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Liu Y, Ritchie SC, Teo SM, Ruuskanen MO, Kambur O, Zhu Q, Sanders J, Vázquez-Baeza Y, Verspoor K, Jousilahti P, Lahti L, Niiranen T, Salomaa V, Havulinna AS, Knight R, Méric G, Inouye M. Integration of polygenic and gut metagenomic risk prediction for common diseases. Nat Aging 2024; 4:584-594. [PMID: 38528230 PMCID: PMC11031402 DOI: 10.1038/s43587-024-00590-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Accepted: 02/13/2024] [Indexed: 03/27/2024]
Abstract
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.
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Affiliation(s)
- Yang Liu
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia.
- Department of Clinical Pathology, Melbourne Medical School, University of Melbourne, Melbourne, Victoria, Australia.
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK.
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
| | - Scott C Ritchie
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Cambridge Centre of Research Excellence, School of Clinical Medicine, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
| | - Shu Mei Teo
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
- Centre for Youth Mental Health, University of Melbourne, Melbourne, Victoria, Australia
| | - Matti O Ruuskanen
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
- Department of Computing, University of Turku, Turku, Finland
| | - Oleg Kambur
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Qiyun Zhu
- School of Life Sciences, Arizona State University, Tempe, AZ, USA
- Biodesign Center for Fundamental and Applied Microbiomics, Arizona State University, Tempe, AZ, USA
| | - Jon Sanders
- Department of Ecology and Evolutionary Biology, Cornell University, Ithaca, NY, USA
| | - Yoshiki Vázquez-Baeza
- Center for Microbiome Innovation, Jacobs School of Engineering, University of California San Diego, La Jolla, CA, USA
| | - Karin Verspoor
- School of Computing Technologies, RMIT University, Melbourne, Victoria, Australia
- School of Computing and Information Systems, University of Melbourne, Melbourne, Victoria, Australia
| | - Pekka Jousilahti
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Leo Lahti
- Department of Computing, University of Turku, Turku, Finland
| | - Teemu Niiranen
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
- Division of Medicine, Turku University Hospital and University of Turku, Turku, Finland
| | - Veikko Salomaa
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Aki S Havulinna
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
- Institute for Molecular Medicine Finland, FIMM-HiLIFE, University of Helsinki, Helsinki, Finland
| | - Rob Knight
- Center for Microbiome Innovation, Jacobs School of Engineering, University of California San Diego, La Jolla, CA, USA
- Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, USA
- Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA
| | - Guillaume Méric
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
- Central Clinical School, Monash University, Melbourne, Victoria, Australia
- Department of Cardiometabolic Health, University of Melbourne, Melbourne, Victoria, Australia
- Department of Cardiovascular Research, Translation and Implementation, La Trobe University, Melbourne, Victoria, Australia
- Department of Medical Sciences, Molecular Epidemiology, Uppsala University, Uppsala, Sweden
| | - Michael Inouye
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia.
- Department of Clinical Pathology, Melbourne Medical School, University of Melbourne, Melbourne, Victoria, Australia.
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK.
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
- British Heart Foundation Cambridge Centre of Research Excellence, School of Clinical Medicine, University of Cambridge, Cambridge, UK.
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK.
- The Alan Turing Institute, London, UK.
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Koponen K, Kambur O, Joseph B, Ruuskanen MO, Jousilahti P, Salido R, Brennan C, Jain M, Meric G, Inouye M, Lahti L, Niiranen T, Havulinna AS, Knight R, Salomaa V. Role of Gut Microbiota in Statin-Associated New-Onset Diabetes-A Cross-Sectional and Prospective Analysis of the FINRISK 2002 Cohort. Arterioscler Thromb Vasc Biol 2024; 44:477-487. [PMID: 37970720 PMCID: PMC10805357 DOI: 10.1161/atvbaha.123.319458] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Accepted: 10/31/2023] [Indexed: 11/17/2023]
Abstract
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 (β=0.37; SE=0.13; q=0.02) and the strongest negative association with Bacteroides cellulosilyticus (β=-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.
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Affiliation(s)
- Kari Koponen
- Finnish Institute for Health and Welfare (THL), Helsinki, Finland (K.K., O.K., B.J., P.J., T.N., A.S.H., V.S.)
| | - Oleg Kambur
- Finnish Institute for Health and Welfare (THL), Helsinki, Finland (K.K., O.K., B.J., P.J., T.N., A.S.H., V.S.)
| | - Bijoy Joseph
- Finnish Institute for Health and Welfare (THL), Helsinki, Finland (K.K., O.K., B.J., P.J., T.N., A.S.H., V.S.)
| | | | - Pekka Jousilahti
- Finnish Institute for Health and Welfare (THL), Helsinki, Finland (K.K., O.K., B.J., P.J., T.N., A.S.H., V.S.)
| | - Rodolfo Salido
- Department of Pediatrics (R.S., C.B., R.K.), University of California San Diego, La Jolla
- Department of Bioengineering (R.S., R.K.), University of California San Diego, La Jolla
| | - Caitriona Brennan
- Department of Pediatrics (R.S., C.B., R.K.), University of California San Diego, La Jolla
| | - Mohit Jain
- Department of Medicine and Pharmacology (M.J.), University of California San Diego, La Jolla
| | - Guillaume Meric
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Australia (G.M., M.I.)
- Department of Infectious Diseases, Central Clinical School, Monash University, Melbourne, Australia (G.M.)
| | - Michael Inouye
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Australia (G.M., M.I.)
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, United Kingdom (M.I.)
| | - Leo Lahti
- Department of Computing, University of Turku, Finland (M.O.R., L.L.)
| | - Teemu Niiranen
- Finnish Institute for Health and Welfare (THL), Helsinki, Finland (K.K., O.K., B.J., P.J., T.N., A.S.H., V.S.)
- Department of Medicine, Turku University Hospital and University of Turku, Finland (T.N.)
| | - Aki S. Havulinna
- Finnish Institute for Health and Welfare (THL), Helsinki, Finland (K.K., O.K., B.J., P.J., T.N., A.S.H., V.S.)
- Institute for Molecular Medicine Finland, FiMM-HiLIFE, Helsinki, Finland (A.S.H.)
| | - Rob Knight
- Department of Pediatrics (R.S., C.B., R.K.), University of California San Diego, La Jolla
- Department of Bioengineering (R.S., R.K.), University of California San Diego, La Jolla
- Department of Computer Science and Engineering (R.K.), University of California San Diego, La Jolla
- Center for Microbiome Innovation (R.K.), University of California San Diego, La Jolla
| | - Veikko Salomaa
- Finnish Institute for Health and Welfare (THL), Helsinki, Finland (K.K., O.K., B.J., P.J., T.N., A.S.H., V.S.)
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Ruuskanen MO, Vats D, Potbhare R, RaviKumar A, Munukka E, Ashma R, Lahti L. Towards standardized and reproducible research in skin microbiomes. Environ Microbiol 2022; 24:3840-3860. [PMID: 35229437 PMCID: PMC9790573 DOI: 10.1111/1462-2920.15945] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 02/15/2022] [Accepted: 02/16/2022] [Indexed: 12/30/2022]
Abstract
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.
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Affiliation(s)
- Matti O. Ruuskanen
- Department of Computing, Faculty of TechnologyUniversity of TurkuTurkuFinland
| | - Deepti Vats
- Department of Zoology, Centre of Advanced StudySavitribai Phule Pune UniversityPuneIndia
| | - Renuka Potbhare
- Department of Zoology, Centre of Advanced StudySavitribai Phule Pune UniversityPuneIndia
| | - Ameeta RaviKumar
- Institute of Bioinformatics and BiotechnologySavitribai Phule Pune UniversityPuneIndia
| | - Eveliina Munukka
- Microbiome Biobank, Institute of BiomedicineUniversity of TurkuTurkuFinland
| | - Richa Ashma
- Department of Zoology, Centre of Advanced StudySavitribai Phule Pune UniversityPuneIndia
| | - Leo Lahti
- Department of Computing, Faculty of TechnologyUniversity of TurkuTurkuFinland
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Ruuskanen MO, Erawijantari PP, Havulinna AS, Liu Y, Méric G, Tuomilehto J, Inouye M, Jousilahti P, Salomaa V, Jain M, Knight R, Lahti L, Niiranen TJ. Gut Microbiome Composition Is Predictive of Incident Type 2 Diabetes in a Population Cohort of 5,572 Finnish Adults. Diabetes Care 2022; 45:811-818. [PMID: 35100347 PMCID: PMC9016732 DOI: 10.2337/dc21-2358] [Citation(s) in RCA: 37] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Accepted: 01/05/2022] [Indexed: 02/03/2023]
Abstract
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.
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Affiliation(s)
- Matti O. Ruuskanen
- Department of Computing, University of Turku, Turku, Finland
- Corresponding author: Matti O. Ruuskanen,
| | | | - Aki S. Havulinna
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Science, Helsinki, Finland
| | - Yang Liu
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
- Department of Clinical Pathology, Melbourne Medical School, University of Melbourne, Melbourne, Victoria, Australia
| | - Guillaume Méric
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
- Department of Infectious Diseases, Central Clinical School, Monash University, Melbourne, Victoria, Australia
| | - Jaakko Tuomilehto
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
- Department of Public Health, University of Helsinki, Helsinki, Finland
- Saudi Diabetes Research Group, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Michael Inouye
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
- Department of Public Health and Primary Care, Cambridge University, Cambridge, U.K
| | - Pekka Jousilahti
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Veikko Salomaa
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Mohit Jain
- Department of Medicine, University of California San Diego, La Jolla, CA
- Department of Pharmacology, University of California San Diego, La Jolla, CA
| | - Rob Knight
- Jacobs School of Engineering, University of California San Diego, La Jolla, CA
- Center for Microbiome Innovation, University of California San Diego, La Jolla, CA
- Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA
- Department of Computer Science & Engineering, University of California San Diego, La Jolla, CA
| | - Leo Lahti
- Department of Computing, University of Turku, Turku, Finland
| | - Teemu J. Niiranen
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
- Division of Medicine, Turku University Hospital, Turku, Finland
- Department of Internal Medicine, University of Turku, Turku, Finland
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Palmu J, Tikkanen E, Havulinna AS, Vartiainen E, Lundqvist A, Ruuskanen MO, Perola M, Ala-Korpela M, Jousilahti P, Würtz P, Salomaa V, Lahti L, Niiranen T. Comprehensive biomarker profiling of hypertension in 36 985 Finnish individuals. J Hypertens 2022; 40:579-587. [PMID: 34784307 PMCID: PMC8815836 DOI: 10.1097/hjh.0000000000003051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Revised: 11/04/2021] [Accepted: 11/04/2021] [Indexed: 11/30/2022]
Abstract
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 ± 14.2; 53.1% women) and longitudinal (N = 4197; age 49.4 ± 11.8, 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.
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Affiliation(s)
- Joonatan Palmu
- Department of Medicine, Turku University Hospital and University of Turku, Turku
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare
| | | | - Aki S. Havulinna
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, Helsinki
| | - Erkki Vartiainen
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare
| | - Annamari Lundqvist
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare
| | | | - Markus Perola
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare
- Estonian Genome Center, University of Tartu, Tartu, Estonia
- Institute for Molecular Medicine, University of Helsinki, Helsinki
| | - Mika Ala-Korpela
- Computational Medicine, Faculty of Medicine, University of Oulu and Biocenter Oulu
- Center for Life Course Health Research, University of Oulu, Oulu
- NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio, Finland
| | - Pekka Jousilahti
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare
| | | | - Veikko Salomaa
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare
| | - Leo Lahti
- Department of Computing, University of Turku, Turku, Finland
| | - Teemu Niiranen
- Department of Medicine, Turku University Hospital and University of Turku, Turku
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare
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6
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Koponen KK, Salosensaari A, Ruuskanen MO, Havulinna AS, Männistö S, Jousilahti P, Palmu J, Salido R, Sanders K, Brennan C, Humphrey GC, Sanders JG, Meric G, Cheng S, Inouye M, Jain M, Niiranen TJ, Valsta LM, Knight R, Salomaa VV. Associations of healthy food choices with gut microbiota profiles. Am J Clin Nutr 2021; 114:605-616. [PMID: 34020448 PMCID: PMC8326043 DOI: 10.1093/ajcn/nqab077] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Accepted: 02/25/2021] [Indexed: 02/06/2023] Open
Abstract
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 (β/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.
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Affiliation(s)
- Kari K Koponen
- Department of Food and Nutrition, University of Helsinki, Helsinki, Finland
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Aaro Salosensaari
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Turku, Finland
- Department of Future Technologies, University of Turku, Turku, Finland
| | - Matti O Ruuskanen
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
- Department of Medicine, Turku University Hospital and University of Turku, Turku, Finland
| | - Aki S Havulinna
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
- Institute for Molecular Medicine Finland, Helsinki, Finland
| | - Satu Männistö
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Pekka Jousilahti
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Joonatan Palmu
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Turku, Finland
- Department of Medicine, Turku University Hospital and University of Turku, Turku, Finland
| | - Rodolfo Salido
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
| | - Karenina Sanders
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
| | - Caitriona Brennan
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
| | - Gregory C Humphrey
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
| | - Jon G Sanders
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
- Cornell Institute for Host-Microbe Interaction and Disease, Cornell University, Ithaca, NY, USA
| | - Guillaume Meric
- Department of Infectious Diseases, Central Clinical School, Monash University, Melbourne, Australia
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Australia
| | - Susan Cheng
- Division of Cardiology, Brigham and Women's Hospital, Boston, MA, USA
- Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Michael Inouye
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Australia
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
| | - Mohit Jain
- Departments of Medicine and Pharmacology, University of California San Diego, La Jolla, CA, USA
| | - Teemu J Niiranen
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Turku, Finland
- Department of Medicine, Turku University Hospital and University of Turku, Turku, Finland
| | - Liisa M Valsta
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Rob Knight
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
| | - Veikko V Salomaa
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
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7
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Ruuskanen MO, Sommeria-Klein G, Havulinna AS, Niiranen TJ, Lahti L. Modelling spatial patterns in host-associated microbial communities. Environ Microbiol 2021; 23:2374-2388. [PMID: 33734553 DOI: 10.1111/1462-2920.15462] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Revised: 03/02/2021] [Accepted: 03/11/2021] [Indexed: 12/12/2022]
Abstract
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.
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Affiliation(s)
- Matti O Ruuskanen
- Department of Internal Medicine, University of Turku, Turku, Finland.,Finnish Institute for Health and Welfare, Helsinki, Finland
| | | | - Aki S Havulinna
- Finnish Institute for Health and Welfare, Helsinki, Finland.,Institute for Molecular Medicine Finland, FIMM-HiLIFE, Helsinki, Finland
| | - Teemu J Niiranen
- Department of Internal Medicine, University of Turku, Turku, Finland.,Finnish Institute for Health and Welfare, Helsinki, Finland.,Division of Medicine, Turku University Hospital, Turku, Finland
| | - Leo Lahti
- Department of Computing, University of Turku, Turku, Finland
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8
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Ruuskanen MO, Åberg F, Männistö V, Havulinna AS, Méric G, Liu Y, Loomba R, Vázquez-Baeza Y, Tripathi A, Valsta LM, Inouye M, Jousilahti P, Salomaa V, Jain M, Knight R, Lahti L, Niiranen TJ. Links between gut microbiome composition and fatty liver disease in a large population sample. Gut Microbes 2021; 13:1-22. [PMID: 33651661 PMCID: PMC7928040 DOI: 10.1080/19490976.2021.1888673] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/17/2020] [Revised: 01/14/2021] [Accepted: 01/28/2021] [Indexed: 02/08/2023] Open
Abstract
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.
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Affiliation(s)
- Matti O. Ruuskanen
- Department of Internal Medicine, University of Turku, Turku, Finland
- Department of Public Health Solutions, Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Fredrik Åberg
- Transplantation and Liver Surgery Clinic, Helsinki University Hospital, University of Helsinki, Helsinki, Finland
- Transplant Institute, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Ville Männistö
- Department of Medicine, Kuopio University Hospital, University of Eastern Finland, Kuopio, Finland
- Department of Experimental Vascular Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Aki S. Havulinna
- Department of Public Health Solutions, Finnish Institute for Health and Welfare, Helsinki, Finland
- Institute for Molecular Medicine Finland, FIMM - HiLIFE, Helsinki, Finland
| | - Guillaume Méric
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
- Department of Infectious Diseases, Central Clinical School, Monash University, Melbourne, Victoria, Australia
| | - Yang Liu
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
- Department of Clinical Pathology, The University of Melbourne, Melbourne, Victoria, Australia
| | - Rohit Loomba
- Department of Medicine, NAFLD Research Center, La Jolla, CA, USA
- Department of Medicine, University of California San Diego, La Jolla, CA, USA
| | - Yoshiki Vázquez-Baeza
- Center for Microbiome Innovation, Jacobs School of Engineering, University of California San Diego, La Jolla, CA, USA
| | - Anupriya Tripathi
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA
- Division of Biological Sciences, University of California San Diego, La Jolla, CA, USA
| | - Liisa M. Valsta
- Department of Public Health Solutions, Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Michael Inouye
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
- Department of Public Health and Primary Care, Cambridge University, Cambridge, UK
| | - Pekka Jousilahti
- Department of Public Health Solutions, Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Veikko Salomaa
- Department of Public Health Solutions, Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Mohit Jain
- Department of Medicine, University of California San Diego, La Jolla, CA, USA
- Department of Pharmacology, University of California San Diego, La Jolla, California, USA
| | - Rob Knight
- Center for Microbiome Innovation, Jacobs School of Engineering, University of California San Diego, La Jolla, CA, USA
- Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, California, USA
- Department of Computer Science & Engineering, University of California San Diego, La Jolla, California, USA
| | - Leo Lahti
- Deparment of Computing, University of Turku, Turku, Finland
| | - Teemu J. Niiranen
- Department of Internal Medicine, University of Turku, Turku, Finland
- Department of Public Health Solutions, Finnish Institute for Health and Welfare, Helsinki, Finland
- Division of Medicine, Turku University Hospital, Turku, Finland
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9
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Colby GA, Ruuskanen MO, St.Pierre KA, St.Louis VL, Poulain AJ, Aris-Brosou S. Warming Climate Is Reducing the Diversity of Dominant Microbes in the Largest High Arctic Lake. Front Microbiol 2020; 11:561194. [PMID: 33133035 PMCID: PMC7579425 DOI: 10.3389/fmicb.2020.561194] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2020] [Accepted: 08/28/2020] [Indexed: 11/13/2022] Open
Abstract
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.
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Affiliation(s)
- Graham A. Colby
- Department of Biology, University of Ottawa, Ottawa, ON, Canada
| | | | - Kyra A. St.Pierre
- Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada
| | - Vincent L. St.Louis
- Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada
| | | | - Stéphane Aris-Brosou
- Department of Biology, University of Ottawa, Ottawa, ON, Canada
- Department of Mathematics and Statistics, University of Ottawa, Ottawa, ON, Canada
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Ruuskanen MO, St Pierre KA, St Louis VL, Aris-Brosou S, Poulain AJ. Physicochemical Drivers of Microbial Community Structure in Sediments of Lake Hazen, Nunavut, Canada. Front Microbiol 2018; 9:1138. [PMID: 29922252 PMCID: PMC5996194 DOI: 10.3389/fmicb.2018.01138] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2018] [Accepted: 05/14/2018] [Indexed: 11/13/2022] Open
Abstract
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.
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Affiliation(s)
| | - Kyra A St Pierre
- Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada
| | - Vincent L St Louis
- Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada
| | - Stéphane Aris-Brosou
- Department of Biology, University of Ottawa, Ottawa, ON, Canada.,Department of Mathematics and Statistics, University of Ottawa, Ottawa, ON, Canada
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