Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

PREDICT Study ASN Presentation June 2020

7,213 views

Published on

Sarah Berry, Nicola Segata, Jose Ordovas and Tim Spector reveal novel findings from the world's largest ongoing nutrition study, PREDICT. The presentation shares learnings on how we metabolize food, the importance of food sequencing and combining, the gut microbiome and inflammation. These findings are some of the most cutting edge in the field of nutrition science, highlighting the need for precision nutrition. Learn more at www.joinzoe.com/science

Published in: Health & Medicine
  • Be the first to comment

  • Be the first to like this

PREDICT Study ASN Presentation June 2020

  1. 1. Findings from PREDICT: The Personalized Responses to Dietary Composition Trial 1
  2. 2. Speakers Tim Spector, Ph.D Professor of Genetic Epidemiology King’s College London, UK Sarah Berry, Ph.D Senior Lecturer, Department of Nutritional Sciences Nicola Segata, Ph.D Associate Professor, Computational Metagenomics Jose Ordovas, Ph.D Director and Senior Scientist, Nutrition and Genomics Lab 2
  3. 3. The PREDICT Programme Tim Spector, Ph.D Professor of Genetic Epidemiology King’s College London, UK 3
  4. 4. Largest ongoing program to measure postprandial responses to food in nutritional science Nutrition academia Tech companies 4Tim Spector King’s College London, UK
  5. 5. Jun 2018 - May 2019 Jan 2019 - May 2019 n=1,100 PREDICT - Carbs n=100 Healthy 60% TwinsUK Healthy Compliant individuals from PREDICT 1 Postprandial responses Meal order Time of day Clinic & Home (UK/US) Home (UK) STATUS: Complete STATUS: Complete Validation of remote study delivery Postprandial responses Microbiome profiling Jun 2019 – Mar 2020 n=1,000 Healthy Ethnicity Home (US) STATUS: Complete PREDICT - Cardio Sep 2019 - Ongoing n=50 High/Low CVD risk subset of PREDICT 1 Plus Cardiometabolic end-points Liver fat Vascular function Clinic & Home (UK) Continuation of PREDICT 1 Postprandial responses PREDICT 1 Plus n=900 Jun 2019 - Ongoing Healthy TwinsUK Clinic & Home (UK) STATUS: n=250 Postprandial responses Dietary assessment Microbiome profiling July 2020 n=10,000 Healthy Ethnicity Chronic disease Home (US) STATUS: Pending enrolment P R E D I C T 2 PREDICT Postprandial responses P R E D I C T 3 5Tim Spector King’s College London, UK P R E D I C T 1 STATUS: Complete
  6. 6. Nutritional advice in the past and present…evidence is constantly evolving Guidelines take on ‘one-size fits all’ approach 6Tim Spector King’s College London, UK Lack of consensus even amongst expert nutritional scientists
  7. 7. Even the world leading experts don’t agree… 7Tim Spector King’s College London, UK With perfect agreement 1 2 3 1 2 3 4 5 6 7 8 9 10 11 12 13 In reality 13 independent experts 105 food items tested Why? We are complicated and food is complicated
  8. 8. Whole almonds Ground Almonds 0 50 100 150 200 Kcal/serving • Thousands chemicals/nutrients in each food; not just the nutrients listed on a label • Nutrient-nutrient interactions • Food matrix • Processing Why is food complicated? Meals/foods containing same ingredients and/or composition but differing in matrix can have completely different effects What does this mean? That’s because food is complicated… 8Tim Spector King’s College London, UK
  9. 9. How meaningful is the mean? 9Tim Spector King’s College London, UK Large inter-individual variability in responses to food Nutritional studies typically show the mean response Manipulation of lipid bioaccessibility of almond seeds influences postprandial lipemia in healthy human subjects. Berry et al (2007) The need for personalisation Nutritional advice is based on population averages A difference of 106 kcal per serving ~742 kcal per week 4.6 2.3 kcal - 6.0 kcal range 62 kcal 168 kcal “Poor” metabolisers “Good” metabolisers Average serving 28g Metabolizable Energy from Nuts kcal Discrepancy between the Atwater factor predicted and empirically measured energy values of almonds in human diets. Novotny et al (2012) But how many of us are average?
  10. 10. Quality&Precision Quantity Digital devices • Mobile phone apps • Clinical devices, continuous glucose monitors, activity monitors Remote clinical testing DNA, microbiome, blood tests Citizen science Today we can look beyond the mean 10 Traditional Randomized Controlled Trials Big Data Opportunities Tim Spector King’s College London, UK Duplicate Diet Records Epidemiological Studies Food Frequency Questionnaires
  11. 11. Nutrition academia Tech companies Quality and quantity 11 Largest ongoing program to measure postprandial responses to food in nutritional science 11Tim Spector King’s College London, UK
  12. 12. What explains these differences? Can we PREDICT individual responses using machine learning? MEAL CONTEXT GENETICSMICROBIOME AGE / SEX / BMI MEAL COMPOSITION 2. 3. How much variability between people? 1. By using genetic, metabolomic, metagenomic and meal-context information to predict individuals’ response to food 12Tim Spector King’s College London, UK
  13. 13. What makes our work so unique….we are looking at the integrated response and interrelated multi-directional pathways…. 13Tim Spector King’s College London, UK
  14. 14. How we respond to food… using postprandial responses Single Meal 2 1.5 1.0 0.5 0 100 200 300 400 500 Minutes since breakfast 7 6 5 Traditional measures of disease risk Typical Day 50g fat, 85g carb. AJCN. 2011. 94, 1433-41. n=50 2.0 1.5 1.0 06:00 08:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00 00:00 02:00 04:00Time 7 6 5 Breakfast Lunch DinnerSnack Snack Snack PLASMA TRIACYLGLYCEROL PLASMA GLUCOSE INSULIN PlasmaTriacylglycerol(mmol/l) PlasmaTriacylglycerol(mmol/l) PlasmaGlucose(mmol/l) PlasmaGlucose(mmol/l) 14Tim Spector King’s College London, UK
  15. 15. Time since breakfast Astley, C. M. et al. Clin Chem 64, 192-200, doi:10.1373/clinchem.2017.280727 (2018). Bansal, S. et al. JAMA 298, 309-316, doi:10.1001/jama.298.3.309 (2007). Lindman, A. S., . et al. . Eur J Epidemiol 25, 789-798, doi: 10.1007/s10654-010-9501-1 (2010). Nordestgaard, B. G., . et al. . JAMA 298, 299-308, doi:10.1001/jama.298.3.299 (2007). Levitan, . et al. . Arch Intern Med 164, 2147-2155, doi:10.1001/archinte.164.19.2147 (2004). Why do post-prandial peaks matter? 15 PLASMA TRIACYLGLYCEROL PLASMA GLUCOSE INSULIN • Lipoprotein Re-modelling • Oxidative Stress Inflammation • Endothelial Dysfunction • Raised Insulin Secretion • Hunger & Appetite Impact… • Cardiovascular Disease • Metabolic Disease (Type 2 Diabetes, Fatty Liver, Insulin Resistance) • Weight gain Increased risk for… Tim Spector King’s College London, UK
  16. 16. Controlled Time (Mins) BLOOD Fasting 30 6015 120 180 240 2700 300 360 BLOOD BLOOD BLOOD BLOOD BLOOD BLOOD BLOOD BLOOD BLOOD Questionnaires FFQ, Lifestyle & Medical Anthropometry DEXA, waist/hip & BMI Blood pressure and heart rate Genetics, Clinical assays & Metabolomics Metabolomics, Saliva & Urine SALIVA SALIVA Metabolic challenge Other test meals Aims Use genetic, metabolomic, metagenomic and meal-context information to predict individuals’ postprandial responses to food. Validation Cohort n=100 Main Cohort n=1,002 Home Phase (Days 2-14) Dietary Assessment Standardised meals Blood Spot tests Digital devices Stool samples Study app; weighed records; in-study support Nutritionally varied test breakfasts and lunches TAG, C-peptide assays Continuous glucose, physical activity and sleep monitoring Microbiome profiling Clinic Day 2 3 4 5 6 7 9 10 13 1411 128 Baseline Clinic Visit (Day 1) 16Tim Spector King’s College London, UKJun 2018 – May 2019 IRAS 236407 IRB 2018P002078 NCT03479866
  17. 17. 2,022,000 CGM glucose readings Mean (SD) Age (yr) 45.7 (12.0) BMI (kg/m2) 25.6 (5.0) Sex (%) 72 F/ 28 M Triacylglycerol (mmol/L) 1.1 (0.5) Insulin (IU/mL) 6.1 (4.3) Glucose (mmol/L) 5.0 (0.5) Total cholesterol (mmol/L) 5.0 (1.0) n=1,002 MZ Twins 479 DZ Twins 172 Non-Twins 351 Drop-out 2.5% The scale of the PREDICT 1 study 32,000 Muffins consumed 28,000 TAG readings 132,000 Meals logged 17Tim Spector King’s College London, UK
  18. 18. Decoding human responses to food for precision nutrition 18 Sarah Berry, Ph.D Senior Lecturer, Department of Nutritional Sciences King’s College London
  19. 19. Significant variability between healthy individuals Baseline 6h rise CV 50% 103% Triacylglycerol Glucose Insulin 19Sarah Berry King’s College London, UK Baseline 2h iAUC CV 10% 68% Baseline 2h iAUC CV 69% 59% TAG(mmol/L) Glucose(mmol/L) Insulin(mmol/L) Clinic day data, n = 1,002
  20. 20. Intra-individual variability is lower than inter-individual variability (6h rise, n=1018 meals at home and in clinic) Interindividual CV is calculated for identical meals, between random pairs of individuals. Intraindividual CV is calculated between pairs of nutritionally identical meals for the same individual Triacylglycerol Glucose (iAUC 0-2h, n=7898 meals at home) 20Sarah Berry King’s College London, UK Differences between individuals are repeatable Intra-individual Inter-individual CV 36% 68% Intra-individual Inter-individual CV 24% 40%
  21. 21. Identical twins have very different responses Height Glucose (iAUC 0-2h) Triacylglycerol (6h iAUC) ACE Heritability Modelling Key: 21Sarah Berry King’s College London, UK Genetics do not explain most nutritional differences Genetics Upbringing Environment 48% 5 % 48% 48% 52%46% 50% 4% TwinMZ2GLU0-2hiAUC Twin MZ 1 GLU 0-2h iAUC Twin MZ 1 Log scaled 6h TAG iAUCTwin MZ 1 Height TwinMZ2Height TwinMZ2Logscaled6hTAGiAUC
  22. 22. What causes the variability in responses? 22Sarah Berry King’s College London, UK Glucose iAUC 0-2h R2 adjusted Triacylglycerol 6hr rise R2 adjusted * P<0.05, ** P<0.01, *** P<0.001
  23. 23. Machine Learning can predict individual responses Machine learning model correlates 77% to measured glucose responses 23 Machine Learning model uses test results to predict responses to new meals Individual takes test Sarah Berry King’s College London, UK Pearson R = 0.77; p = 0
  24. 24. High resolution and high density measures allows deep dive 24Sarah Berry King’s College London, UK Meal context Time of day, Meal sequence, Exercise Lipoprotein re-modelling Oxidative stress Inflammation Endothelial dysfunction Raised insulin secretion Meal composition Genetics Microbiome Age Blood pressure Serum measures Anthropometry Habitual diet Sex Hunger & appetite
  25. 25. 0 2000 4000 6000 8000 10000 12000 14000 MealiAUC(mmol/l*2h) Breakfast Lunch Time of day influences the postprandial response, but as we age, this largely disappears 13:00 25Sarah Berry King’s College London, UK 9:00 +4h
  26. 26. Home-based Intervention (Days 2-13) Study app; weighed diet records; in-study support Set-up Day 1 2 3 4 5 6 7 9 10 118 Continuous glucose, physical activity and sleep monitoring TAG TAG 12 13 Nutritionally varied test meals: breakfast, lunch, snack, sweetener preloads Daily intervention timeline Free-livingFasted 3-4h fast 3h fast 2h fast Breakfast Lunch Snack Microbiome profiling Dietary Assessment Standardised meals Blood Spot tests Digital devices Stool samples 1. Effects on glycaemic responses of: i. carbohydrate staple foods ii. composite meals iii. sweetener preloads iv. time-of-day v. meal-sequence 2. Collect free-living dietary intake and energy expenditure data to validate the Zoe app Aims Single-staple breakfasts White bread Rye bread Pasta Mashed potatoes Rice Composite staple lunches White bread & cheese Rye bread & cheese Spaghetti bolognaise Cottage pie Chickpeas & chicken Staple snacks & sweeteners Sucralose, Aspartame, Stevia preloads Biscuits and juicePotato crisps Chocolate bar Dietary Intervention n=100 26Sarah Berry King’s College London, UK 26Jan 2019 – May 2019 IRAS 236407 NCT03479866
  27. 27. 27INTERIM UNPUBLISHED DATA Using carbohydrate-rich test meals at breakfast/lunch Type of breakfast can impact glycaemic response at lunch eaten 4 hours later Sarah Berry King’s College London, UK Lunchtime response affected by breakfast meal
  28. 28. Breakfasts Time since breakfast (min) Glucose dip Divergent responses seen 2-3 hours after a meal 28Sarah Berry King’s College London, UKINTERIM UNPUBLISHED DATA
  29. 29. High resolution and high density measures allows deep dive 29Sarah Berry King’s College London, UK Lipoprotein re-modelling Oxidative stress Inflammation Endothelial dysfunction Raised insulin secretion Hunger & appetite Meal context Time of day, Meal sequence, Exercise Meal composition Genetics Microbiome Age Blood pressure Serum measures Anthropometry Habitual diet Sex
  30. 30. Impact of glucose ‘dips’ on hunger and calorie intake 30 Individuals with smallest 25% of Glucose Drop (Q1), versus largest 25% of Glucose Drop (Q4) Sarah Berry King’s College London, UK Glucose dip UK average meal (mmol/L) Large inter-individual variability in ‘dippers’ – even in response to the same meal Frequency +9 (+/-4) -2 (+/-2) -25 mins(+/-10) +79 Kcal(+/-28) +321 Kcal(+/-87) Hunger+2-3hpost-meal–pre-meal Alertness+2-3hpost-meal Minutessincefirstmeal(mins) Calories(Kcal) Calories(Kcal) Q1 Q4 Q1 Q4 Q1 Q4 Q1 Q4 Q1 Q4 Increase in hunger Alertness level Time until next meal Calories eaten 3-4h Calories eaten in full day n=689, meals=5693
  31. 31. iAUC? Increase from fasting? Duration? Peak concentration? 1.8 1.6 1.4 1.2 1.0 0.8 0.6 0 100 200 300 400 500 PlasmaTriacylglycerol(mmol/l) Minutes since breakfast Late postprandial rise? The response curve is complicated – what feature matters most? 31Sarah Berry King’s College London, UK • Peak Concentration? • iAUC? • Increase from fasting? • Duration? • Late postprandial rise? AJCN. 2011. 94, 1433-41
  32. 32. Postprandial Lipoprotein remodelling associated with CVD Inflammatory marker associated with CVD Outcomes from the metabolomic analysis include measures of cardiovascular inflammation and atherogenic lipoprotein remodelling Lipoprotein subclasses Lipoprotein compositional re-modelling 32Sarah Berry King’s College London, UK HDL VLDL CE TAG PL CE TAG PL Cholesteryl ester Transfer protein Cholesteryl ester Transfer protein Diameter (nm) Density(g/ml) Postprandial-induced inflammation IL-6 and GlycA Incident CVD; FINRISK (n=7256), SABRE (n=2622), British Woman's Health Study (n=3563); Circulation, 2015.
  33. 33. The 6h increase in TAG from fasting values is most strongly associated with intermediary cardiometabolic risk factors 33Sarah Berry King’s College London, UK Atherogenic lipoproteins and inflammatory markers most closely associated with a prolonged postprandial response 0.1 0.2 0.3 0.4 0.5 n=1,002 Remnant-C HDL-C Gp XL-VLD-C XL-VLD-P XXL-VLD-C XXL-VLD-P 4h TAG iAUC 6h TAG iAUC 4h TAG Concentration 4h TAG increase 6h TAG Concentration 6h TAG increase
  34. 34. Food-induced inflammation is highly variable, clinically relevant and determined mainly by postprandial lipaemia IL-6 GlycA • Significant, clinically relevant increase postprandially • Correlated with peak in glucose (r=0.239) and TG (r=0.832) • ML shows lipaemia is stronger determinant, specifically TG 6h-rise • Increases postprandially • Not correlated with glycaemica or lipaemia Time (min) 0 (Breakfast) 240 (Lunch) 360 10 15 20 25 30 Interleukin-6(mmol/l) 10 15 20 25 30 Glycoproteinacetylation(mmol/l) Time (min) 0 (Breakfast) 240 (Lunch) 360 34Sarah Berry King’s College London, UK Doubled ASCVD risk for individuals with >90th percentile GlycA increase
  35. 35. Neck-to-knee XMR imaging Liver lipid quantification PREDICT 1 PlusPREDICT - Cardio Pulse wave velocity Plaque grading Carotid intima-media thickness 1. Measure clinical intermediary cardiometabolic outcomes, in fasted PREDICT 1 Plus participants 2. Determine the link between postprandial glycaemic/ lipaemic responses and cardiometabolic disease Clinic Day 1 2 3 4 5 6 7 9 10 118 Aims 35Sarah Berry King’s College London, UK Sub-cohort of PREDICT 1 Plus n=50 Sep 2019 – Feb 2020 IRAS 236407 NCT03479866
  36. 36. Summary 36Sarah Berry King’s College London, UK • Everyone is unique in food response – even identical twins • Genetics explains only a fraction of how we respond to foods • The composition of the meal explains <30% of our responses • How and when we eat can impact our response to food • Dissecting the integrated, inter- related multidirectional pathways with large scale, high resolution data makes precision nutrition achievable
  37. 37. The hidden human microbiome diversity and personalized host- microbiome interaction 37 Nicola Segata, Ph.D Associate Professor, Computational Metagenomics, CIBIO, University of Trento @nsegata
  38. 38. Species-level features #samples 232 121 110 253 344 96 Cirrhosis Colorectal IBD Obesity T2D WT2D Pasolli et al., PLoS Comput Biol, 2016 Human Microbiome Project, MetaHIT Thomas, Manghi et al., Nat Med, 2019 Nine total (and concordant) datasets for Colorectal cancer But what about the microbiome in “health” levels and pre-disease? What is health and what is disease for the microbiome? 38Nicola Segata University of Trento, Italy The microbiome in disease The microbiome in health
  39. 39. On the road to link the gut microbiome with food and metabolic health 39Nicola Segata University of Trento, Italy
  40. 40. Truth is, we are all unique Even identical twins have very different responses to food 40Nicola Segata University of Trento, Italy
  41. 41. Subjects from around the world (~3000 sbj from 4 continents) Subjects from US (from two universities) Subjects from EU (6 countries) Samples from same subjects collected at ~6 months Subjects from EU Subjects from US Truong et al., Genome Research, 2017 Truth is, also our microbiome is unique 0.250 0.275 0.300 0.325 0.350 0.400 0.425 0.375 Commonspecies(%) Twin pair Unrelated 41Nicola Segata University of Trento, Italy
  42. 42. The PREDICT 1 study at ZOE 42 1,002 samples Predict 1 (UK Cohort) 100 samples Predict 1 (US Cohort) Food Frequency Questionnaire Clinic data Serum metabolomics Continuous Glucose Monitor Stool metagenomics The microbiome data 769 species 29 spp. 90% prevalent 91 spp. 50% prevalent 174 spp. 20% prevalent UniRef90 1,910,069 UniRef50 878,520 KEGG KOs 6,163 Pathways 445 Taxonomic Functional Assembly 48,181 MAGs 29,035 MQ 19,146 HQ 8.8 avg. 2.2 sd. Gb/sample 58.3 avg 14.6 sd. Mreads/sample Metadata Age, BMI, weight, waist/hip ratio, visceral fat, antibiotic usage, blood pressure Personal Tot. 18 Foods, Food groups, Nutrients, Nutrients % kcal, Dietary patterns Habitual diet Tot. 275 Lipoproteins, ApoLipoproteins, Risk scores, Glucose mediated, FA’s metabolism Fasting Tot. 247 All fasting measures up to 7 timepoints including max values and rises Post prandial Tot. 484 42Nicola Segata University of Trento, Italy
  43. 43. The workflow of shotgun metagenomics 43Nicola Segata University of Trento, ItalyQuince, Walker, Simpson, Loman, and Segata. Nature Biotechnology, 2017
  44. 44. The PREDICT 1 study at ZOE 4444Nicola Segata University of Trento, Italy
  45. 45. How strongly is the microbiome linked with habitual diet? 45Nicola Segata University of Trento, Italy
  46. 46. And the associations re reproducible in the US cohort! aMed scoreHFD 46Nicola Segata University of Trento, Italy
  47. 47. What are the microbes most associated with foods? 47 Beef, poultry, sausages, pork, ham Shellfish, oily fish, eggs Red wine YogurtJam Dark Chocolate Tomato Ketchup Sauces Support for health-association for the two clusters: p-value < 1e-20 Baked beans 47Nicola Segata University of Trento, Italy
  48. 48. What are the microbes most associated with foods? 48 Healthy plant-based Less healthy plant-based Unhealthy animal-based Healthy animal-based 48Nicola Segata University of Trento, Italy
  49. 49. What are the microbes most associated with habitual diet? 4949Nicola Segata University of Trento, Italy
  50. 50. From microbe-food links to microbe-obesity links 50Nicola Segata University of Trento, Italy
  51. 51. Postprandial Lipoprotein remodelling associated with CVD Inflammatory marker associated with CVD Metabolomics for measures of cardiometabolic health Incident CVD; FINRISK (n=7256), SABRE (n=2622), British Woman's Health Study (n=3563); Circulation, 2015. Lipoprotein subclasses Lipoprotein compositional re-modelling HDL VLDL CE TAG PL CE TAG PL Cholesteryl ester Transfer protein Cholesteryl ester Transfer protein Diameter (nm) Density(g/ml) Postprandial-induced inflammation IL-6 and GlycA 51Nicola Segata University of Trento, Italy
  52. 52. The gut microbiome and fasting cardio-metabolic markers 52Nicola Segata University of Trento, Italy
  53. 53. 53 The gut microbiome and fasting cardio-metabolic markers 53Nicola Segata University of Trento, Italy
  54. 54. Fasting and postprandial level are more predictable than rises Total lipid concentrationsInflammatory measures Lipoprotein particle sizeLipoprotein concentrations Glycaemic mediatedApoLipoproteins 54Nicola Segata University of Trento, Italy
  55. 55. 5555Nicola Segata University of Trento, Italy
  56. 56. An overall signature of the “healthy” microbiome “in health” 56Nicola Segata University of Trento, Italy
  57. 57. • Thousands of unknown species • Millions of unsampled genes • Missing links with diseases and conditions …but a lot of the human microbiome diversity is still unexplored Mapping reads (%) Yes No 0 20 40 60 80 100 Average mapping reads (%) Skin Oral Cavity Stool Vagina 0 20 40 60 80 100 Westernized 57Nicola Segata University of Trento, Italy
  58. 58. The future of precision Nutrition 58 Jose Ordovas, Ph.D Director and Senior Scientist, Nutrition and Genomics Lab, Tufts University
  59. 59. International Food Information Council Foundation: 2018 Food and Health Survey. 2018. https://foodinsight.org/wp-content/uploads/2018/05/2018-FHS-Report-FINAL.pdf. 59Jose Ordovas Tufts, USA Before moving into the future let’s peek the present: Cardiovascular Health Top Desired Benefit from Food Weight loss, energy, and brain function also rank as top benefits consumers are interested in getting from food. 24% of African Americans ranked weight loss as a top three health benefit, compared to 41% of non-Hispanic whites More older adults (65+) ranked bone health and diabetes management in top 3 benefits from food Interest in health benefits from food and nutrients 59
  60. 60. Source: https://www.foodnavigator.com/Article/2019/02/06/Food-confusion-prevalent-in-Europe-says-Arla-Foods# Eighty per cent of consumers report finding information about food and nutrition conflicting. Fifty-nine per cent say that conflicting information makes them doubt their choices. This significant consumer segment also experiences heightened stress while shopping Jose Ordovas Tufts, USA International Food Information Council Foundation: 2018 Food and Health Survey. 2018. https://foodinsight.org/wp-content/uploads/2018/05/2018-FHS-Report-FINAL.pdf. The Concern: Consumer confusion about food and nutrition is a prevalent issue 60
  61. 61. Jose Ordovas Tufts, USA International Food Information Council Foundation: 2018 Food and Health Survey. 2018. https://foodinsight.org/wp-content/uploads/2018/05/2018-FHS-Report-FINAL.pdf. Conflicting information creates “Confusion” 61
  62. 62. Jose Ordovas Tufts, USA International Food Information Council Foundation: 2018 Food and Health Survey. 2018. https://foodinsight.org/wp-content/uploads/2018/05/2018-FHS-Report-FINAL.pdf. Level of trust vs. Reliance as a source Health professionals trusted and used by consumers to guide health and food decisions Relation between trust and reliance 62
  63. 63. With limited access/knowledge to clear, science-based, unbiased nutrition information, public trust in generalized nutrition guidelines is compromised. Jose Ordovas Tufts, USA International Food Information Council Foundation: 2018 Food and Health Survey. 2018. https://foodinsight.org/wp-content/uploads/2018/05/2018-FHS-Report-FINAL.pdf. Familiarity with the MyPlate graphic 59% Have seen the MyPlate graphic 69% of parents with children under 18 have seen the MyPlate graphic Younger consumers. Those in better health, parents and women are particularly familiar with the icon More education is needed: Three in ten know a lot/fair amount about MyPlate 63
  64. 64. • In 2017 ~11 million global deaths and 255 million disability-adjusted life years (DALYs) could be attributed to dietary risk factors. • Treating Chronic Diseases within the current healthcare model accounts for a staggering 90% of the United States $3.3 trillion healthcare costs. • Global nutrition recommendations have failed to reduce the incidence of chronic disease. Jose Ordovas Tufts, USA Why is precision nutrition needed? 64 1995 2000 2005 2010 2003 2015 2011 Dietary guidelines for Americans USDA
  65. 65. A “Proof of Principle” study of Personalised Nutrition across Europe: The Food4Me intervention study This project has received funding from the European Union’s Seventh Framework Programme for research, technological development and demonstration. (Contract n°265494) Jose Ordovas Tufts, USA Does precision nutrition work? 65
  66. 66. Is personalised nutrition advice more effective than general healthy eating guidelines? Is phenotypic or genotypic information more effective than diet-based advice alone? Is the internet a successful delivery method? Research questions Jose Ordovas Tufts, USA 66
  67. 67. Level 0: Generic dietary advice (Control) Level 1: Personalization based on DIETARY analysis Level 2: Personalization based on DIETARY + PHENOTYPIC analysis Level 3: Personalization based on DIETARY + PHENOTYPIC + GENOMIC analysis Randomized to 4 Arms Jose Ordovas Tufts, USA 67
  68. 68. Celis-Morales C et al. (2017) Int. J. Epidemiol. 46, 578-588 Jose Ordovas Tufts, USA Personalized nutrition improved dietary behaviour (All participants) RedMeatIntake (g.day-1) HealthyEating index Control (LO) Personalized nutrition (Mean L1, L2, L3) L1 L2 L3 68
  69. 69. Celis-Morales C et al. (2017) Int. J. Epidemiol. 46, 578-588 Jose Ordovas Tufts, USA PN improved dietary behaviour (participants receiving targeted advice) SaturatedFatIntake (%fromtotalenergy) FolateIntake (μg.day-1) Control (LO) Personalized nutrition (Mean L1, L2, L3) L1 L2 L3 69
  70. 70. Challenge - Nutrition research: Data management, study design and translation. Meeting the challenge: Precision Nutrition research and data (PREDICT) Large Datasets Nutrition trials Integrated responses Dynamic responses Jose Ordovas Tufts, USA Meeting the Challenges of Nutrition with Precision Nutrition 70
  71. 71. Summary Tim Spector, Ph.D Professor of Genetic Epidemiology King’s College London, UK 71
  72. 72. Traditional targets for personalisation Integrating multiple factors for comprehensive personalisation Past, present, and future of precision nutrition The past (1950s – present) The present (2020) Meal composition Genetics Meal context Serum glycemic markers Microbiome Age Serum lipid markers Blood pressure Anthropometry Other serum markers FFQ Sex New Studies • PREDICT 2 • PREDICT 3 New Technologies • Non-invasive Real time Biosensors • POC Lab-on-a-chip • Artificial Intelligence 2.0 New Knowledge for Professionals and Individuals • Empowering the Health Professional • Empowering the Individual • Precision Public Health • Insurance Coverage The future 72Tim Spector King’s College London, UK
  73. 73. 73Tim Spector King’s College London, UK PREDICT - Carbs PREDICT - Cardio PREDICT 1 Plus P R E D I C T 2 PREDICT P R E D I C T 3 PREDICT1 J F M A M J J A S O N D J F M A M J J AJ J A S O N D 2018 2019 2020 Completed Ongoing Pending enrolment The PREDICT program PREDICT Ongoing
  74. 74. King’s College London Tim Spector Sarah Berry Deborah Hart ZOE Richard Davies Jonathan Wolf George Hadjigeorgiou University of Trento, Italy Nicola Segata (PI) Francesco Asnicar Massachusetts General Hospital & Harvard University Linda Delahanty Oxford University Leanne Hodson Mark McCarthy Massachusetts General Hospital Andrew T. Chan David Drew Lund University, Sweden Paul Franks Acknowledgements Harvard University Curtis Huttenhower University of Leeds John Blundell University of Nottingham Ana Valdes Tufts University Jose Ordovas

×