasting 100 g OGTT also enrolled. Maternal serum glucose, AGE, soluble receptor for AGE (sRAGE), interleukin (IL)-6, and C-reactive protein (CRP) were immunoassayed. Total radical-trapping antioxidant parameter (TRAP) estimated with antioxidant capacity-peroxyl assay. Data corrected for gestational age and maternal body mass index.

RESULTS:

During 50 g OGTT there was a decrease in systolic blood pressure not accompanied by the onset of adverse clinical symptoms. There was a decrease in serum glucose levels 1 hour after water ( P = .019) but not glucose ingestion. Serum CRP ( P = .001) but not IL-6 was increased. The AGE, sRAGE, and TRAP levels remained unchanged. Similar results were seen during 100 g OGTT, except serum glucose was significantly elevated after 1 hour.

CONCLUSION:

Results suggest screening tools for gestational diabetes are safe and clinically well tolerated during pregnancy. Clinical Trial Registration: ClinicalTrials.gov NCT03029546.

A proteomic signature that reflects pancreatic beta-cell function

AIM

Proteomics has the potential to enhance early identification of beta-cell dysfunction, in conjunction with monitoring the various stages of type 2 diabetes onset. The most routine method of assessing pancreatic beta-cell function is an oral glucose tolerance test, however this method is time consuming and carries a participant burden. The objectives of this research were to identify protein signatures and pathways related to pancreatic beta-cell function in fasting blood samples.

METHODS

Beta-cell function measures were calculated for MECHE study participants who completed an oral glucose tolerance test and had proteomic data (n = 100). Information on 1,129 protein levels was obtained using the SOMAscan assay. Receiver operating characteristic curves were used to assess discriminatory ability of proteins of interest. Subsequent in vitro experiments were performed using the BRIN-BD11 pancreatic beta-cell line. Replication of findings were achieved in a second human cohort where possible.

RESULTS

Twenty-two proteins measured by aptamer technology were significantly associated with beta-cell function/HOMA-IR while 17 proteins were significantly associated with the disposition index (p ≤ 0.01). Receiver operator characteristic curves determined the protein panels to have excellent discrimination between low and high beta-cell function. Linear regression analysis determined that beta-endorphin and IL-17F have strong associations with beta-cell function/HOMA-IR, β = 0.039 (p = 0.005) and β = -0.027 (p = 0.013) respectively. Calcineurin and CRTAM were strongly associated with the disposition index (β = 0.005 and β = 0.005 respectively, p = 0.012). In vitro experiments confirmed that IL-17F modulated insulin secretion in the BRIN-BD11 cell line, with the lower concentration of 10 ng/mL significantly increasing glucose stimulated insulin secretion (p = 0.043).

CONCLUSIONS

Early detection of compromised beta-cell function could allow for implementation of nutritional and lifestyle interventions before progression to type 2 diabetes.

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

SCOPE

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

METHODS AND RESULTS

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

CONCLUSION

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

Personalised nutrition and health

Jose Ordovas and colleagues consider that nutrition interventions tailored to individual characteristics and behaviours have promise but more work is needed before they can deliver

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

Health has been defined as the capability of the organism to adapt to challenges. In this study, we tested to what extent comprehensively phenotyped individuals reveal differences in metabolic responses to a standardized mixed meal tolerance test (MMTT) and how these responses change when individuals experience moderate weight loss. Metabolome analysis was used in 70 healthy individuals. with profiling of ∼300 plasma metabolites during an MMTT over 8 h. Multivariate analysis of plasma markers of fatty acid catabolism identified 2 distinct metabotype clusters (A and B). Individuals from metabotype B showed slower glucose clearance, had increased intra-abdominal adipose tissue mass and higher hepatic lipid levels when compared with individuals from metabotype A. An NMR-based urine analysis revealed that these individuals also to have a less healthy dietary pattern. After a weight loss of ∼5.6 kg over 12 wk, only the subjects from metabotype B showed positive changes in the glycemic response during the MMTT and in markers of metabolic diseases. Our study in healthy individuals demonstrates that more comprehensive phenotyping can reveal discrete metabotypes with different outcomes in a dietary intervention and that markers of lipid catabolism in plasma could allow early detection of the metabolic syndrome.-Fiamoncini, J., Rundle, M., Gibbons, H., Thomas, E. L., Geillinger-Kästle, K., Bunzel, D., Trezzi, J.-P., Kiselova-Kaneva, Y., Wopereis, S., Wahrheit, J., Kulling, S. E., Hiller, K., Sonntag, D., Ivanova, D., van Ommen, B., Frost, G., Brennan, L., Bell, J. Daniel, H. Plasma metabolome analysis identifies distinct human metabotypes in the postprandial state with different susceptibility to weight loss-mediated metabolic improvements.

Four Plasma Glucose and Insulin Responses to a 75 g OGTT in Healthy Young Japanese Women

The incidence of diabetes has been gradually increasing, not only in middle-aged individuals but also in young individuals. However, insulin and glucose patterns have not been investigated in apparently healthy young individuals, as they are typically grouped as controls. In this study, we investigated and classified glucose and insulin patterns in healthy young women. Sixty-two nonobese women without metabolic disease were recruited. The subjects underwent a 75 g oral glucose tolerance test (OGTT), physical measurements, and a biochemical examination. Two subjects displayed impaired glucose tolerance. The 62 subjects were categorized into four patterns by plasma glucose and insulin peak time during OGTT: normal type (n = 39), insulin-late type (n = 11), insulin- and glucose-late type (n = 7), and insulin-very late type (n = 5). OGTT glucose and insulin levels at all time points, insulinogenic index, HOMA-IR, and glucose area under the curve (AUC) significantly differed among the four groups. However, insulin AUC did not significantly differ. We did not detect significant differences in body condition or biochemical measurements. Our study demonstrated that some healthy young individuals might have delayed insulin secretion by OGTT. Early detection of altered glucose metabolism might be helpful to improve lifestyle choices and prevent progression to diabetes.

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

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

Associations of Dairy Intake with Incident Prediabetes or Diabetes in Middle-Aged Adults Vary by Both Dairy Type and Glycemic Status

Background: Inconsistent evidence describes the association between dietary intake of dairy and milk-based products and type 2 diabetes (T2D) risk.Objective: Our objective was to assess associations between consumption of milk-based products, incident prediabetes, and progression to T2D in the Framingham Heart Study Offspring Cohort.Methods: Total dairy and milk-based product consumption was assessed by ≤4 food-frequency questionnaires across a mean of 12 y of follow-up in 2809 participants [mean ± SD age: 54.0 ± 9.7 y; body mass index (in kg/m²): 27.1 ± 4.7; 54% female]. Prediabetes was defined as the first occurrence of fasting plasma glucose ≥5.6 to <7.0 mmol/L (≥100 to <126 mg/dL), and T2D was defined as the first occurrence of fasting plasma glucose ≥7.0 mmol/L (≥126 mg/dL) or diabetes treatment. Proportional hazards models were used to estimate the risk of incident outcomes relative to dairy product intake in subsets of the cohort who were at risk of developing the outcomes. Spline regressions were used to examine potential nonlinear relations.Results: Of 1867 participants free of prediabetes at baseline, 902 (48%) developed prediabetes. Total, low-fat, and high-fat dairy consumptions were associated with a 39%, 32%, and 25% lower risk of incident prediabetes, respectively, in the highest compared with the lowest intakes (≥14 compared with <4 servings/wk). Total, low-fat and skim milk, whole-milk, and yogurt intakes were associated nonlinearly with incident prediabetes; moderate intake was associated with the greatest relative risk reduction. Neither cheese nor cream and butter was associated with prediabetes. Of 925 participants with prediabetes at baseline, 196 (21%) developed T2D. Only high-fat dairy and cheese showed evidence of dose-response, inverse associations with incident T2D, with 70% and 63% lower risk, respectively, of incident T2D between the highest and lowest intake categories (≥14 compared with <1 serving/wk for high-fat dairy, ≥4 compared with <1 serving/wk for cheese).Conclusion: Associations of dairy with incident prediabetes or diabetes varied both by dairy product and type and by baseline glycemic status in this middle-aged US population. Baseline glycemic status may partially underlie prior equivocal evidence regarding the role of dairy intake in diabetes.

Multi-parameter comparison of a standardized mixed meal tolerance test in healthy and type 2 diabetic subjects: the PhenFlex challenge

BACKGROUND

A key feature of metabolic health is the ability to adapt upon dietary perturbations. Recently, it was shown that metabolic challenge tests in combination with the new generation biomarkers allow the simultaneous quantification of major metabolic health processes. Currently, applied challenge tests are largely non-standardized. A systematic review defined an optimal nutritional challenge test, the "PhenFlex test" (PFT). This study aimed to prove that PFT modulates all relevant processes governing metabolic health thereby allowing to distinguish subjects with different metabolic health status. Therefore, 20 healthy and 20 type 2 diabetic (T2D) male subjects were challenged both by PFT and oral glucose tolerance test (OGTT). During the 8-h response time course, 132 parameters were quantified that report on 26 metabolic processes distributed over 7 organs (gut, liver, adipose, pancreas, vasculature, muscle, kidney) and systemic stress.

RESULTS

In healthy subjects, 110 of the 132 parameters showed a time course response. Patients with T2D showed 18 parameters to be significantly different after overnight fasting compared to healthy subjects, while 58 parameters were different in the post-challenge time course after the PFT. This demonstrates the added value of PFT in distinguishing subjects with different health status. The OGTT and PFT response was highly comparable for glucose metabolism as identical amounts of glucose were present in both challenge tests. Yet the PFT reports on additional processes, including vasculature, systemic stress, and metabolic flexibility.

CONCLUSION

The PFT enables the quantification of all relevant metabolic processes involved in maintaining or regaining homeostasis of metabolic health. Studying both healthy subjects and subjects with impaired metabolic health showed that the PFT revealed new processes laying underneath health. This study provides the first evidence towards adopting the PFT as gold standard in nutrition research.