The gut microbiome is shaped by diet and influences host metabolism, but these links are complex and can be unique to each individual. We performed deep metagenomic sequencing of >1,100 gut microbiomes from individuals with detailed long-term diet information, as well as hundreds of fasting and same-meal postprandial cardiometabolic blood marker measurements. We found strong associations between microbes and specific nutrients, foods, food groups, and general dietary indices, driven especially by the presence and diversity of healthy and plant-based foods. Microbial biomarkers of obesity were reproducible across cohorts, and blood markers of cardiovascular disease and impaired glucose tolerance were more strongly associated with microbiome structure. While some microbes such as Prevotella copri and Blastocystis spp., were indicators of reduced postprandial glucose metabolism, several species were more directly predictive for postprandial triglycerides and C-peptide. The panel of intestinal species associated with healthy dietary habits overlapped with those associated with favourable cardiometabolic and postprandial markers, indicating our large-scale resource can potentially stratify the gut microbiome into generalizable health levels among individuals without clinically manifest disease. Fig. 1: The PREDICT 1 study associates gut microbiome structure with habitual diet and blood cardiometabolic markers. (A)The PREDICT 1 study assessed the gut microbiome of 1,098 volunteers from the UK and US via metagenomic sequencing of stool samples. Phenotypic data obtained through in-person assessment, blood/biospecimen collection, and the return of validated study questionnaires queried a range of relevant host/environmental factors including (1) personal characteristics, such as age, BMI, and estimated visceral fat; (2) habitual dietary intake using semi-quantitative food frequency questionnaires (FFQs);(3) fasting; and (4) postprandial cardiometabolic blood and inflammatory markers, total lipid and lipoprotein concentrations, lipoprotein particle sizes, apolipoproteins, derived metabolic risk scores, glycaemic-mediated metabolites, and metabolites related to fatty acid metabolism. (B) Overall microbiome alpha diversity, estimated as the total number of confidently identified microbial species in a given sample (richness), was correlated with HDL-D (positive) and estimated hepatic steatosis (negative). Up to ten strongest absolute Spearman correlations are reported for each category with q<0.05. Top species based on Shannon diversity are reported in Supplementary Fig. 1A and all correlations are in Supplementary Table 1. Microbial diversity and composition are linked with diet and fasting and postprandial biomarkersWe first leveraged a unique subpopulation of our study comprised of 480 twins to disentangle the confounding effects of shared genetics from other factors on microbiome composition. Our data confirmed that host genetics influences microbiome composition only to a small extent 18 , as intra-twin pair microbiome ...
(Figure 2c), and less than 1% of variation for postprandial triglyceride and postprandial C-peptide (Figure 2b and 2d). Gut microbiome (16S rRNA). We estimated the contribution of gut microbiome composition using relative bacterial taxonomic abundances and measures of community diversity and richness, derived from 16S rRNA high-throughput sequencing of baseline stool specimens (Supplemental Table 4). We found that without adjusting for any other individual characteristics the gut microbiome composition explained 7.5% of postprandial triglyceride6h-rise, 6.4% of postprandial glucoseiAUC0-2h and 5.8% of postprandial C-peptide1h-rise. Meal composition, habitual diet and meal context. To determine the impact of the macronutrient composition of meals, we measured triglyceride6h-rise and C-peptide1h-rise for two standardized home phase meals of contrasting macronutrient compositions (for triglyceride, comparison of meals 1 and 7: 85 vs 28g of carbohydrate and 50 vs 40 g of fat at breakfast, both followed by a lunch of 71g carbohydrate and 22g fat; for C-peptide, comparison of meal 2 and 3: 71 vs 41 g of carbohydrate and 22 vs 35 g of fat; Supplement Table 2) in subsets of participants (n=712 and n=186, respectively). GlucoseiAUC0-2h was measured for seven standardized meals (comparison of meals 1, 2, 4, 5, 6, 7 and 8: 28 -95 g carbohydrate; 0 -53 g fat) totalling 9,102 meals in 920 individuals. The proportions of variance explained by meal composition, habitual diet, and by meal context are shown for triglyceride6h-risein Figure 2b, for glucoseiAUC0-2hin Figure 2c, and for C-peptide1h-risein Figure 2d. A multivariate regression model (meals 1, 2, 4, 5, 6, 7 and 8) revealed that the Glucosei AUC0-2h (mmol/L*s) was significantly (P<0.001) reduced by 79, 142 and 185 for every 1g fat, fiber and protein respectively, after adjustment for carbohydrate consumption. Machine learning model. To estimate the unbiased predictive utility of the factors analysed, we used a machine learning approach robust to overfitting 22 . Random Forest regression models 23 were fitted using all the informative features (meal composition, habitual diet, meal context, anthropometry, genetics, microbiome, clinical and biochemical parameters) to predict triglyceride6h10 described in the Methods, we considered not only the effect of the meal macronutrient and energy content in the response (meal composition), but also considered how each individual responded on average to all their set meals relative to the population (individual glucose scaling), as well as the effect of the individual's meal-specific response, the error attributable to the glucose measurement and other sources of variation (including modifiable sources of variation such as sleep, circadian rhythm and exercise). We found that, consistent with the linear models described earlier, the ANOVA models show that there are three meal-related factors explaining individual glycemic responses. Meal macronutrient composition alters iAUC by 16.73% (95%CI 15.37 -18.92%), but the individual glucose...
Background and aimsGut transit time is a key modulator of host–microbiome interactions, yet this is often overlooked, partly because reliable methods are typically expensive or burdensome. The aim of this single-arm, single-blinded intervention study is to assess (1) the relationship between gut transit time and the human gut microbiome, and (2) the utility of the ‘blue dye’ method as an inexpensive and scalable technique to measure transit time.MethodsWe assessed interactions between the taxonomic and functional potential profiles of the gut microbiome (profiled via shotgun metagenomic sequencing), gut transit time (measured via the blue dye method), cardiometabolic health and diet in 863 healthy individuals from the PREDICT 1 study.ResultsWe found that gut microbiome taxonomic composition can accurately discriminate between gut transit time classes (0.82 area under the receiver operating characteristic curve) and longer gut transit time is linked with specific microbial species such as Akkermansia muciniphila, Bacteroides spp and Alistipes spp (false discovery rate-adjusted p values <0.01). The blue dye measure of gut transit time had the strongest association with the gut microbiome over typical transit time proxies such as stool consistency and frequency.ConclusionsGut transit time, measured via the blue dye method, is a more informative marker of gut microbiome function than traditional measures of stool consistency and frequency. The blue dye method can be applied in large-scale epidemiological studies to advance diet-microbiome-health research. Clinical trial registry website https://clinicaltrials.gov/ct2/show/NCT03479866 and trial number NCT03479866.
Thirty-six of 43 maternally related members of a large African American family experienced hearing loss. A muscle biopsy specimen from the proband showed cytochrome c oxidase (COX)-deficient fibers but no ragged-red fibers; biochemical analysis showed marked reduction of COX activity. A novel T7511C point mutation in the tRNA(Ser(UCN)) gene was present in almost homoplasmic levels (>95%) in the blood of 18 of 20 family members, and was also found in lower abundance in the other two. Single-fiber PCR showed that the mutational load was greater in COX-deficient muscle fibers. The tRNA(ser(UCN)) gene may be a "hot spot" for mutations associated with maternally transmitted hearing loss.
Depressive symptoms are common in AD, but their prevalence decreases over time. Examination of the temporal relationship between depressive symptoms and risk factors suggests that decline in function but not in cognition precedes the first episode of depressive symptoms in patients with probable AD.
Alzheimer's disease (AD) is a progressive neurodegenerative disorder and the most common cause of dementia. Beta-amyloid (Aβ) deposition and neurofibrillary tangles (NFTs) of abnormal hyperphosphorylated tau protein are the pathological hallmarks of the disease, accompanied by other pathological processes such as microglia activation. Functional and molecular nuclear medicine imaging with single-photon emission computed tomography (SPECT) and positron emission tomography (PET) techniques provides valuable information about the underlying pathological processes, many years before the appearance of clinical symptoms. Nuclear neuroimaging in AD has made great progress in the past two decades and has extended beyond the traditional role of brain perfusion and glucose metabolism evaluation. Intense efforts in radiopharmaceuticals research have led to the development of various probes able to detect Aβ deposits, tau protein accumulation, microglia activation and neuroinflammation. As a result, SPECT and PET have proposed to serve as biomarkers in recently revised diagnostic clinical criteria for the early diagnosis of AD and the prediction of progression to AD in mild cognitive impairment (MCI) subjects.
The muscle histopathology and respiratory chain enzyme defects may be accounted for by the decreased mtDNA amount and by the presence of mtDNA deleted molecules; however, relative levels of mtDNA seem to correlate with life span in these patients. The combination of partial depletion and multiple deletions of mtDNA might indicate the derangement of a common genetic mechanism controlling mtDNA copy number and integrity.
The G209A mutation in the alpha-synuclein gene has been associated with autosomal dominant PD (ADPD) in a family from Contursi, Italy, and three apparently unrelated Greek families. Several groups around the world failed to identify the G209A mutation in a sizable series of familial and sporadic cases of PD. The authors present two additional Greek families with ADPD associated with the G209A mutation. In both families, asymptomatic carriers older than the expected age at onset were found.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.