Personalized Nutrition by Prediction of Glycemic Responses

1. Personalized Nutrition by Prediction of Glycemic Responses How to Predict the postprandial glycemic responses/PPGR

2. Background • Postprandial hyperglycemia contributes to elevated A1C levels, with its relative contribution being greater at A1C levels that are closer to 7%[1]. • A1c is the primary predictor of diabetes-associated complications[1]. • Factors that may affect PPGR: Genetics, lifestyle, insulin sensitivity, exocrine pancreatic and glucose transporters activity levels (GLUT4), and gut microbiota[1]. • Two sequenced cohort studies (main cohort, validation cohort) and one RCT with 26 individuals are available[2]. References: 1.American Diabetes Association. Standards of medical care in diabetes----2015. Diabetes Care 2015;38 (Suppl. 1):S37 2.David Z., Tal K., Niv Z. et al. Personalized nutrition by prediction of glycemic responses. Cell 2015;163 (5):1079-94

3. Questions to answer • What clinical and microbiome factors could positively or negatively affect the PPGRs? • Whether clinical and microbiome factors could be integrated into an algorithm that predicts individualized PPGRs? • Whether personally tailored dietary interventions based on the algorithm could improve PPGRs?

4. Structure and summary • Part 1 – A cohort with 800 participants • Cohort profile (800): Age, Sex, BMI, A1c, TC, HDL-C, Waist-to-hip, etc. • Data input: CGM (blinded), food intake, lifestyle, exercise, sleep, microbitota, etc. • Duration: 1 week • Part 2 – A cohort with 100 participants • Cohort profile (100): Age, Sex, BMI, A1c, TC, HDL, Waist-to-hip, etc. • Data input: CGM (blinded), food intake, lifestyle, exercise, sleep, microbiota, etc. • Duration: 1 week • Part 3 – A two-arm blinded RCT with 26 participants • Data input: CGM, food intake, lifestyle, exercise, sleep, microbitota, etc. • Experiment vs. control • Duration: 1 week “profile” week + 2 weeks trial phase Abbreviations: CGM: Continuous glucose monitor RCT: Randomized Controlled trial BMI: Body mass index TC: Total cholesterol HDL-C: High-density lipoprotein cholesterol

5. Part 1 A cohort with 800 participants

6. Part 1 A cohort with 800 participants • Baseline data • Food frequency • Lifestyle • Medical background questionnaires • Anthropometric measures • A panel of blood tests • A single stool sample • Glucose levels (including PPGR) – CGM • Subsequently data (Logging into a website) • Food intake (type, weight) • Exercise • Sleep Abbreviations: PPGR: Postprandial glycemic responses

7. Part 1 A cohort with 800 participants • Intrapersonal variability (standard meals) • Not significant • Interpersonal variability (standard meals) • Significant • Found in participants having high PPGRs • Found in participants having normal PPGRs

8. Part 1 A cohort with 800 participants

9. Part 1 A cohort with 800 participants • Interpersonal variability (real-life meals) • Only examined meals that contained 20-40 g of carbohydrates and had a single dominant food component whose carbohydrate content exceeded 50% of the meal’s carbohydrate content

10. Part 1 A cohort with 800 participants

11. Part 1 A cohort with 800 participants • Risk factors positively associated with PPGR • Well established: BMI, A1c, wakeup glucose, systolic BP, age • Meal content (relative), sleep times • ALT, CRP • Hips circumference • Gut microbiota/16S rRNA: Actinobacteria, Coriobacteriia, Coriobacteriales • Gut microbiota/Metagenomics: Gammaproteobacteria, etc. • Gut microbiota/KEGG pathways: ko02020, ko02030, ko02040, etc. • Gut microbiota/KEGG modules: M00226, M00226, etc. • Beneificial factors negatively associated with PPGR • Meal content (relative), exercise • Non-fasting HDL • Gut microbiota/16S rRNA: Tenericutes • Gut microbiota/KEGG pathways: ko02010, ko00240, ko00300, etc. • Gut microbiota/KEGG modules: M00233 Metagenomics: is the study of genetic material recovered directly from environmental samples 16S rRNA: A component of the 30S small subunit of prokaryotic ribosomes ALT: Alanine aminotransferase CRP: C-reactive protein KEGG: Kyoto encyclopedia of genes and genomes is a collection of databases dealing with genomes, biological pathways, diseases, drugs, and chemical substances.

12. Part 2 A cohort with 100 participants

13. Part 2 A cohort with 100 participants

14. Part 2 A cohort with 100 participants • PDP – Factors positively associated with PPGR • Amount of carbohydrate (however, interpersonal variability) • Meal sodium, meal water • Time from last sleep • Fibers (short-term) • Gut microbitoa • PDF – Factors negatively associated with PPGR • Meal’s ratio of fat to carbohydrates or total fat content (however, interpersonal variability) • Fibers (long-term) • Short effect • Gut microbiota PDP: Partial dependence plots

15. Part 3 A RCT with 26 participants • All participants with one week input • Data: microbiome, blood parameters, CGM, etc. • Standard breakfast • Other meals are complied by a dietitian • Control group • Blindly assigned to each arms • One week of “good diet” and another week of “bad diet” compiled by the dietitian • Experimental group • One week “good diet” or “bad diet” (sequence was randomly determined) • Another week the residual type of diet Good diet: A diet composed of the meals predicted by the algorithm or experts to have low PPGRs Bad diet: A diet composed of the meals predicted by the algorithm or experts to have high PPGRs

16. Part 3 A RCT with 26 participants