Gut microbiota and its metabolites such as short chain fatty acids (SCFA), lipopolysaccharides (LPS), and trimethylamine-N-oxide (TMAO) impact cardiovascular health. In this review, we discuss how gut microbiota and gut metabolites can affect hypertension and atherosclerosis. Hypertensive patients were shown to have lower alpha diversity, lower abundance of SCFA-producing microbiota, and higher abundance of gram-negative bacteria, which are a source of LPS. Animal studies point towards a direct role for SCFAs in blood pressure regulation and show that LPS has pro-inflammatory effects. Translocation of LPS into the systemic circulation is a consequence of increased gut permeability. Atherosclerosis, a multifactorial disease, is influenced by the gut microbiota through multiple pathways. Many studies have focused on the pro-atherogenic role of TMAO, however, it is not clear if this is a causal factor. In addition, gut microbiota play a key role in bile acid metabolism and some interventions targeting bile acid receptors tend to decrease atherosclerosis. Concluding, gut microbiota affect hypertension and atherosclerosis through many pathways, providing a wide range of potential therapeutic targets. Challenges ahead include translation of findings and mechanisms to humans and development of therapeutic interventions that target cardiovascular risk by modulation of gut microbes and metabolites.

Aims Preliminary evidence from animal and human studies shows that gut microbiota composition and levels of microbiota-derived metabolites, including short-chain fatty acids (SCFAs), are associated with blood pressure (BP). We hypothesized that faecal microbiota composition and derived metabolites may be differently associated with BP across ethnic groups. Methods and results We included 4672 subjects (mean age 49.8 ± 11.7 years, 52% women) from six different ethnic groups participating in the HEalthy Life In an Urban Setting (HELIUS) study. The gut microbiota was profiled using 16S rRNA gene amplicon sequencing. Associations between microbiota composition and office BP were assessed using machine learning prediction models. In the subgroups with the largest associations, faecal SCFA levels were compared in 200 subjects with lower or higher systolic BP. Faecal microbiota composition explained 4.4% of the total systolic BP variance. Best predictors for systolic BP included Roseburia spp., Clostridium spp., Romboutsia spp., and Ruminococcaceae spp. Explained variance of the microbiota composition was highest in Dutch subjects (4.8%), but very low in South-Asian Surinamese, African Surinamese, Ghanaian, Moroccan and Turkish descent groups (explained variance <0.8%). Faecal SCFA levels, including acetate (P < 0.05) and propionate (P < 0.01), were lower in young Dutch participants with low systolic BP. Conclusions Faecal microbiota composition is associated with BP, but with strongly divergent associations between ethnic groups. Intriguingly, while Dutch participants with lower BP had higher abundances of several SCFA-producing microbes, they had lower faecal SCFA levels. Intervention studies with SCFAs could provide more insight in the effects of these metabolites on BP.

IntroductionSeveral studies have reported alterations in gut microbiota composition of Alzheimer’s disease (AD) patients. However, the observed differences are not consistent across studies. We aimed to investigate associations between gut microbiota composition and AD biomarkers using machine learning models in patients with AD dementia, mild cognitive impairment (MCI) and subjective cognitive decline (SCD).Materials and MethodsWe included 170 patients from the Amsterdam Dementia Cohort, comprising 33 with AD dementia (66 ± 8 years, 46%F, mini-mental state examination (MMSE) 21[19-24]), 21 with MCI (64 ± 8 years, 43%F, MMSE 27[25-29]) and 116 with SCD (62 ± 8 years, 44%F, MMSE 29[28-30]). Fecal samples were collected and gut microbiome composition was determined using 16S rRNA sequencing. Biomarkers of AD included cerebrospinal fluid (CSF) amyloid-beta 1-42 (amyloid) and phosphorylated tau (p-tau), and MRI visual scores (medial temporal atrophy, global cortical atrophy, white matter hyperintensities). Associations between gut microbiota composition and dichotomized AD biomarkers were assessed with machine learning classification models. The two models with the highest area under the curve (AUC) were selected for logistic regression, to assess associations between the 20 best predicting microbes and the outcome measures from these machine learning models while adjusting for age, sex, BMI, diabetes, medication use, and MMSE.ResultsThe machine learning prediction for amyloid and p-tau from microbiota composition performed best with AUCs of 0.64 and 0.63. Highest ranked microbes included several short chain fatty acid (SCFA)-producing species. Higher abundance of [Clostridium] leptum and lower abundance of [Eubacterium] ventriosum group spp., Lachnospiraceae spp., Marvinbryantia spp., Monoglobus spp., [Ruminococcus] torques group spp., Roseburia hominis, and Christensenellaceae R-7 spp., was associated with higher odds of amyloid positivity. We found associations between lower abundance of Lachnospiraceae spp., Lachnoclostridium spp., Roseburia hominis and Bilophila wadsworthia and higher odds of positive p-tau status.ConclusionsGut microbiota composition was associated with amyloid and p-tau status. We extend on recent studies that observed associations between SCFA levels and AD CSF biomarkers by showing that lower abundances of SCFA-producing microbes were associated with higher odds of positive amyloid and p-tau status.

Cognitive development in patients with tuberous sclerosis complex is highly variable. Predictors in the infant years would be valuable to counsel parents and to support development. The aim of this study was to confirm factors that have been reported to be independently correlated with cognitive development. 102 patients included in this study were treated at the ENCORE-TSC expertise center of the Erasmus Medical Center-Sophia Children’s Hospital. Data from the first 24 months of life were used, including details on epilepsy, motor development and mutation status. Outcome was defined as cognitive development (intellectual equivalent, IE) as measured using tests appropriate to the patients age and cognitive abilities (median age at testing 8.2 years, IQR 4.7–12.0). Univariable and multivariable regression analyses were used. In a univariable analysis, predictors of lower IE were: the presence of infantile spasms (β = −18.3, p = 0.000), a larger number of antiepileptic drugs used (β = −6.3, p = 0.000), vigabatrin not used as first drug (β = −14.6, p = 0.020), corticosteroid treatment (β = −33.2, p = 0.005), and a later age at which the child could walk independently (β = −2.1, p = 0.000). An older age at seizure onset predicted higher IE (β = 1.7, p = 0.000). In a multivariable analysis, only age at seizure onset was significantly correlated to IE (β = 1.2, p = 0.005), contributing to 28% of the variation in IE. In our cohort, age at seizure onset was the only variable that independently predicted IE. Factors predicting cognitive development could aid parents and physicians in finding the appropriate support and schooling for these patients.Electronic supplementary materialThe online version of this article (doi:10.1007/s00415-016-8335-5) contains supplementary material, which is available to authorized users.

Emerging evidence suggests that both central and peripheral immunological processes play an important role in the pathogenesis of Alzheimer’s disease (AD), but regulatory mechanisms remain unknown. The gut microbiota and its key metabolites are known to affect neuroinflammation by modulating the activity of peripheral and brain-resident immune cells, yet an overview on how the gut microbiota contribute to immunological alterations in AD is lacking. In this review, we discuss current literature on microbiota composition in AD patients and relevant animal models. Next, we highlight how microbiota and their metabolites may contribute to peripheral and central immunological changes in AD. Finally, we offer a future perspective on the translation of these findings into clinical practice by targeting gut microbiota to modulate inflammation in AD. Since we find that gut microbiota alterations in AD can induce peripheral and central immunological changes via the release of microbial metabolites, we propose that modulating their composition may alter ongoing inflammation and could therefore be a promising future strategy to fight progression of AD.

Nutrition is one of the modifiable risk factors for cognitive decline and Alzheimer’s disease (AD) dementia, and is therefore highly relevant in the context of prevention. However, knowledge of dietary quality in clinical populations on the spectrum of AD dementia is lacking, therefore we studied the association between dietary quality and cognitive impairment in Alzheimer’s disease (AD) dementia, mild cognitive impairment (MCI) and controls. We included 357 participants from the NUDAD project (134 AD dementia, 90 MCI, 133 controls). We assessed adherence to dietary guidelines (components: vegetables, fruit, fibers, fish, saturated fat, trans-fat, salt, and alcohol), and cognitive performance (domains: memory, language, visuospatial functioning, attention, and executive functioning). In the total population, linear regression analyses showed a lower vegetable intake is associated with poorer global cognition, visuospatial functioning, attention and executive functioning. In AD dementia, lower total adherence to dietary guidelines and higher alcohol intake were associated with poorer memory, a lower vegetable intake with poorer global cognition and executive functioning, and a higher trans-fat intake with poorer executive functioning. In conclusion, a suboptimal diet is associated with more severely impaired cognition—this association is mostly attributable to a lower vegetable intake and is most pronounced in AD dementia.

Introduction Weight loss is associated with higher mortality and progression of cognitive decline, but its associations with magnetic resonance imaging (MRI) changes related to Alzheimer's disease (AD) are unknown. Methods We included 412 patients from the NUDAD project, comprising 129 with AD dementia, 107 with mild cognitive impairment (MCI), and 176 controls. Associations between nutritional status and MRI measures were analyzed using linear regression, adjusted for age, sex, education, cognitive functioning, and cardiovascular risk factors. Results Lower body mass index (BMI), fat mass (FM), and fat free mass index were associated with higher medial temporal atrophy (MTA) scores. Lower BMI, FM, and waist circumference were associated with more microbleeds. Stratification by diagnosis showed that the observed associations with microbleeds were only significant in MCI. Discussion Lower indicators of nutritional status were associated with more MTA and microbleeds, with largest effect sizes in MCI.

Background Gut microbiome composition is shaped by a combination of host genetic make-up and dietary habits. In addition, large ethnic differences exist in microbiome composition. Several studies in humans and animals have shown that differences in gut microbiota and its metabolites, including short chain fatty acids (SCFA), are associated with blood pressure (BP). We hypothesized that gut microbiome composition and its metabolites may be differently associated with BP across ethnic groups. Purpose To investigate associations of gut microbiome composition and fecal SCFA levels with BP across different ethnic groups. Methods We assessed the association between gut microbiome composition and office BP among 4672 subjects (mean age 49.8±11.7 years, 52%F) of 6 different ethnic groups participating in the HELIUS study. Gut microbiome composition was determined using 16S rRNA sequencing. Associations between microbiome composition and blood pressure were assessed using machine learning prediction models. The resulting best predictors were correlated with BP using Spearman's rank correlations. Fecal SCFA levels were measured with high-performance liquid chromatography in an age- and body mass index (BMI)-matched subgroup of 200 participants with either extreme low or high systolic BP. Differences in abundances of best predictors and fecal SCFA levels between high and low BP groups were assessed with Mann-Whitney U tests. Results Gut microbiome composition explained 4.4% of systolic BP variance. Best predictors for systolic BP included Roseburia spp. (ρ −0.15, p<0.001), Clostridium spp. (ρ −0.14, p<0.001), Romboutsia spp. (ρ −0.10, p<0.001), and Ruminococceae spp. (ρ −0.15, p<0.001) (Figure 1). Explained variance of the microbiome composition was highest in Dutch subjects (4.8%), but very low in African Surinamese, Ghanaian, and Turkish ethnic groups (ranging from 0–0.77%) Hence, we selected only participants with Dutch ethnicity for the matched subgroup. Participants with high BP had lower abundance of Roseburia hominis (p<0.01) and Roseburia spp. (p<0.05) compared to participants with low BP. However, fecal acetate (p<0.05) and propionate (p<0.01) levels were higher in participants with high BP. Conclusions In this cross-sectional study, gut microbiome composition was moderately associated with BP. Associations were strongly divergent between ethnic groups, with strongest associations in Dutch participants. Intriguingly, while Dutch participants with high BP had lower abundances of several SCFA-producing microbes, they had higher fecal SCFA levels. Intervention studies with SCFAs could provide more insight in the effects of these metabolites on BP. Funding Acknowledgement Type of funding source: Public Institution(s). Main funding source(s): The Academic Medical Center (AMC) of Amsterdam and the Public Health Service of Amsterdam (GGD Amsterdam) provided core financial support for HELIUS. The HELIUS study is also funded by research grants of the Dutch Heart Foundation (Hartstichting; grant no. 2010T084), the Netherlands Organization for Health Research and Development (ZonMw; grant no. 200500003), the European Integration Fund (EIF; grant no. 2013EIF013) and the European Union (Seventh Framework Programme, FP-7; grant no. 278901).