Characterizing human postprandial metabolic response using multiway data analysis.

INTRODUCTION: Analysis of time-resolved postprandial metabolomics data can improve our understanding of the human metabolism by revealing similarities and differences in postprandial responses of individuals. Traditional data analysis methods often rely on data summaries or univariate approaches focusing on one metabolite at a time. OBJECTIVES: Our goal is to provide a comprehensive picture in terms of the changes in the human metabolism in response to a meal challenge test, by revealing static and dynamic markers of phenotypes, i.e., subject stratifications, related clusters of metabolites, and their temporal profiles. METHODS: We analyze Nuclear Magnetic Resonance (NMR) spectroscopy measurements of plasma samples collected during a meal challenge test from 299 individuals from the COPSAC2000 cohort using a Nightingale NMR panel at the fasting and postprandial states (15, 30, 60, 90, 120, 150, 240 min). We investigate the postprandial dynamics of the metabolism as reflected in the dynamic behaviour of the measured metabolites. The data is arranged as a three-way array: subjects by metabolites by time. We analyze the fasting state data to reveal static patterns of subject group differences using principal component analysis (PCA), and fasting state-corrected postprandial data using the CANDECOMP/PARAFAC (CP) tensor factorization to reveal dynamic markers of group differences. RESULTS: Our analysis reveals dynamic markers consisting of certain metabolite groups and their temporal profiles showing differences among males according to their body mass index (BMI) in response to the meal challenge. We also show that certain lipoproteins relate to the group difference differently in the fasting vs. dynamic state. Furthermore, while similar dynamic patterns are observed in males and females, the BMI-related group difference is observed only in males in the dynamic state. CONCLUSION: The CP model is an effective approach to analyze time-resolved postprandial metabolomics data, and provides a compact but a comprehensive summary of the postprandial data revealing replicable and interpretable dynamic markers crucial to advance our understanding of changes in the metabolism in response to a meal challenge.

Associations between Alcohol Consumption and HDL Subspecies Defined by ApoC3, ApoE and ApoJ: the Cardiovascular Health Study.

Alcohol consumption increases circulating high-density lipoprotein cholesterol (HDL-C), but HDL protein cargo may better reflect HDL function. This study examined the associations between alcohol intake and HDL subspecies containing or lacking apoC3, apoE, and apoJ in a well-phenotyped cohort. We performed a cross-sectional analysis of 2092 Cardiovascular Health Study participants aged 70 or older with HDL subspecies measured in stored specimens from 1998 to 1999. Associations between alcohol intake and apoA1 defined HDL subspecies lacking or containing apoC3, apoE, and apoJ, and circulating levels of total apoA1, apoC3, apoE, and apoJ were examined. HDL subspecies lacking and containing apoC3, apoE, and apoJ were all positively associated with alcohol intake, with ∼1% per additional drink per week or ∼7% per additional drink per day (subspecies without the apolipoproteins, P ≤ 2 × 10-9, subspecies with the apolipoproteins, P ≤ 3 × 10-5). Total apoA1 was also directly associated with alcohol consumption, with a 1% increase per additional drink per week (P = 1 × 10-14). Total apoC3 blood levels were 0.5% higher per additional drink per week (P = 0.01), but the association was driven by a few heavily drinking men. Alcohol intake was positively associated with HDL subspecies lacking and containing apoC3, apoE, or apoJ, and with total plasma apoA1. ApoC3 was directly, albeit not as robustly associated with alcohol intake. HDL protein cargo is crucial for its anti-atherosclerotic functions, but it remains to be determined whether HDL subspecies play a role in the putative association between limited alcohol intake and lower risk of coronary heart disease.

Safety and efficacy of liraglutide versus colesevelam for the treatment of bile acid diarrhoea: a randomised, double-blind, active-comparator, non-inferiority clinical trial.

BACKGROUND: Bile acid diarrhoea is an underdiagnosed disease estimated to affect 1-2% of the general population. Case reports indicate that the glucagon-like peptide 1 receptor agonist liraglutide might be an effective treatment for bile acid diarrhoea. We aimed to investigate the safety and efficacy of liraglutide for the treatment of bile acid diarrhoea. METHODS: We conducted a randomised, double-blind, active-comparator, double-dummy, non-inferiority clinical trial at the Center for Clinical Metabolic Research at Copenhagen University Hospital-Herlev and Gentofte, Hellerup, Denmark. Patients aged 18-75 years with 75selenium-homotaurocholic acid test (SeHCAT)-verified moderate-to-severe primary bile acid diarrhoea were randomly assigned (1:1) to receive liraglutide (one daily subcutaneous injection uptitrated from 0·6-1·8 mg per day over 3 weeks) or colesevelam (three capsules of 625 mg twice daily), the standard of care, for 6 weeks following one run-in week with no treatment. The primary endpoint was the proportion of participants experiencing a reduction in daily stool frequency of 25% or greater after 6 weeks. Data from all participants were included in the analysis of the primary outcome. The non-inferiority limit was set to 15% in favour of colesevelam. This trial is registered with EudraCT (2018-003575-34) and is completed. FINDINGS: Between April 1, 2019, and Jan 31, 2021, 52 patients were enrolled; 26 were assigned to liraglutide and 26 to colesevelam. 20 (77%) of 26 participants on liraglutide and 13 (50%) of 26 on colesevelam experienced a 25% or greater reduction in stool frequency, corresponding to a significant risk difference of -27% in favour of liraglutide (one-sided 95% CI -100 to -6). Liraglutide was therefore superior to colesevelam in reducing daily stool frequency. Mild nausea with a duration of 10-21 days was reported by six participants in the liraglutide group and by one participant in the colesevelam group. No other adverse events were reported. INTERPRETATION: The superiority of liraglutide compared with colesevelam in reducing stool frequency suggests consideration of liraglutide as a potential new treatment modality for bile acid diarrhoea, although larger confirmatory trials powered for superiority are warranted. FUNDING: Novo Nordisk, Novo Nordisk Foundation, Foundation for the Advancement of Medical Science under The A.P. Møller and Chastine Mc-Kinney Møller Foundation.

Moderate alcohol consumption and lipoprotein subfractions: a systematic review of intervention and observational studies.

CONTEXT: Moderate alcohol consumption is associated with decreased risk of cardiovascular disease (CVD) and improvement in cardiovascular risk markers, including lipoproteins and lipoprotein subfractions. OBJECTIVE: To systematically review the relationship between moderate alcohol intake, lipoprotein subfractions, and related mechanisms. DATA SOURCES: Following PRISMA, all human and ex vivo studies with an alcohol intake up to 60 g/d were included from 8 databases. DATA EXTRACTION: A total of 17 478 studies were screened, and data were extracted from 37 intervention and 77 observational studies. RESULTS: Alcohol intake was positively associated with all HDL subfractions. A few studies found lower levels of small LDLs, increased average LDL particle size, and nonlinear relationships to apolipoprotein B-containing lipoproteins. Cholesterol efflux capacity and paraoxonase activity were consistently increased. Several studies had unclear or high risk of bias, and heterogeneous laboratory methods restricted comparability between studies. CONCLUSIONS: Up to 60 g/d alcohol can cause changes in lipoprotein subfractions and related mechanisms that could influence cardiovascular health. SYSTEMATIC REVIEW REGISTRATION: PROSPERO registration no. 98955.

Bifidobacterium species associated with breastfeeding produce aromatic lactic acids in the infant gut.

Breastfeeding profoundly shapes the infant gut microbiota, which is critical for early life immune development, and the gut microbiota can impact host physiology in various ways, such as through the production of metabolites. However, few breastmilk-dependent microbial metabolites mediating host-microbiota interactions are currently known. Here, we demonstrate that breastmilk-promoted Bifidobacterium species convert aromatic amino acids (tryptophan, phenylalanine and tyrosine) into their respective aromatic lactic acids (indolelactic acid, phenyllactic acid and 4-hydroxyphenyllactic acid) via a previously unrecognized aromatic lactate dehydrogenase (ALDH). The ability of Bifidobacterium species to convert aromatic amino acids to their lactic acid derivatives was confirmed using monocolonized mice. Longitudinal profiling of the faecal microbiota composition and metabolome of Danish infants (n = 25), from birth until 6 months of age, showed that faecal concentrations of aromatic lactic acids are correlated positively with the abundance of human milk oligosaccharide-degrading Bifidobacterium species containing the ALDH, including Bifidobacterium longum, B. breve and B. bifidum. We further demonstrate that faecal concentrations of Bifidobacterium-derived indolelactic acid are associated with the capacity of these samples to activate in vitro the aryl hydrocarbon receptor (AhR), a receptor important for controlling intestinal homoeostasis and immune responses. Finally, we show that indolelactic acid modulates ex vivo immune responses of human CD4+ T cells and monocytes in a dose-dependent manner by acting as an agonist of both the AhR and hydroxycarboxylic acid receptor 3 (HCA3). Our findings reveal that breastmilk-promoted Bifidobacterium species produce aromatic lactic acids in the gut of infants and suggest that these microbial metabolites may impact immune function in early life.

Meat and Human Health-Current Knowledge and Research Gaps.

Meat is highly nutritious and contributes with several essential nutrients which are difficult to obtain in the right amounts from other food sources. Industrially processed meat contains preservatives including salts, possibly exerting negative effects on health. During maturation, some processed meat products develop a specific microbiota, forming probiotic metabolites with physiological and biological effects yet unidentified, while the concentration of nutrients also increases. Meat is a source of saturated fatty acids, and current WHO nutrition recommendations advise limiting saturated fat to less than ten percent of total energy consumption. Recent meta-analyses of both observational and randomized controlled trials do not support any effect of saturated fat on cardiovascular disease or diabetes. The current evidence regarding the effect of meat consumption on health is potentially confounded, and there is a need for sufficiently powered high-quality trials assessing the health effects of meat consumption. Future studies should include biomarkers of meat intake, identify metabolic pathways and include detailed study of fermented and other processed meats and their potential of increasing nutrient availability and metabolic effects of compounds.

Whole-Grain Intake and Pancreatic Cancer Risk-The Danish, Diet, Cancer and Health Cohort.

BACKGROUND: Pancreatic cancer is a highly deadly disease with a poor prognosis. There is limited knowledge about prevention of the disease; thus, identification of risk factors is important to reduce the disease incidence. OBJECTIVE: The aim of the present study was to prospectively investigate associations between incidence of pancreatic cancer and whole-grain intake measured in 2 ways: as whole-grain product intake (g whole-grain products/d) and as whole-grain intake (grams of whole grains/d). Moreover, the intake of subgroups of these was also investigated: whole-grain products (rye bread, whole-grain bread, and oatmeal/muesli) and cereals (rye, wheat, and oats). METHODS: In total, 55,995 Danish adults aged 50-64 y, of whom 446 developed pancreatic cancer (17.5 y mean follow-up), were included in the study. Detailed information on daily intake of whole-grain products was available from a validated self-administered FFQ, and intake of whole-grain cereals (wheat, rye, and oats) was estimated using information from a 24-h dietary recall. The association between the whole-grain exposures and incidence of pancreatic cancer was investigated by Cox regression analyses adjusted for potential confounders. RESULTS: Total whole-grain product intake was associated with a 7% lower incidence of pancreatic cancer per serving (50 g/d) (HR: 0.93; 95% CI: 0.86, 1.00), and in the sex-specific analyses, an inverse association was found only in men. No association was found for total whole-grain intake (per 16-g serving size; HR: 0.96; 95% CI: 0.89, 1.03). When investigating specific whole-grain products and cereals individually, none were alone associated with lower incidence of pancreatic cancer. CONCLUSION: Our findings indicate that intake of whole grains is associated with lower risk of pancreatic cancer in middle-aged men. Consuming ample amounts of whole grains may prove beneficial in terms of lowering pancreatic cancer risk.

Effects of brown seaweeds on postprandial glucose, insulin and appetite in humans - A randomized, 3-way, blinded, cross-over meal study.

BACKGROUND & AIMS: Seaweed including brown seaweeds with rich bioactive components may be efficacious for a glycaemic management strategy and appetite control. We investigated the effects of two brown edible seaweeds, Laminaria digitata (LD) and Undaria pinnatifida (UP), on postprandial glucose metabolism and appetite following a starch load in a human meal study. METHODS: Twenty healthy subjects were enrolled in a randomized, 3-way, blinded cross-over trial. The study was registered under ClinicalTrials.gov Identifier no. NCT00123456. At each test day, the subjects received one of three meals comprising 30 g of starch with 5 g of LD or UP or an energy-adjusted control meal containing pea protein. Fasting and postprandial blood glucose, insulin, C-peptide and glucagon-like peptide-1 (GLP-1) concentrations were measured. Subjective appetite sensations were scored using visual analogue scales (VAS). RESULTS: Linear mixed model (LMM) analysis showed a lower blood glucose, insulin and C-peptide response following the intake of LD and UP, after correction for body weight. Participants weighing ≤ 63 kg had a reduced glucose response compared to control meal between 40 and 90 min both following LD and UP meals. Furthermore, LMM analysis for C-peptide showed a significantly lower response after intake of LD. Compared to the control meal, GLP-1 response was higher after the LD meal, both before and after the body weight adjustment. The VAS scores showed a decreased appetite sensation after intake of the seaweeds. Ad-libitum food intake was not different three hours after the seaweed meals compared to control. CONCLUSIONS: Concomitant ingestion of brown seaweeds may help improving postprandial glycaemic and appetite control in healthy and normal weight adults, depending on the dose per body weight. CLINICAL TRIAL REGISTRY NUMBER: Clinicaltrials.gov (ID# NCT02608372).

The anserine to carnosine ratio: an excellent discriminator between white and red meats consumed by free-living overweight participants of the PREVIEW study.

BACKGROUND: Biomarkers of meat intake hold promise in clarifying the health effects of meat consumption, yet the differentiation between red and white meat remains a challenge. We measure meat intake objectively in a free-living population by applying a newly developed, three-step strategy for biomarker-based assessment of dietary intakes aimed to indicate if (1) any meat was consumed, (2) what type it was and (3) the quantity consumed. METHODS: Twenty-four hour urine samples collected in a four-way crossover RCT and in a cross-sectional analysis of a longitudinal lifestyle intervention (the PREVIEW Study) were analyzed by untargeted LC-MS metabolomics. In the RCT, healthy volunteers consumed three test meals (beef, pork and chicken) and a control; in PREVIEW, overweight participants followed a diet with high or moderate protein levels. PLS-DA modeling of all possible combinations between six previously reported, partially validated, meat biomarkers was used to classify meat intake using samples from the RCT to predict consumption in PREVIEW. RESULTS: Anserine best separated omnivores from vegetarians (AUROC 0.94-0.97), while the anserine to carnosine ratio best distinguished the consumption of red from white meat (AUROC 0.94). Carnosine showed a trend for dose-response between non-consumers, low consumers and high consumers for all meat categories, while in combination with other biomarkers the difference was significant. CONCLUSION: It is possible to evaluate red meat intake by using combinations of existing biomarkers of white and general meat intake. Our results are novel and can be applied to assess qualitatively recent meat intake in nutritional studies. Further work to improve quantitation by biomarkers is needed.

Donor Fecal Microbiota Transplantation Alters Gut Microbiota and Metabolites in Obese Individuals With Steatohepatitis.

The intestinal microbiota has been linked to the development and prevalence of steatohepatitis in humans. Interestingly, steatohepatitis is significantly lower in individuals taking a plant-based, low-animal-protein diet, which is thought to be mediated by gut microbiota. However, data on causality between these observations in humans is scarce. In this regard, fecal microbiota transplantation (FMT) using healthy donors is safe and is capable of changing microbial composition in human disease. We therefore performed a double-blind randomized controlled proof-of-principle study in which individuals with hepatic steatosis on ultrasound were randomized to two study arms: lean vegan donor (allogenic n = 10) or own (autologous n = 11) FMT. Both were performed three times at 8-week intervals. A liver biopsy was performed at baseline and after 24 weeks in every subject to determine histopathology (Nonalcoholic Steatohepatitis Clinical Research Network) classification and changes in hepatic gene expression based on RNA sequencing. Secondary outcome parameters were changes in intestinal microbiota composition and fasting plasma metabolomics. We observed a trend toward improved necro-inflammatory histology, and found significant changes in expression of hepatic genes involved in inflammation and lipid metabolism following allogenic FMT. Intestinal microbial community structure changed following allogenic FMT, which was associated with changes in plasma metabolites as well as markers of . Conclusion: Allogenic FMT using lean vegan donors in individuals with hepatic steatosis shows an effect on intestinal microbiota composition, which is associated with beneficial changes in plasma metabolites and markers of steatohepatitis.

Autocrine negative feedback regulation of lipolysis through sensing of NEFAs by FFAR4/GPR120 in WAT.

OBJECTIVES: Long-chain fatty acids (LCFAs) released from adipocytes inhibit lipolysis through an unclear mechanism. We hypothesized that the LCFA receptor, FFAR4 (GPR120), which is highly expressed in adipocytes, may be involved in this feedback regulation. METHODS AND RESULTS: Liquid chromatography mass spectrometry (LC-MS) analysis of conditioned media from isoproterenol-stimulated primary cultures of murine and human adipocytes demonstrated that most of the released non-esterified free fatty acids (NEFAs) are known agonists for FFAR4. In agreement with this, conditioned medium from isoproterenol-treated adipocytes stimulated signaling strongly in FFAR4 transfected COS-7 cells as opposed to non-transfected control cells. In transfected 3T3-L1 cells, FFAR4 agonism stimulated Gi- and Go-mini G protein binding more strongly than Gq, effects which were blocked by the selective FFAR4 antagonist AH7614. In primary cultures of murine white adipocytes, the synthetic, selective FFAR4 agonist CpdA inhibited isoproterenol-induced intracellular cAMP accumulation in a manner similar to the antilipolytic control agent nicotinic acid acting through another receptor, HCAR2. In vivo, oral gavage with the synthetic, specific FFAR4 agonist CpdB decreased the level of circulating NEFAs in fasting lean mice to a similar degree as nicotinic acid. In agreement with the identified anti-lipolytic effect of FFAR4, plasma NEFAs and glycerol were increased in FFAR4-deficient mice as compared to littermate controls despite having elevated insulin levels, and cAMP accumulation in primary adipocyte cultures was augmented by treatment with the FFAR4 antagonist conceivably by blocking the stimulatory tone of endogenous NEFAs on FFAR4. CONCLUSIONS: In white adipocytes, FFAR4 functions as an NEFA-activated, autocrine, negative feedback regulator of lipolysis by decreasing cAMP though Gi-mediated signaling.

Human urine 1H NMR metabolomics reveals alterations of protein and carbohydrate metabolism when comparing habitual Average Danish diet vs. healthy New Nordic diet.

OBJECTIVES: The aim of this study was to investigate the alteration of the human urine metabolome by means of diet and to compare the metabolic effects of the nutritionally healthy New Nordic Diet (NND) with an Average Danish Diet (ADD). The NND was designed a decade ago by scientists and chefs, based on local and sustainable foods, including fish, shellfish, vegetables, roots, fruit, and berries. The NND has been proven to lower blood pressure, reduce glycemia, and lead to weight loss. METHODS: The human urine metabolome was measured by untargeted proton nuclear magnetic resonance spectroscopy in samples from 142 centrally obese Danes (20-66 years old), randomized to consume the ADD or the NND. The resulting metabolomics data was processed and analyzed using advanced multivariate data analysis methods to reveal effects related to the design factors, including diet, season, sex, and changes in body weight. RESULTS: Exploration of the nuclear magnetic resonance profiles revealed unique metabolite markers reflecting changes in protein and carbohydrate metabolism between the two diets. Glycine betaine, glucose, trimethylamine N-oxide and creatinine were increased in urine of the individuals following the NND compared with the ADD population, whereas relative concentrations of tartrate, dimethyl sulfone, and propylene glycol were decreased. Propylene glycol had a strong association with the homeostatic model assessment for insulin resistance in the NND group. The food intake biomarkers found in this study confirm the importance of these as tools for nutritional research. CONCLUSIONS: Findings from this study provided new insights into the effects of a healthy diet on glycemia, reduction of inflammation, and weight loss among obese individuals, and alteration of the gut microbiota metabolism.

Correlation of plasma metabolites with glucose and lipid fluxes in human insulin resistance.

OBJECTIVE: Insulin resistance develops prior to the onset of overt type 2 diabetes, making its early detection vital. Direct accurate evaluation is currently only possible with complex examinations like the stable isotope-based hyperinsulinemic euglycemic clamp (HIEC). Metabolomic profiling enables the detection of thousands of plasma metabolites, providing a tool to identify novel biomarkers in human obesity. DESIGN: Liquid chromatography mass spectrometry-based untargeted plasma metabolomics was applied in 60 participants with obesity with a large range of peripheral insulin sensitivity as determined via a two-step HIEC with stable isotopes [6,6-2H2]glucose and [1,1,2,3,3-2H5]glycerol. This additionally enabled measuring insulin-regulated lipolysis, which combined with metabolomics, to the knowledge of this research group, has not been reported on before. RESULTS: Several plasma metabolites were identified that significantly correlated with glucose and lipid fluxes, led by plasma (gamma-glutamyl)citrulline, followed by betaine, beta-cryptoxanthin, fructosyllysine, octanylcarnitine, sphingomyelin (d18:0/18:0, d19:0/17:0) and thyroxine. Subsequent machine learning analysis showed that a panel of these metabolites derived from a number of metabolic pathways may be used to predict insulin resistance, dominated by non-essential amino acid citrulline and its metabolite gamma-glutamylcitrulline. CONCLUSION: This approach revealed a number of plasma metabolites that correlated reasonably well with glycemic and lipolytic flux parameters, measured using gold standard techniques. These metabolites may be used to predict the rate of glucose disposal in humans with obesity to a similar extend as HOMA, thus providing potential novel biomarkers for insulin resistance.

Perspective: Dietary Biomarkers of Intake and Exposure-Exploration with Omics Approaches.

While conventional nutrition research has yielded biomarkers such as doubly labeled water for energy metabolism and 24-h urinary nitrogen for protein intake, a critical need exists for additional, equally robust biomarkers that allow for objective assessment of specific food intake and dietary exposure. Recent advances in high-throughput MS combined with improved metabolomics techniques and bioinformatic tools provide new opportunities for dietary biomarker development. In September 2018, the NIH organized a 2-d workshop to engage nutrition and omics researchers and explore the potential of multiomics approaches in nutritional biomarker research. The current Perspective summarizes key gaps and challenges identified, as well as the recommendations from the workshop that could serve as a guide for scientists interested in dietary biomarkers research. Topics addressed included study designs for biomarker development, analytical and bioinformatic considerations, and integration of dietary biomarkers with other omics techniques. Several clear needs were identified, including larger controlled feeding studies, testing a variety of foods and dietary patterns across diverse populations, improved reporting standards to support study replication, more chemical standards covering a broader range of food constituents and human metabolites, standardized approaches for biomarker validation, comprehensive and accessible food composition databases, a common ontology for dietary biomarker literature, and methodologic work on statistical procedures for intake biomarker discovery. Multidisciplinary research teams with appropriate expertise are critical to moving forward the field of dietary biomarkers and producing robust, reproducible biomarkers that can be used in public health and clinical research.

An Isocaloric Nordic Diet Modulates RELA and TNFRSF1A Gene Expression in Peripheral Blood Mononuclear Cells in Individuals with Metabolic Syndrome-A SYSDIET Sub-Study.

A healthy dietary pattern is associated with a lower risk of metabolic syndrome (MetS) and reduced inflammation. To explore this at the molecular level, we investigated the effect of a Nordic diet (ND) on changes in the gene expression profiles of inflammatory and lipid-related genes in peripheral blood mononuclear cells (PBMCs) of individuals with MetS. We hypothesized that the intake of an ND compared to a control diet (CD) would alter the expression of inflammatory genes and genes involved in lipid metabolism. The individuals with MetS underwent an 18/24-week randomized intervention to compare a ND with a CD. Eighty-eight participants (66% women) were included in this sub-study of the larger SYSDIET study. Fasting PBMCs were collected before and after the intervention and changes in gene expression levels were measured using TaqMan Array Micro Fluidic Cards. Forty-eight pre-determined inflammatory and lipid related gene transcripts were analyzed. The expression level of the gene tumor necrosis factor (TNF) receptor superfamily member 1A (TNFRSF1A) was down-regulated (p = 0.004), whereas the nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) subunit, RELA proto-oncogene, was up-regulated (p = 0.016) in the ND group compared to the CD group. In conclusion, intake of an ND in individuals with the MetS may affect immune function.

Biomarkers of seaweed intake.

Seaweeds are marine macroalgae, some of which are edible. They are rich in specific dietary fibers and also contain other characteristic biological constituents. Biological activities have been investigated mainly in animal studies, while very few results are available from human studies. Biomarkers of food intake (BFIs) specific to seaweed could play an important role as objective measurements in observational studies and dietary intervention studies. Thus, the health effects of seaweeds can be explored and understood by discovering and applying BFIs. This review summarizes studies to identify candidate BFIs of seaweed intake. These BFIs are evaluated by a structured validation scheme. Hydroxytrifuhalol A, 7-hydroxyeckol, C-O-C dimer of phloroglucinol, diphloroethol, fucophloroethol, dioxinodehydroeckol, and/or their glucuronides or sulfate esters which all belong to the phlorotannins are considered candidate biomarkers for brown seaweed. Fucoxanthinol, the main metabolite of fucoxanthin, is also regarded as a candidate biomarker for brown seaweed. Further validation will be needed due to the very limited number of human studies. Further studies are also needed to identify additional candidate biomarkers, relevant specifically for the red and green seaweeds, for which no candidate biomarkers emerged from the literature search. Reliable BFIs should also ideally be found for the whole seaweed food group.

Associations between Urinary Advanced Glycation End Products and Cardiometabolic Parameters in Metabolically Healthy Obese Women.

Advanced glycation end products (AGEs) have been implicated in the pathophysiology of type 2 diabetes and cardiovascular disease. We aimed to determine the associations of urinary carboxymethyl-lysine (CML) and methylglyoxal-hydroimidazolone (MG-H1) levels with cardiometabolic parameters in metabolically healthy obese women. Anthropometric, glycemic, cardiovascular, and urinary AGE parameters were measured in 58 metabolically healthy obese women (age: 39.98 ± 8.72 years; body mass index (BMI): 32.29 ± 4.05 kg/m2). Urinary CML levels were positively associated with BMI (r = 0.29, p = 0.02). After adjustment for age and BMI, there was a trend for positive associations between urinary CML levels and fasting (p = 0.06) and 2 h insulin (p = 0.05) levels, and insulin resistance measured by homeostatic model assessment (HOMA-IR) (p = 0.06). Urinary MG-H1 levels were positively associated with systolic and diastolic blood pressure, pulse pressure, mean arterial pressure, and total and low-density lipoprotein cholesterol after adjustment for age, BMI, and HOMA-IR (all p ˂ 0.05). There were no associations between urinary CML levels and cardiovascular parameters, and between urinary MG-H1 levels and glycemic measurements. Our data support a role of urinary AGEs in the pathophysiology of insulin resistance and cardiovascular disease; however, future studies are highly warranted.

Comparison of Bi- and Tri-Linear PLS Models for Variable Selection in Metabolomic Time-Series Experiments.

Metabolomic studies with a time-series design are widely used for discovery and validation of biomarkers. In such studies, changes of metabolic profiles over time under different conditions (e.g., control and intervention) are compared, and metabolites responding differently between the conditions are identified as putative biomarkers. To incorporate time-series information into the variable (biomarker) selection in partial least squares regression (PLS) models, we created PLS models with different combinations of bilinear/trilinear X and group/time response dummy Y. In total, five PLS models were evaluated on two real datasets, and also on simulated datasets with varying characteristics (number of subjects, number of variables, inter-individual variability, intra-individual variability and number of time points). Variables showing specific temporal patterns observed visually and determined statistically were labelled as discriminating variables. Bootstrapped-VIP scores were calculated for variable selection and the variable selection performance of five PLS models were assessed based on their capacity to correctly select the discriminating variables. The results showed that the bilinear PLS model with group × time response as dummy Y provided the highest recall (true positive rate) of 83-95% with high precision, independent of most characteristics of the datasets. Trilinear PLS models tend to select a small number of variables with high precision but relatively high false negative rate (lower power). They are also less affected by the noise compared to bilinear PLS models. In datasets with high inter-individual variability, bilinear PLS models tend to provide higher recall while trilinear models tend to provide higher precision. Overall, we recommend bilinear PLS with group x time response Y for variable selection applications in metabolomics intervention time series studies.

Combined Markers to Assess Meat Intake-Human Metabolomic Studies of Discovery and Validation.

SCOPE: Biomarkers of red meat may clarify the relationship between meat intake and health. This paper explores the discovery of biomarkers of intake for three types of meat with varying heme iron content. Candidate biomarkers for red and general meat are further evaluated based on defined validation criteria. METHODS AND RESULTS: In a randomized cross-over meal study, healthy volunteers consume a randomized sequence of four test meals: chicken, pork, beef, and a control made of egg white and pea. Fasting and postprandial urine samples are collected to cover 48 h and profiled by untargeted LC-ESI-qTOF-MS metabolomics. The profiles following the meal challenges are explored by univariate and multivariate analyses. Nine red, four white, and eight general meat biomarkers are selected as putative biomarkers, originating from collagen degradation, flavour compounds, and amino acid metabolism. Heme-related metabolites are masked by the chlorophyll content of the control meal. The candidate biomarkers are confirmed in an independent meal study and validated for plausibility, robustness, time-response, and prediction performance. Combinations of biomarkers are more efficient than single markers in predicting meat intake. CONCLUSION: New combinations of partially validated biomarkers are proposed to assess terrestrial meat intake and thus help disentangle the effects of meat consumption on human health.