Glaucoma Patients Have a Lower Abundance of Butyrate-Producing Taxa in the Gut.

Purpose: Glaucoma is an eye disease that is the most common cause of irreversible blindness worldwide. It has been suggested that gut microbiota can produce reactive oxygen species and pro-inflammatory cytokines that may travel from the gastric mucosa to distal sites, for example, the optic nerve head or trabecular meshwork. There is evidence for a gut-eye axis, as microbial dysbiosis has been associated with retinal diseases. We investigated the microbial composition in patients with glaucoma and healthy controls. Moreover, we analyzed the association of the gut microbiome with intraocular pressure (IOP; risk factor of glaucoma) and vertical cup-to-disc ratio (VCDR; quantifying glaucoma severity). Methods: The discovery analyses included participants of the Rotterdam Study and the Erasmus Glaucoma Cohort. A total of 225 patients with glaucoma and 1247 age- and sex-matched participants without glaucoma were included in our analyses. Stool samples were used to generate 16S rRNA gene profiles. We assessed associations with 233 genera and species. We used data from the TwinsUK and the Study of Health in Pomerania (SHIP) to replicate our findings. Results: Several butyrate-producing taxa (e.g. Butyrivibrio, Caproiciproducens, Clostridium sensu stricto 1, Coprococcus 1, Ruminococcaceae UCG 007, and Shuttleworthia) were less abundant in people with glaucoma compared to healthy controls. The same taxa were also associated with lower IOP and smaller VCDR. The replication analyses confirmed the findings from the discovery analyses. Conclusions: Large human studies exploring the link between the gut microbiome and glaucoma are lacking. Our results suggest that microbial dysbiosis plays a role in the pathophysiology of glaucoma.

Association of blood cell-based inflammatory markers with gut microbiota and cancer incidence in the Rotterdam study.

The immune response-gut microbiota interaction is implicated in various human diseases, including cancer. Identifying the link between the gut microbiota and systemic inflammatory markers and their association with cancer will be important for our understanding of cancer etiology. The current study was performed on 8090 participants from the population-based Rotterdam study. We found a significant association (false discovery rate [FDR] ≤0.05) between lymphocytes and three gut microbial taxa, namely the family Streptococcaceae, genus Streptococcus, and order Lactobacillales. In addition, we identified 95 gut microbial taxa that were associated with inflammatory markers (p < 0.05). Analyzing the cancer data, we observed a significant association between higher systemic immune-inflammation index (SII) levels at baseline (hazard ratio (HR): 1.65 [95% confidence interval (CI); 1.10-2.46, p ≤ 0.05]) and a higher count of lymphocytes (HR: 1.38 [95% CI: 1.15-1.65, p ≤ 0.05]) and granulocytes (HR: 1.69 [95% CI: 1.40-2.03, p ≤ 0.05]) with increased risk of lung cancer after adjusting for age, sex, body mass index (BMI), and study cohort. This association was lost for SII and lymphocytes after additional adjustment for smoking (SII = HR:1.46 [95% CI: 0.96-2.22, p = 0.07] and lymphocytes = HR: 1.19 [95% CI: 0.97-1.46, p = 0.08]). In the stratified analysis, higher count of lymphocyte and granulocytes at baseline were associated with an increased risk of lung cancer in smokers after adjusting for age, sex, BMI, and study cohort (HR: 1.33 [95% CI: 1.09-1.62, p ≤0.05] and HR: 1.57 [95% CI: 1.28-1.92, p ≤0.05], respectively). Our study revealed a positive association between gut microbiota, higher SII levels, and higher lymphocyte and granulocyte counts, with an increased risk of developing lung cancer.

Early-life stress and the gut microbiome: A comprehensive population-based investigation.

Early-life stress (ELS) has been robustly associated with a range of poor mental and physical health outcomes. Recent studies implicate the gut microbiome in stress-related mental, cardio-metabolic and immune health problems, but research on humans is scarce and thus far often based on small, selected samples, often using retrospective reports of ELS. We examined associations between ELS and the human gut microbiome in a large, population-based study of children. ELS was measured prospectively from birth to 10 years of age in 2,004 children from the Generation R Study. We studied overall ELS, as well as unique effects of five different ELS domains, including life events, contextual risk, parental risk, interpersonal risk, and direct victimization. Stool microbiome was assessed using 16S rRNA sequencing at age 10 years and data were analyzed at multiple levels (i.e. α- and ß-diversity indices, individual genera and predicted functional pathways). In addition, we explored potential mediators of ELS-microbiome associations, including diet at age 8 and body mass index at 10 years. While no associations were observed between overall ELS (composite score of five domains) and the microbiome after multiple testing correction, contextual risk - a specific ELS domain related to socio-economic stress, including risk factors such as financial difficulties and low maternal education - was significantly associated with microbiome variability. This ELS domain was associated with lower α-diversity, with ß-diversity, and with predicted functional pathways involved, amongst others, in tryptophan biosynthesis. These associations were in part mediated by overall diet quality, a pro-inflammatory diet, fiber intake, and body mass index (BMI). These results suggest that stress related to socio-economic adversity - but not overall early life stress - is associated with a less diverse microbiome in the general population, and that this association may in part be explained by poorer diet and higher BMI. Future research is needed to test causality and to establish whether modifiable factors such as diet could be used to mitigate the negative effects of socio-economic adversity on the microbiome and related health consequences.

Advanced Glycation End Products (AGEs) in Diet and Skin in Relation to Stool Microbiota: The Rotterdam Study.

BACKGROUND: Advanced glycation end products (AGEs) are involved in age-related diseases, but the interaction of gut microbiota with dietary AGEs (dAGEs) and tissue AGEs in the population is unknown. OBJECTIVE: Our objective was to investigate the association of dietary and tissue AGEs with gut microbiota in the population-based Rotterdam Study, using skin AGEs as a marker for tissue accumulation and stool microbiota as a surrogate for gut microbiota. DESIGN: Dietary intake of three AGEs (dAGEs), namely carboxymethyl-lysine (CML), N-(5-hydro-5-methyl-4-imidazolon-2-yl)-ornithine (MGH1), and carboxyethyl-lysine (CEL), was quantified at baseline from food frequency questionnaires. Following up after a median of 5.7 years, skin AGEs were measured using skin autofluorescence (SAF), and stool microbiota samples were sequenced (16S rRNA) to measure microbial composition (including alpha-diversity, beta-dissimilarity, and taxonomic abundances) as well as predict microbial metabolic pathways. Associations of both dAGEs and SAF with microbial measures were investigated using multiple linear regression models in 1052 and 718 participants, respectively. RESULTS: dAGEs and SAF were not associated with either the alpha-diversity or beta-dissimilarity of the stool microbiota. After multiple-testing correction, dAGEs were not associated with any of the 188 genera tested, but were nominally inversely associated with the abundance of Barnesiella, Colidextribacter, Oscillospiraceae UCG-005, and Terrisporobacter, in addition to being positively associated with Coprococcus, Dorea, and Blautia. A higher abundance of Lactobacillus was associated with a higher SAF, along with several nominally significantly associated genera. dAGEs and SAF were nominally associated with several microbial pathways, but none were statistically significant after multiple-testing correction. CONCLUSIONS: Our findings did not solidify a link between habitual dAGEs, skin AGEs, and overall stool microbiota composition. Nominally significant associations with several genera and functional pathways suggested a potential interaction between gut microbiota and AGE metabolism, but validation is required. Future studies are warranted, to investigate whether gut microbiota modifies the potential impact of dAGEs on health.

Interplay of Metabolome and Gut Microbiome in Individuals With Major Depressive Disorder vs Control Individuals.

Importance: Metabolomics reflect the net effect of genetic and environmental influences and thus provide a comprehensive approach to evaluating the pathogenesis of complex diseases, such as depression. Objective: To identify the metabolic signatures of major depressive disorder (MDD), elucidate the direction of associations using mendelian randomization, and evaluate the interplay of the human gut microbiome and metabolome in the development of MDD. Design, Setting and Participants: This cohort study used data from participants in the UK Biobank cohort (n = 500 000; aged 37 to 73 years; recruited from 2006 to 2010) whose blood was profiled for metabolomics. Replication was sought in the PREDICT and BBMRI-NL studies. Publicly available summary statistics from a 2019 genome-wide association study of depression were used for the mendelian randomization (individuals with MDD = 59 851; control individuals = 113 154). Summary statistics for the metabolites were obtained from OpenGWAS in MRbase (n = 118 000). To evaluate the interplay of the metabolome and the gut microbiome in the pathogenesis of depression, metabolic signatures of the gut microbiome were obtained from a 2019 study performed in Dutch cohorts. Data were analyzed from March to December 2021. Main Outcomes and Measures: Outcomes were lifetime and recurrent MDD, with 249 metabolites profiled with nuclear magnetic resonance spectroscopy with the Nightingale platform. Results: The study included 6811 individuals with lifetime MDD compared with 51 446 control individuals and 4370 individuals with recurrent MDD compared with 62 508 control individuals. Individuals with lifetime MDD were younger (median [IQR] age, 56 [49-62] years vs 58 [51-64] years) and more often female (4447 [65%] vs 2364 [35%]) than control individuals. Metabolic signatures of MDD consisted of 124 metabolites spanning the energy and lipid metabolism pathways. Novel findings included 49 metabolites, including those involved in the tricarboxylic acid cycle (ie, citrate and pyruvate). Citrate was significantly decreased (ß [SE], -0.07 [0.02]; FDR = 4 × 10-04) and pyruvate was significantly increased (ß [SE], 0.04 [0.02]; FDR = 0.02) in individuals with MDD. Changes observed in these metabolites, particularly lipoproteins, were consistent with the differential composition of gut microbiota belonging to the order Clostridiales and the phyla Proteobacteria/Pseudomonadota and Bacteroidetes/Bacteroidota. Mendelian randomization suggested that fatty acids and intermediate and very large density lipoproteins changed in association with the disease process but high-density lipoproteins and the metabolites in the tricarboxylic acid cycle did not. Conclusions and Relevance: The study findings showed that energy metabolism was disturbed in individuals with MDD and that the interplay of the gut microbiome and blood metabolome may play a role in lipid metabolism in individuals with MDD.

Whole-exome sequence analysis of anthropometric traits illustrates challenges in identifying effects of rare genetic variants.

Anthropometric traits, measuring body size and shape, are highly heritable and significant clinical risk factors for cardiometabolic disorders. These traits have been extensively studied in genome-wide association studies (GWASs), with hundreds of genome-wide significant loci identified. We performed a whole-exome sequence analysis of the genetics of height, body mass index (BMI) and waist/hip ratio (WHR). We meta-analyzed single-variant and gene-based associations of whole-exome sequence variation with height, BMI, and WHR in up to 22,004 individuals, and we assessed replication of our findings in up to 16,418 individuals from 10 independent cohorts from Trans-Omics for Precision Medicine (TOPMed). We identified four trait associations with single-nucleotide variants (SNVs; two for height and two for BMI) and replicated the LECT2 gene association with height. Our expression quantitative trait locus (eQTL) analysis within previously reported GWAS loci implicated CEP63 and RFT1 as potential functional genes for known height loci. We further assessed enrichment of SNVs, which were monogenic or syndromic variants within loci associated with our three traits. This led to the significant enrichment results for height, whereas we observed no Bonferroni-corrected significance for all SNVs. With a sample size of ∼20,000 whole-exome sequences in our discovery dataset, our findings demonstrate the importance of genomic sequencing in genetic association studies, yet they also illustrate the challenges in identifying effects of rare genetic variants.

The gut microbiome and child mental health: A population-based study.

The link between the gut microbiome and the brain has gained increasing scientific and public interest for its potential to explain psychiatric risk. While differences in gut microbiome composition have been associated with several mental health problems, evidence to date has been largely based on animal models and human studies with modest sample sizes. In this cross-sectional study in 1,784 ten-year-old children from the multi-ethnic, population-based Generation R Study, we aimed to characterize associations of the gut microbiome with child mental health problems. Gut microbiome was assessed from stool samples using 16S rRNA sequencing. We focused on overall psychiatric symptoms as well as with specific domains of emotional and behavioral problems, assessed via the maternally rated Child Behavior Checklist. While we observed lower gut microbiome diversity in relation to higher overall and specific mental health problems, associations were not significant. Likewise, we did not identify any taxonomic feature associated with mental health problems after multiple testing correction, although suggestive findings indicated depletion of genera previously associated with psychiatric disorders, including Hungatella, Anaerotruncus and Oscillospiraceae. The identified compositional abundance differences were found to be similar across all mental health problems. Finally, we did not find significant enrichment for specific microbial functions in relation to mental health problems. In conclusion, based on the largest sample examined to date, we do not find clear evidence of associations between gut microbiome diversity, taxonomies or functions and mental health problems in the general pediatric population. In future, the use of longitudinal designs with repeated measurements of microbiome and psychiatric outcomes will be critical to identify whether and when associations between the gut microbiome and mental health emerge across development and into adulthood.

Gut microbiota analysis in pediatric-onset multiple sclerosis compared to pediatric monophasic demyelinating syndromes and pediatric controls.

BACKGROUND AND PURPOSE: Gut microbiota dysbiosis may lead to proinflammatory conditions contributing to multiple sclerosis (MS) etiology. Pediatric-onset MS patients are close to biological disease onset and less exposed to confounders. Therefore, this study investigated gut microbiota composition and functional pathways in pediatric-onset MS, compared to monophasic acquired demyelinating syndromes (mADS) and healthy controls (HCs). METHODS: Pediatric participants were selected from the Dutch national prospective cohort study including ADS patients and HCs <18 years old. Amplicon sequence variants (ASVs) were generated from sequencing the V3/4 regions of the 16S rRNA gene. Functional MetaCyc microbial pathways were predicted based on Enzyme Commission numbers. Gut microbiota composition (alpha/beta diversity and individual microbe abundance at ASV to phylum level) and predicted functional pathways were tested using nonparametric tests, permutational multivariate analysis of variance, and linear regression. RESULTS: Twenty-six pediatric-onset MS (24 with disease-modifying therapy [DMT]), 25 mADS, and 24 HC subjects were included. Alpha/beta diversity, abundance of individual resident microbes, and microbial functional features were not different between these participant groups. Body mass index (BMI) showed significant differences, with obese children having a lower alpha diversity (Chao1 Index p = 0.015, Shannon/Simpson Diversity Index p = 0.014/p = 0.023), divergent beta diversity (R2  = 3.7%, p = 0.013), and higher abundance of numerous individual resident microbes and functional microbial pathways. CONCLUSIONS: Previous results of gut microbiota composition and predicted functional features could not be validated in this Dutch pediatric-onset MS cohort using a more sensitive 16S pipeline, although it was limited by sample size and DMT use. Notably, several other host-related factors were found to associate with gut microbiota variation, especially BMI.

Gut microbiome-wide association study of depressive symptoms.

Depression is one of the most poorly understood diseases due to its elusive pathogenesis. There is an urgency to identify molecular and biological mechanisms underlying depression and the gut microbiome is a novel area of interest. Here we investigate the relation of fecal microbiome diversity and composition with depressive symptoms in 1,054 participants from the Rotterdam Study cohort and validate these findings in the Amsterdam HELIUS cohort in 1,539 subjects. We identify association of thirteen microbial taxa, including genera Eggerthella, Subdoligranulum, Coprococcus, Sellimonas, Lachnoclostridium, Hungatella, Ruminococcaceae (UCG002, UCG003 and UCG005), LachnospiraceaeUCG001, Eubacterium ventriosum and Ruminococcusgauvreauiigroup, and family Ruminococcaceae with depressive symptoms. These bacteria are known to be involved in the synthesis of glutamate, butyrate, serotonin and gamma amino butyric acid (GABA), which are key neurotransmitters for depression. Our study suggests that the gut microbiome composition may play a key role in depression.

The gut microbiota and depressive symptoms across ethnic groups.

The gut microbiome is thought to play a role in depressive disorders, which makes it an attractive target for interventions. Both the microbiome and depressive symptom levels vary substantially across ethnic groups. Thus, any intervention for depression targeting the microbiome requires understanding of microbiome-depression associations across ethnicities. Analysing data from the HELIUS cohort, we characterize the gut microbiota and its associations with depressive symptoms in 6 ethnic groups (Dutch, South-Asian Surinamese, African Surinamese, Ghanaian, Turkish, Moroccan; N = 3211), living in the same urban area. Diversity of the gut microbiota, both within (α-diversity) and between individuals (ß-diversity), predicts depressive symptom levels, taking into account demographic, behavioural, and medical differences. These associations do not differ between ethnic groups. Further, ß-diversity explains 29%-18% of the ethnic differences in depressive symptoms. Bacterial genera associated with depressive symptoms belong to mulitple families, prominently including the families Christensenellaceae, Lachnospiraceae, and Ruminococcaceae. In summary, the results show that the gut microbiota are linked to depressive symptom levels and that this association generalizes across ethnic groups. Moreover, the results suggest that ethnic differences in the gut microbiota may partly explain parallel disparities in depression.

Exome sequencing identifies rare damaging variants in ATP8B4 and ABCA1 as risk factors for Alzheimer's disease.

Alzheimer's disease (AD), the leading cause of dementia, has an estimated heritability of approximately 70%1. The genetic component of AD has been mainly assessed using genome-wide association studies, which do not capture the risk contributed by rare variants2. Here, we compared the gene-based burden of rare damaging variants in exome sequencing data from 32,558 individuals-16,036 AD cases and 16,522 controls. Next to variants in TREM2, SORL1 and ABCA7, we observed a significant association of rare, predicted damaging variants in ATP8B4 and ABCA1 with AD risk, and a suggestive signal in ADAM10. Additionally, the rare-variant burden in RIN3, CLU, ZCWPW1 and ACE highlighted these genes as potential drivers of respective AD-genome-wide association study loci. Variants associated with the strongest effect on AD risk, in particular loss-of-function variants, are enriched in early-onset AD cases. Our results provide additional evidence for a major role for amyloid-ß precursor protein processing, amyloid-ß aggregation, lipid metabolism and microglial function in AD.

Whole-exome sequencing of 14 389 individuals from the ESP and CHARGE consortia identifies novel rare variation associated with hemostatic factors.

Plasma levels of fibrinogen, coagulation factors VII and VIII and von Willebrand factor (vWF) are four intermediate phenotypes that are heritable and have been associated with the risk of clinical thrombotic events. To identify rare and low-frequency variants associated with these hemostatic factors, we conducted whole-exome sequencing in 10 860 individuals of European ancestry (EA) and 3529 African Americans (AAs) from the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium and the National Heart, Lung and Blood Institute's Exome Sequencing Project. Gene-based tests demonstrated significant associations with rare variation (minor allele frequency < 5%) in fibrinogen gamma chain (FGG) (with fibrinogen, P = 9.1 × 10-13), coagulation factor VII (F7) (with factor VII, P = 1.3 × 10-72; seven novel variants) and VWF (with factor VIII and vWF; P = 3.2 × 10-14; one novel variant). These eight novel rare variant associations were independent of the known common variants at these loci and tended to have much larger effect sizes. In addition, one of the rare novel variants in F7 was significantly associated with an increased risk of venous thromboembolism in AAs (Ile200Ser; rs141219108; P = 4.2 × 10-5). After restricting gene-based analyses to only loss-of-function variants, a novel significant association was detected and replicated between factor VIII levels and a stop-gain mutation exclusive to AAs (rs3211938) in CD36 molecule (CD36). This variant has previously been linked to dyslipidemia but not with the levels of a hemostatic factor. These efforts represent the largest integration of whole-exome sequence data from two national projects to identify genetic variation associated with plasma hemostatic factors.

The diagnostic value of nasal microbiota and clinical parameters in a multi-parametric prediction model to differentiate bacterial versus viral infections in lower respiratory tract infections.

BACKGROUND: The ability to accurately distinguish bacterial from viral infection would help clinicians better target antimicrobial therapy during suspected lower respiratory tract infections (LRTI). Although technological developments make it feasible to rapidly generate patient-specific microbiota profiles, evidence is required to show the clinical value of using microbiota data for infection diagnosis. In this study, we investigated whether adding nasal cavity microbiota profiles to readily available clinical information could improve machine learning classifiers to distinguish bacterial from viral infection in patients with LRTI. RESULTS: Various multi-parametric Random Forests classifiers were evaluated on the clinical and microbiota data of 293 LRTI patients for their prediction accuracies to differentiate bacterial from viral infection. The most predictive variable was C-reactive protein (CRP). We observed a marginal prediction improvement when 7 most prevalent nasal microbiota genera were added to the CRP model. In contrast, adding three clinical variables, absolute neutrophil count, consolidation on X-ray, and age group to the CRP model significantly improved the prediction. The best model correctly predicted 85% of the 'bacterial' patients and 82% of the 'viral' patients using 13 clinical and 3 nasal cavity microbiota genera (Staphylococcus, Moraxella, and Streptococcus). CONCLUSIONS: We developed high-accuracy multi-parametric machine learning classifiers to differentiate bacterial from viral infections in LRTI patients of various ages. We demonstrated the predictive value of four easy-to-collect clinical variables which facilitate personalized and accurate clinical decision-making. We observed that nasal cavity microbiota correlate with the clinical variables and thus may not add significant value to diagnostic algorithms that aim to differentiate bacterial from viral infections.

Association of Insulin Resistance and Type 2 Diabetes With Gut Microbial Diversity: A Microbiome-Wide Analysis From Population Studies.

Importance: Previous studies have indicated that gut microbiome may be associated with development of type 2 diabetes. However, these studies are limited by small sample size and insufficient for confounding. Furthermore, which specific taxa play a role in the development of type 2 diabetes remains unclear. Objective: To examine associations of gut microbiome composition with insulin resistance and type 2 diabetes in a large population-based setting controlling for various sociodemographic and lifestyle factors. Design, Setting, and Participants: This cross-sectional analysis included 2166 participants from 2 Dutch population-based prospective cohorts: the Rotterdam Study and the LifeLines-DEEP study. Exposures: The 16S ribosomal RNA method was used to measure microbiome composition in stool samples collected between January 1, 2012, and December 31, 2013. The α diversity (Shannon, richness, and Inverse Simpson indexes), ß diversity (Bray-Curtis dissimilarity matrix), and taxa (from domain to genus level) were identified to reflect gut microbiome composition. Main Outcomes and Measures: Associations among α diversity, ß diversity, and taxa with the Homeostatic Model Assessment of Insulin Resistance (HOMA-IR) and with type 2 diabetes were examined. Glucose and insulin were measured to calculate the HOMA-IR. Type 2 diabetes cases were identified based on glucose levels and medical records from January 2012 to December 2013. Analyses were adjusted for technical covariates, lifestyle, sociodemographic, and medical factors. Data analysis was performed from January 1, 2018, to December 31, 2020. Results: There were 2166 participants in this study: 1418 from the Rotterdam Study (mean [SD] age, 62.4 [5.9] years; 815 [57.5%] male) and 748 from the LifeLines-DEEP study (mean [SD] age, 44.7 [13.4] years; 431 [57.6%] male); from this total, 193 type 2 diabetes cases were identified. Lower microbiome Shannon index and richness were associated with higher HOMA-IR (eg, Shannon index, -0.06; 95% CI, -0.10 to -0.02), and patients with type 2 diabetes had a lower richness than participants without diabetes (odds ratio [OR], 0.93; 95% CI, 0.88-0.99). The ß diversity (Bray-Curtis dissimilarity matrix) was associated with insulin resistance (R2 = 0.004, P = .001 in the Rotterdam Study and R2 = 0.005, P = .002 in the LifeLines-DEEP study). A total of 12 groups of bacteria were associated with HOMA-IR or type 2 diabetes. Specifically, a higher abundance of Christensenellaceae (ß = -0.08; 95% CI, -0.12 to -0.03: P < .001), Christensenellaceae R7 group (ß = -0.07; 95% CI, -0.12 to -0.03; P < .001), Marvinbryantia (ß = -0.07; 95% CI, -0.11 to -0.03; P < .001), Ruminococcaceae UCG005 (ß = -0.09; 95% CI, -0.13 to -0.05; P < .001), Ruminococcaceae UCG008 (ß = -0.07; 95% CI, -0.11 to -0.03; P < .001), Ruminococcaceae UCG010 (ß = -0.08; 95% CI, -0.12 to -0.04; P < .001), or Ruminococcaceae NK4A214 group (ß = -0.09; 95% CI, -0.13 to -0.05; P < .001) was associated with lower HOMA-IR. A higher abundance of Clostridiaceae 1 (OR, 0.51; 95% CI, 0.41-0.65; P < .001), Peptostreptococcaceae (OR, 0.56; 95% CI, 0.45-0.70; P < .001), C sensu stricto 1 (OR, 0.51; 95% CI, 0.40-0.65; P < .001), Intestinibacter (OR, 0.60; 95% CI, 0.48-0.76; P < .001), or Romboutsia (OR, 0.55; 95% CI, 0.44-0.70; P < .001) was associated with less type 2 diabetes. These bacteria are all known to produce butyrate. Conclusions and Relevance: In this cross-sectional study, higher microbiome α diversity, along with more butyrate-producing gut bacteria, was associated with less type 2 diabetes and with lower insulin resistance among individuals without diabetes. These findings could help provide insight into the etiology, pathogenesis, and treatment of type 2 diabetes.

Composition of cutaneous bacterial microbiome in seborrheic dermatitis patients: A cross-sectional study.

BACKGROUND: Seborrheic dermatitis (SD) is a chronic inflammatory skin disease with a multifactorial aetiology. Malassezia yeasts have been associated with the disease but the role of bacterial composition in SD has not been thoroughly investigated. OBJECTIVES: To profile the bacterial microbiome of SD patients and compare this with the microbiome of individuals with no inflammatory skin disease (controls). METHODS: This was a cross sectional study embedded in a population-based study. Skin swabs were taken from naso-labial fold from patients with seborrheic dermatitis (lesional skin: n = 22; non-lesional skin SD: n = 75) and controls (n = 465). Sample collection began in 2016 at the research facility and is still ongoing. Shannon and Chao1 α- diversity metrics were calculated per group. Associations between the microbiome composition of cases and controls was calculated using multivariate statistics (permANOVA) and univariate statistics. RESULTS: We found an increased α-diversity between SD lesional cases versus controls (Shannon diversity: Kruskal-Wallis rank sum: Chi-squared: 19.06; global p-value = 7.7x10-5). Multivariate statistical analysis showed significant associations in microbiome composition when comparing lesional SD skin to controls (p-value = 0.03;R2 = 0.1%). Seven out of 13 amplicon sequence variants (ASVs) that were significantly different between controls and lesional cases were members of the genus Staphylococcus, most of which showed increased composition in lesional cases, and were closely related to S. capitis S. caprae and S. epidermidis. CONCLUSION: Microbiome composition differs in patients with seborrheic dermatitis and individuals without diseases. Differences were mainly found in the genus Staphylococcus.

A population-based study on associations of stool microbiota with atopic diseases in school-age children.

BACKGROUND: Infants with less diverse gut microbiota seem to have higher risks of atopic diseases in early life, but any associations at school age are unclear. OBJECTIVES: This study sought to examine the associations of diversity, relative abundance, and functional pathways of stool microbiota with atopic diseases in school-age children. METHODS: We performed a cross-sectional study within an existing population-based prospective cohort among 1440 children 10 years of age. On stool samples, 16S ribosomal RNA gene sequencing was performed, and taxonomic and functional tables were produced. Physician-diagnosed eczema, allergy, and asthma were measured by questionnaires, allergic sensitization by skin prick tests, and lung function by spirometry. RESULTS: The α-diversity of stool microbiota was associated with a decreased risk of eczema (odds ratio [OR], 0.98; 95% CI, 0.97, 1.00), and ß-diversity was associated with physician-diagnosed inhalant allergy (R2 = 0.001; P = .047). Lachnospiraceae, Ruminococcaceae_UCG-005, and Christensenellaceae_R-7_group species were associated with decreased risks of eczema, inhalant allergic sensitization, and physician-diagnosed inhalant allergy (OR range, 0.88-0.94; 95% CI range, 0.79-0.96 to 0.88-0.98), while Agathobacter species were associated with an increased risk of physician-diagnosed inhalant allergy (OR, 1.23; 95% CI, 1.08-1.42). Functional pathways related to heme and terpenoid biosynthesis were associated with decreased risks of physician-diagnosed inhalant allergy and asthma (OR range, 0.89-0.86; 95% CI range, 0.80-0.99 to 0.73-1.02). No associations of stool microbiota with lung function were observed. CONCLUSIONS: The diversity, relative abundance and functional pathways of stool microbiota were most consistently associated with physician-diagnosed inhalant allergy in school-age children and less consistently with other atopic diseases.

Gut Microbiota Composition Is Related to AD Pathology.

Introduction: Several 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 Methods: We 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. Results: The 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. Conclusions: Gut 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.

Microbiomics, Metabolomics, Predicted Metagenomics, and Hepatic Steatosis in a Population-Based Study of 1,355 Adults.

BACKGROUND AND AIMS: Previous small studies have appraised the gut microbiome (GM) in steatosis, but large-scale studies are lacking. We studied the association of the GM diversity and composition, plasma metabolites, predicted functional metagenomics, and steatosis. APPROACH AND RESULTS: This is a cross-sectional analysis of the prospective population-based Rotterdam Study. We used 16S ribosomal RNA gene sequencing and determined taxonomy using the SILVA reference database. Alpha diversity and beta diversity were calculated using the Shannon diversity index and Bray-Curtis dissimilarities. Differences were tested across steatosis using permutational multivariate analysis of variance. Hepatic steatosis was diagnosed by ultrasonography. We subsequently selected genera using regularized regression. The functional metagenome was predicted based on the GM using Kyoto Encyclopedia of Genes and Genomes pathways. Serum metabolomics were assessed using high-throughput proton nuclear magnetic resonance. All analyses were adjusted for age, sex, body mass index, alcohol, diet, and proton-pump inhibitors. We included 1,355 participants, of whom 472 had steatosis. Alpha diversity was lower in steatosis (P = 1.1∙10-9 ), and beta diversity varied across steatosis strata (P = 0.001). Lasso selected 37 genera of which three remained significantly associated after adjustment (Coprococcus3: ß = -65; Ruminococcus Gauvreauiigroup: ß = 62; and Ruminococcus Gnavusgroup: ß = 45, Q-value = 0.037). Predicted metagenome analyses revealed that pathways of secondary bile-acid synthesis and biotin metabolism were present, and D-alanine metabolism was absent in steatosis. Metabolic profiles showed positive associations for aromatic and branched chain amino acids and glycoprotein acetyls with steatosis and R. Gnavusgroup, whereas these metabolites were inversely associated with alpha diversity and Coprococcus3. CONCLUSIONS: We confirmed, on a large-scale, the lower microbial diversity and association of Coprococcus and Ruminococcus Gnavus with steatosis. We additionally showed that steatosis and alpha diversity share opposite metabolic profiles.