systems wide perspectives on diabetic kidney disease and coronary artery disease - villie-petteri...
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Systems-wide perspectives on diabetic kidney disease and coronary artery disease
Ville-Petteri Mäkinen, DSc (Tech)University of California Los Angeles
Genes Transcripts Proteins Metabolites Health
National Health and Nutrition Examination Survey participants 2005–2010, age ≥ 20
United States Renal Data System 2012 Annual Data Report
Annual mortality dueto cardiovascular disease
Age
Moody WE, Edwards NC, Chue CD et al. Heart 2013:365–372Foley RN et al. Am J Kidney Dis 1998:S112–19
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 symptoms
Supervised classificationPrediction of end-points
Metabolomics for health care
Disease definition DiagnosticsConsensus cutoffs of established biomarker(s)Physically observable symptoms
Supervised classificationPrediction of end-points
Metabolomics for health care
Mӓkinen et al. Magn ResonMater Phy 19:281-296, 2006
Disease definition DiagnosticsConsensus cutoffs of established biomarker(s)Physically observable symptoms
Supervised classificationPrediction of end-points
Biomolecular profileClinical characteristicsAge, gender, education, habits etc.Physically observable symptoms
Unsupervised analysisMetabolic phenotypes
Metabolomics for health care
Metabolomics for systems biology
Disease definition DiagnosticsConsensus cutoffs of established biomarker(s)Physically observable symptoms
Supervised classificationPrediction of end-points
Biomolecular profileClinical characteristicsDiagnostic categoriesPhysically observable symptoms
Unsupervised analysisMetabolic phenotypes
Metabolomics for health care
Metabolomics for systems biology
Re-defined disease
Biomolecular profileClinical characteristicsDiagnostic categoriesPhysically observable symptoms
Unsupervised analysisMetabolic phenotypes
Metabolomics for systems biology
Relative risk of death in T1DMTwo presentations
AER < 3030 < AER < 300
AER > 300End-stagerenal disease
0.82.7
9.2
18.3
Groop et al. (2009) Diabetes 58:1651-1658
Mäkinen et al. (2008) Diabetes 57:2480-2487
AER = Urinary albuminexcretion rate (mg/24h)
Multidimensional “barchart”
Age-adjusted prevalence of coronaryheart disease among adults
Geographic mapDistance between individuals = physical distance
Coloring indicates regional demographics
Metabolic mapDistance between individuals= difference in biomolecular profile
Coloring indicates demographics ofmetabolically similar individuals
Prevalence of kidney diseasein the FinnDiane Study, 2008
Behavioral Risk Factor SurveillanceSystem, USA, 2010
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!
Men
Women
Relative risk of death
Diabetic complicationsMäkinen et al. Mol Syst Biol 4:167, 2008
Diabetic complicationsMäkinen et al. Mol Syst Biol 4:167, 2008
Mäkinen et al. J Proteome Res 11:1782-1790, 2012Kidney disease progression
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 composition
Lipoprotein subclasses in type 1 diabetesMäkinen et al. J Intern Med, in press
Tukiainen et al. Biochem Biophys Res Commun, 375:356-361, 2008
Cognitive decline and Alzheimer's
Intima media thicknessWurtz et al 2010
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-up of their physical activity habits (Prof Urho Kujala).
● Limited power due to multiple testing...
● ...but systems-wide shift was detected (P = 0.003).
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 genomics
Papers 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)
Schunkert et al. Nat Genet 43:333-338, 2011
Cholesterol biosynthesisLipoprotein metabolismBile acid metabolismFatty acid oxidation
ProteasomeLysosome
Peroxisome
Cell cycle
Human leukocyteantigen genes
Coagulation cascadesand complement system
Coagulation & wound healing
Antigenprocessing
Immunity I & II
Lipid I
Unknown II
Proteolysis
Glyoxalase Igene
Plasminogengene
Preparing for the future...
Chen R et al. Cell 2012
Constant change defines biology
Normalrange
Continuous glucose monitoring curve, a person with insulin-treated type 1 diabetes
Gordin et al.
Individuality defines people
Lehto et al. unpublished
BreakfastLunch
Dinner
PlatformsGenomicsTranscriptomicsProteomicsMetabolomics
PlatformsGenomicsTranscriptomicsProteomicsMetabolomics
AnalysesHypothesis testingPattern recognitionMachine learningComplex networks
PlatformsGenomicsTranscriptomicsProteomicsMetabolomics
DynamicsRepetitive stimuliAcute perturbationsChronic perturbations
AnalysesHypothesis testingPattern recognitionMachine learningComplex networks
PlatformsGenomicsTranscriptomicsProteomicsMetabolomics
DynamicsRepetitive stimuliAcute perturbationsChronic perturbations
Study designSupercases and controlsFamily studiesTime-collapse follow-upMaximal diversity cohortsPopulation-based studies
AnalysesHypothesis testingPattern recognitionMachine learningComplex networks
PlatformsGenomicsTranscriptomicsProteomicsMetabolomics
DynamicsRepetitive stimuliAcute perturbationsChronic perturbations
Study designSupercases and controlsFamily studiesTime-collapse follow-upMaximal diversity cohortsPopulation-based studies
ExperimentCell culturesModel organismsHuman studiesIntervention trialsDeep environment data
AnalysesHypothesis testingPattern recognitionMachine learningComplex networks
PlatformsGenomicsTranscriptomicsProteomicsMetabolomics
DynamicsRepetitive stimuliAcute perturbationsChronic perturbations
Study designSupercases and controlsFamily studiesTime-collapse follow-upMaximal diversity cohortsPopulation-based studies
ExperimentCell culturesModel organismsHuman studiesIntervention trialsDeep environment data
How to turn information into knowledge?
AnalysesHypothesis testingPattern recognitionMachine learningComplex networks
PlatformsGenomicsTranscriptomicsProteomicsMetabolomics
DynamicsRepetitive stimuliAcute perturbationsChronic perturbations
Study designSupercases and controlsFamily studiesTime-collapse follow-upMaximal diversity cohortsPopulation-based studies
ExperimentCell culturesModel organismsHuman studiesIntervention trialsDeep environment data
How to turn information into knowledge?
AnalysesHypothesis testingPattern recognitionMachine learningComplex networks
MessagePatientsGeneral publicIndustryPoliticians
Biology Technology
Relevant questions
Possible questions
Acknowledgments
United StatesUniv of California Los AngelesProf Xia YangDr Qing-Ying Meng
FinlandFolkhälsan Research CenterThe FinnDiane GroupProf Per-Henrik GroopCarol ForsblomMarkku LehtoLena M ThornValma Harjutsalo
University of Oulu &University of Eastern FinlandProf Mika Ala-KorpelaPasi SoininenTuulia TynkkynenAntti Kangas
FinlandAalto UniversitySchool of Science and Tech.Prof Kimmo KaskiTomi Peltola
United KingdomImperial College LondonProf Marjo-Riitta Järvelin
Institute for MolecularMedicine FinlandPeter Würtz
FinlandUniversity of JyväskyläProf Urho Kujala