340 likes | 450 Views
Genomic Approaches in Personalized Dietary Recommendations: The EURRECA Example. Ben van Ommen. Sources of variability of individual nutrient intake requirements: biological variation. Lifestyle, physiological or psychological stress. Life stage. Disease state. Age, gender, body size.
E N D
Genomic Approaches in Personalized Dietary Recommendations: The EURRECA Example Ben van Ommen
Sources of variability of individual nutrient intake requirements: biological variation Lifestyle, physiological or psychological stress Life stage Disease state Age, gender, body size (epi)genetic variation Biological variation in absorption, distribution, metabolism excretion Biological variation in health effect
Start with “tox” Include safety margins adjust for unknowns The “modern classical” approach Renwick & Walker, Tox letters 180 (2008) 123-130
We don`t really know… What about genetics? – Folate and Neural Tube Defect Linden et al, Proc Nut Soc 65 (2006) 204-215
Conclusion so far: • Many mechanistic leads are known for micronutrient activity • Single micronutrient research is the golden standard • Current approaches do not allow to “go personal” • Many genetic variations are known but health impact is difficult to assess.
So, what are the challenges? • From micronutrient status to micronutrient health • From a single micronutrient to all relevant (micro)nutrients • From a population to a person
Nutrition relates to health optimizing, so needs to target the homeostasis of overarching processes instead of the disease process. Colon cancer infection gingivitis Oxidative stress Alzheimer Inflammatory stress Joint pain Diabetes metabolic stress Arthritis Asthma Metabolic syndrome Eye disorders obesity CVD allergies IBD
Breast cancer Colon cancer gingivitis Oxidative stress Ulcerative Colitis Inflammatory stress Joint pain Diabetes metabolic stress Asthma Arthritis Metabolic syndrome Eye disorders obesity CVD allergies IBD Micronutrients primarily serve to optimize the performance and resilience of these overarching processes. Vit E Vit D Fe Folate Choline Vit C EPA, DHA, ARA Pantothenic acid Riboflavin Biotin Coenzyme Q
This Selenium biological network translated in plasma biomarkers Intracellular Plasma
Other Selenoproteins are influenced by Se, and have an effect on oxidation and inflammation. The “selenoproteome”
So, many micronutrient interact in maintaining homeostasis in metabolism, oxidation and inflammation
Cellular machinery maintaining homeostasis in metabolism, oxidation,inflammation Healthy male adult Insulin resistant male adult Obese male adult - How does this profile change with changing Riboflavin? - when will it “crack”? - How does this profile look like for a MHTFR TT-er? - What happens in case of low Selenium? - What if I was a top athlete? - What is normal for a child?
Metabolomics: ~ 350 metabolites quantified in human plasma GC-MS chromatogram
? What happens with the oxidation node if the inflammation part is challenged? Which micronutrients might be “finetuned” to optimally absorb this challenge? A correlational network of all Observed changes metabolism node inflammation node Folate VitE oxidation node EPA Se Clish et al
Visualisation of transcriptome, proteome and metabolome changes (wikipathways => Genmapp)
All information on all relevant genetic variation of the whole network “one click away”
The plasma metabolite pool will be linked to all relevant physiological processes in various organs. muscle processes liver processes All measures plasma metabolites adipose processes gut processes
-- What information is available? -- • Intake • Status • Function • Health • Intermediate endpoints
So, can we set optimal micronutrient recommendations based on phenotypic information (including genotype)? Levels of biomarkers • Status • function • Omics (micronutrient-health relationship) • accepted intermediary endpoint • Established (disease) endpoint • RA2 provides subgroup specific established endpoints
B12 relates to optimizing health overarching processes instead of the disease process DNA damage (incl neoplasm) Eye disorders Oxidative stress Frailty syndrome Inflammatory stress Cognitive impaiment (dementia) Skin lesions metabolic stress Osteoporosis Nervous system disease Cardiovascular disease (incl. infarction) Anemia (incl. pancytopenia, leucopenia & thrombocytopenia)
Zinc relates to optimizing health overarching processes instead of the disease process Compromised immune function Ischemic heart disease Oxidative stress Inflammatory stress Anorexia Dermatitis metabolic stress Balance Hypogeusia (loss of appetite) Carcinogenesis Depression Impaired cognitive function (dementia) Diabetes (reduced glucose tolerance)
Folate relates to optimizing health overarching processes instead of the disease process Neurodegenaration Cancer Oxidative stress Inflammatory stress Osteoporosis metabolic stress CVD Stroke CVD / homocysteine
Predict, based on Cohort observations Nutrient status Health status Micronutrient status Functional assay Micronutrient active molecule Function endpoint Predict, based on individual observations Micronutrient target organ concentration Organ function Function homeostasis Micronutrient metabolite Health reporter Disease reporter What can we measure?
0.6 Cell cycle disorders metabolic disorders 0.5 0.4 Insulin resistance effects 0.3 GI-tract effects obese 0.2 Scores on D 2 low folate 0.1 low folate (specific genotype) overweight healthy 0 low DHA -0.1 exercise Oxidative stress disorders Inflammation related disorders Immune effects -0.2 -0.3 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 Scores on D 1 the micronutrient biomarker health space
I think it may work but I`m not exactly sure about the modeling part … So I`m organizing a workshop on modeling of phenotypic information for personalized micronutrient recommendationThis fall, nice place in Europe
Jim Kaput Ahmed El-Sohemy Richard Cotton Thanksto: Please join this community effort!