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Background: Interactions between the skin barrier, immune system, and microbiome underlie the development of atopic dermatitis (AD). Objective: To investigate the skin and nasal microbiome in relation to filaggrin gene (FLG) mutations. Methods: A cross-sectional study including 77 children with difficult-to-treat AD. The entire encoding region of FLG was screened for mutations using single molecule molecular inversion probes and next-generation sequencing. Bacterial swabs from the anterior nares, lesional and nonlesional skin were analyzed using 16S rRNA sequencing. For skin samples, additional qPCR was performed for Staphylococcus aureus and Staphylococcus epidermidis. Results: The prevalence of patients with a mutation in FLG was 40%, including 10 different mutations. Analyzing bacterial swabs from all three niches showed a significant effect for both niche and FLG mutation status on the overall microbiome composition. Using a subset analysis to test the effect of FLG mutation status per niche separately did not show a significant association to the microbiome. Shannon diversity and S. aureus abundance were significantly affected by the niche, but not by the presence of an FLG mutation. Conclusions: Our results suggest only a minor role for FLG mutation status on the overall microbiome, which is rather caused by differences in the present genera than by microbe richness and evenness.

Digital health technologies may support the management and prevention of disease through personalized lifestyle interventions. Wearables and smartphones are increasingly used to continuously monitor health and disease in everyday life, targeting health maintenance. Here, we aim to demonstrate the potential of wearables and smartphones to (1) detect eating moments and (2) predict and explain individual glucose levels in healthy individuals, ultimately supporting health self-management. Twenty-four individuals collected continuous data from interstitial glucose monitoring, food logging, activity, and sleep tracking over 14 days. We demonstrated the use of continuous glucose monitoring and activity tracking in detecting eating moments with a prediction model showing an accuracy of 92.3% (87.2–96%) and 76.8% (74.3–81.2%) in the training and test datasets, respectively. Additionally, we showed the prediction of glucose peaks from food logging, activity tracking, and sleep monitoring with an overall mean absolute error of 0.32 (+/−0.04) mmol/L for the training data and 0.62 (+/−0.15) mmol/L for the test data. With Shapley additive explanations, the personal lifestyle elements important for predicting individual glucose peaks were identified, providing a basis for personalized lifestyle advice. Pending further validation of these digital biomarkers, they show promise in supporting the prevention and management of type 2 diabetes through personalized lifestyle recommendations.

Poor taste and smell function are widely thought to contribute to the development of poor appetite and undernutrition in older adults. It has been hypothesized that the oral microbiota play a role as well, but evidence is scarce. In a cross-sectional cohort of 356 older adults, we performed taste and smell tests, collected anthropometric measurements and tongue swabs for analysis of microbial composition (16S rRNA sequencing) and Candida albicans abundance (qPCR). Older age, edentation, poor smell and poor appetite were associated with lower alpha diversity and explained a significant amount of beta diversity. Moreover, a lower Streptococcus salivarius abundance was associated with poor smell identification score, whereas high C. albicans abundance seemed to be associated with poor smell discrimination score. In our population, neither the tongue microbiota, nor C. albicans were associated with poor taste or directly with undernutrition. Our findings do suggest a host-microbe interaction with regard to smell perception and appetite.

Background: The skin microbiome, characterized by an overgrowth of Staphylococcus aureus, plays an important role in the pathogenesis of atopic dermatitis (AD). Multidisciplinary treatment in alpine climate is known for its positive effect on disease severity in children with AD and can result in a different immune response compared with moderate maritime climate. However, the effect on the composition of the skin microbiome in AD is unknown. Objective: To determine the effect of treatment in alpine climate and moderate maritime climate on the microbiome for lesional and non-lesional skin in children with difficult to treat AD. Results: Alpine climate treatment led to a significant change in the microbiota on lesional skin, whereas no significant change was found after moderate maritime climate. On both lesional and non-lesional skin, we observed a significant increase in Shannon diversity and a significant decrease in both Staphylococcus abundance and S aureus load after alpine climate treatment. The decrease in S aureus was significantly larger on lesional skin following alpine climate treatment compared with moderate maritime climate treatment. Staphylococcus epidermidis load was stable over time. Conclusions and clinical relevance: Alpine climate treatment leads to significant changes in the composition of the skin microbiome in children with AD, mainly caused by a reduction in the Staphylococcus genus. This study shows new perspectives in the potential mode of action for therapies in AD. © 2019 John Wiley & Sons Ltd

Graphical abstract, Highlights • Type 2 diabetes (T2DM) increases the risk of recurrence in myocardial infarction patients. • A gut microbiota profile is associated to the further T2DM development. • Microbiome data improved the prediction of T2DM development when added to clinical parameters. • A risk score including the most predictive genera was associated with the probability of T2DM. • A high risk score was associated with a higher hepatic insulin resistance and β-cell dysfunction., Introduction A distinctive gut microbiome have been linked to type 2 diabetes mellitus (T2DM). Objectives We aimed to evaluate whether gut microbiota composition, in addition to clinical biomarkers, could improve the prediction of new incident cases of diabetes in patients with coronary heart disease. Methods All the patients from the CORDIOPREV (Clinical Trials.gov.Identifier: NCT00924937) study without T2DM at baseline were included (n = 462). Overall, 107 patients developed it after a median of 60 months. The gut microbiota composition was determined by 16S rRNA gene sequencing and predictive models were created using hold-out method. Results A gut microbiota profile associated with T2DM development was determined through a microbiome-based predictive model. The addition of microbiome data to clinical parameters (variables included in FINDRISC risk score and the diabetes risk score of the American Diabetes Association, HDL, triglycerides and HbA1c) improved the prediction increasing the area under the curve from 0.632 to 0.946. Furthermore, a microbiome-based risk score including the ten most discriminant genera, was associated with the probability of develop T2DM. Conclusion These results suggest that a microbiota profile is associated to the T2DM development. An integrate predictive model of microbiome and clinical data that can improve the prediction of T2DM is also proposed, if is validated in independent populations to prevent this disease.

In public health initiatives, generic nutrition advice (GNA) from national guidelines has a limited effect on food-intake improvement. Personalized nutrition advice (PNA) may enable dietary behavior change. A monocentric, randomized, parallel, controlled clinical trial was performed in males (n = 55) and females (n = 100) aged 25 to 70 years. Participants were allocated to control, GNA or PNA groups. The PNA group consisted of automatically generated dietary advice based on personal metabolic health parameters, dietary intake, anthropometric and hemodynamic measures, gender and age. Participants who received PNA (n = 51) improved their nutritional intake status for fruits P (p &lt, 0.0001), whole grains (p = 0.008), unsalted nuts (p &lt, 0.0001), fish (p = 0.0003), sugar-sweetened beverages (p = 0.005), added salt (p = 0.003) and less unhealthy choices (p = 0.002), whereas no improvements were observed in the control and GNA group. PNA participants were encouraged to set a goal for one or multiple food categories. Goal-setting led to greater improvement of food categories within the PNA group including, unsalted nuts (p &lt, 0.0001), fruits (p = 0.0001), whole grains (p = 0.005), fish (p = 0.0001), dairy (p = 0.007), vegetables (p = 0.01) and unhealthy choices (p = 0.02). In a healthy population, participants receiving PNA changed their food-intake behavior more favorably than participants receiving GNA or no advice. When personal goals were set, nutritional behavior was more prone to change.

Inconsistent associations between lipids and circulating markers of fat-soluble vitamin and carotenoid status have been reported. The aim of this hypothesis-generating study was to examine the contribution of the LC-MS-based lipidome, characterized by lipid class, carbon count, and the number of unsaturated bonds, to the interindividual variability in circulating concentrations of retinol, carotenoids, 25-hydroxyvitamin D3, α-tocopherol, γ-tocopherol, and phylloquinone in 35 overweight and obese, but healthy men. A sparse partial least-squares method was used to accomplish this aim. Highly abundant phospholipids and triglycerides (TGs) contributed to the interindividual variability in phylloquinone, α-tocopherol, and γ-tocopherol. Interindividual variability in lycopene concentrations was driven by concentrations of low-abundant TG. 25-Hydroxyvitamin D3, retinol, and the other carotenoids were not influenced by lipids. Except for lycopene, evaluation of lipids beyond class does not appear to further explain the interindividual variability in circulating concentrations of fat-soluble vitamins and carotenoids., Lipidomics does not appear to explain the interindividual variability in most circulating concentrations of fat-soluble vitamins and carotenoids beyond the interindividual variability explained by class.

Background & Aims The incidence of nonalcoholic steatohepatitis (NASH) is increasing. The pathophysiological mechanisms of NASH and the sequence of events leading to hepatic fibrosis are incompletely understood. The aim of this study was to gain insight into the dynamics of key molecular processes involved in NASH and to rank early markers for hepatic fibrosis. Methods A time-course study in low-density lipoprotein–receptor knockout. Leiden mice on a high-fat diet was performed to identify the temporal dynamics of key processes contributing to NASH and fibrosis. An integrative systems biology approach was used to elucidate candidate markers linked to the active fibrosis process by combining transcriptomics, dynamic proteomics, and histopathology. The translational value of these findings were confirmed using human NASH data sets. Results High-fat-diet feeding resulted in obesity, hyperlipidemia, insulin resistance, and NASH with fibrosis in a time-dependent manner. Temporal dynamics of key molecular processes involved in the development of NASH were identified, including lipid metabolism, inflammation, oxidative stress, and fibrosis. A data-integrative approach enabled identification of the active fibrotic process preceding histopathologic detection using a novel molecular fibrosis signature. Human studies were used to identify overlap of genes and processes and to perform a network biology-based prioritization to rank top candidate markers representing the early manifestation of fibrosis. Conclusions An early predictive molecular signature was identified that marked the active profibrotic process before histopathologic fibrosis becomes manifest. Early detection of the onset of NASH and fibrosis enables identification of novel blood-based biomarkers to stratify patients at risk, development of new therapeutics, and help shorten (pre)clinical experimental time frames., Graphical abstract

Personalized nutrition is fast becoming a reality due to a number of technological, scientific, and societal developments that complement and extend current public health nutrition recommendations. Personalized nutrition tailors dietary recommendations to specific biological requirements on the basis of a person’s health status and goals. The biology underpinning these recommendations is complex, and thus any recommendations must account for multiple biological processes and subprocesses occurring in various tissues and must be formed with an appreciation for how these processes interact with dietary nutrients and environmental factors. Therefore, a systems biology–based approach that considers the most relevant interacting biological mechanisms is necessary to formulate the best recommendations to help people meet their wellness goals. Here, the concept of “systems flexibility” is introduced to personalized nutrition biology. Systems flexibility allows the real-time evaluation of metabolism and other processes that maintain homeostasis following an environmental challenge, thereby enabling the formulation of personalized recommendations. Examples in the area of macro- and micronutrients are reviewed. Genetic variations and performance goals are integrated into this systems approach to provide a strategy for a balanced evaluation and an introduction to personalized nutrition. Finally, modeling approaches that combine personalized diagnosis and nutritional intervention into practice are reviewed.

Chronic exposure of children in sub-Saharan Africa to aflatoxins has been associated with low birth weight, stunted growth, immune suppression, and liver function damage. Lactobacillus species have been shown to reduce aflatoxin contamination during the process of food fermentation. Twenty-three Lactobacillus strains were isolated from fecal samples obtained from a cohort of rural Ugandan children at the age of 54 to 60 months, typed by 16S rRNA gene sequencing, and characterized in terms of their ability to bind aflatoxin B1 in vitro. Evidence for chronic exposure of these children to aflatoxin B1 in the study area was obtained by analysis of local foods (maize flour and peanuts), followed by the identification of the breakdown product aflatoxin M1 in their urine samples. Surprisingly, Lactobacillus in the gut microbiota of 140 children from the same cohort at 24 and 36 months showed the highest positive correlation coefficient with stunting among all bacterial genera identified in the stool samples. This correlation was interpreted to be associated with dietary changes from breastfeeding to plant-based solid foods that pose an additional risk for aflatoxin contamination, on one hand, and lead to increased intake of Lactobacillus species on the other.

OBJECTIVES: A key to a successful personalized nutrition (PN) strategy is tailoring the behavior intervention to achieve sustained behavior change consistent with desired health outcomes. The objective of this study was to evaluate the impact of a digitally-delivered, systems biology-based PN program (Habit, LLC, Oakland, CA), that included nutrition and behavior advice, and meals. METHODS: In this single-arm, multi-phase, open-label study (clinicaltrials.gov: NCT03424395), participants (n = 107; 67% female), 30–59 years of age, were provided a Fitbit® Activity Tracker and instructions to complete an at-home kit that included collection of DNA and capillary blood before and after (30 and 120 min) consuming a mixed macronutrient challenge beverage. Lifestyle behavior data and activity levels were collected at baseline. Participants completed a 10-week control phase and a 10-week PN program intervention phase that included tailored nutrition and behavior advice, and meals (2 meals/day, 5 days/week). Validated quality of life, perceived stress, diet self-efficacy, and eating behavior questionnaires were administered to assess behavior before and after the intervention. Data from 73 compliant participants (66% female) were included in the analysis. Anthropometric and biological results were used to categorize participants into subgroups including Protein Seekers (PS) and Range Seekers (RS). RESULTS: Participants reported feeling fuller faster after the intervention, relative to before (P = 0.002), which could be linked to the dietary recommendations and behavior advice. Analysis by gender showed men reported more control over their eating patterns during negative events at baseline, compared to women (P = 0.005). This difference was not evident at the end of the intervention with women reporting slightly more, and men slightly less control. PS entered the study with a higher weight and BMI (P

Background: Undernutrition impairs child development outcomes and growth. In this follow-up study of an open cluster-randomized intervention trial we examined the effects of an education package delivered to mothers in rural Uganda on their children's development, growth and gut microbiota at 36 months of age.Methods: The parental trial included 511 mother-child pairs recruited when the children were 6-8 months. In that trial, a nutrition, stimulation and hygiene education was delivered to mothers in the intervention group while the control group received routine health care. A follow-up sample of 155 pairs (intervention n = 77, control n = 78) were re-enrolled when the children were 24 months. Developmental outcomes were assessed with the Bayley Scales of Infant and Toddler Development (BSID-III) composite scores for cognitive (primary endpoint), language and motor development. Development outcomes were also evaluated using the Ages and Stages Questionnaire (ASQ) and the Mullen Scales of Early Learning (MSEL). Other outcomes included growth and gut microbiota composition.Results: The demographic characteristics were not different (P > 0.05) between the intervention and control groups and similar to those of the parental study. The intervention group had higher BSID-III scores than controls, with mean difference 10.13 (95% confidence interval (CI): 3.31-17.05, P = 0.002); 7.59 (1.62-13.66, P = 0.01); 9.00 (2.92-15.40, P = 0.005), for cognitive, language and motor composite scores, respectively. An improvement in the intervention compared to the control group was obtained for both the ASQ and the MSEL scores. The mean difference in height-for-age z-score was higher in the intervention compared to the control group: 0.50 (0.25-0.75, P = 0.0001). Gut microbiota composition did not differ significantly between the two study groups.Conclusions: The maternal education intervention had positive effects on child development and growth at three years, but did not alter gut microbiota composition. This intervention may be applicable in other low-resource settings.Trial registration: ClinicalTrials.gov registration number NCT02098031.

Personalized nutrition may be more effective in changing lifestyle behaviors compared to population-based guidelines. This single-arm exploratory study evaluated the impact of a 10-week personalized systems nutrition (PSN) program on lifestyle behavior and health outcomes. Healthy men and women (n = 82) completed the trial. Individuals were grouped into seven diet types, for which phenotypic, genotypic and behavioral data were used to generate personalized recommendations. Behavior change guidance was also provided. The intervention reduced the intake of calories (−256.2 kcal, p &lt, 0.0001), carbohydrates (−22.1 g, 0.0039), sugar (−13.0 g, 0.0001), total fat (−17.3 g, 0.0001), saturated fat (−5.9 g, p = 0.0003) and PUFA (−2.5 g, p = 0.0065). Additionally, BMI (−0.6 kg/m2, 0.0001), body fat (−1.2%, p = 0.0192) and hip circumference (−5.8 cm, 0.0001) were decreased after the intervention. In the subgroup with the lowest phenotypic flexibility, a measure of the body’s ability to adapt to environmental stressors, LDL (−0.44 mmol/L, p = 0.002) and total cholesterol (−0.49 mmol/L, 0.0001) were reduced after the intervention. This study shows that a PSN program in a workforce improves lifestyle habits and reduces body weight, BMI and other health-related outcomes. Health improvement was most pronounced in the compromised phenotypic flexibility subgroup, which indicates that a PSN program may be effective in targeting behavior change in health-compromised target groups.

Background Antibiotic therapy is commonly used in animal agriculture. Antibiotics excreted by the animals can contaminate farming environments, resulting in long term exposure of animals to sub-inhibitory levels of antibiotics. Little is known on the effect of this exposure on antibiotic resistance. In this study, we aimed to investigate the long term effects of sub-inhibitory levels of antibiotics on the gut microbiota composition and resistome of veal calves in vivo. Forty-two veal calves were randomly assigned to three groups. The first group (OTC-high) received therapeutic oral dosages of 1 g oxytetracycline (OTC), twice per day, during 5 days. The second group (OTC-low) received an oral dose of OTC of 100–200 μg per day during 7 weeks, mimicking animal exposure to environmental contamination. The third group (CTR) did not receive OTC, serving as unexposed control. Antibiotic residue levels were determined over time. The temporal effects on the gut microbiota and antibiotic resistance gene abundance was analysed by metagenomic sequencing. Results In the therapeutic group, OTC levels exceeded MIC values. The low group remained at sub-inhibitory levels. The control group did not reach any significant OTC levels. 16S rRNA gene-based analysis revealed significant changes in the calf gut microbiota. Time-related changes accounted for most of the variation in the sequence data. Therapeutic application of OTC had transient effect, significantly impacting gut microbiota composition between day 0 and day 2. By metagenomic sequence analysis we identified six antibiotic resistance genes representing three gene classes (tetM, floR and mel) that differed in relative abundance between any of the intervention groups and the control. qPCR was used to validate observations made by metagenomic sequencing, revealing a peak of tetM abundance at day 28–35 in the OTC-high group. No increase in resistance genes abundance was seen in the OTC-low group. Conclusions Under the conditions tested, sub-therapeutic administration of OTC did not result in increased tetM resistance levels as observed in the therapeutic group. Electronic supplementary material The online version of this article (10.1186/s12864-018-5419-x) contains supplementary material, which is available to authorized users.

Recently, the concept of prebiotics has been revisited to expand beyond non-digestible oligosaccharides, and the requirements for selective stimulation were extended to include microbial groups other than, and additional to, bifidobacteria and lactobacilli. Here, the gut microbiota-modulating effects of well-known and novel prebiotics were studied. An in vitro fermentation screening platform (i-screen) was inoculated with adult fecal microbiota, exposed to different dietary fibers that had a range of concentrations (inulin, alpha-linked galacto-oligosaccharides (alpha-GOS), beta-linked GOS, xylo-oligosaccharides (XOS) from corn cobs and high-fiber sugar cane, and beta-glucan from oats), and compared to a positive fructo-oligosaccharide (FOS) control and a negative control (no fiber addition). All dietary fibers displayed prebiotic activity, with beta-glucan showing more distinct effects on the microbial composition and metabolism compared to the other fibers. Beta-glucan induced the growth of Prevotella and Roseburia with a concomitant increase in propionate production. Inulin and both forms of GOS and XOS had a strong bifidogenic effect on the microbial composition. A dose-response effect was observed for butyrate when exposed to beta-glucan and inulin. The findings of this study support the potential for alpha-GOS, XOS, and oat beta-glucan to serve as novel prebiotics, due to their association with the positive shifts in microbiome composition and short-chain fatty acid production that point to potential health benefits.

Background: The oral cavity harbors a complex microbial ecosystem, intimately related to oral health and disease. The use of polyol-sweetened gum is believed to benefit oral health through stimulation of salivary flow and impacting oral pathogenic bacteria. Maltitol is often used as sweetener in food products. This study aimed to establish the in vivo effects of frequent consumption of maltitol-sweetened chewing gum on the dental plaque microbiota in healthy volunteers and to establish the cellular and molecular effects by in vitro cultivation and transcriptional analysis. Results: An intervention study was performed in 153 volunteers, randomly assigned to three groups (www.trialregister.nl; NTR4165). One group was requested to use maltitol gum five times daily, one group used gum-base, and the third group did not use chewing gum. At day 0 and day 28, 24 h-accumulated supragingival plaque was collected at the lingual sites of the lower jaw and the buccal sites of the upper jaw and analyzed by 16S ribosomal rRNA gene sequencing. At day 42, 2 weeks after completion of the study, lower-jaw samples were collected and analyzed. The upper buccal plaque microbiota composition had lower bacterial levels and higher relative abundances of (facultative) aerobic species compared to the lower lingual sites. There was no difference in bacterial community structure between any of the three study groups (PERMANOVA). Significant lower abundance of several bacterial phylotypes was found in maltitol gum group compared to the gum-base group, including Actinomyces massiliensis HOT 852 and Lautropia mirabilis HOT 022. Cultivation studies confirmed growth inhibition of A. massiliensis and A. johnsonii by maltitol at levels of 1% and higher. Transcriptome analysis of A. massiliensis revealed that exposure to maltitol resulted in changes in the expression of genes linked to osmoregulation, biofilm formation, and central carbon metabolism. Conclusion: The results showed that chewing itself only marginally impacted the plaque microbiota composition. Use of maltitol-sweetened gum lowered abundance of several bacterial species. Importantly, the species impacted play a key role in the early formation of dental biofilms. Further studies are required to establish if frequent use of maltitol gum impacts early dental-plaque biofilm development.

Background Vitamins and carotenoids are key micronutrients facilitating the maintenance of health, as evidenced by the increased risk of disease with low intake. Optimal phenotypic flexibility, i.e., the ability to respond to a physiological challenge, is an essential indicator of health status. Therefore, health can be measured by applying a challenge test and monitoring the response of relevant phenotypic processes. In this study, we assessed the correlation of three fat-soluble vitamins, (i.e., vitamin A or retinol, vitamin D3, two homologues of vitamin E) and four carotenoids (i.e., α-carotene, β-carotene, β-cryptoxanthin, and lycopene), with characteristics of metabolic and inflammatory parameters at baseline and in response to a nutritional challenge test (NCT) in a group of 36 overweight and obese male subjects, using proteomics and metabolomics platforms. The phenotypic flexibility concept implies that health can be measured by the ability to adapt to a NCT, which may offer a more sensitive way to assess changes in health status of healthy subjects. Results Correlation analyses of results after overnight fasting revealed a rather evenly distributed network in a number of relatively strong correlations per micronutrient, with minor overlap between correlation profiles of each compound. Correlation analyses of challenge response profiles for metabolite and protein parameters with micronutrient status revealed a network that is more skewed towards α-carotene and γ-tocopherol suggesting a more prominent role for these micronutrients in the maintenance of phenotypic flexibility. Comparison of the networks revealed that there is merely overlap of two parameters (inositol and oleic acid (C18:1)) affirming that there is a specific biomarker response profile upon NCT. Conclusions Our study shows that applying the challenge test concept is able to reveal previously unidentified correlations between specific micronutrients and health-related processes, with potential relevance for maintenance of health that were not observed by correlating homeostatic measurements. This approach will contribute to insights on the influence of micronutrients on health and help to create efficient micronutrient intervention programs. Electronic supplementary material The online version of this article (doi:10.1186/s12263-017-0553-7) contains supplementary material, which is available to authorized users.

OBJECTIVES: This study evaluates the impact of a digitally-delivered systems biology-based personalized nutrition (PN) program (Habit LLC, Oakland, CA), that included nutrition and behavior advice, and meals. METHODS: In this single-arm, multi-phase, open-label study (clinicaltrials.gov: NCT03424395), participants (n = 107; 67% female) were provided a Fitbit® Activity Tracker and completed an at-home kit that included collection of DNA and capillary blood before and after (30 and 120 min) consuming a mixed macronutrient challenge beverage. Participants completed a 10-week run-in and 10-week PN program intervention phase. The intervention included personalized nutrition and behavior advice and meals (2 meals/day; 5 days/week) tailored to dietary subgroups. Here we report on changes in diet, anthropometrics, and biomarkers of two subgroups (Range Seekers [RS] and Protein Seekers [PS]) that differed in anthropometrics and biological measures at baseline and were provided different dietary recommendations. RESULTS: At baseline, there were no differences between groups in nutrients assessed, however, compared to RS (n = 48), PS (n = 22) had higher BMI, weight, body fat %, waist and hip circumference (all P

Bile acids (BA) are signaling molecules with a wide range of biological effects, also identified among the most responsive plasma metabolites in the postprandial state. We here describe this response to different dietary challenges and report on key determinants linked to its interindividual variability. Healthy men and women (n = 72, 62 ± 8 yr, mean ± SE) were enrolled into a 12-wk weight loss intervention. All subjects underwent an oral glucose tolerance test and a mixed-meal tolerance test before and after the intervention. BA were quantified in plasma by liquid chromatography-tandem mass spectrometry combined with whole genome exome sequencing and fecal microbiota profiling. Considering the average response of all 72 subjects, no effect of the successful weight loss intervention was found on plasma BA profiles. Fasting and postprandial BA profiles revealed high interindividual variability, and three main patterns in postprandial BA response were identified using multivariate analysis. Although the women enrolled were postmeno-pausal, effects of sex difference in BA response were evident. Exome data revealed the contribution of preselected genes to the observed interindividual variability. In particular, a variant in the SLCO1A2 gene, encoding the small intestinal BA transporter organic anion-transporting polypeptide-1A2 (OATP1A2), was associated with delayed postprandial BA increases. Fecal microbiota analysis did not reveal evidence for a significant influence of bacterial diversity and/or composition on plasma BA profiles. The analysis of plasma BA profiles in response to two different dietary challenges revealed a high interindividual variability, which was mainly determined by genetics and sex difference of host with minimal effects of the microbiota. NEW & NOTEWORTHY Considering the average response of all 72 subjects, no effect of the successful weight loss intervention was found on plasma bile acid (BA) profiles. Despite high interindividual variability, three main patterns in postprandial BA response were identified using multivariate analysis. A variant in the SLCO1A2 gene, encoding the small intestinal BA transporter organic anion-transporting polypeptide-1A2 (OATP1A2), was associated with delayed postprandial BA increases in response to both the oral glucose tolerance test and the mixed-meal tolerance test. © 2017, American Physiological Society. All rights reserved. Chemicals/CAS: chenodeoxycholic acid, 474-25-9; cholic acid, 32500-01-9, 361-09-1, 81-25-4; deoxycholic acid, 83-44-3; glycine, 56-40-6, 6000-43-7, 6000-44-8; glycochenodeoxycholic acid, 640-79-9; glycocholic acid, 475-31-0; glycodeoxycholic acid, 16409-34-0, 360-65-6; taurine, 107-35-7; taurochenodeoxycholic acid, 516-35-8; taurocholic acid, 145-42-6, 59005-70-8, 81-24-3; taurodeoxycholic acid, 1180-95-6, 516-50-7; tauroursodeoxycholic acid, 14605-22-2; ursodeoxycholic acid, 128-13-2, 2898-95-5; Bile Acids and Salts