Mika Ala-Korpela, Ph.D.
Johannes Kettunen, Ph.D.
University of Oulu, Biocenter Oulu, Faculty of Medicine, Computational Medicine
Notable advances have taken place in the understanding and treatment of cardiometabolic diseases (CMDs). However, they remain the leading cause of morbidity and mortality worldwide. Risk assessment at early stages, where the disease is still reversible, would allow targeted primary prevention. Identification of those individuals that would benefit from life-style decisions or preventive medication is the key for cost-effective healthcare to prevent disease development and the associated societal cost burden. However, established risk factors – dyslipidaemia, smoking, hypertension and diabetes – often fail to identify those who will eventually develop, for example, coronary heart disease. Our work in this project aims to improve individual (cardio)metabolic risk assessment via high-throughput NMR-based metabolic profiling of serum samples in extensive population cohorts at unprecedented sample-sizes. The population-scale of the project allows us to go beyond biomarker discovery.
The key aim is to evaluate CMD risk models in various metabolic strata, e.g., men and women, obese individuals, people with low LDL cholesterol or people grouped on the basis of their comprehensive systemic metabolic profile. Our recent findings have shown that biomarkers and risk models of clinical outcome differ in regard to metabolic status and specific CMD outcomes, e.g., heart attack and stroke. The high-throughput serum NMR metabolomics platform enables cost-effective production of individual systemic metabolic profiles with around 250 measures, unique in large-scale epidemiology. The research will involve record numbers of individuals – the abovementioned metabolomics platform has already been applied to analyse almost 500,000 blood samples. The work is supported by international leaders in the field of cardiometabolic science via collaboration and data from population-based epidemiological cohorts and clinical trials. This is an exceptional international endeavour of high scientific and societal importance.
During the past ten years the Team has developed a novel fully automated NMR metabolomics platform for comprehensive metabolite quantification of human serum and plasma. This platform has been used to profile almost 500,000 blood samples and it has been utilised to identify and replicate novel biomarkers of future CVD, diabetes and mortality. Data from the platform have further been used to elucidate the genetic architecture of metabolism and to characterise cardiovascular risk factors and metabolic health (the Team’s up-to-date publication list is at http://computationalmedicine.fi/publications). The serum NMR metabolomics platform provides quantitative molecular data on ~250 metabolic measures, including 14 lipoprotein subclasses, their lipid concentrations and composition, apolipoprotein A-I and B, multiple cholesterol and triglyceride measures, albumin, various fatty acids as well as numerous low-molecular-weight metabolites, including amino acids, glycolysis-related measures and ketone bodies (http://computationalmedicine.fi/platform). Metabolites are quantified in physiological units (e.g. mmol/L), which enhances interpretability and is essential for applications in genetics, epidemiology and medicine. All commercial IPRs of the methodology belong currently to a spin-off company, Brainshake Ltd. (http://www.brainshake.fi) The company is expanding the metabolic coverage of the platform by adding ~20–30 metabolic measures, including additional amino acids and some other low-molecular-weight metabolites. They are also working for official laboratory accreditation and a medical device CE marking.
Metabolic profiling captures an individual’s metabolic status across multiple molecular pathways. The technological advances have made metabolite profiling of blood samples feasible and affordable for those dealing with large epidemiological cohorts and biobanks. The quantification of amino acids, fatty acids, glycolysis metabolites and lipoprotein subclasses is possible, along with the standard clinical lipids. Examination of all these metabolic measures in relation to cardiovascular disease incidence in a recent large study revealed novel biomarkers and improved understanding of the underlying mechanisms (Würtz P, et al. Circulation 131:774-785, 2015). The wide metabolic coverage also resulted in improvement in the prediction of cardiovascular risk in comparison with the use of established risk factors. In this recent study we applied our high-throughput NMR metabolomics platform to profile three population-based cohorts of 13,441 individuals with 12 to 23 years of follow-up (with 1,740 incident events). Thirty-three of the 68 metabolic measures analysed were associated with incident cardiovascular disease. In analyses adjusted for standard lipids, we identified circulating phenylalanine and monounsaturated fatty acids as novel biomarkers of higher cardiovascular risk. Omega-3 and omega-6 fatty acids were associated with lower cardiovascular disease risk. In combination, the four biomarkers improved cardiovascular risk assessment, in particular for persons classified as being at intermediate risk, based on established risk factors. Thus, this study substantiated the fact that high-throughput metabolite profiling is a powerful method to uncover novel cardiovascular biomarkers and enhance risk prediction. Further studies with more detailed patient stratification and more specific end points are underway in this project to evaluate the prospects for clinical use. Because the metabolomics platform quantifies amino acids and fatty acids simultaneously with the standard lipids, these novel biomarkers could be implemented to augment risk prediction without a need for additional clinical chemistry.
We have already illustrated that combining genetic and metabolomics data is a powerful way to discover genetic determinants underlying systemic metabolism. We have just completed our second large genome-wide association study (GWAS) on genetic influences on circulating metabolic traits quantified by our metabolomics platform from almost 25,000 individuals. In this study we identified eight novel genetic loci for amino acids, pyruvate and fatty acids (Kettunen J, et al., Nature Communications 7:11122, 2016). This work was a continuation of the first metabolomics GWAS, which was carried out by J. Kettunen and co-workers (Nature Genetics 44:269-276, 2012).
The combination of gene expression data and circulating biomarkers provides a systems biological view of disease processes. We have also finalised a concept study of how mechanistic understanding of a biomarker can be improved by such an approach (Ritchie SC, et al. Cell Systems 1:293-301, 2015). We studied our previously discovered biomarker for all-cause mortality, glycoprotein acetylation (GlycA), to uncover the mechanism behind increased mortality risk. In this work we leveraged multiple omics data and discovered a link between GlycA and premature death in >10,000 individuals across three population-based cohorts together with hospitalization and death records. One of our findings was that elevated levels of GlycA correspond to a higher state of chronic inflammation in the body. GlycA was associated with various immune signalling molecules including interleukins and other inflammatory mediators indicating low level chronic inflammation. We also found that high blood levels of GlycA were strongly linked to incident hospitalization and death from severe infections. These results are likely to form the foundation for forthcoming studies involving investigation of the role of GlycA in the human body. Similar approaches are to be used to improve our molecular understanding of processes behind new CMD biomarkers that are emerging from this project.
Statins have become first-line therapy for cardiovascular prevention, making them the most widely prescribed drug class worldwide. However, their systemic effects across lipoprotein subclasses, fatty acids and circulating metabolites remain incompletely characterized. In large international research collaboration, led by the Computational Medicine Research Team, we determined comprehensive metabolic effects of statin therapy by conducting metabolic profiling at two time-points in four population-based cohorts. To verify that the observed lipoprotein, fatty acid and metabolic changes were due to the effects of statins, the results were corroborated via Mendelian randomization by using a genetic variant in the HMGCR gene as a proxy for the pharmacological action of statins. Metabolic profiling of statin use in longitudinal cohorts uncovered an intricate association pattern of circulating lipoprotein, fatty acid and metabolite changes, which add to our understanding of the LDL-C independent effects of statins. The exquisite match between the metabolic association patterns from observational and genetic analyses serves as a proof-of-concept illustrating how the combination of metabolomics and genetic proxies for drug mechanisms can facilitate the assessment of pharmacological action and on‑target effects for known therapies and novel drug targets. The insights into an extensively studied therapeutic illustrate how metabolomics, combined with genetic proxies mimicking pharmacological action, can elucidate the molecular effects of known targets, clarify treatment indication and potentially be used to inform drug development. As extensive metabolomics and genetic data are becoming increasingly available in large biobanks, we anticipate that comprehensive molecular profiling will prove as a cost-effective solution to augment drug development in both pre-clinical and clinical trial stages. This work was published in the Journal of the American College of Cardiology (67:1200-1210, 2016) and also accompanied by an Editorial entitled Pharmacometabolomics meets genetics (67:1211–1213, 2016).
In October 2016 International Journal of Epidemiology published a Themed Issue on Metabolic Phenotyping in Epidemiology, co-edited by Prof Mika Ala-Korpela and Prof George Davey Smith, who also wrote the Editorial entitled Metabolic profiling – multitude of technologies with great research potential, but (when) will translation emerge? (45:1311-1316, 2016). In this Themed Issue the Team published also 4 original contributions. These were fused on the effects of hormonal contraception on systemic metabolism (45:1445-1457, 2016), on the metabolic profiling of alcohol consumption (45:1493-1506, 2016), on the characterization of the metabolic profile associated with serum 25-hydroxyvitamin D (45, 1469-1481, 2016) and on the metabolic signatures of birth weight in adolescents and adults (45:1539-1550, 2016) – 3 of these works were led by the Team.
A specific short-term goal in this particular project is the third round of a metabolic GWAS that will include over 150,000 individuals with comprehensive systemic metabolite, fatty acid and lipoprotein data from our platform as well as GW data. This work will obviously involve collaboration with multiple laboratories in Europe. We will also pay more and more attention to causality and will be increasingly using the Mendelian randomization framework to infer causality between biomarkers and disease outcomes. The new genetic and causal molecular information to be obtained is central in connecting biomarker levels and pathways to the development of atherosclerosis, thereby improving individual CVD risk assessment.
In general, integration of complementary omics platforms provides unprecedented biological details of underlying disease risk and is leading to a conceptual shift in disease risk assessment from individual biomarkers to multi-marker profiles. Ultimately, the application and value of new biomarkers and profiles will depend on their predictive power vs. traditional risk assessment, on their reproducibility in multiple cohorts and on the practicalities and cost-effectiveness of their integration into clinical routines and laboratories. Our recent results show that combining multiple omics platforms will take us from biomarker discovery to understanding disease processes that the biomarkers are descriptive of. Such detailed biological information will likely aid in designing treatment strategies and improve healthcare in the future.
Five selected key publications in 2016 led by the Team are given below; please see http://computationalmedicine.fi/publications for an up-to-date list of Team’s all publications.
Würtz P, Wang Q, Soininen P, Kangas AJ, Fatemifar G, Tynkkynen T, Tiainen M, Perola M, Tillin T, Hughes AD, Mäntyselkä P, Kähönen M, Lehtimäki T, Sattar N, Hingorani AD, Casas JP, Salomaa V, Kivimäki M, Järvelin MR, Davey Smith G, Vanhala M, Lawlor DA, Raitakari OT, Chaturvedi N, Kettunen J, Ala-Korpela M. Metabolomic profiling of statin use and genetic inhibition of HMG-CoA reductase. J Am Coll Cardiol 2016 Mar 15;67(10):1200-10.
In relation to this paper, JACC published an editorial by D. Voora & S. H. Shah, entitled Pharmacometabolomics meets genetics; Journal of the American College of Cardiology, 67, 1211–1213, 2016.
Statins have become first-line therapy for cardiovascular prevention, making them the most widely prescribed drug class worldwide. However, their systemic effects across lipoprotein subclasses, fatty acids and circulating metabolites remain incompletely characterized. In this work we determined comprehensive metabolic effects of statin therapy by conducting metabolic profiling at two time-points in four population-based cohorts. To verify that the observed lipoprotein, fatty acid and metabolic changes were due to the effects of statins, the results were corroborated via Mendelian randomization by using a genetic variant in the HMGCR gene as a proxy for the pharmacological action of statins. Metabolic profiling of statin use in longitudinal cohorts uncovered an intricate association pattern of circulating lipoprotein, fatty acid and metabolite changes, which add to our understanding of the LDL-C independent effects of statins. The exquisite match between the metabolic association patterns from observational and genetic analyses serves as a proof-of-concept illustrating how the combination of metabolomics and genetic proxies for drug mechanisms can facilitate the assessment of pharmacological action and on-target effects for known therapies and novel drug targets. The insights into an extensively studied therapeutic illustrate how metabolomics, combined with genetic proxies mimicking pharmacological action, can elucidate the molecular effects of known targets, clarify treatment indication and potentially be used to inform drug development. As extensive metabolomics and genetic data are becoming increasingly available in large biobanks, we anticipate that comprehensive molecular profiling will prove as a cost-effective solution to augment drug development in both pre-clinical and clinical trial stages.
Kettunen J, Demirkan A, Würtz P, Draisma HH, Haller T, Rawal R, Vaarhorst A, Kangas AJ, Lyytikäinen LP, Pirinen M, Pool R, Sarin AP, Soininen P, Tukiainen T, Wang Q, Tiainen M, Tynkkynen T, Amin N, Zeller T, Beekman M, Deelen J, van Dijk KW, Esko T, Hottenga JJ, van Leeuwen EM, Lehtimäki T, Mihailov E, Rose RJ, de Craen AJ, Gieger C, Kähönen M, Perola M, Blankenberg S, Savolainen MJ, Verhoeven A, Viikari J, Willemsen G, Boomsma DI, van Duijn CM, Eriksson J, Jula A, Järvelin MR, Kaprio J, Metspalu A, Raitakari O, Salomaa V, Slagboom PE, Waldenberger M, Ripatti S, Ala-Korpela M. Genome-wide study for circulating metabolites identifies 62 loci and reveals novel systemic effects of LPA. Nat Commun. 2016 Mar 23;7:11122.
Wang Q, Würtz P, Auro K, Morin-Papunen L, Kangas AJ, Soininen P, Tiainen M, Tynkkynen T, Joensuu A, Havulinna AS, Aalto K, Salmi M, Blankenberg S, Zeller T, Viikari J, Kähönen M, Lehtimäki T, Salomaa V, Jalkanen S, Järvelin MR, Perola M, Raitakari OT, Lawlor DA, Kettunen J, Ala-Korpela M. Effects of hormonal contraception on systemic metabolism: cross-sectional and longitudinal evidence. Int J Epidemiol. 2016 Oct;45(5):1445-1457.
Würtz P, Wang Q, Niironen M, Tynkkynen T, Tiainen M, Drenos F, Kangas AJ, Soininen P, Skilton MR, Heikkilä K, Pouta A, Kähönen M, Lehtimäki T, Rose RJ, Kajantie E, Perola M, Kaprio J, Eriksson JG, Raitakari OT, Lawlor DA, Davey Smith G, Järvelin MR, Ala-Korpela M, Auro K. Metabolic signatures of birthweight in 18 288 adolescents and adults. Int J Epidemiol. 2016 Oct;45(5):1539-1550.
Wang Q, Würtz P, Auro K, Mäkinen VP, Kangas AJ, Soininen P, Tiainen M, Tynkkynen T, Jokelainen J, Santalahti K, Salmi M, Blankenberg S, Zeller T, Viikari J, Kähönen M, Lehtimäki T, Salomaa V, Perola M, Jalkanen S, Järvelin MR, Raitakari OT, Kettunen J, Lawlor DA, Ala-Korpela M. Metabolic profiling of pregnancy: cross-sectional and longitudinal evidence. BMC Med. 2016 Dec 13;14(1):205.
Individual biographies can be found at http://computationalmedicine.fi/teamandorganisation.
Aho V, Ollila HM, Kronholm E, Bondia-Pons I, Soininen P, Kangas AJ, et al (incl Ala-Korpela M). Prolonged sleep restriction induces changes in pathways involved in cholesterol metabolism and inflammatory responses. Sci Rep 22;6:24828, 2016.
Ala-Korpela M, Davey Smith G. Metabolic profiling-multitude of technologies with great research potential, but (when) will translation emerge? Int J Epidemiol 45(5):1311–8, 2016.
Ala-Korpela M. Metabolomics in cardiovascular medicine: Not personalised, not diagnostic. Eur J Prev Cardiol 23(17):1821–2, 2016.
Beaney KE, Cooper JA, McLachlan S, Wannamethee SG, Jefferis BJ, Whincup P, et al (incl Ala-Korpela M). Variant rs10911021 that associates with coronary heart disease in type 2 diabetes, is associated with lower concentrations of circulating HDL cholesterol and large HDL particles but not with amino acids. Cardiovasc Diabetol 15(1):115, 2016.
Bogl LH, Kaye SM, Rämö JT, Kangas AJ, Soininen P, Hakkarainen A, et al (incl Ala-Korpela M). Abdominal obesity and circulating metabolites: A twin study approach. Metabolism 65(3):111–21, 2016.
Cichonska A, Rousu J, Marttinen P, Kangas AJ, Soininen P, Lehtimäki T, et al (incl Ala-Korpela M). metaCCA: summary statistics-based multivariate meta-analysis of genome-wide association studies using canonical correlation analysis. Bioinformatics 1;32(13):1981–9, 2016.
Drenos F, Davey Smith G, Ala-Korpela M, Kettunen J, Würtz P, Soininen P, et al. Metabolic Characterization of a Rare Genetic Variation Within APOC3 and Its Lipoprotein Lipase-Independent Effects. Circ Cardiovasc Genet 9(3):231–9, 2016.
Gillberg J, Marttinen P, Pirinen M, Kangas AJ, Soininen P, Ali M, et al (incl Ala-Korpela M). Multiple Output Regression with Latent Noise. J Mach Learn Res 17:1–35, 2016.
Kaikkonen JE, Kresanov P, Ahotupa M, Jula A, Mikkilä V, Viikari JSA, et al (incl Ala-Korpela M, Soininen P). Longitudinal study of circulating oxidized LDL and HDL and fatty liver: the Cardiovascular Risk in Young Finns Study. Free Radic Res 50(4):396–404, 2016.
Kaikkonen JE, Würtz P, Suomela E, Lehtovirta M, Kangas AJ, Jula A, et al (incl Ala-Korpela M, P. Soininen). Metabolic profiling of fatty liver in young and middle-aged adults: Cross-sectional and prospective analyses of the Young Finns Study. Hepatology (65(2):491-500, 2017.
Kettunen J, Demirkan A, Würtz P, Draisma HHM, Haller T, Rawal R, et al (incl Ala-Korpela M) . Genome-wide study for circulating metabolites identifies 62 loci and reveals novel systemic effects of LPA. Nat Commun 23;7:11122, 2016.
Kyttälä A, Moraghebi R, Valensisi C, Kettunen J, Andrus C, Pasumarthy KK, et al. Genetic Variability Overrides the Impact of Parental Cell Type and Determines iPSC Differentiation Potential. Stem cell reports 6(2):200–12, 2016.
Laurila P-P, Soronen J, Kooijman S, Forsström S, Boon MR, Surakka I, et al (incl Kettunen J). USF1 deficiency activates brown adipose tissue and improves cardiometabolic health. Sci Transl Med 8(323):323ra13, 2016.
Lemmelä S, Solovieva S, Shiri R, Benner C, Heliövaara M, Kettunen J, et al. Genome-Wide Meta-Analysis of Sciatica in Finnish Population. PLoS One 11(10):e0163877, 2016.
Mäkinen V-P, Ala-Korpela M. Metabolomics of aging requires large-scale longitudinal studies with replication. Proc Natl Acad Sci U S A 113(25):E3470, 2016.
Ollila HM, Kronholm E, Kettunen J, Silander K, Perola M, Porkka-Heiskanen T, et al. Insomnia does not mediate or modify the association between MTNR1B risk variant rs10830963 and glucose levels. Diabetologia 59(5):1070–2, 2016.
Preiss D, Rankin N, Welsh P, Holman RR, Kangas AJ, Soininen P, et al (incl Ala-Korpela M). Effect of metformin therapy on circulating amino acids in a randomized trial: the CAMERA study. Diabet Med 33(11):1569–74, 2016.
Raitoharju E, Seppälä I, Lyytikäinen L-P, Viikari J, Ala-Korpela M, Soininen P, et al. Blood hsa-miR-122-5p and hsa-miR-885-5p levels associate with fatty liver and related lipoprotein metabolism-The Young Finns Study. Sci Rep 6:38262, 2016.
Shu L, Zhao Y, Kurt Z, Byars SG, Tukiainen T, Kettunen J, et al. Mergeomics: multidimensional data integration to identify pathogenic perturbations to biological systems. BMC Genomics 17(1):874, 2016.
Wang Q, Würtz P, Auro K, Morin-Papunen L, Kangas AJ, Soininen P, et al (incl Kettunen J, Ala-Korpela M). Effects of hormonal contraception on systemic metabolism: cross-sectional and longitudinal evidence. Int J Epidemiol 45(5):1445–57, 2016.
Wang Q, Würtz P, Auro K, Mäkinen V-P, Kangas AJ, Soininen P, et al (incl Kettunen J, Ala-Korpela M). Metabolic profiling of pregnancy: cross-sectional and longitudinal evidence. BMC Med 14(1):205, 2016.
Vogt S, Wahl S, Kettunen J, Breitner S, Kastenmüller G, Gieger C, et al (incl Ala-Korpela M). Characterization of the metabolic profile associated with serum 25-hydroxyvitamin D: a cross-sectional analysis in population-based data. Int J Epidemiol 45(5):1469–81, 2016.
Würtz P, Cook S, Wang Q, Tiainen M, Tynkkynen T, Kangas AJ, et al (incl Ala-Korpela M). Metabolic profiling of alcohol consumption in 9778 young adults. Int J Epidemiol 45(5):1493–506, 2016.
Würtz P, Wang Q, Niironen M, Tynkkynen T, Tiainen M, Drenos F, et al (incl Ala-Korpela M). Metabolic signatures of birthweight in 18 288 adolescents and adults. Int J Epidemiol 45(5):1539–50, 2016.
Würtz P, Wang Q, Soininen P, Kangas AJ, Fatemifar G, Tynkkynen T, et al (incl Kettunen J, Ala-Korpela M). Metabolomic Profiling of Statin Use and Genetic Inhibition of HMG-CoA Reductase. J Am Coll Cardiol 67(10):1200–10, 2016.
Ahola-Olli A V, Würtz P, Havulinna AS, Aalto K, Pitkänen N, Lehtimäki T, et al (incl Kettunen J). Genome-wide association study identifies 27 loci influencing concentrations of circulating cytokines and growth factors. Am J Hum Genet 5;100(1):40–50, 2017.
Wahl S, Drong A, Lehne B, Loh M, Scott WR, Kunze S, et al (incl Ala-Korpela M). Epigenome-wide association study of body mass index, and the adverse outcomes of adiposity. Nature 541(7635):81–6, 2017.
Würtz P, Kangas AJ, Soininen P, Lawlor DA, Davey Smith G, Ala-Korpela M. Quantitative serum NMR metabolomics in large-scale epidemiology: a primer on -omic technology. Am J Epidemiol, in press.
Mika Ala-Korpela, Ph.D., Professor (University of Oulu, University of Bristol)
Johannes Kettunen, Ph.D., Associate Professor (Academy of Finland, University of Oulu)
Senior and Post-doctoral Investigators:
Pasi Soininen, Ph.D., Assistant Professor (University of Eastern Finland)
Ville-Petteri Mäkinen, Ph.D., Associate Professor (EMBL Australia, University of Adelaide & South Australian Health and Medical Research Institute, University of Oulu)
Pauli Ohukainen, Ph.D. (Academy of Finland)
Fotios Drenos, Ph.D. (University of Bristol)
Tuulia Tynkkynen, Ph.D. (University of Eastern Finland, Sigrid Juselius Foundation)
Mari Karsikas, Ph.D. (Biocenter Oulu, University of Oulu)
Sanna Kuusisto, Ph.D. (Academy of Finland, Sigrid Juselius Foundation)
Amit Bansal, MBBS, M.Phil. (Biocenter Oulu)
Qin Wang, M.Sc. (University of Oulu)
Olga Anufrieva, M.Sc. (Biocenter Oulu)
Jussi Ekholm, M.Sc.Eng., B.Med. (Sigrid Juselius Foundation)
Marita Kalaoja, M.Sc. (Academy of Finland)
Laboratory Technicians, 2 (University of Eastern Finland and University of Bristol)
Foreign Scientists, 4
Group Members Who Spent More Than Two Weeks in Foreign Laboratories During 2016
Prof Mika Ala-Korpela, University of Bristol, UK
Assoc Prof Ville-Petteri Mäkinen, EMBL Australia, University of Adelaide & South Australian Health and Medical Research Institute (SAHMRI), Australia
Dr Fotios Drenos, University of Bristol, UK
Dr Peter Würtz, University of Bristol, UK
Dr Mari Karsikas, University of Adelaide & South Australian Health and Medical Research Institute (SAHMRI), Australia
Co-operation With Finnish and Foreign Companies
Brainshake Ltd., Helsinki – development and applications of serum metabolomics methodologies (http://www.brainshake.fi)
Last updated: 10.5.2017