Integration of polygenic and gut metagenomic risk prediction for common diseases.

Multiomics has shown promise in noninvasive risk profiling and early detection of various common diseases. In the present study, in a prospective population-based cohort with ~18 years of e-health record follow-up, we investigated the incremental and combined value of genomic and gut metagenomic risk assessment compared with conventional risk factors for predicting incident coronary artery disease (CAD), type 2 diabetes (T2D), Alzheimer disease and prostate cancer. We found that polygenic risk scores (PRSs) improved prediction over conventional risk factors for all diseases. Gut microbiome scores improved predictive capacity over baseline age for CAD, T2D and prostate cancer. Integrated risk models of PRSs, gut microbiome scores and conventional risk factors achieved the highest predictive performance for all diseases studied compared with models based on conventional risk factors alone. The present study demonstrates that integrated PRSs and gut metagenomic risk models improve the predictive value over conventional risk factors for common chronic diseases.

Role of Gut Microbiota in Statin-Associated New-Onset Diabetes-A Cross-Sectional and Prospective Analysis of the FINRISK 2002 Cohort.

BACKGROUND: Dyslipidemia is treated effectively with statins, but treatment has the potential to induce new-onset type-2 diabetes. Gut microbiota may contribute to this outcome variability. We assessed the associations of gut microbiota diversity and composition with statins. Bacterial associations with statin-associated new-onset type-2 diabetes (T2D) risk were also prospectively evaluated. METHODS: We examined shallow-shotgun-sequenced fecal samples from 5755 individuals in the FINRISK-2002 population cohort with a 17+-year-long register-based follow-up. Alpha-diversity was quantified using Shannon index and beta-diversity with Aitchison distance. Species-specific differential abundances were analyzed using general multivariate regression. Prospective associations were assessed with Cox regression. Applicable results were validated using gradient boosting. RESULTS: Statin use associated with differing taxonomic composition (R2, 0.02%; q=0.02) and 13 differentially abundant species in fully adjusted models (MaAsLin; q<0.05). The strongest positive association was with Clostridium sartagoforme (ß=0.37; SE=0.13; q=0.02) and the strongest negative association with Bacteroides cellulosilyticus (ß=-0.31; SE=0.11; q=0.02). Twenty-five microbial features had significant associations with incident T2D in statin users, of which only Bacteroides vulgatus (HR, 1.286 [1.136-1.457]; q=0.03) was consistent regardless of model adjustment. Finally, higher statin-associated T2D risk was seen with [Ruminococcus] torques (ΔHRstatins, +0.11; q=0.03), Blautia obeum (ΔHRstatins, +0.06; q=0.01), Blautia sp. KLE 1732 (ΔHRstatins, +0.05; q=0.01), and beta-diversity principal component 1 (ΔHRstatin, +0.07; q=0.03) but only when adjusting for demographic covariates. CONCLUSIONS: Statin users have compositionally differing microbiotas from nonusers. The human gut microbiota is associated with incident T2D risk in statin users and possibly has additive effects on statin-associated new-onset T2D risk.

Early prediction of incident liver disease using conventional risk factors and gut-microbiome-augmented gradient boosting.

The gut microbiome has shown promise as a predictive biomarker for various diseases. However, the potential of gut microbiota for prospective risk prediction of liver disease has not been assessed. Here, we utilized shallow shotgun metagenomic sequencing of a large population-based cohort (N > 7,000) with ∼15 years of follow-up in combination with machine learning to investigate the predictive capacity of gut microbial predictors individually and in conjunction with conventional risk factors for incident liver disease. Separately, conventional and microbial factors showed comparable predictive capacity. However, microbiome augmentation of conventional risk factors using machine learning significantly improved the performance. Similarly, disease-free survival analysis showed significantly improved stratification using microbiome-augmented models. Investigation of predictive microbial signatures revealed previously unknown taxa for liver disease, as well as those previously associated with hepatic function and disease. This study supports the potential clinical validity of gut metagenomic sequencing to complement conventional risk factors for prediction of liver diseases.

Towards standardized and reproducible research in skin microbiomes.

Skin is a complex organ serving a critical role as a barrier and mediator of interactions between the human body and its environment. Recent studies have uncovered how resident microbial communities play a significant role in maintaining the normal healthy function of the skin and the immune system. In turn, numerous host-associated and environmental factors influence these communities' composition and diversity across the cutaneous surface. In addition, specific compositional changes in skin microbiota have also been connected to the development of several chronic diseases. The current era of microbiome research is characterized by its reliance on large data sets of nucleotide sequences produced with high-throughput sequencing of sample-extracted DNA. These approaches have yielded new insights into many previously uncharacterized microbial communities. Application of standardized practices in the study of skin microbial communities could help us understand their complex structures, functional capacities, and health associations and increase the reproducibility of the research. Here, we overview the current research in human skin microbiomes and outline challenges specific to their study. Furthermore, we provide perspectives on recent advances in methods, analytical tools and applications of skin microbiomes in medicine and forensics.

Gut Microbiome Composition Is Predictive of Incident Type 2 Diabetes in a Population Cohort of 5,572 Finnish Adults.

OBJECTIVE: To examine the previously unknown long-term association between gut microbiome composition and incident type 2 diabetes in a representative population cohort. RESEARCH DESIGN AND METHODS: We collected fecal samples from 5,572 Finns (mean age 48.7 years; 54.1% women) in 2002 who were followed up for incident type 2 diabetes until 31 December 2017. The samples were sequenced using shotgun metagenomics. We examined associations between gut microbiome composition and incident diabetes using multivariable-adjusted Cox regression models. We first used the eastern Finland subpopulation to obtain initial findings and validated these in the western Finland subpopulation. RESULTS: Altogether, 432 cases of incident diabetes occurred over the median follow-up of 15.8 years. We detected four species and two clusters consistently associated with incident diabetes in the validation models. These four species were Clostridium citroniae (hazard ratio [HR] 1.21; 95% CI 1.04-1.42), C. bolteae (HR 1.20; 95% CI 1.04-1.39), Tyzzerella nexilis (HR 1.17; 95% CI 1.01-1.36), and Ruminococcus gnavus (HR 1.17; 95% CI 1.01-1.36). The positively associated clusters, cluster 1 (HR 1.18; 95% CI 1.02-1.38) and cluster 5 (HR 1.18; 95% CI 1.02-1.36), mostly consisted of these same species. CONCLUSIONS: We observed robust species-level taxonomic features predictive of incident type 2 diabetes over long-term follow-up. These findings build on and extend previous mainly cross-sectional evidence and further support links between dietary habits, metabolic diseases, and type 2 diabetes that are modulated by the gut microbiome. The gut microbiome can potentially be used to improve disease prediction and uncover novel therapeutic targets for diabetes.

Comprehensive biomarker profiling of hypertension in 36 985 Finnish individuals.

OBJECTIVE: Previous studies on the association between metabolic biomarkers and hypertension have been limited by small sample sizes, low number of studied biomarkers, and cross-sectional study design. In the largest study to date, we assess the cross-sectional and longitudinal associations between high-abundance serum biomarkers and blood pressure (BP). METHODS: We studied cross-sectional (N = 36 985; age 50.5 ±â€Š14.2; 53.1% women) and longitudinal (N = 4197; age 49.4 ±â€Š11.8, 55.3% women) population samples of Finnish individuals. We included 53 serum biomarkers and other detailed lipoprotein subclass measures in our analyses. We studied the associations between serum biomarkers and BP using both conventional statistical methods and a machine learning algorithm (gradient boosting) while adjusting for clinical risk factors. RESULTS: Fifty-one of 53 serum biomarkers were cross-sectionally related to BP (adjusted P < 0.05 for all). Conventional linear regression modeling demonstrated that LDL cholesterol, remnant cholesterol, apolipoprotein B, and acetate were positively, and HDL particle size was negatively, associated with SBP change over time (adjusted P < 0.05 for all). Adding serum biomarkers (cross-sectional root-mean-square error: 16.27 mmHg; longitudinal: 17.61 mmHg) in the model with clinical measures (cross-sectional: 16.70 mmHg; longitudinal 18.52 mmHg) improved the machine learning model fit. Glucose, albumin, triglycerides in LDL, glycerol, VLDL particle size, and acetoacetate had the highest importance scores in models related to current or future BP. CONCLUSION: Our results suggest that serum lipids, and particularly LDL-derived and VLDL-derived cholesterol measures, and glucose metabolism abnormalities are associated with hypertension onset. Use of serum metabolite determination could improve identification of individuals at high risk of developing hypertension.

Associations of healthy food choices with gut microbiota profiles.

BACKGROUND: Diet has a major influence on the human gut microbiota, which has been linked to health and disease. However, epidemiological studies on associations of a healthy diet with the microbiota utilizing a whole-diet approach are still scant. OBJECTIVES: To assess associations between healthy food choices and human gut microbiota composition, and to determine the strength of association with functional potential. METHODS: This population-based study sample consisted of 4930 participants (ages 25-74; 53% women) in the FINRISK 2002 study. Intakes of recommended foods were assessed using a food propensity questionnaire, and responses were transformed into healthy food choices (HFC) scores. Microbial diversity (alpha diversity) and compositional differences (beta diversity) and their associations with the HFC score and its components were assessed using linear regression. Multiple permutational multivariate ANOVAs were run from whole-metagenome shallow shotgun-sequenced samples. Associations between specific taxa and HFC were analyzed using linear regression. Functional associations were derived from Kyoto Encyclopedia of Genes and Genomes orthologies with linear regression models. RESULTS: Both microbial alpha diversity (ß/SD, 0.044; SE, 6.18 × 10-5; P = 2.21 × 10-3) and beta diversity (R2, 0.12; P ≤ 1.00 × 10-3) were associated with the HFC score. For alpha diversity, the strongest associations were observed for fiber-rich breads, poultry, fruits, and low-fat cheeses (all positive). For beta diversity, the most prominent associations were observed for vegetables, followed by berries and fruits. Genera with fiber-degrading and SCFA-producing capacities were positively associated with the HFC score. The HFC score was associated positively with functions such as SCFA metabolism and synthesis, and inversely with functions such as fatty acid biosynthesis and the sulfur relay system. CONCLUSIONS: Our results from a large, population-based survey confirm and extend findings of other, smaller-scale studies that plant- and fiber-rich dietary choices are associated with a more diverse and compositionally distinct microbiota, and with a greater potential to produce SCFAs.

Modelling spatial patterns in host-associated microbial communities.

Microbial communities exhibit spatial structure at different scales, due to constant interactions with their environment and dispersal limitation. While this spatial structure is often considered in studies focusing on free-living environmental communities, it has received less attention in the context of host-associated microbial communities or microbiota. The wider adoption of methods accounting for spatial variation in these communities will help to address open questions in basic microbial ecology as well as realize the full potential of microbiome-aided medicine. Here, we first overview known factors affecting the composition of microbiota across diverse host types and at different scales, with a focus on the human gut as one of the most actively studied microbiota. We outline a number of topical open questions in the field related to spatial variation and patterns. We then review the existing methodology for the spatial modelling of microbiota. We suggest that methodology from related fields, such as systems biology and macro-organismal ecology, could be adapted to obtain more accurate models of spatial structure. We further posit that methodological developments in the spatial modelling and analysis of microbiota could in turn broadly benefit theoretical and applied ecology and contribute to the development of novel industrial and clinical applications.

Links between gut microbiome composition and fatty liver disease in a large population sample.

Fatty liver disease is the most common liver disease in the world. Its connection with the gut microbiome has been known for at least 80 y, but this association remains mostly unstudied in the general population because of underdiagnosis and small sample sizes. To address this knowledge gap, we studied the link between the Fatty Liver Index (FLI), a well-established proxy for fatty liver disease, and gut microbiome composition in a representative, ethnically homogeneous population sample of 6,269 Finnish participants. We based our models on biometric covariates and gut microbiome compositions from shallow metagenome sequencing. Our classification models could discriminate between individuals with a high FLI (≥60, indicates likely liver steatosis) and low FLI (<60) in internal cross-region validation, consisting of 30% of the data not used in model training, with an average AUC of 0.75 and AUPRC of 0.56 (baseline at 0.30). In addition to age and sex, our models included differences in 11 microbial groups from class Clostridia, mostly belonging to orders Lachnospirales and Oscillospirales. Our models were also predictive of the high FLI group in a different Finnish cohort, consisting of 258 participants, with an average AUC of 0.77 and AUPRC of 0.51 (baseline at 0.21). Pathway analysis of representative genomes of the positively FLI-associated taxa in (NCBI) Clostridium subclusters IV and XIVa indicated the presence of, e.g., ethanol fermentation pathways. These results support several findings from smaller case-control studies, such as the role of endogenous ethanol producers in the development of the fatty liver.

Warming Climate Is Reducing the Diversity of Dominant Microbes in the Largest High Arctic Lake.

Temperatures in the Arctic are expected to increase dramatically over the next century, and transform high latitude watersheds. However, little is known about how microbial communities and their underlying metabolic processes will be affected by these environmental changes in freshwater sedimentary systems. To address this knowledge gap, we analyzed sediments from Lake Hazen, NU Canada. Here, we exploit the spatial heterogeneity created by varying runoff regimes across the watershed of this uniquely large high-latitude lake to test how a transition from low to high runoff, used as one proxy for climate change, affects the community structure and functional potential of dominant microbes. Based on metagenomic analyses of lake sediments along these spatial gradients, we show that increasing runoff leads to a decrease in taxonomic and functional diversity of sediment microbes. Our findings are likely to apply to other, smaller, glacierized watersheds typical of polar or high latitude ecosystems; we can predict that such changes will have far reaching consequences on these ecosystems by affecting nutrient biogeochemical cycling, the direction and magnitude of which are yet to be determined.

Swift evolutionary response of microbes to a rise in anthropogenic mercury in the Northern Hemisphere.

Anthropogenic mercury remobilization has considerably increased since the Industrial Revolution in the late 1700s. The Minamata Convention on Mercury is a United Nations treaty (2017) aiming at curbing mercury emissions. Unfortunately, evaluating the effectiveness of such a global treaty is hampered by our inability to determine the lag in aquatic ecosystem responses to a change in atmospheric mercury deposition. Whereas past metal concentrations are obtained from core samples, there are currently no means of tracking historical metal bioavailability or toxicity. Here, we recovered DNA from nine dated sediment cores collected in Canada and Finland, and reconstructed the past demographics of microbes carrying genes coding for the mercuric reductase (MerA)-an enzyme involved in Hg detoxification-using Bayesian relaxed molecular clocks. We found that the evolutionary dynamics of merA exhibited a dramatic increase in effective population size starting from 1783.8 ± 3.9 CE, which coincides with both the Industrial Revolution, and with independent measurements of atmospheric Hg concentrations. We show that even low levels of anthropogenic mercury affected the evolutionary trajectory of microbes in the Northern Hemisphere, and that microbial DNA encoding for detoxification determinants stored in environmental archives can be used to track historical pollutant toxicity.

Physicochemical Drivers of Microbial Community Structure in Sediments of Lake Hazen, Nunavut, Canada.

The Arctic is undergoing rapid environmental change, potentially affecting the physicochemical constraints of microbial communities that play a large role in both carbon and nutrient cycling in lacustrine environments. However, the microbial communities in such Arctic environments have seldom been studied, and the drivers of their composition are poorly characterized. To address these gaps, we surveyed the biologically active surface sediments in Lake Hazen, the largest lake by volume north of the Arctic Circle, and a small lake and shoreline pond in its watershed. High-throughput amplicon sequencing of the 16S rRNA gene uncovered a community dominated by Proteobacteria, Bacteroidetes, and Chloroflexi, similar to those found in other cold and oligotrophic lake sediments. We also show that the microbial community structure in this Arctic polar desert is shaped by pH and redox gradients. This study lays the groundwork for predicting how sediment microbial communities in the Arctic could respond as climate change proceeds to alter their physicochemical constraints.

Ionic Strength Differentially Affects the Bioavailability of Neutral and Negatively Charged Inorganic Hg Complexes.

Mercury (Hg) bioavailability to bacteria in marine systems is the first step toward its bioamplification in food webs. These systems exhibit high salinity and ionic strength that will both alter Hg speciation and properties of the bacteria cell walls. The role of Hg speciation on Hg bioavailability in marine systems has not been teased apart from that of ionic strength on cell wall properties, however. We developed and optimized a whole-cell Hg bioreporter capable of functioning under aerobic and anaerobic conditions and exhibiting no physiological limitations of signal production to changes in ionic strength. We show that ionic strength controls the bioavailability of Hg species, regardless of their charge, possibly by altering properties of the bacterial cell wall. The unexpected anaerobic bioavailability of negatively charged halocomplexes may help explain Hg methylation in marine systems such as the oxygen-deficient zone in the oceanic water column, sea ice or polar snow.

Fertilizing with Animal Manure Disseminates Antibiotic Resistance Genes to the Farm Environment.

The dissemination of antibiotic resistance genes to the environment is an important factor causing increased prevalence of resistant pathogens. Manure is an important fertilizer, but it contains diverse resistance genes. Therefore, its application to fields may lead to increased abundance of resistance genes in the environment. Farming environments exposed to animal manure have not been studied extensively in countries with comparably low antibiotic use, such as Finland. The effects of manure storage and application to fields on the abundance of resistance genes were studied on two dairy cattle farms and two swine farms in southern Finland. Samples were taken from farms during the 2013 cropping season. Copy numbers of carbapenem (), sulfonamide (), and tetracycline () resistance genes were measured with quantitative polymerase chain reaction, and the data were analyzed using linear mixed models. The relative abundance of antibiotic resistance genes increased about fourfold in soil after manure application. Carbapenemase encoding was detected on all of the studied farms, which indicated that the gene is dispersed in the farm environment. The relative abundance of antibiotic resistance genes increased in stored manure compared with fresh manure roughly fivefold. This study shows that antibiotic resistance genes are disseminated on Finnish production animal farms. The spreading of resistance genes in farm-associated environments could possibly be limited by experimenting with new manure handling methods that could reduce the abundance of the genes in manure used for land application.