systems wide perspectives on diabetic kidney disease and coronary artery disease - villie-petteri...

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