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Presentation of EdgeLeap's CEO Marijana Radonjic at NuGO Week (11 September 2014, Castellammare di Stabia, Italy). This presentation shows how organizing and linking diverse data (food databases, disease associations, genomics, 23andMe genetics profiles) allows us to map a personal food-genomics-health landscape and identify dietary patterns which may affect personal disease risk. For more details see: http://www.edgeleap.com/news/edgeleap-at-nugo-week-2014/
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Marijana Radonjic, PhD
ORGANIZING DATA TO
EMPOWER DECISION MAKING
This presentation remains property of EdgeLeap B.V. and is licensed for reuse under a Creative Commons Attribution 4.0 International License (see http://creativecommons.org/licenses/by/4.0/).
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DATA
DECISION
INFORMATION
KNOWLEDGE
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NuGO week 2014Participant list(hard copy)
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Institute
Person
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In Marijana’s LinkedIn
Not (yet) in Marijana’s LinkedIn
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COMPLEXITY OF HEALTH FACTORS
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PEAK INTO THE FUTURE
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JAN FEB MAR APR MAY JUN
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FOOD - GENOMICS - HEALTH
LANDSCAPE
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BRIDGING FOOD AND HEALTH
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Ingredient GeneFooDB: 9,955 food
ingredients, 858 foods
HMDB: 4,025 associated
enzymes & transporters
Disease
UMLS: 8,521 diseasesDisgeNET: 13,828 associated genes & proteins
GeneticsBiomarkersMechanistic
EnzymesTransporters
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BRIDGING FOOD AND HEALTH
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Ingredient Gene
Disease
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FOOD - GENOMICS - HEALTH NETWORK
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Significance filter(Monte Carlo randomization
procedure p < 0.001)
Projection to disease-
ingredient space
Bipartitedisease-gene-ingredient
network
Disease-Ingredientnetwork
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Neoplasms
Metabolic syndrome
and complications
Mental disorders817 connected diseases316 connected ingredients1,961 disease-ingredient relations(p < 0.001)Network cluster coefficient = 0.42 (v.s. random graph ~0.006)
FOOD - GENOMICS - HEALTH
LANDSCAPE
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NETWORK RECONSTRUCTS DISEASE AND
INGREDIENTS CLASSES
Most or all topology based clusters (47/67 for diseases and 10/10 for ingredients) are significantly enriched with one or more predefined MeSH disease and/or HMDB chemical classes (p < 0.05).
Significant enrichment (p < 0.01)
Clusters enriched with main disease classes
Clusters enriched with main ingredient classes
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Raw data
GOING PERSONALIZED
Rs1234(A;A)
Rs1234(G;G)
Increased risk
Decreased risk
Disconnected information
Personal disease-ingredient network
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PERSONAL FOOD - GENOMICS - HEALTH
LANDSCAPE
Disease
Decreased risk
Increased risk
Mixed evidence
Linked to variant
Not linked to variant
Ingredient
AtherosclerosisCoronary diseaseHypertensionObesityDiabetesDementia
Cancers
PsychosisAutismMigrane disordersDepression‘as if’ personalityNicotine dependence
Cluster headacheHypertension
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NUTS – ATHEROSCLEROSIS ASSOCIATION
Disease
Increased risk
Linked to variant
Not linked to variant
Ingredient
Vegetables;Fruits;Herbs and Spices;Nuts;Cereals and cereal products;Pulses;Teas;Gourds;Coffee
CETP
Fats and oils
Herbs and Spices
Cocoa and cocoa products;Fats and oils
Cocoa and cocoa products;Fats and oils
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WHAT ELSE WILL BE AFFECTED?
Disease
Increased risk
Mixed evidence
Linked to variant
Ingredient
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FROM INGREDIENTS TO DIET
23andMe SNP profiles(11 individuals)
Whole foods for which their ingredients enriched in personal networks
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CHALLENGES AHEAD
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• From ingredients to diets: diets in genetics context
• Beyond genetics: involve broader phenotypic data (dietary
questionnaires, activity measurements, self monitoring devices,
medical history)
• Time resolved data (current status, future risk)
• Standardization of knowledge:
• Ontologies to map between resources
• Publish in structured format
• Add directionality of relations: from associations to causal
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MAKING DECISIONS
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Understand big picture beyond single SNP-
ingredient associations
Improve advice on dietary lifestyle
Develop healthy food
(Re)position existing product
What to eat to stay healthy?
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THANKS
Georg SummerPhD Student, co-supervised by Maastricht University / TNO / EdgeLeap
Thomas KelderEdgeLeap
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edgeleap
@EdgeLeap
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