Systems-wide perspectives on diabetic kidneydisease and coronary artery diseaseVille-Petteri Mäkinen, DSc (Tech)University...
National Health and Nutrition Examination Surveyparticipants 2005–2010, age ≥ 20United States Renal Data System 2012 Annua...
Annual mortality dueto cardiovascular diseaseAgeMoody WE, Edwards NC, Chue CD et al. Heart 2013:365–372Foley RN et al. Am ...
Australian Institute of Health and Welfare
Australian Institute of Health and Welfare$ 900 000 000
Disease definition DiagnosticsConsensus cutoffs of established biomarker(s)Physically observable symptomsSupervised classi...
Disease definition DiagnosticsConsensus cutoffs of established biomarker(s)Physically observable symptomsSupervised classi...
Disease definition DiagnosticsConsensus cutoffs of established biomarker(s)Physically observable symptomsSupervised classi...
Disease definition DiagnosticsConsensus cutoffs of established biomarker(s)Physically observable symptomsSupervised classi...
Biomolecular profileClinical characteristicsDiagnostic categoriesPhysically observable symptomsUnsupervised analysisMetabo...
Relative risk of death in T1DMTwo presentationsAER < 3030 < AER < 300AER > 300End-stagerenal disease0.82.79.218.3Groop et ...
Age-adjusted prevalence of coronaryheart disease among adultsGeographic mapDistance between individuals = physical distanc...
Relative risk of death
Relative risk of death
Relative risk of death
Relative risk of death
Relative risk of death
Treatment target: Stay away from the high-risk zone!MenWomenRelative risk of death
Diabetic complicationsMäkinen et al. Mol Syst Biol 4:167, 2008
Diabetic complicationsMäkinen et al. Mol Syst Biol 4:167, 2008Mäkinen et al. J Proteome Res 11:1782-1790, 2012Kidney disea...
Kumpula et al. J Lipid Res 51:431-439, 2010Lipoprotein structure and composition
Kumpula et al. J Lipid Res 51:431-439, 2010Lipoprotein structure and composition
Kumpula et al. J Lipid Res 51:431-439, 2010Lipoprotein structure and compositionLipoprotein subclasses in type 1 diabetesM...
Tukiainen et al. Biochem Biophys Res Commun, 375:356-361, 2008Cognitive decline and AlzheimersIntima media thicknessWurtz ...
Can we do something to avoidhigh-risk metabolic zones?
Persistent physical activity and the metabolome● 16 pairs of discordant twins (32 individuals).● 30-year continuous follow...
Correlations between metabolites and gene expression
Kujala, Mäkinen & Ala-Korpela, Circulation, 127:340-348, 2013
Can we blame bad genes?
SNP = single nucleotide polymorphism, a DNA variant at at a specific location in the genome
Existing knowledge
Integrative genomicsPapers by Xia Yang, Eric Schadt, Jun Zhu, Steve Horvath and Jake Lusis
Large-scale association analysis identifies 13 new susceptibility loci forcoronary artery disease (CARDIoGRAM Consortium)S...
Cholesterol biosynthesisLipoprotein metabolismBile acid metabolismFatty acid oxidation
ProteasomeLysosomePeroxisome
Cell cycle
Human leukocyteantigen genes
Coagulation cascadesand complement system
Coagulation & wound healingAntigenprocessingImmunity I & IILipid IUnknown IIProteolysis
Glyoxalase Igene
Plasminogengene
Preparing for the future...
Chen R et al. Cell 2012
Constant change defines biologyNormalrangeContinuous glucose monitoring curve, a person with insulin-treated type 1 diabet...
Individuality defines peopleLehto et al. unpublishedBreakfastLunchDinner
PlatformsGenomicsTranscriptomicsProteomicsMetabolomics
PlatformsGenomicsTranscriptomicsProteomicsMetabolomicsAnalysesHypothesis testingPattern recognitionMachine learningComplex...
PlatformsGenomicsTranscriptomicsProteomicsMetabolomicsDynamicsRepetitive stimuliAcute perturbationsChronic perturbationsAn...
PlatformsGenomicsTranscriptomicsProteomicsMetabolomicsDynamicsRepetitive stimuliAcute perturbationsChronic perturbationsSt...
PlatformsGenomicsTranscriptomicsProteomicsMetabolomicsDynamicsRepetitive stimuliAcute perturbationsChronic perturbationsSt...
PlatformsGenomicsTranscriptomicsProteomicsMetabolomicsDynamicsRepetitive stimuliAcute perturbationsChronic perturbationsSt...
PlatformsGenomicsTranscriptomicsProteomicsMetabolomicsDynamicsRepetitive stimuliAcute perturbationsChronic perturbationsSt...
Biology TechnologyRelevant questionsPossible questions
AcknowledgmentsUnited StatesUniv of California Los AngelesProf Xia YangDr Qing-Ying MengFinlandFolkhälsan Research CenterT...
Systems wide perspectives on diabetic kidney disease and coronary artery disease - Villie-Petteri Makinen
Systems wide perspectives on diabetic kidney disease and coronary artery disease - Villie-Petteri Makinen
Systems wide perspectives on diabetic kidney disease and coronary artery disease - Villie-Petteri Makinen
Systems wide perspectives on diabetic kidney disease and coronary artery disease - Villie-Petteri Makinen
Systems wide perspectives on diabetic kidney disease and coronary artery disease - Villie-Petteri Makinen
Systems wide perspectives on diabetic kidney disease and coronary artery disease - Villie-Petteri Makinen
Systems wide perspectives on diabetic kidney disease and coronary artery disease - Villie-Petteri Makinen
Systems wide perspectives on diabetic kidney disease and coronary artery disease - Villie-Petteri Makinen
Systems wide perspectives on diabetic kidney disease and coronary artery disease - Villie-Petteri Makinen
Systems wide perspectives on diabetic kidney disease and coronary artery disease - Villie-Petteri Makinen
Systems wide perspectives on diabetic kidney disease and coronary artery disease - Villie-Petteri Makinen
Systems wide perspectives on diabetic kidney disease and coronary artery disease - Villie-Petteri Makinen
Upcoming SlideShare
Loading in...5
×

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

198

Published on

Published in: Technology, Health & Medicine
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total Views
198
On Slideshare
0
From Embeds
0
Number of Embeds
1
Actions
Shares
0
Downloads
8
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

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

  1. 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. 2. National Health and Nutrition Examination Surveyparticipants 2005–2010, age ≥ 20United States Renal Data System 2012 Annual Data Report
  3. 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. 4. Australian Institute of Health and Welfare
  5. 5. Australian Institute of Health and Welfare$ 900 000 000
  6. 6. Disease definition DiagnosticsConsensus cutoffs of established biomarker(s)Physically observable symptomsSupervised classificationPrediction of end-pointsMetabolomics for health care
  7. 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. 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. 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. 10. Biomolecular profileClinical characteristicsDiagnostic categoriesPhysically observable symptomsUnsupervised analysisMetabolic phenotypesMetabolomics for systems biology
  11. 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. 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. 13. Relative risk of death
  14. 14. Relative risk of death
  15. 15. Relative risk of death
  16. 16. Relative risk of death
  17. 17. Relative risk of death
  18. 18. Treatment target: Stay away from the high-risk zone!MenWomenRelative risk of death
  19. 19. Diabetic complicationsMäkinen et al. Mol Syst Biol 4:167, 2008
  20. 20. Diabetic complicationsMäkinen et al. Mol Syst Biol 4:167, 2008Mäkinen et al. J Proteome Res 11:1782-1790, 2012Kidney disease progression
  21. 21. Kumpula et al. J Lipid Res 51:431-439, 2010Lipoprotein structure and composition
  22. 22. Kumpula et al. J Lipid Res 51:431-439, 2010Lipoprotein structure and composition
  23. 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. 24. Tukiainen et al. Biochem Biophys Res Commun, 375:356-361, 2008Cognitive decline and AlzheimersIntima media thicknessWurtz et al 2010
  25. 25. Can we do something to avoidhigh-risk metabolic zones?
  26. 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. 27. Correlations between metabolites and gene expression
  28. 28. Kujala, Mäkinen & Ala-Korpela, Circulation, 127:340-348, 2013
  29. 29. Can we blame bad genes?
  30. 30. SNP = single nucleotide polymorphism, a DNA variant at at a specific location in the genome
  31. 31. Existing knowledge
  32. 32. Integrative genomicsPapers by Xia Yang, Eric Schadt, Jun Zhu, Steve Horvath and Jake Lusis
  33. 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. 34. Cholesterol biosynthesisLipoprotein metabolismBile acid metabolismFatty acid oxidation
  35. 35. ProteasomeLysosomePeroxisome
  36. 36. Cell cycle
  37. 37. Human leukocyteantigen genes
  38. 38. Coagulation cascadesand complement system
  39. 39. Coagulation & wound healingAntigenprocessingImmunity I & IILipid IUnknown IIProteolysis
  40. 40. Glyoxalase Igene
  41. 41. Plasminogengene
  42. 42. Preparing for the future...
  43. 43. Chen R et al. Cell 2012
  44. 44. Constant change defines biologyNormalrangeContinuous glucose monitoring curve, a person with insulin-treated type 1 diabetesGordin et al.
  45. 45. Individuality defines peopleLehto et al. unpublishedBreakfastLunchDinner
  46. 46. PlatformsGenomicsTranscriptomicsProteomicsMetabolomics
  47. 47. PlatformsGenomicsTranscriptomicsProteomicsMetabolomicsAnalysesHypothesis testingPattern recognitionMachine learningComplex networks
  48. 48. PlatformsGenomicsTranscriptomicsProteomicsMetabolomicsDynamicsRepetitive stimuliAcute perturbationsChronic perturbationsAnalysesHypothesis testingPattern recognitionMachine learningComplex networks
  49. 49. PlatformsGenomicsTranscriptomicsProteomicsMetabolomicsDynamicsRepetitive stimuliAcute perturbationsChronic perturbationsStudy designSupercases and controlsFamily studiesTime-collapse follow-upMaximal diversity cohortsPopulation-based studiesAnalysesHypothesis testingPattern recognitionMachine learningComplex networks
  50. 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. 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. 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. 53. Biology TechnologyRelevant questionsPossible questions
  54. 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
  1. A particular slide catching your eye?

    Clipping is a handy way to collect important slides you want to go back to later.

×