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Systems wide perspectives on diabetic kidney disease and coronary artery disease - Villie-Petteri Makinen

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  • 1. Systems-wide perspectives on diabetic kidneydisease and coronary artery diseaseVille-Petteri Mäkinen, DSc (Tech)University of California Los AngelesGenes Transcripts Proteins Metabolites Health
  • 2. National Health and Nutrition Examination Surveyparticipants 2005–2010, age ≥ 20United States Renal Data System 2012 Annual Data Report
  • 3. Annual mortality dueto cardiovascular diseaseAgeMoody WE, Edwards NC, Chue CD et al. Heart 2013:365–372Foley RN et al. Am J Kidney Dis 1998:S112–19
  • 4. Australian Institute of Health and Welfare
  • 5. Australian Institute of Health and Welfare$ 900 000 000
  • 6. Disease definition DiagnosticsConsensus cutoffs of established biomarker(s)Physically observable symptomsSupervised classificationPrediction of end-pointsMetabolomics for health care
  • 7. Disease definition DiagnosticsConsensus cutoffs of established biomarker(s)Physically observable symptomsSupervised classificationPrediction of end-pointsMetabolomics for health careMӓkinen et al. Magn ResonMater Phy 19:281-296, 2006
  • 8. Disease definition DiagnosticsConsensus cutoffs of established biomarker(s)Physically observable symptomsSupervised classificationPrediction of end-pointsBiomolecular profileClinical characteristicsAge, gender, education, habits etc.Physically observable symptomsUnsupervised analysisMetabolic phenotypesMetabolomics for health careMetabolomics for systems biology
  • 9. Disease definition DiagnosticsConsensus cutoffs of established biomarker(s)Physically observable symptomsSupervised classificationPrediction of end-pointsBiomolecular profileClinical characteristicsDiagnostic categoriesPhysically observable symptomsUnsupervised analysisMetabolic phenotypesMetabolomics for health careMetabolomics for systems biologyRe-defined disease
  • 10. Biomolecular profileClinical characteristicsDiagnostic categoriesPhysically observable symptomsUnsupervised analysisMetabolic phenotypesMetabolomics for systems biology
  • 11. Relative risk of death in T1DMTwo presentationsAER < 3030 < AER < 300AER > 300End-stagerenal disease0.82.79.218.3Groop et al. (2009) Diabetes 58:1651-1658Mäkinen et al. (2008) Diabetes 57:2480-2487AER = Urinary albuminexcretion rate (mg/24h)Multidimensional “barchart”
  • 12. Age-adjusted prevalence of coronaryheart disease among adultsGeographic mapDistance between individuals = physical distanceColoring indicates regional demographicsMetabolic mapDistance between individuals= difference in biomolecular profileColoring indicates demographics ofmetabolically similar individualsPrevalence of kidney diseasein the FinnDiane Study, 2008Behavioral Risk Factor SurveillanceSystem, USA, 2010
  • 13. Relative risk of death
  • 14. Relative risk of death
  • 15. Relative risk of death
  • 16. Relative risk of death
  • 17. Relative risk of death
  • 18. Treatment target: Stay away from the high-risk zone!MenWomenRelative risk of death
  • 19. Diabetic complicationsMäkinen et al. Mol Syst Biol 4:167, 2008
  • 20. Diabetic complicationsMäkinen et al. Mol Syst Biol 4:167, 2008Mäkinen et al. J Proteome Res 11:1782-1790, 2012Kidney disease progression
  • 21. Kumpula et al. J Lipid Res 51:431-439, 2010Lipoprotein structure and composition
  • 22. Kumpula et al. J Lipid Res 51:431-439, 2010Lipoprotein structure and composition
  • 23. Kumpula et al. J Lipid Res 51:431-439, 2010Lipoprotein structure and compositionLipoprotein subclasses in type 1 diabetesMäkinen et al. J Intern Med, in press
  • 24. Tukiainen et al. Biochem Biophys Res Commun, 375:356-361, 2008Cognitive decline and AlzheimersIntima media thicknessWurtz et al 2010
  • 25. Can we do something to avoidhigh-risk metabolic zones?
  • 26. Persistent physical activity and the metabolome● 16 pairs of discordant twins (32 individuals).● 30-year continuous follow-up of their physical activity habits(Prof Urho Kujala).● Limited power due to multiple testing...● ...but systems-wide shift was detected (P = 0.003).
  • 27. Correlations between metabolites and gene expression
  • 28. Kujala, Mäkinen & Ala-Korpela, Circulation, 127:340-348, 2013
  • 29. Can we blame bad genes?
  • 30. SNP = single nucleotide polymorphism, a DNA variant at at a specific location in the genome
  • 31. Existing knowledge
  • 32. Integrative genomicsPapers by Xia Yang, Eric Schadt, Jun Zhu, Steve Horvath and Jake Lusis
  • 33. Large-scale association analysis identifies 13 new susceptibility loci forcoronary artery disease (CARDIoGRAM Consortium)Schunkert et al. Nat Genet 43:333-338, 2011
  • 34. Cholesterol biosynthesisLipoprotein metabolismBile acid metabolismFatty acid oxidation
  • 35. ProteasomeLysosomePeroxisome
  • 36. Cell cycle
  • 37. Human leukocyteantigen genes
  • 38. Coagulation cascadesand complement system
  • 39. Coagulation & wound healingAntigenprocessingImmunity I & IILipid IUnknown IIProteolysis
  • 40. Glyoxalase Igene
  • 41. Plasminogengene
  • 42. Preparing for the future...
  • 43. Chen R et al. Cell 2012
  • 44. Constant change defines biologyNormalrangeContinuous glucose monitoring curve, a person with insulin-treated type 1 diabetesGordin et al.
  • 45. Individuality defines peopleLehto et al. unpublishedBreakfastLunchDinner
  • 46. PlatformsGenomicsTranscriptomicsProteomicsMetabolomics
  • 47. PlatformsGenomicsTranscriptomicsProteomicsMetabolomicsAnalysesHypothesis testingPattern recognitionMachine learningComplex networks
  • 48. PlatformsGenomicsTranscriptomicsProteomicsMetabolomicsDynamicsRepetitive stimuliAcute perturbationsChronic perturbationsAnalysesHypothesis testingPattern recognitionMachine learningComplex networks
  • 49. PlatformsGenomicsTranscriptomicsProteomicsMetabolomicsDynamicsRepetitive stimuliAcute perturbationsChronic perturbationsStudy designSupercases and controlsFamily studiesTime-collapse follow-upMaximal diversity cohortsPopulation-based studiesAnalysesHypothesis testingPattern recognitionMachine learningComplex networks
  • 50. PlatformsGenomicsTranscriptomicsProteomicsMetabolomicsDynamicsRepetitive stimuliAcute perturbationsChronic perturbationsStudy designSupercases and controlsFamily studiesTime-collapse follow-upMaximal diversity cohortsPopulation-based studiesExperimentCell culturesModel organismsHuman studiesIntervention trialsDeep environment dataAnalysesHypothesis testingPattern recognitionMachine learningComplex networks
  • 51. PlatformsGenomicsTranscriptomicsProteomicsMetabolomicsDynamicsRepetitive stimuliAcute perturbationsChronic perturbationsStudy designSupercases and controlsFamily studiesTime-collapse follow-upMaximal diversity cohortsPopulation-based studiesExperimentCell culturesModel organismsHuman studiesIntervention trialsDeep environment dataHow to turn information into knowledge?AnalysesHypothesis testingPattern recognitionMachine learningComplex networks
  • 52. PlatformsGenomicsTranscriptomicsProteomicsMetabolomicsDynamicsRepetitive stimuliAcute perturbationsChronic perturbationsStudy designSupercases and controlsFamily studiesTime-collapse follow-upMaximal diversity cohortsPopulation-based studiesExperimentCell culturesModel organismsHuman studiesIntervention trialsDeep environment dataHow to turn information into knowledge?AnalysesHypothesis testingPattern recognitionMachine learningComplex networksMessagePatientsGeneral publicIndustryPoliticians
  • 53. Biology TechnologyRelevant questionsPossible questions
  • 54. AcknowledgmentsUnited StatesUniv of California Los AngelesProf Xia YangDr Qing-Ying MengFinlandFolkhälsan Research CenterThe FinnDiane GroupProf Per-Henrik GroopCarol ForsblomMarkku LehtoLena M ThornValma HarjutsaloUniversity of Oulu &University of Eastern FinlandProf Mika Ala-KorpelaPasi SoininenTuulia TynkkynenAntti KangasFinlandAalto UniversitySchool of Science and Tech.Prof Kimmo KaskiTomi PeltolaUnited KingdomImperial College LondonProf Marjo-Riitta JärvelinInstitute for MolecularMedicine FinlandPeter WürtzFinlandUniversity of JyväskyläProf Urho Kujala