Helen M Roche. Ireland

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1 Smart Science ~ Good Food Understanding the potential of Personalised Nutrition for Metabolic Disease Helen M Roche Nutrigenomics Research Group, University College Dublin, Ireland

2 Personalised Nutrition & Metabolic Health The anomalies High risk v low risk Respond v not respond The answer or an excuse.

3 Personalised Nutrition& Metabolic Health

4 Obesity and Diabetes OBESITY Cardiovascular Disease (CVD) Metabolic Health DIABETES news.bbc.co.uk

Obesity Insulin Resistance Glucose Intolerance

5 Diabesity Pathogenesis & Progression Sensitive Genotype Progressive phenotype Metabolic Stress Obesity Insulin Resistance Glucose Intolerance Metabolic Syndrome Inflammation Molecular mechanisms T 2 DM

6 Obesity: a state of chronic inflammation IRS-1 pakt GLUT-4 M2 Anti-Inflammatory Insulin Sensitivity IL-10 M1 Pro-Inflammatory Insulin Resistance TNF-α IL-1β

7 Inflammation and coronary artery disease (CAD)

8 Translational perspectives from LIPGENE

9 LIPGENE Partners Human Nutrition University College Dublin University of Reading University of Oslo University of Bergen INSERM University of Cordoba Maastricht University Uppsala University University of Krakow Unilever Best Foods Hitachi, Europe Limited Animal Nutrition University of Reading Rowett Research Institute MTT Agrifood Finland INRA Plant Biotechnology BASF Plant Science GmbH University of York Rothamsted Research Ecomonic Science LIPGENE LMC International Consumer Science University of Ulster University of Porto Dissemination British Nutrition Foundation

10 Molecular mechanisms Progressive phenotype Basic hypothesis Dietary fat composition + genotype Metabolic syndrome LIPGENE Sensitive Genotype Metabolic Stress Obesity Insulin Resistance Inflammation Glucose Intolerance Metabolic Syndrome T 2 DM

11 Genes & Dietary Fat MetS Prospective Case-Control Study LIPGENE The interaction between genes and dietary fat in the development of the MetS Subjects chosen from the SU.VI.MAX study of 13,000 individuals ( ) individuals (877 cases, 877 controls) Women aged years and men aged years Cases being individuals who developed 3 elements of the MetS, over the 7.5 year follow up period versus control subjects who did not World Health Organisation and the NCEP-ATP III criteria to define the MetS Case and controls were matched according to age (± 5 y), gender and number of dietary records available Cohort details expanded to include Baseline and 7-year follow-up data Metabolic markers associated with the MetS Genetic markers as previously described Dietary & plasma fatty acid composition, markers of inflammation & lipid metabolism

12 LIPGENE: Case Control Study Cases Controls P LIPGENE n Male/Female % 60/40 60/40 Age, yrs 58.3± ± BMI, kg/m ± ±0.08 < Waist, cm 96.3± ±0.31 < Systolic blood pressure, mm Hg 139.9± ±0.37 < Diastolic blood pressure, mm Hg 86.9± ±0.25 < Glucose, mmol/l 5.64± ±0.01 < Insulin, mu/l 10.28± ±0.08 < C-Peptide, nmol/l 0.96± ±0.01 < NEFA, mmol/l 0.96± ±

13 LIPGENE: Case Control Study Plasma fatty acid composition Plasma fatty acids, % Cases Controls p LIPGENE SFA (14:0, 16:0,18:0) 34.55± ± SFA (14:0 + 16:0) 26.87± ±0.17 <0.001 PUFA 42.43± ±0.20 < n-6 PUFA 38.10± ±0.19 < n-3 PUFA 4.32± ± LC n-3 PUFA (EPA+DHA) 3.21± ± MUFA (16:1 (n-7), 18:1 (n-9), 20:1 (n-9)) 23.04± ± 0.11 < Fatty acid ratios SFA/MUFA 1.57± ±0.02 < PUFA/SFA 1.27± ±0.01 < (C14:0+C16:0)/(MUFA+PUFA) 0.42± ± n-6/n ± ±

Associated with IR & postprandial TAG metabolism MetS, T2DM & CVD risk

14 Compliment component 3 (C3) Acute phase protein, innate immune response Upon activation C3 converted to APS NEFA uptake Adipocytes important source of C3 Associated with IR & postprandial TAG metabolism MetS, T2DM & CVD risk Genetic & environmental determinants How does dietary fat, obesity & smoking affect C3?

17 LIPGENE

21 Findings replicated in Dietary Intervention Cohort The protective rs GG genotype Associated with enhanced insulin sensitivity (P<0.035) More responsive to LC n-3 PUFA intervention Improved NEFA & TAG metabolism (P=0.027) No difference between genotypes in the placebo group The at risk rs A allele carriers versus GG homozygotes Decreased insulin sensitivity, increased BMI Genetic influence was modified by LC n-3 PUFA intervention A allele carriers achieved 35% improved IS (P=0.02) No genotype effect in placebo intervention Study design differences functional studies

Dietary Fatty Acids, Adipose Inflammation & Insulin Resistance Translational perspectives» Dietary fatty acids, inflammation & MetS / T2D risk» Prospective evidence» Consistent interaction with

22 Dietary Fatty Acids, Adipose Inflammation & Insulin Resistance Translational perspectives» Dietary fatty acids, inflammation & MetS / T2D risk» Prospective evidence» Consistent interaction with inflammation and effect of SFA on IR» Intervention / dietary efficacy» More complex when dealing beyond weight loss Mechanistic perspectives» SFA v MUFA : NLRP3 inflammasome activation, IL-1b and IR

23 Teen Nutrition Study Anti-inflammatory nutritional strategies & metabolic health in overweight and obese adolescents Clinical Outcomes Molecular Mechanisms

24 Prevalence of Childhood Overweight and Obesity in Ireland 3yrs 25% * 13yrs 26% * * Growing Up In Ireland, 2012 as defined by IOTF cut-offs

25 IκB IκB IL6R IL1R Bad diet obesity Saturated Fat (Signal 1) IR Glucose TLR4 TNFR ATP TIRAP IRS pser x AKT AS160 ROS x Glucose Lipid accumulation IKKβ AMPK Fatty acid oxidation IKKβ MyD88 IRAK TAK TRAF ERK MAPK p38 JNK Signal 2 K+ efflux ROS ROS NF-κB JNK NLRP3 ASC ER stress NF-κB Pro-caspase-1 caspase-1 Pro-IL-1β IL-1β IL-6, IL-10, SOCS, MIF, TNF-α, pro-il-1β

IκB IκB IL6R IL1R Bad diet obesity Saturated Fat (Signal 1) IR Glucose TLR4 TNFR ATP TIRAP IRS pser x AKT AS160 ROS x Glucose Lipid accumulation IKKβ AMPK Fatty acid oxidation IKKβ

26 IκB IκB IL6R IL1R Bad diet obesity Saturated Fat (Signal 1) IR Glucose TLR4 TNFR ATP TIRAP IRS pser x AKT AS160 ROS x Glucose Lipid accumulation IKKβ AMPK Fatty acid oxidation IKKβ MyD88 IRAK TAK TRAF ERK MAPK p38 JNK Signal 2 K+ efflux ROS ROS NF-κB JNK NLRP3 ASC ER stress NF-κB Pro-caspase-1 caspase-1 Pro-IL-1β IL-1β IL-6, IL-10, SOCS, MIF, TNF-α, pro-il-1β

27 Primary Outcome Objective 1 To examine the effect of the supplement on plasma adiponectin Secondary Outcome Objective 2 To determine predictors of responsiveness to intervention Nutritional Efficacy Personalised Nutrition & Health

28 JAMA, July 8, 2009 Vol 302, No. 2 Adiponectin is among the strongest and most consistent biochemical predictors of type 2 diabetes

29 Study Design Randomized, double-blind, placebo controlled trial ClinicaTrials.gov Identifier: NCT N=60 teens (13-18 year olds, overweight and obese) N=30 Active Supplement N=30 Placebo Supplement 8 weeks on active supplement or placebo 3 month wash out period 8 weeks on active supplement or placebo

30 Personalised Nutrition High risk v low risk Respond v not respond The answer or an excuse.

31 Defining Responsiveness to Intervention Responders were classified by a unit improvement in HOMA-IR -0.6 HOMA-IR response to intervention (95% CI) Mean Intervention Effect on HOMA-IR -0.28

32 Baseline metabotype but not BMI differed between responders and non-responders Responders Non-Responders

33 Teen Nutrition Study Conclusions Adiponectin future risk attenuated.? Selective improvement in insulin resistance personalised therapy.? Despite similar BMI, adverse phenotype obese & metabolically unhealthy responded more favourably to anti-inflammatory cocktail Improved insulin resistance was related to inflammatory profile CD163 & adiponectin More concrete molecular perspectives.

34 Next Steps Molecular Mechanisms PBMCs Epigenetics Metabolomics..? CVD risk..?

35 What is FHI? Partnership between: Public Research Organizations Irish food industry Funded by Enterprise Ireland and Industry partners Budget 19,2 mio plus in-kind contributions Second 5-year term, since September 2013 Industry-led research agenda: Research on functionality of milk components Infant nutrition, glycemic management, appetite modulation, healthy ageing, healthy cheeses, technology Strong market position of industry partners FHI combines worldclass science and industry expertise to improve health, wellness and quality of life through innovation in food

36 FHI target groups and health benefit areas Target groups Infants, Mothers Obese, Diabetics Elderly, Athletes Target health benefits Functionality/ composition of mother s milk Appetite Modulation Healthy ageing and muscle health Maternal nutrition Glycemic Management Performance nutrition Nutritional food matrix Healthy Cheeses Health benefits of cheese Technology Enabler Generation, characterization, upscaling, delivery matrices

37 All Irish food science capabilities working together for FHI Research on Health and wellness food / food ingredients for the global food industry

38 Five competitors working together in FHI Competitors in the market, pre-competitive research together World class research requires collaboration Results of research brought to consumers in competitive way Model for Open Innovation Direct Industry input makes program highly relevant to industry customers Access to FHI research and inventions through the industry partners

39 Personalised Nutrition& Metabolic Health The anomalies High risk v low risk Respond v not respond The answer or an excuse.

40 Acknowledgements Nutrigenomics Research Group» Fiona McGillicuddy» Orla Finucane» Clare Reynolds» Niamh Healy» Maeve McArdle» Kieran Holohan» Claire Lyons» Aoife Murphy» Ruth Connaughton» Aoibheann McMorrow» Elizabeth Oliver» Melissa Morine» Catherine Phillips» Audrey Tierney» Jane Ferguson Trinity College Dublin» James Gibney» Kingston Mills» Luke O Nelll Teagasc» Kanishka Nilaweera Science Foundation Ireland» Principal Investigator Programme Wellcome Trust» Career Development Fellowship National Children s Research Centre Dept of Agriculture & Food» Food Institutional Research Measure EU Framework 6/7» LIPGENE Collaborators» NuGO Collaborators

Tackling the Metabolic Syndrome

Diabet Med 998;5:539-53 The Metabolic Syndrome Tackling the Metabolic Syndrome WHO definition Metabolic syndrome and chronic disease Insulin resistance and metabolic syndrome Across Europe approx 0-25%