Predicting weight loss success on a new Nordic diet: an untargeted multi-platform metabolomics and machine learning approach.

Background and aim: Results from randomized controlled trials indicate that no single diet performs better than other for all people living with obesity. Regardless of the diet plan, there is always large inter-individual variability in weight changes, with some individuals losing weight and some not losing or even gaining weight. This raises the possibility that, for different individuals, the optimal diet for successful weight loss may differ. The current study utilized machine learning to build a predictive model for successful weight loss in subjects with overweight or obesity on a New Nordic Diet (NND). Methods: Ninety-one subjects consumed an NND ad libitum for 26 weeks. Based on their weight loss, individuals were classified as responders (weight loss ≥5%, n = 46) or non-responders (weight loss <2%, n = 24). We used clinical baseline data combined with baseline urine and plasma untargeted metabolomics data from two different analytical platforms, resulting in a data set including 2,766 features, and employed symbolic regression (QLattice) to develop a predictive model for weight loss success. Results: There were no differences in clinical parameters at baseline between responders and non-responders, except age (47 ± 13 vs. 39 ± 11 years, respectively, p = 0.009). The final predictive model for weight loss contained adipic acid and argininic acid from urine (both metabolites were found at lower levels in responders) and generalized from the training (AUC 0.88) to the test set (AUC 0.81). Responders were also able to maintain a weight loss of 4.3% in a 12 month follow-up period. Conclusion: We identified a model containing two metabolites that were able to predict the likelihood of achieving a clinically significant weight loss on an ad libitum NND. This work demonstrates that models based on an untargeted multi-platform metabolomics approach can be used to optimize precision dietary treatment for obesity.

Human blood plasma biomarkers of diet and weight loss among centrally obese subjects in a New Nordic Diet intervention.

Scope: The New Nordic Diet (NND) has been shown to promote weight loss and lower blood pressure amongst obese people. This study investigates blood plasma metabolite and lipoprotein biomarkers differentiating subjects who followed Average Danish Diet (ADD) or NND. The study also evaluates how the individual response to the diet is reflected in the metabolic differences between NND subjects who lost or maintained their pre-intervention weight. Methods: Centrally obese Danes (BMI >25) followed NND (90 subjects) or ADD (56 subjects) for 6 months. Fasting blood plasma samples, collected at three time-points during the intervention, were screened for metabolites and lipoproteins (LPs) using proton nuclear magnetic resonance spectroscopy. In total, 154 metabolites and 65 lipoproteins were analysed. Results: The NND showed a relatively small but significant effect on the plasma metabolome and lipoprotein profiles, with explained variations ranging from 0.6% for lipoproteins to 4.8% for metabolites. A total of 38 metabolites and 11 lipoproteins were found to be affected by the NND. The primary biomarkers differentiating the two diets were found to be HDL-1 cholesterol, apolipoprotein A1, phospholipids, and ketone bodies (3-hydroxybutyric acid, acetone, and acetoacetic acid). The increased levels of ketone bodies detected in the NND group inversely associated with the decrease in diastolic blood pressure of the NND subjects. The study also showed that body weight loss among the NND subjects was weakly associated with plasma levels of citrate. Conclusion: The main plasma metabolites associated with NND were acetate, methanol and 3-hydroxybutyrate. The metabolic changes associated with the NND-driven weight loss are mostly pronounced in energy and lipid metabolism.

Effects of a wholegrain-rich diet on markers of colonic fermentation and bowel function and their associations with the gut microbiome: a randomised controlled cross-over trial.

Background: Diets rich in whole grains are associated with health benefits. Yet, it remains unclear whether the benefits are mediated by changes in gut function and fermentation. Objective: We explored the effects of whole-grain vs. refined-grain diets on markers of colonic fermentation and bowel function, as well as their associations with the gut microbiome. Methods: Fifty overweight individuals with increased metabolic risk and a high habitual intake of whole grains (~69 g/day) completed a randomised cross-over trial with two 8-week dietary intervention periods comprising a whole-grain diet (≥75 g/day) and a refined-grain diet (<10 g/day), separated by a washout period of ≥6 weeks. A range of markers of colonic fermentation and bowel function were assessed before and after each intervention. Results: The whole-grain diet increased the levels of faecal butyrate (p = 0.015) and caproate (p = 0.013) compared to the refined-grain diet. No changes in other faecal SCFA, BCFA or urinary levels of microbial-derived proteolytic markers between the two interventions were observed. Similarly, faecal pH remained unchanged. Faecal pH did however increase (p = 0.030) after the refined-grain diet compared to the baseline. Stool frequency was lower at the end of the refined-grain period compared to the end of the whole-grain diet (p = 0.001). No difference in faecal water content was observed between the intervention periods, however, faecal water content increased following the whole-grain period compared to the baseline (p = 0.007). Dry stool energy density was unaffected by the dietary interventions. Nevertheless, it explained 4.7% of the gut microbiome variation at the end of the refined-grain diet, while faecal pH and colonic transit time explained 4.3 and 5%, respectively. Several butyrate-producers (e.g., Faecalibacterium, Roseburia, Butyriciococcus) were inversely associated with colonic transit time and/or faecal pH, while the mucin-degraders Akkermansia and Ruminococcaceae showed the opposite association. Conclusion: Compared with the refined-grain diet, the whole-grain diet increased faecal butyrate and caproate concentrations as well as stool frequency, emphasising that differences between whole and refined grains affect both colonic fermentation and bowel habits.

Associations between sheep meat intake frequency and blood plasma levels of metabolites and lipoproteins in healthy Uzbek adults.

INTRODUCTION: Uzbekistan is one of the countries with the highest number of diet-related chronic diseases, which is believed to be associated with high animal fat intake. Sheep meat is high in fats (~ 5% in muscle), including saturated and monounsaturated fatty acids, and it contains nearly twice the higher amounts of n-3 polyunsaturated fatty acids and conjugated linoleic acids compared to beef. Nevertheless, sheep meat is considered health promoting by the locals in Uzbekistan and it accounts for around 1/3 of red meat intake in the country. OBJECTIVES: The aim of this study was to apply a metabolomics approach to investigate if sheep meat intake frequency (SMIF) is associated with alterations in fasting blood plasma metabolites and lipoproteins in healthy Uzbek adults. METHODS: The study included 263 subjects, 149 females and 114 males. For each subject a food intake questionnaire, including SMIF, was recorded and fasting blood plasma samples were collected for metabolomics. Blood plasma metabolites and lipoprotein concentrations were determined using 1H NMR spectroscopy. RESULTS AND CONCLUSION: The results showed that SMIF was confounded by nationality, sex, body mass index (BMI), age, intake frequency of total meat and fish in ascending order (p < 0.01). Multivariate and univariate data analyses showed differences in the levels of plasma metabolites and lipoproteins with respect to SMIF. The effect of SMIF after statistical adjustment by nationality, sex, BMI, age, intake frequency of total meat and fish decreased but remained significant. Pyruvic acid, phenylalanine, ornithine, and acetic acid remained significantly lower in the high SMIF group, whereas choline, asparagine, and dimethylglycine showed an increasing trend. Levels of cholesterol, apolipoprotein A1, as well as low- and high-density lipoprotein subfractions all displayed a decreasing trend with increased SMIF although the difference were not significant after FDR correction.

Biomarkers of moderate alcohol intake and alcoholic beverages: a systematic literature review.

The predominant source of alcohol in the diet is alcoholic beverages, including beer, wine, spirits and liquors, sweet wine, and ciders. Self-reported alcohol intakes are likely to be influenced by measurement error, thus affecting the accuracy and precision of currently established epidemiological associations between alcohol itself, alcoholic beverage consumption, and health or disease. Therefore, a more objective assessment of alcohol intake would be very valuable, which may be established through biomarkers of food intake (BFIs). Several direct and indirect alcohol intake biomarkers have been proposed in forensic and clinical contexts to assess recent or longer-term intakes. Protocols for performing systematic reviews in this field, as well as for assessing the validity of candidate BFIs, have been developed within the Food Biomarker Alliance (FoodBAll) project. The aim of this systematic review is to list and validate biomarkers of ethanol intake per se excluding markers of abuse, but including biomarkers related to common categories of alcoholic beverages. Validation of the proposed candidate biomarker(s) for alcohol itself and for each alcoholic beverage was done according to the published guideline for biomarker reviews. In conclusion, common biomarkers of alcohol intake, e.g., as ethyl glucuronide, ethyl sulfate, fatty acid ethyl esters, and phosphatidyl ethanol, show considerable inter-individual response, especially at low to moderate intakes, and need further development and improved validation, while BFIs for beer and wine are highly promising and may help in more accurate intake assessments for these specific beverages.

The Nordic diet can potentially prevent and treat cardiovascular diseases.

The Nordic diet is characterized by a high content of plant-based food and a limited content of animal and processed food. Intervention studies show with moderate evidence that Nordic diet reduces risk factors for cardiovascular diseases (blood pressure, total and low-density lipoprotein cholesterol). Observational studies show with weak evidence that Nordic diet reduces the risk of cardiovascular diseases e.g. stroke and myocardial infarcts and with moderate evidence reduces cardiovascular death. Thus, Nordic diet appears beneficial for cardiovascular health as well as for the climate and the environment, as argued in this review.

Advancing human gut microbiota research by considering gut transit time.

Accumulating evidence indicates that gut transit time is a key factor in shaping the gut microbiota composition and activity, which are linked to human health. Both population-wide and small-scale studies have identified transit time as a top covariate contributing to the large interindividual variation in the faecal microbiota composition. Despite this, transit time is still rarely being considered in the field of the human gut microbiome. Here, we review the latest research describing how and why whole gut and segmental transit times vary substantially between and within individuals, and how variations in gut transit time impact the gut microbiota composition, diversity and metabolism. Furthermore, we discuss the mechanisms by which the gut microbiota may causally affect gut motility. We argue that by taking into account the interindividual and intraindividual differences in gut transit time, we can advance our understanding of diet-microbiota interactions and disease-related microbiome signatures, since these may often be confounded by transient or persistent alterations in transit time. Altogether, a better understanding of the complex, bidirectional interactions between the gut microbiota and transit time is required to better understand gut microbiome variations in health and disease.

1-2 Drinks Per Day Affect Lipoprotein Composition after 3 Weeks-Results from a Cross-Over Pilot Intervention Trial in Healthy Adults Using Nuclear Magnetic Resonance-Measured Lipoproteins and Apolipoproteins.

Alcohol consumption ranging from 1−2 drinks/day associates with a lower risk of coronary heart disease in some studies. The underlying mechanisms are unclear. The Metabolic Imprints of Alcoholic Beverages (MetAl) trial aimed to explore the short-term effects of moderate alcohol consumption on cardiovascular biomarkers. A 2 × 3-week cross-over single-blinded intervention trial investigating the effect of 1−2 drinks/day (~12−24 g) compared with abstention on 1H Nuclear Magnetic Resonance-measured main lipoproteins and subfractions was performed in 26 healthy adults. Volunteers were classified as occasional or habitual drinkers based on their habitual alcohol intakes (<2 or ≥2 drinks/week). Compared with abstention, 1−2 drinks/day increased HDL2a-C (p = 0.004), HDL3-C (p = 0.008), and HDL non-significantly (p = 0.19). Total apoA1 and apoA1 in HDL and its subfractions increased (p < 0.05). Novel findings were a decreased apoB/apoA1 ratio (p = 0.02), and increased HDL2a phospholipid content (p = 0.04). In women alone, the results were similar but attenuated, and LDL-P decreased. Thus, changes in apoA1- and HDL-related biomarkers occur within weeks in moderate drinkers. Compared with abstention, 1−2 drinks/day increased total apoA1 more strongly than HDL-C and increased the cholesterol, apoA1, and phospholipid content of several HDL subfractions. Whether this provides a cardiovascular benefit requires further study. Clinicaltrials.gov: NCT03384147.

A Comprehensive Database for DNA Adductomics.

The exposure of human DNA to genotoxic compounds induces the formation of covalent DNA adducts, which may contribute to the initiation of carcinogenesis. Liquid chromatography (LC) coupled with high-resolution mass spectrometry (HRMS) is a powerful tool for DNA adductomics, a new research field aiming at screening known and unknown DNA adducts in biological samples. The lack of databases and bioinformatics tool in this field limits the applicability of DNA adductomics. Establishing a comprehensive database will make the identification process faster and more efficient and will provide new insight into the occurrence of DNA modification from a wide range of genotoxicants. In this paper, we present a four-step approach used to compile and curate a database for the annotation of DNA adducts in biological samples. The first step included a literature search, selecting only DNA adducts that were unequivocally identified by either comparison with reference standards or with nuclear magnetic resonance (NMR), and tentatively identified by tandem HRMS/MS. The second step consisted in harmonizing structures, molecular formulas, and names, for building a systematic database of 279 DNA adducts. The source, the study design and the technique used for DNA adduct identification were reported. The third step consisted in implementing the database with 303 new potential DNA adducts coming from different combinations of genotoxicants with nucleobases, and reporting monoisotopic masses, chemical formulas, .cdxml files, .mol files, SMILES, InChI, InChIKey and IUPAC nomenclature. In the fourth step, a preliminary spectral library was built by acquiring experimental MS/MS spectra of 15 reference standards, generating in silico MS/MS fragments for all the adducts, and reporting both experimental and predicted fragments into interactive web datatables. The database, including 582 entries, is publicly available (https://gitlab.com/nexs-metabolomics/projects/dna_adductomics_database). This database is a powerful tool for the annotation of DNA adducts measured in (HR)MS. The inclusion of metadata indicating the source of DNA adducts, the study design and technique used, allows for prioritization of the DNA adducts of interests and/or to enhance the annotation confidence. DNA adducts identification can be further improved by integrating the present database with the generation of authentic MS/MS spectra, and with user-friendly bioinformatics tools.

Analysis of the SYSDIET Healthy Nordic Diet randomized trial based on metabolic profiling reveal beneficial effects on glucose metabolism and blood lipids.

BACKGROUND & AIMS: Intake assessment in multicenter trials is challenging, yet important for accurate outcome evaluation. The present study aimed to characterize a multicenter randomized controlled trial with a healthy Nordic diet (HND) compared to a Control diet (CD) by plasma and urine metabolic profiles and to associate them with cardiometabolic markers. METHODS: During 18-24 weeks of intervention, 200 participants with metabolic syndrome were advised at six centres to eat either HND (e.g. whole-grain products, berries, rapeseed oil, fish and low-fat dairy) or CD while being weight stable. Of these 166/159 completers delivered blood/urine samples. Metabolic profiles of fasting plasma and 24 h pooled urine were analysed to identify characteristic diet-related patterns. Principal components analysis (PCA) scores (i.e. PC1 and PC2 scores) were used to test their combined effect on blood glucose response (primary endpoint), serum lipoproteins, triglycerides, and inflammatory markers. RESULTS: The profiles distinguished HND and CD with AUC of 0.96 ± 0.03 and 0.93 ± 0.02 for plasma and urine, respectively, with limited heterogeneity between centers, reflecting markers of key foods. Markers of fish, whole grain and polyunsaturated lipids characterized HND, while CD was reflected by lipids containing palmitoleic acid. The PC1 scores of plasma metabolites characterizing the intervention is associated with HDL (ß = 0.05; 95% CI: 0.02, 0.08; P = 0.001) and triglycerides (ß = -0.06; 95% CI: -0.09, -0.03; P < 0.001). PC2 scores were related with glucose metabolism (2 h Glucose, ß = 0.1; 95% CI: 0.05, 0.15; P < 0.001), LDL (ß = 0.06; 95% CI: 0.01, 0.1; P = 0.02) and triglycerides (ß = 0.11; 95% CI: 0.06, 0.15; P < 0.001). For urine, the scores were related with LDL cholesterol. CONCLUSIONS: Plasma and urine metabolite profiles from SYSDIET reflected good compliance with dietary recommendations across the region. The scores of metabolites characterizing the diets associated with outcomes related with cardio-metabolic risk. Our analysis therefore offers a novel way to approach a per protocol analysis with a balanced compliance assessment in larger multicentre dietary trials. The study was registered at clinicaltrials.gov with NCT00992641.

Effects of changing from a diet with saturated fat to a diet with n-6 polyunsaturated fat on the serum metabolome in relation to cardiovascular disease risk factors.

PURPOSE: Replacing saturated fatty acids (SFA) with polyunsaturated fatty acids (PUFA) is associated with a reduced risk of cardiovascular disease. Yet, the changes in the serum metabolome after this replacement is not well known. Therefore, the present study aims to identify the metabolites differentiating diets where six energy percentage SFA is replaced with PUFA and to elucidate the association of dietary metabolites with cardiometabolic risk markers. METHODS: In an 8-week, double-blind, randomized, controlled trial, 99 moderately hyper-cholesterolemic adults (25-70 years) were assigned to a control diet (C-diet) or an experimental diet (Ex-diet). Both groups received commercially available food items with different fatty acid compositions. In the Ex-diet group, products were given where SFA was replaced mostly with n-6 PUFA. Fasting serum samples were analysed by untargeted ultra-performance liquid chromatography high-resolution mass spectrometry (UPLC-HRMS). Pre-processed data were analysed by double cross-validated Partial Least-Squares Discriminant Analysis (PLS-DA) to detect features differentiating the two diet groups. RESULTS: PLS-DA differentiated the metabolic profiles of the Ex-diet and the C-diet groups with an area under the curve of 0.83. The Ex-diet group showed higher levels of unsaturated phosphatidylcholine plasmalogens, an unsaturated acylcarnitine, and a secondary bile acid. The C-diet group was characterized by odd-numbered phospholipids and a saturated acylcarnitine. The Principal Component analysis scores of the serum metabolic profiles characterizing the diets were significantly associated with low-density lipoprotein cholesterol, total cholesterol, and triglyceride levels but not with glycaemia. CONCLUSION: The serum metabolic profiles confirmed the compliance of the participants based on their diet-specific metabolome after replacing SFA with mostly n-6 PUFA. The participants' metabolic profiles in response to the change in diet were associated with cardiovascular disease risk markers. This study was registered at clinicaltrials.gov as NCT01679496 on September 6th 2012.

Discovery of Urinary Biomarkers of Spinach Consumption Using Untargeted LC-MS Metabolomics in a Human Intervention Trial.

SCOPE: Biomarkers for intake of green leafy vegetables such as spinach can help investigate their health effects. However, only few potential intake markers have been reported in the literature so far. METHODS AND RESULTS: Based on a cross-over study on whole leaf and minced spinach, we investigate changes in metabolites before and after spinach intake and differences between the two treatments and health status. Nineteen volunteers (12 healthy subjects and 7 short bowel patients) completed the study within 48 days. Urine samples (24-h intervals before and after spinach intake) and serum samples (baseline, post 8 d, and post 15 d) are collected and analyzed by ultra-high performance liquid chromatography quadrupole time-of-flight mass spectrometry (UHPLC-QTOF-MS). The acquired data is analyzed by multivariate and univariate analyses. Three candidate biomarkers are observed in urine only after the spinach intake, including des-amino arginine pentenol ester, D/L-malic acid ester of cis-p-coumarate, D/L-malic acid ester of trans-p-coumarate, and 69 metabolites are present before spinach intake but showing an altered level after treatment. These metabolites are related to dietary habits or meal structure, and some changes are possibly affected by spinach intake. The candidate biomarkers are independent of spinach pre-processing and healthy status. No markers are discovered in serum samples. CONCLUSION: We propose structures for three candidate spinach intake biomarkers; these markers will need further validation in independent studies.

Bile acid-farnesoid X receptor-fibroblast growth factor 19 axis in patients with short bowel syndrome: The randomized, glepaglutide phase 2 trial.

BACKGROUND: The gut-liver axis and enterohepatic circulation have gained increasing attention lately. Patients with short bowel syndrome (SBS) are, in fact, human knock-out models that may assist in the understanding of bile acid synthesis and regulation. We evaluated effect of glepaglutide (a long-acting glucagon-like peptide-2 analog) on bile acid synthesis (the enterohepatic circulation of bile acids and liver biochemistry in patients with SBS). METHOD: In a single-center, double-blinded, dose-finding, crossover phase 2 trial, 18 patients with SBS were randomly assigned to 2 of 3 treatment arms (0.1, 1, and 10 mg) with daily subcutaneous injections of glepaglutide for 3 weeks. The washout period between the 2 treatment periods was 4-8 weeks. Measurements were performed at baseline and at the end of each treatment period and included postprandial plasma samples for fibroblast growth factor 19 (FGF19), 7α-hydroxy-4-cholesten-3-one (C4), total excretion of fecal bile acids, gene expression of farnesoid X receptor (FXR) in intestinal mucosal biopsies, total plasma bile acids, and liver biochemistry. RESULTS: Compared with baseline, the median (interquartile range) postprandial response (area under the curve 0-2h) of FGF19 increased by 150 h × ng/L (41, 195; P = 0.001) and C4 decreased by 82 h × µg/L (-169, -28; p = 0.010) in the 10-mg dose. FXR gene expression did not change in any of the groups. Alkaline phosphatase significantly decreased. CONCLUSION: Glepaglutide may stimulate the bile acid/FXR/FGF19 axis, leading to increased plasma concentrations of FGF19. Thereby, glepaglutide may ameliorate the accelerated de novo bile acid synthesis and play a role in the prevention and/or treatment of intestinal failure-associated liver disease.

Combined Urinary Biomarkers to Assess Coffee Intake Using Untargeted Metabolomics: Discovery in Three Pilot Human Intervention Studies and Validation in Cross-Sectional Studies.

Coffee is a widely consumed beverage worldwide and has a high content of chlorogenic acids, polyphenols, methylxanthines, and volatile flavor compounds. Scientific evidence to support the beneficial health effects of coffee is limited, and validated urinary biomarkers of coffee intake are therefore needed. We observed 23 common putative biomarkers of coffee intake in three separate parallel intervention studies by ultra-high-performance liquid chromatography-electrospray ionization-quadrupole time-of-flight-mass spectrometry (UHPLC-ESI-QTOF-MS) and multivariate analyses. Baseline samples from the NU-AGE study were used to confirm and validate 16 of these candidate biomarkers, including their robustness, time response, and dose response. These validated candidate biomarkers are N-methylpyridinium cation, 1-methyl-1H-pyrrole-2-carboxaldehyde, 1H-pyrrole-2-carboxaldehyde sulfate, 3-piperidinemethanol, furfurylidene-furfurylamine, 2-furoylglycine, N-substituted-5-(aminoethyl) furan-2-carbaldehyde derivative, 3',4'-dihydroxyacetophenone sulfate, caffeine, dihydroxystyrene glucuronide, ferulic acid sulfate, 4-ethylcatechol glucuronide, 3-feruloylquinic acid, 3,4-dihydroxystyrene sulfate, one unknown glucuronide, and one unknown sulfate. Combinations of candidate biomarkers gave a better prediction of coffee consumption than individual biomarkers. The robustness of the combined biomarkers requires additional validation in cohort studies covering other populations.

Data sharing in PredRet for accurate prediction of retention time: Application to plant food bioactive compounds.

Prediction of retention times (RTs) is increasingly considered in untargeted metabolomics to complement MS/MS matching for annotation of unidentified peaks. We tested the performance of PredRet (http://predret.org/) to predict RTs for plant food bioactive metabolites in a data sharing initiative containing entry sets of 29-103 compounds (totalling 467 compounds, >30 families) across 24 chromatographic systems (CSs). Between 27 and 667 predictions were obtained with a median prediction error of 0.03-0.76 min and interval width of 0.33-8.78 min. An external validation test of eight CSs showed high prediction accuracy. RT prediction was dependent on shape and type of LC gradient, and number of commonly measured compounds. Our study highlights PredRet's accuracy and ability to transpose RT data acquired from one CS to another CS. We recommend extensive RT data sharing in PredRet by the community interested in plant food bioactive metabolites to achieve a powerful community-driven open-access tool for metabolomics annotation.

New Advanced Glycation End Products Observed in Rat Urine by Untargeted Metabolomics after Feeding with Heat-Treated Skimmed Milk Powder.

SCOPE: Milk powder is commonly consumed throughout the world. However, advanced glycation end products (AGEs) will form in milk powder during thermal processing and long-term storage. This study aimed to identify such compounds with potential as new urinary biomarkers of intake of heat-treated skimmed milk powder (HSMP). METHODS AND RESULTS: A parallel study is performed with different dosages of HSMP as well as hydrolyzed HSMP and untreated skimmed milk powder (SMP) in 36 rats. The 24-h urine samples on day 7 or 8 are collected and profiled by untargeted UPLC-Qtof-MS metabolomics. Statistical analysis revealed 25 metabolites differentiating SMP and HSMP; nineteen of these structures are proposed as lysine- and arginine-derived AGEs, and heterocyclic compounds. CONCLUSION: These metabolites may potentially serve as biomarkers of food intake pending further validation to assess intakes of heat-processed dairy foods and thus help to elucidate the effects of HSMP consumption or dietary AGEs on human health.

Maternal milk microbiota and oligosaccharides contribute to the infant gut microbiota assembly.

Breastfeeding protects against diseases, with potential mechanisms driving this being human milk oligosaccharides (HMOs) and the seeding of milk-associated bacteria in the infant gut. In a cohort of 34 mother-infant dyads we analyzed the microbiota and HMO profiles in breast milk samples and infant's feces. The microbiota in foremilk and hindmilk samples of breast milk was compositionally similar, however hindmilk had higher bacterial load and absolute abundance of oral-associated bacteria, but a lower absolute abundance of skin-associated Staphylococcus spp. The microbial communities within both milk and infant's feces changed significantly over the lactation period. On average 33% and 23% of the bacterial taxa detected in infant's feces were shared with the corresponding mother's milk at 5 and 9 months of age, respectively, with Streptococcus, Veillonella and Bifidobacterium spp. among the most frequently shared. The predominant HMOs in feces associated with the infant's fecal microbiota, and the dominating infant species B. longum ssp. infantis and B. bifidum correlated inversely with HMOs. Our results show that breast milk microbiota changes over time and within a feeding session, likely due to transfer of infant oral bacteria during breastfeeding and suggest that milk-associated bacteria and HMOs direct the assembly of the infant gut microbiota.

An Untargeted Urine Metabolomics Approach for Autologous Blood Transfusion Detection.

PURPOSE: Autologous blood transfusion is performance enhancing and prohibited in sport but remains difficult to detect. This study explored the hypothesis that an untargeted urine metabolomics analysis can reveal one or more novel metabolites with high sensitivity and specificity for detection of autologous blood transfusion. METHODS: In a randomized, double-blinded, placebo-controlled, crossover design, exercise-trained men (n = 12) donated 900 mL blood or were sham phlebotomized. After 4 wk, red blood cells or saline were reinfused. Urine samples were collected before phlebotomy and 2 h and 1, 2, 3, 5, and 10 d after reinfusion and analyzed by ultraperformance liquid chromatography-quadrupole time-of-flight mass spectrometry. Models of unique metabolites reflecting autologous blood transfusion were attained by partial least-squares discriminant analysis. RESULTS: The strongest model was obtained 2 h after reinfusion with a misclassification error of 6.3% and 98.8% specificity. However, combining only a few of the strongest metabolites selected by this model provided a sensitivity of 100% at days 1 and 2 and 66% at day 3 with 100% specificity. Metabolite identification revealed the presence of secondary di-2-ethylhexyl phtalate metabolites and putatively identified the presence of (iso)caproic acid glucuronide as the strongest candidate biomarker. CONCLUSIONS: Untargeted urine metabolomics revealed several plasticizers as the strongest metabolic pattern for detection of autologous blood transfusion for up to 3 d. Importantly, no other metabolites in urine seem of value for antidoping purposes.

Lipidomics of human adipose tissue reveals diversity between body areas.

BACKGROUND AND AIMS: Adipose tissue plays a pivotal role in storing excess fat and its composition reflects the history of person's lifestyle and metabolic health. Broad profiling of lipids with mass spectrometry has potential for uncovering new knowledge on the pathology of obesity, metabolic syndrome, diabetes and other related conditions. Here, we developed a lipidomic method for analyzing human subcutaneous adipose biopsies. We applied the method to four body areas to understand the differences in lipid composition between these areas. MATERIALS AND METHODS: Adipose tissue biopsies from 10 participants were analyzed using ultra-high-performance liquid chromatography coupled to quadrupole time-of-flight mass spectrometry. The sample preparation optimization included the optimization of the lipid extraction, the sample amount and the sample dilution factor to detect lipids in an appropriate concentration range. Lipidomic analyses were performed for adipose tissue collected from the abdomen, breast, thigh and lower back. Differences in lipid levels between tissues were visualized with heatmaps. RESULTS: Lipidomic analysis on human adipose biopsies lead to the identification of 186lipids in 2 mg of sample. Technical variation of the lipid-class specific internal standards were below 5%, thus indicating acceptable repeatability. Triacylglycerols were highly represented in the adipose tissue samples, and lipids from 13 lipid classes were identified. Long polyunsaturated triacylglycerols in higher levels in thigh (q<0.05), when compared with the abdomen, breast and lower back, indicating that the lipidome was area-specific. CONCLUSION: The method presented here is suitable for the analysis of lipid profiles in 2 mg of adipose tissue. The amount of fat across the body is important for health but we argue that also the distribution and the particular profile of the lipidome may be relevant for metabolic outcomes. We suggest that the method presented in this paper could be useful for detecting such aberrations.