ORCID Profile
0000-0002-4787-8959
Current Organisations
Finnish Institute for Health and Welfare
,
Institute for Molecular Medicine Finland
,
University of Helsinki
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Publisher: Cold Spring Harbor Laboratory
Date: 11-11-2021
DOI: 10.1101/2021.11.10.21266163
Abstract: To examine the previously unknown long-term association between gut microbiome composition and incident type 2 diabetes in a representative population cohort. We collected fecal s les of 5 572 Finns (mean age 48.7 years, 54.1% women) in 2002 who were followed up for incident type 2 diabetes until Dec 31 st , 2017. The s les were sequenced using shotgun metagenomics. We examined associations between gut microbiome compositions and incident diabetes using multivariable-adjusted Cox regression models. We first used the Eastern Finland sub-population to obtain initial findings and validated these in the Western Finland sub-population. Altogether 432 cases of incident diabetes occurred over the median follow-up of 15.8 years. We detected 4 species and 2 clusters consistently associated with incident diabetes in the validation models. These 4 species were Clostridium citroniae (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. We observed robust species-level taxonomic features predictive of incident type 2 diabetes over a 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 could potentially be used to improve risk prediction and to uncover novel therapeutic targets for diabetes.
Publisher: Springer Science and Business Media LLC
Date: 30-09-2022
DOI: 10.1038/S41586-022-05165-3
Abstract: Previous genome-wide association studies (GWASs) of stroke — the second leading cause of death worldwide — were conducted predominantly in populations of European ancestry 1,2 . Here, in cross-ancestry GWAS meta-analyses of 110,182 patients who have had a stroke (five ancestries, 33% non-European) and 1,503,898 control in iduals, we identify association signals for stroke and its subtypes at 89 (61 new) independent loci: 60 in primary inverse-variance-weighted analyses and 29 in secondary meta-regression and multitrait analyses. On the basis of internal cross-ancestry validation and an independent follow-up in 89,084 additional cases of stroke (30% non-European) and 1,013,843 control in iduals, 87% of the primary stroke risk loci and 60% of the secondary stroke risk loci were replicated ( P 0.05). Effect sizes were highly correlated across ancestries. Cross-ancestry fine-mapping, in silico mutagenesis analysis 3 , and transcriptome-wide and proteome-wide association analyses revealed putative causal genes (such as SH3PXD2A and FURIN ) and variants (such as at GRK5 and NOS3 ). Using a three-pronged approach 4 , we provide genetic evidence for putative drug effects, highlighting F11, KLKB1, PROC, GP1BA, LAMC2 and VCAM1 as possible targets, with drugs already under investigation for stroke for F11 and PROC. A polygenic score integrating cross-ancestry and ancestry-specific stroke GWASs with vascular-risk factor GWASs (integrative polygenic scores) strongly predicted ischaemic stroke in populations of European, East Asian and African ancestry 5 . Stroke genetic risk scores were predictive of ischaemic stroke independent of clinical risk factors in 52,600 clinical-trial participants with cardiometabolic disease. Our results provide insights to inform biology, reveal potential drug targets and derive genetic risk prediction tools across ancestries.
Publisher: Cold Spring Harbor Laboratory
Date: 03-09-2021
Abstract: The number of publicly available microbiome s les is continually growing. As data set size increases, bottlenecks arise in standard analytical pipelines. Faith's phylogenetic ersity (Faith's PD) is a highly utilized phylogenetic alpha ersity metric that has thus far failed to effectively scale to trees with millions of vertices. Stacked Faith's phylogenetic ersity (SFPhD) enables calculation of this widely adopted ersity metric at a much larger scale by implementing a computationally efficient algorithm. The algorithm reduces the amount of computational resources required, resulting in more accessible software with a reduced carbon footprint, as compared to previous approaches. The new algorithm produces identical results to the previous method. We further demonstrate that the phylogenetic aspect of Faith's PD provides increased power in detecting ersity differences between younger and older populations in the FINRISK study's metagenomic data.
Publisher: Cold Spring Harbor Laboratory
Date: 13-09-2020
DOI: 10.1101/2020.09.12.20193045
Abstract: Co-evolution between humans and the microbial communities colonizing them has resulted in an intimate assembly of thousands of microbial species mutualistically living on and in their body and impacting multiple aspects of host physiology and health. Several studies examining whether human genetic variation can affect gut microbiota suggest a complex combination of environmental and host factors. Here, we leverage a single large-scale population-based cohort of 5,959 genotyped in iduals with matched gut microbial shotgun metagenomes, dietary information and health records up to 16 years post-s ling, to characterize human genetic variations associated with microbial abundances, and predict possible causal links with various diseases using Mendelian randomization (MR). Genome-wide association study (GWAS) identified 583 independent SNP-taxon associations at genome-wide significance ( p .0×10 -8 ), which included notable strong associations with LCT ( p =5.02×10 -35 ), ABO ( p =1.1×10 -12 ), and MED13L ( p =1.84×10 -12 ). A combination of genetics and dietary habits was shown to strongly shape the abundances of certain key bacterial members of the gut microbiota, and explain their genetic association. Genetic effects from the LCT locus on Bifidobacterium and three other associated taxa significantly differed according to dairy intake. Variation in mucin-degrading Faecalicatena lactaris abundances were associated with ABO , highlighting a preferential utilization of secreted A/B/AB-antigens as energy source in the gut, irrespectively of fibre intake. Enterococcus faecalis levels showed a robust association with a variant in MED13L , with putative links to colorectal cancer. Finally, we identified putative causal relationships between gut microbes and complex diseases using MR, with a predicted effect of Morganella on major depressive disorder that was consistent with observational incident disease analysis. Overall, we present striking ex les of the intricate relationship between humans and their gut microbial communities, and highlight important health implications.
Publisher: Springer Science and Business Media LLC
Date: 27-07-2023
DOI: 10.1038/S41587-023-01845-1
Abstract: Studies using 16S rRNA and shotgun metagenomics typically yield different results, usually attributed to PCR lification biases. We introduce Greengenes2, a reference tree that unifies genomic and 16S rRNA databases in a consistent, integrated resource. By inserting sequences into a whole-genome phylogeny, we show that 16S rRNA and shotgun metagenomic data generated from the same s les agree in principal coordinates space, taxonomy and phenotype effect size when analyzed with the same tree.
Publisher: Springer Science and Business Media LLC
Date: 14-11-2022
Publisher: Springer Science and Business Media LLC
Date: 18-10-2023
Publisher: American Society for Microbiology
Date: 26-04-2022
DOI: 10.1128/MSYSTEMS.00167-22
Abstract: Shotgun metagenomics is a powerful, yet computationally challenging, technique compared to 16S rRNA gene licon sequencing for decoding the composition and structure of microbial communities. Current analyses of metagenomic data are primarily based on taxonomic classification, which is limited in feature resolution.
Publisher: Springer Science and Business Media LLC
Date: 23-11-2016
DOI: 10.1038/NCOMMS13357
Abstract: Large consortia have revealed hundreds of genetic loci associated with anthropometric traits, one trait at a time. We examined whether genetic variants affect body shape as a composite phenotype that is represented by a combination of anthropometric traits. We developed an approach that calculates averaged PCs (AvPCs) representing body shape derived from six anthropometric traits (body mass index, height, weight, waist and hip circumference, waist-to-hip ratio). The first four AvPCs explain % of the variability, are heritable, and associate with cardiometabolic outcomes. We performed genome-wide association analyses for each body shape composite phenotype across 65 studies and meta-analysed summary statistics. We identify six novel loci: LEMD2 and CD47 for AvPC1, RPS6KA5 / C14orf159 and GANAB for AvPC3, and ARL15 and ANP32 for AvPC4. Our findings highlight the value of using multiple traits to define complex phenotypes for discovery, which are not captured by single-trait analyses, and may shed light onto new pathways.
Publisher: Springer Science and Business Media LLC
Date: 12-03-2018
Publisher: Elsevier BV
Date: 05-2023
Publisher: Springer Science and Business Media LLC
Date: 22-01-2021
Publisher: Springer Science and Business Media LLC
Date: 02-2022
DOI: 10.1038/S41588-021-00991-Z
Abstract: Human genetic variation affects the gut microbiota through a complex combination of environmental and host factors. Here we characterize genetic variations associated with microbial abundances in a single large-scale population-based cohort of 5,959 genotyped in iduals with matched gut microbial metagenomes, and dietary and health records (prevalent and follow-up). We identified 567 independent SNP-taxon associations. Variants at the LCT locus associated with Bifidobacterium and other taxa, but they differed according to dairy intake. Furthermore, levels of Faecalicatena lactaris associated with ABO, and suggested preferential utilization of secreted blood antigens as energy source in the gut. Enterococcus faecalis levels associated with variants in the MED13L locus, which has been linked to colorectal cancer. Mendelian randomization analysis indicated a potential causal effect of Morganella on major depressive disorder, consistent with observational incident disease analysis. Overall, we identify and characterize the intricate nature of host-microbiota interactions and their association with disease.
Publisher: Cold Spring Harbor Laboratory
Date: 08-2020
DOI: 10.1101/2020.07.30.20164962
Abstract: Fatty liver disease is the most common liver disease in the world. It is characterized by a buildup of excess fat in the liver that can lead to cirrhosis and liver failure. The link between fatty liver disease and gut microbiome has been known for at least 80 years. However, this association remains mostly unstudied in the general population because of underdiagnosis and small s le 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 s le in Finland. We based our models on biometric covariates and gut microbiome compositions from shallow metagenome sequencing. Our classification models could discriminate between in iduals with a high FLI (≥ 60, indicates likely liver steatosis) and low FLI ( 60) in our validation set, consisting of 30% of the data not used in model training, with an average AUC of 0.75. In addition to age and sex, our models included differences in 11 microbial groups from class Clostridia , mostly belonging to orders Lachnospirales and Oscillospirales . Pathway analysis of representative genomes of the FLI-associated taxa in (NCBI) Clostridium subclusters IV and XIVa indicated the presence of e.g ., ethanol fermentation pathways. Through modeling the fatty liver index, our results provide with high resolution associations between gut microbiota composition and fatty liver in a large representative population cohort and support the role of endogenous ethanol producers in the development of fatty liver.
Publisher: Springer Science and Business Media LLC
Date: 11-05-2021
DOI: 10.1038/S41467-021-22962-Y
Abstract: The collection of fecal material and developments in sequencing technologies have enabled standardised and non-invasive gut microbiome profiling. Microbiome composition from several large cohorts have been cross-sectionally linked to various lifestyle factors and diseases. In spite of these advances, prospective associations between microbiome composition and health have remained uncharacterised due to the lack of sufficiently large and representative population cohorts with comprehensive follow-up data. Here, we analyse the long-term association between gut microbiome variation and mortality in a well-phenotyped and representative population cohort from Finland ( n = 7211). We report robust taxonomic and functional microbiome signatures related to the Enterobacteriaceae family that are associated with mortality risk during a 15-year follow-up. Our results extend previous cross-sectional studies, and help to establish the basis for examining long-term associations between human gut microbiome composition, incident outcomes, and general health status.
Publisher: Research Square Platform LLC
Date: 04-01-2022
DOI: 10.21203/RS.3.RS-1175817/V1
Abstract: Previous genome-wide association studies (GWAS) of stroke, the second leading cause of death, have been conducted in populations of predominantly European ancestry.1,2 We undertook cross-ancestry GWAS meta-analyses of stroke and its subtypes in 110,182 stroke patients (33% non-European) and 1,503,898 control in iduals of five ancestries from population- and clinic-based studies, nearly doubling the number of cases in previous stroke GWAS. We identified association signals at 89 independent loci, of which 61 were novel. Effect sizes were overall highly correlated across ancestries. Cross-ancestry fine-mapping, in silico mutagenesis analysis using a novel machine-learning approach,3 transcriptome and proteome-wide association analyses revealed putative causal genes (e.g. SH3PXD2A and FURIN) and variants (e.g. at GRK5 and NOS3). Using a novel three-pronged approach,4 we provided genetic evidence for putative drug effects, highlighting F11, KLKB1, PROC, GP1BA, and VCAM1 as possible targets, with drugs already under investigation for stroke for F11 and PROC. A polygenic score integrating cross-ancestry and ancestry-specific stroke GWAS with vascular risk factor GWAS (iPGS) showed strong prediction of ischemic stroke risk in European and, for the first time, East-Asian populations.5,6 The iPGS performed better than stroke PGS alone and better than previous best iPGS, in Europeans and East-Asians. Transferability of European-specific iPGS to East-Asians was limited. Stroke genetic risk scores were predictive of ischemic stroke independent of clinical risk factors in 52,600 clinical trial participants with cardiometabolic disease and performed considerably better than previous scores, both in Europeans and East-Asians. Altogether our results provide critical insight to inform biology, reveal potential drug targets for intervention, and provide genetic risk prediction tools across ancestries for targeted prevention.
Publisher: Ovid Technologies (Wolters Kluwer Health)
Date: 04-08-2020
Abstract: Several small‐scale animal studies have suggested that gut microbiota and blood pressure (BP) are linked. However, results from human studies remain scarce and conflicting. We wanted to elucidate the multivariable‐adjusted association between gut metagenome and BP in a large, representative, well‐phenotyped population s le. We performed a focused analysis to examine the previously reported inverse associations between sodium intake and Lactobacillus abundance and between Lactobacillus abundance and BP. We studied a population s le of 6953 Finns aged 25 to 74 years (mean age, 49.2±12.9 years 54.9% women). The participants underwent a health examination, which included BP measurement, stool collection, and 24‐hour urine s ling (N=829). Gut microbiota was analyzed using shallow shotgun metagenome sequencing. In age‐ and sex‐adjusted models, the α (within‐s le) and β (between‐s le) ersities of taxonomic composition were strongly related to BP indexes ( P .001 for most). In multivariable‐adjusted models, β ersity was only associated with diastolic BP ( P =0.032). However, we observed significant, mainly positive, associations between BP indexes and 45 microbial genera ( P .05), of which 27 belong to the phylum Firmicutes . Interestingly, we found mostly negative associations between 19 distinct Lactobacillus species and BP indexes ( P .05). Of these, greater abundance of the known probiotic Lactobacillus paracasei was associated with lower mean arterial pressure and lower dietary sodium intake ( P .001 for both). Although the associations between overall gut taxonomic composition and BP are weak, in iduals with hypertension demonstrate changes in several genera. We demonstrate strong negative associations of certain Lactobacillus species with sodium intake and BP, highlighting the need for experimental studies.
Publisher: Ovid Technologies (Wolters Kluwer Health)
Date: 06-10-2020
Abstract: Epidemiological and animal studies have associated systemic inflammation with blood pressure (BP). However, the mechanistic factors linking inflammation and BP remain unknown. Fatty acid–derived eicosanoids serve as mediators of inflammation and have been suggested to regulate renal vascular tone, peripheral resistance, renin‐angiotensin system, and endothelial function. We hypothesize that specific proinflammatory and anti‐inflammatory eicosanoids are linked with BP. We studied a population s le of 8099 FINRISK 2002 participants randomly drawn from the Finnish population register (53% women mean age, 48±13 years) and, for external validation, a s le of 2859 FHS (Framingham Heart Study) Offspring study participants (55% women mean age, 66±9 years). Using nontargeted liquid chromatography–mass spectrometry, we profiled 545 distinct high‐quality eicosanoids and related oxylipin mediators in plasma. Adjusting for conventional hypertension risk factors, we observed 187 (34%) metabolites that were significantly associated with systolic BP ( P Bonferroni‐corrected threshold of 0.05/545). We used forward selection linear regression modeling in FINRISK to define a general formula for in idual eicosanoid risk score. In iduals of the top risk score quartile in FINRISK had a 9.0 (95% CI, 8.0–10.1) mm Hg higher systolic BP compared with in iduals in the lowest quartile in fully adjusted models. Observed metabolite associations were consistent across FINRISK and FHS. Plasma eicosanoids demonstrate strong associations with BP in the general population. As eicosanoid compounds affect numerous physiological processes that are central to BP regulation, they may offer new insights about the pathogenesis of hypertension, as well as serve as potential targets for therapeutic intervention.
Publisher: Cold Spring Harbor Laboratory
Date: 20-12-2022
DOI: 10.1101/2022.12.19.520774
Abstract: 16S rRNA and shotgun metagenomics studies typically yield different results, usually attributed to biases in PCR lification of 16S rRNA genes. Here, we introduce Greengenes2 and show that differences in reference phylogeny are more important. By inserting sequences into a whole-genome phylogeny, we show that 16S rRNA and shotgun metagenomic data generated from the same s les agree in principal coordinates space, taxonomy, and in phenotype effect size when analyzed with the same tree.
Publisher: Cold Spring Harbor Laboratory
Date: 12-10-2023
Publisher: Ovid Technologies (Wolters Kluwer Health)
Date: 18-10-2022
DOI: 10.1212/WNL.0000000000201006
Abstract: Current genome-wide association studies of ischemic stroke have focused primarily on late-onset disease. As a complement to these studies, we sought to identify the contribution of common genetic variants to risk of early-onset ischemic stroke. We performed a meta-analysis of genome-wide association studies of early-onset stroke (EOS), ages 18–59 years, using in idual-level data or summary statistics in 16,730 cases and 599,237 nonstroke controls obtained across 48 different studies. We further compared effect sizes at associated loci between EOS and late-onset stroke (LOS) and compared polygenic risk scores (PRS) for venous thromboembolism (VTE) between EOS and LOS. We observed genome-wide significant associations of EOS with 2 variants in ABO , a known stroke locus. These variants tag blood subgroups O1 and A1, and the effect sizes of both variants were significantly larger in EOS compared with LOS. The odds ratio (OR) for rs529565, tagging O1, was 0.88 (95% confidence interval [CI]: 0.85–0.91) in EOS vs 0.96 (95% CI: 0.92–1.00) in LOS, and the OR for rs635634, tagging A1, was 1.16 (1.11–1.21) for EOS vs 1.05 (0.99–1.11) in LOS p -values for interaction = 0.001 and 0.005, respectively. Using PRSs, we observed that greater genetic risk for VTE, another prothrombotic condition, was more strongly associated with EOS compared with LOS ( p = 0.008). The ABO locus, genetically predicted blood group A, and higher genetic propensity for venous thrombosis are more strongly associated with EOS than with LOS, supporting a stronger role of prothrombotic factors in EOS.
Publisher: Informa UK Limited
Date: 2021
Publisher: Cold Spring Harbor Laboratory
Date: 06-04-2021
DOI: 10.1101/2021.04.04.438427
Abstract: We introduce Operational Genomic Unit (OGU), a metagenome analysis strategy that directly exploits sequence alignment hits to in idual reference genomes as the minimum unit for assessing the ersity of microbial communities and their relevance to environmental factors. This approach is independent from taxonomic classification, granting the possibility of maximal resolution of community composition, and organizes features into an accurate hierarchy using a phylogenomic tree. The outputs are suitable for contemporary analytical protocols for community ecology, differential abundance and supervised learning while supporting phylogenetic methods, such as UniFrac and phylofactorization, that are seldomly applied to shotgun metagenomics despite being prevalent in 16S rRNA gene licon studies. As demonstrated in one synthetic and two real-world case studies, the OGU method produces biologically meaningful patterns from microbiome datasets. Such patterns further remain detectable at very low metagenomic sequencing depths. Compared with taxonomic unit-based analyses implemented in currently adopted metagenomics tools, and the analysis of 16S rRNA gene licon sequence variants, this method shows superiority in informing biologically relevant insights, including stronger correlation with body environment and host sex on the Human Microbiome Project dataset, and more accurate prediction of human age by the gut microbiomes in the Finnish population. We provide Woltka, a bioinformatics tool to implement this method, with full integration with the QIIME 2 package and the Qiita web platform, to facilitate OGU adoption in future metagenomics studies. Shotgun metagenomics is a powerful, yet computationally challenging, technique compared to 16S rRNA gene licon sequencing for decoding the composition and structure of microbial communities. However, current analyses of metagenomic data are primarily based on taxonomic classification, which is limited in feature resolution compared to 16S rRNA licon sequence variant analysis. To solve these challenges, we introduce Operational Genomic Units (OGUs), which are the in idual reference genomes derived from sequence alignment results, without further assigning them taxonomy. The OGU method advances current read-based metagenomics in two dimensions: (i) providing maximal resolution of community composition while (ii) permitting use of phylogeny-aware tools. Our analysis of real-world datasets shows several advantages over currently adopted metagenomic analysis methods and the finest-grained 16S rRNA analysis methods in predicting biological traits. We thus propose the adoption of OGU as standard practice in metagenomic studies.
Publisher: Springer Science and Business Media LLC
Date: 14-11-2018
DOI: 10.1038/S41588-018-0297-3
Abstract: In the version of this article originally published, the name of author Martin H. de Borst was coded incorrectly in the XML. The error has now been corrected in the HTML version of the paper.
Publisher: Cold Spring Harbor Laboratory
Date: 23-03-2022
DOI: 10.1101/2022.03.22.22272736
Abstract: The gut-lung axis is generally recognized, but there are few large studies of the gut microbiome and incident respiratory disease in adults. To investigate the associations between gut microbiome and respiratory disease and to construct predictive models from baseline gut microbiome profiles for incident asthma or chronic obstructive pulmonary disease (COPD). Shallow metagenomic sequencing was performed for stool s les from a prospective, population-based cohort (FINRISK02 N=7,115 adults) with linked national administrative health register derived classifications for incident asthma and COPD up to 15 years after baseline. Generalised linear models and Cox regressions were utilised to assess associations of microbial taxa and ersity with disease occurrence. Predictive models were constructed using machine learning with extreme gradient boosting. Models considered taxa abundances in idually and in combination with other risk factors, including sex, age, body mass index and smoking status. A total of 695 and 392 significant microbial associations at different taxonomic levels were found with incident asthma and COPD, respectively. Gradient boosting decision trees of baseline gut microbiome predicted incident asthma and COPD with mean area under the curves of 0.608 and 0.780, respectively. For both incident asthma and COPD, the baseline gut microbiome had C-indices of 0.623 for asthma and 0.817 for COPD, which were more predictive than other conventional risk factors. The integration of gut microbiome and conventional risk factors further improved prediction capacities. Subgroup analyses indicated gut microbiome was significantly associated with incident COPD in both current smokers and non-smokers, as well as in in iduals who reported never smoking. The gut microbiome is a significant risk factor for incident asthma and incident COPD and is largely independent of conventional risk factors.
Publisher: Springer Science and Business Media LLC
Date: 29-09-2017
DOI: 10.1038/S41467-017-00556-X
Abstract: There are few ex les of robust associations between rare copy number variants (CNVs) and complex continuous human traits. Here we present a large-scale CNV association meta-analysis on anthropometric traits in up to 191,161 adult s les from 26 cohorts. The study reveals five CNV associations at 1q21.1, 3q29, 7q11.23, 11p14.2, and 18q21.32 and confirms two known loci at 16p11.2 and 22q11.21, implicating at least one anthropometric trait. The discovered CNVs are recurrent and rare (0.01–0.2%), with large effects on height ( .4 cm), weight ( kg), and body mass index (BMI) ( .5 kg/m 2 ). Burden analysis shows a 0.41 cm decrease in height, a 0.003 increase in waist-to-hip ratio and increase in BMI by 0.14 kg/m 2 for each Mb of total deletion burden ( P = 2.5 × 10 −10 , 6.0 × 10 −5 , and 2.9 × 10 −3 ). Our study provides evidence that the same genes (e.g., MC4R , FIBIN , and FMO5 ) harbor both common and rare variants affecting body size and that anthropometric traits share genetic loci with developmental and psychiatric disorders.
Publisher: Cold Spring Harbor Laboratory
Date: 02-01-2020
DOI: 10.1101/2019.12.30.19015842
Abstract: The collection of fecal material and developments in sequencing technologies have enabled cost-efficient, standardized, and non-invasive gut microbiome profiling. As a result, microbiome composition data from several large cohorts have been cross-sectionally linked to various lifestyle factors and diseases. 1–5 In spite of these advances, prospective associations between microbiome composition and health have remained uncharacterized due to the lack of sufficiently large and representative population cohorts with comprehensive follow-up data. 6–8 Here, we analyse the long-term association between gut microbiome variation and mortality in a large, well-phenotyped, and representative population cohort ( n = 7211, FINRISK 2002 Finland). 9 We report specific taxonomic and functional signatures related to the Enterobacteriaceae family in the human gut microbiome that predict mortality during a 15-year follow-up. These associations can be observed both in the Eastern and Western Finns who have differing genetic backgrounds, lifestyles, and mortality rates. 10,11 Our results supplement previously reported cross-sectional associations, 1–4,12 and help to establish a methodological and conceptual basis for examining long-term associations between human gut microbiome composition, incident outcomes, and general health status. These findings could serve as a solid framework for microbiome profiling in clinical risk prediction, paving the way towards clinical applications of human microbiome sequencing aimed at prediction, prevention, and treatment of disease.
Publisher: Springer Science and Business Media LLC
Date: 03-06-2019
DOI: 10.1038/S41588-019-0449-0
Abstract: An amendment to this paper has been published and can be accessed via a link at the top of the paper.
No related grants have been discovered for Aki Havulinna.