Effects of polyphenol intake on cardiovascular risk markers

Transcription

1 Effects of polyphenol intake on cardiovascular risk markers OMICS: Advances, Applications and Translation in Nutrition and Epidemiology Boston, May 30, 2017 Rosa M. Lamuela-Raventós Departament of Nutrition, Food Science and Gastronomy School of Pharmacy and Foos Sciencs INSA-University of Barcelona CIBEROBN

2 Polyphenols Most abundant antioxidants in daily diet. Widely spread around plant-derived foods such as fruits, vegetables, cereals, legumes, and beverages.

3 Daily intakes Daily polyphenol intakes in different countries/regions (mg/day) Country/Region Population Number Database Daily polyphenol intakes Study European Adults 36,037 Phenol-Explorer 1,186 EPIC Mediterranean Elderly 304 Phenol-Explorer Italy Elderly 811 Own database 594 InCHIANTI Polish Adults 10,728 Phenol-Explorer 1,756 HAPIEE Finland Adults 2007 USDA 863 Brazil Adults 1,103 Phenol-Explorer FINDIET2002 Health SurveySão Paulo Spain Elderly 7,200 Phenol-Explorer 820 PREDIMED

4 Classification (Phenol Explorer) Polyphenol Phenolic acids Flavonols Stilbenes Flavones Flavonoids Isoflavones Flavanones Lignans Flavanols Others Anthocyanidins

5 Polyphenol Intake and Cardiovascular Health longitudinal Intakes of polyphenols were adjusted for total energy intake (residual method) and cumulative averages were calculated for each participant. FFQ 1 Polyphenol intake 1 FFQ 2 Polyphenol intake 2 Cumulative average 2 FFQ 3 Polyphenol intake 3 Cumulative average 3 FFQ n Polyphenol intake n Cumulative average n Treserra-Rimbau BMC Med May 13;12:77. doi: /

6 Polyphenol Intake and Cardiovascular Health Total polyphenol, flavanols, hydroxybenzoic acids and lignans intake were significantly and linearly associated with CV events after adjusting for confounders. Total polyphenols Flavonoids Anthocyanidins Dihydrochalcones Dihydroflavonols Flavanols Flavanones Flavones Flavonols Phenolic acids Hydroxybenzoic acids Hydroxycinnamic acids Other phenolic acids Stilbenes Lignans Others Total polyphenols HR 0.54; CI 0.33 to 0.91; P-trend=0.04 Flavanols HR 0.40; CI 0.23 to 0.72; P-trend=0.003 Hydroxybenzoic acids HR 0.47; CI 0.26 to 0.86; P-trend=0.02 Lignans HR 0.51; CI 0.30 to 0.86; P-trend= cases 0 0,5 1 1,5 2 Treserra-Rimbau Nutr Metab Cardiovasc Dis Jun;24(6): doi: /j.numecd Epub 2014 Jan 22.

7 Polyphenol Intake and Mortality Risk Treserra-Rimbau BMC Med May 13;12:77. doi: /

8 Polyphenol Intake and Mortality Risk TOTAL POLYPHENOLS AND SUBGROUPS Hazard ratios (95% CI) of total mortality for the highest vs. lowest quintiles of polyphenol intake. Total polyphenols HR 0.63; CI 0.41 to 0.97; P-trend=0.12 Isoflavones HR 0.49; CI 0.28 to 0.84; P-trend=0.009 Stilbenes HR 0.48; CI 0.25 to 0.91; P-trend=0.04 Lignans HR 0.60; CI 0.37 to 0.95; P-trend= deaths Treserra-Rimbau BMC Med May 13;12:77. doi: /

9 In Summary in the PREDIMED CVD Total Polyphenols Lignans Flavanols Hydroxybenzoics Isoflavones Mortality Total Polyphenols Lignans Stilbenes Isoflavones

10 Sources of polyphenols Total polyphenols - PREDIMED cohort - baseline Total polyphenol intakes : 820±323 mg/day Flavonoids intakes: 443±218 mg/day Phenolic acids intakes: 304±156 mg/day Treserra-Rimbau Nutr Metab Cardiovasc Dis Oct;23(10): doi: /j.numecd Epub 2013 Jan 17.

11 Polyphenols and Diabetes Objective To study the association between polyphenol intake (total and by groups) and type 2 diabetes within the PREDIMED cohort. Observational and longitudinal study within the PREDIMED Free of diabetes at baseline 3430 participants 5.5±2.0 years of follow-up 314 new cases of diabetes 7447 participants Treserra-Rimbau J Nutr Mar 9. pii: jn

12 Results Polyphenol Intake and Diabetes 28% 33% HRs (95% CI) of type 2 diabetes incidence for the highest vs. the lowest tertiles of polyphenol intake (fully adjusted model). 41% 43% 43% Treserra-Rimbau J Nutr Mar 9. pii: jn

13 Needs for biomarkers Biomarker of Polyphenol Intake Diversity of polyphenols Difficult in evaluation of habitual food intake Diversity of bioavailability Diversity of metabolism

14 Strengths and Limitations Biomarkers can improve the information acquired by FFQ

15 Urinary Total Polyphenol Excretion Urinary total polyphenol excretion, after a solid phase extraction (SPE) clean-up, analyzed by a Folin-Ciocalteu (F C) assay, could be considered as an accurate biomarker of polyphenol-rich food intake

16 Hypothesis and Aims We hypothesized that a high dietary polyphenol intake, recorded by urinary polyphenol excretion (TPE), could be associated to low CVD risk in an elderly population with high cardiovascular risk. Evaluate the cardiovascular protective role of dietary polyphenols, total polyphenol excretion (TPE) in urine as a reliable biomarker in the PREDIMED population.

17 Polyphenol and CVD Risk Factors Assessed for eligibility (n=650) Participants Finally, 573 were selected for data analysis 5-year of follow-up Excluded (n=38) Not meeting inclusion criteria (n=19) Declined to participate (n=9) Not able to change diet (n=7) Alcoholism (n=3) Randomized (n=612) Excluded because of extremely values (n=39) Allocated into 3 groups according to change of TPE during 5-year of follow-up T1 (n=191) T2 (n=191) T3 (n=191) Guo, X et al. Nutrients May 3;9(5). pii: E452. doi: /nu

18 Polyphenol and CVD Risk Factors Cardiovascular risk factors 573 participants were randomly selected Urinary samples were collected both at baseline and 5-year of intervention. TPE were measured with Folin-Ciocalteu method after solid phase extraction. Our aim was to assess the association between urinary total polyphenols excretion (TPE) and clinical cardiovascular risk factors (glucose, cholesterol, HDL-cholesterol, LDL-cholesterol, triglycerides, systolic blood pressure, diastolic blood pressure, body weight) in a population at high cardiovascular risk. Evaluate dietary polyphenol intake expressed by TPE Evaluate cardioprotective effects of polyphenol intake Evaluate effects of polyphenol intake on body weight Guo, X et al. Nutrients May 3;9(5). pii: E452. doi: /nu

19 Clinical Measurements Measurements Blood Pressure Total cholesterol Cholesterol HDL-cholesterol Glucose LDL-cholesterol Triglyceride Body weight BMI Anthropometic Waist circumference Weight to height ratio

20 Total polyphenol extraction in urine Total polyphenol excretion in urine during 5-year of intervention Total polyphenol excretion (mg GAE/g creatinine ) 180,00 160,00 140,00 120,00 100,00 80,00 60,00 40,00 20,00 0,00 Total Baseline 113,06 5-year 120,64 Male Female Younger Elderly Elder Olive oil Nuts Control Low-fat Smoker Former Somker None smoker P<9 P>9 100,38 124,92 106,54 119,42 109,54 114,75 117,58 98,46 110,36 118,08 111,33 115,45 107,11 133,30 110,94 130,10 121,95 118,22 122,66 105,88 122,32 124,12 120,35 121,03 Baseline 5-year Guo, X et al. Nutrients May 3;9(5). pii: E452. doi: /nu

21 Baseline characteristic of participants Body weight status Smoking status 350 Current 17% Former 22% Never 61% Current Former Never normal overweight obesity Guo, X et al. Nutrients May 3;9(5). pii: E452. doi: /nu

22 Changes in food and nutrients Nutrients Total Carbohydrate Protein Fat MUFA SFA PUFA Fiber Alcohol Cholesterol Changes

23 TPE and CVD risk factors Association between TPE and cardiovascular risk factors Change in Glucose (mg/dl) Change in Triglycerides (mg/dl) Change in Diastolic Blood Pressure (mm Hg) Model 1 Model 2 Model 3 Model 4 Model 1 Model 2 Model 3 Model 4 Model 1 Model 2 Model 3 Model 4 β SE Beta sig % CI Significant inverse associations were found between tertiles of changes in TPE and glucose (β=-4.372; P=0.026), triglycerides (β=-8.572; P=0.006) and DBP (β=-1.156; P=0.031) after adjustment for potential confounders. Guo, X et al. Nutrients May 3;9(5). pii: E452. doi: /nu

24 Stratified analysis for CVD risk factors Male Triglycerides P>9 Cardiovascular risk factors DBP N 236 Male Gender N < N P-14 <9 9 Change in TG (mg/dl) Q1 Q2 Q3 mean SD mean SD mean SD Pa Female P-14 P> Change in TG53.14 (mg/dl) Q1 Q2 Q3 Pa mean SD mean SD mean SD Change in DBP (mg/dl) Q1 Q2 Q3 mean SD mean SD mean SD Pa Guo, X et al. Nutrients May 3;9(5). pii: E452. doi: /nu

25 Polyphenols and Inflammatory Biomarkers Medina-Remón A. et al. Br J Clin Pharmacol Jan;83(1): doi: /bcp.12986

26 Anthropometric parameters at baseline and after 5 years according to quintiles of TPE (mg GAE/g creatinine) 80 79,98 80,5 Body weight Baseline Body weight (Kg) ,2475,07 29,5 76,4276, , ,92 71,52 70, ,53 Q1 29,3 29,09 28,85 28,9 28,4 28,5 Q2 Q3 Q4 Q5 27,5 Q1 99,96 Baseline 97,13 96, year Q3 61,8 Baseline 95,89 95,18 94 Q4 Q5 94,1594, year 61,37 61,5 98,78 98,03 96 Q2 Waist to Height Ratio WHt R(cm/m) Waist circumference (cm) 101, ,44 29,07 28,98 Waist Circumference year Baseline 29, BMI 30 5-year BMI 82 60,95 60,84 60,46 60,5 60,8 60,37 60, ,12 59,92 59,5 59 Q1 Q2 Q3 Q4 Q5 58,5 Q1 Q2 Q3 Q4 Q5

27 Association between TPE and Anthropometrics Multivariate linear regression analyses with obesity indexes and quintiles of TPE (mg GAE/g creatinine) at 5-year BW (kg) BMI WC (cm) WHtR (cm/m) Model 1 Model 2 Model 3 Model 4 Model 1 Model 2 Model 3 Model 4 Model 1 Model 2 Model 3 Model 4 Model 1 Model 2 Model 3 Model 4 β SE Beta P < < < %CI Significant inverse associations were found between quintiles of TPE at 5 years and BW (β=-1.004; P=0.002), BMI (β=-0.320; P=0.005), WC (β=-0.742; P=0.013) and WHtR (β=-0.408; P=0.036) after adjustment for potential confounders.

28 TPE and Prevalence of Obesity Multivariate adjusted odds ratios (95% CI) for obesity (n=213) Model 1 Model 2 Model 3 Model 4 Q1 1 (ref.) 1 (ref.) 1 (ref.) 1 (ref.) Q % CI ( ) ( ) ( ) ( ) Q % CI ( ) ( ) ( ) ( ) Q % CI ( ) ( ) ( ) ( ) Q % CI P-trend ( ) ( ) ( ) ( ) In fully-adjusted models, participants in the category of highest TPE had a lower prevalence of obesity (odds ratio (OR) = 0.346, 95% confidence interval (CI) to 0.678; P-trend, 0.039) than those in the lowest category.

29 TPE and Incidence of Obesity Multivariate adjusted odds ratios (95% CI) for obesity (n=213) In fully-adjusted models, participants in the category of highest TPE had a lower incidence of obesity (odds ratio (OR) = 0.095, 95% confidence interval (CI) to 0.498; P-trend, 0.018) than those in the lowest category.

30 Stratified Analysis Gender Antiobesity Age Male BW Older BMI BW BMI WC WHtR Males showed significant inverse associations with BW (β=-1.004; P=0.031) and BMI (β=-0.298; P=0.036). All adiposity parameters [BW (β=-1.358; P=0.002), BMI (β=-0.466; P=0.003), WC (β=1.061; P=0.012), and WHtR (β=-0.623; P=0.023))] were lower in the older group (age 67).

31 Strengths The prospective design, relatively long-term follow-up and comprehensive data on risk factors and confounders. The evaluation of overall effects of polyphenol intakes on specific cardiovascular risk factors, which provide clinical evidence of the protective role of polyphenol intake against CVD. The use of a reliable biomarker of total polyphenol intakes. The Folin-Ciocalteu assay is a rapid, cheap, and environmentally friendly measurement that can be applied in large intervention studies.

32 Limitations Our sub-study was conducted only among elderly subjects at high cardiovascular risk; therefore, the results should not be extrapolated to the general population. Residual confounding could still exist even though we adjusted for potential confounders related to lifestyles, profiles of participants, family history of CVD, and eating habits. Possible synergistic effects between different types of polyphenols and other dietary components have not been measured. Intrinsic limitations of biomarkers: measurement error from the analysis and variability between individuals.

33 Future Biomarkers analysis of these polyphenols (mainly from microbiota) in urines is necessary to corroborate our results and to evaluate the possible mechanism of action Cueva et al. Molecules 2017, 22(1), 99; doi: /molecules

34 Targeted and Untargeted Metabolomics

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37 Enterolactone monosulfate XIC of -MRM (20 pairs): / Da ID: ED from Sample 73 (ORINA PAO) of LIN_RECU.wiff (Turbo Spray) Max. 1.5e5 cps. XIC of -MRM (20 pairs): / Da ID: ED from Sample 73 (ORINA PAO) of LIN_RECU.wiff (Turbo Spray) Max. 1.5e5 cps. 2.4e6 2.3e62.4e6 2.2e62.3e6 2.1e62.2e6 2.0e62.1e6 XIC of -MRM (20 pairs): / Da ID: ED from Sample 73 (ORINA PAO) of LIN_RECU.wiff (Turbo Spray) 1.9e62.0e6 2.3e6 1.7e61.8e6 2.2e6 2.1e6 1.5e61.6e6 2.0e6 1.4e61.5e6 1.9e6 In te n s ity, c p s In te n s ity, c p s 1.6e61.7e6 1.7e6 1.6e6 1.1e61.2e6 1.5e6 1.0e61.1e6 1.4e6 In te n s ity, c p s 1.2e61.3e6 8.0e59.0e5 1.2e6 1.1e6 1.0e6 6.0e57.0e5 9.0e5 8.0e5 7.0e5 4.0e55.0e5 6.0e5 3.0e54.0e5 5.0e5 2.0e53.0e5 4.0e5 1.0e52.0e5 3.0e e5 0.0 Enterodiol 1.3e6 7.0e58.0e5 5.0e56.0e5 Enterolactone 1.8e6 1.3e61.4e6 9.0e51.0e6 Enterolactone glucuronide 2.4e6 1.8e61.9e6 Max. 1.5e5 cps e5 1.0e Time, 7.0 min Time,Time, min min

38 PREDIMED participants

39 PREDIMED researchers Estruch, Ramón (Hospital Clínic, IDIBAPS, Universidad de Barcelona) Ros, Emilio (Hospital Clínic, IDIBAPS, Barcelona) Salas-Salvado, Jordi (Hospital Universitari Sant Joan, IISPV, eus) Fitó, Montse (Institut de Recerca Hospital del Mar, Barcelona) Corella, Dolores (Universidad de Valencia) Aros, Fernando (Hospital Universitario de Alava, Vitoria) Gómez-Gracia, Enrique (Universidad de Malaga) Ruíz-Gutiérrez, Valentina (CSIC, Sevilla) Fiol, Miguel (Hospital Son Espases, Universidad de las Islas Baleares, Mallorca) Lapetra, José (División de Asistencia Primaria, Centro San Pablo, Sevilla) Lamuela-Raventos, Rosa M (Universidad of Barcelona) Serra-Majem, Lluis (Universidad Las Palmas, Canarias) Pinto, Xavier (Hospital Bellvitge, L Hospitalet) Muñoz, Miguel Ángel (Institut d Investigació en Atenció Primaria-JG, Barcelona) Martínez-González, Miguel Ángel (Universidad de Navarra)

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