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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.

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.

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.

To assess the safety and efficacy of oral immune interventions, it is important and required by regulation to assess the impact of those interventions not only on the immune system, but also on other organs such as the gut as the porte d'entrée. Despite clear indications that the immune system interacts with several physiological functions of the gut, it is still unknown which pathways and molecules are crucial to assessing the impact of nutritional immune interventions on gut functioning. Here we used a network-based systems biology approach to clarify the molecular relationships between immune system and gut functioning and to identify crucial biomarkers to assess effects on gut functions upon nutritional immune interventions. First, the different gut functionalities were categorized based on literature and EFSA guidance documents. Moreover, an overview of the current assays and methods to measure gut function was generated. Secondly, gut-function related biological processes and adverse events were selected and subsequently linked to the physiological functions of the GI tract. Thirdly, database terms and annotations from the Gene ontology database and the Comparative Toxicogenomics Database (CTD) related to the previously selected gut-function related processes were selected. Next, database terms and annotations were used to identify the pathways and genes involved in those gut functionalities. In parallel, information from CTD was used to identify immune disease related genes. The resulting lists of both gut and immune function genes showed an overlap of 753 genes out of 1,296 gut-function related genes indicating the close gut-immune relationship. Using bioinformatics enrichment tools DAVID and Panther, the identified gut-immune markers were predicted to be involved in motility, barrier function, the digestion and absorption of vitamins and fat, regulation of the digestive system and gastric acid, and protection from injurious or allergenic material. Concluding, here we provide a promising systems biology approach to identify genes that help to clarify the relationships between immune system and gut functioning, with the aim to identify candidate biomarkers to monitor nutritional immune intervention assays for safety and efficacy in the general population. This knowledge helps to optimize future study designs to predict effects of nutritional immune intervention on gut functionalities. © Copyright © 2019 Meijerink, van den Broek, Dulos, Garthoff, Knippels, Knipping, Harthoorn, Houben, Verschuren and van Bilsen.

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.

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