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Dietary (poly)phenol intake is associated with cardiometabolic health parameters in adolescents

Emily P. Laveriano-Santosa,b,1, Sara Castro-Barquerob,c,1, Camila Arancibia-Riverosa, Ruiz-León Ana Maríab,c,d, Rosa Casasb,c, Ramón Estruchb,c, Patricia Bodegae,f, Mercedes de Miguele,f, Amaya de Cos-Gandoye,f, Jesús Martínez-Gómezf, Gloria Santos-Beneite,g, Juan M. Fernández-Alviraf, Rodrigo Fernández-Jiménezf,h,i, Rosa M. Lamuela-Raventósa,b(), Anna Tresserra-Rimbaua,b()
Department de Nutrició, Ciències de l’Alimentació I Gastronomia, XIA, Facultat de Farmàcia i Ciències de l’alimentació, Institut de Recerca en Nutrició i Seguretat Alimentària (INSA-UB), Universitat de Barcelona, Barcelona 08028, Spain
Consorcio CIBER, M.P. Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos Ⅲ (ISCⅢ), Madrid 28029, Spain
Department of Internal Medicine, Hospital Clínic, Institutd’Investigacions Biomèdiques August Pi I Sunyer (IDIBAPS), University of Barcelona, Barcelona 08036, Spain
Mediterranean Diet Foundation, Barcelona 08021, Spain
Foundation for Science, Health and Education (SHE), Barcelona 08008, Spain
Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid 28029, Spain
The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York 10029, USA
CIBER de Enfermedades Cardiovasculares (CIBERCV), Madrid 28029, Spain
Hospital Universitario Clínico San Carlos, Madrid 28040, Spain

1 Equally author contribution.

Peer review under responsibility of Tsinghua University Press.

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Highlights

• The highest intake of dietary polyphenols in adolescents was associated with better cardiometabolic parameters.

• Adolescents with a daily intake of 400–600 mg flavanols or flavan-3-ols had a lower waist circumference and blood glucose levels, and higher HDL cholesterol).

• Daily phenolic acid intake of more than 124.7 mg was associated with a lower waist circumference and triglycerides in adolescents compared to the lowest intake (less than 60.9 mg/day).

• Daily stilbene intake of more than 0.30 mg was associated with lower blood glucose and triglyceride levels, and higher HDL cholesterol in adolescents compared to the lowest intake (less than 0.03 mg/day)

Abstract

The protective role of (poly)phenols against metabolic disorders has been extensively studied in adults but not in adolescents. To assess associations of dietary (poly)phenols and their subclasses with cardiometabolic health parameters in adolescents. A cross-sectional study was conducted in 944 individuals aged 11–14 years enrolled in the SI! Program for Secondary Schools trial (NCT03504059). (Poly)phenol intake was assessed using semi-quantitative food frequency questionnaires and the Phenol-Explorer database. The measured cardiometabolic parameters were waist circumference (WC) age-sex Z-score, blood pressure (BP) age-sex Z-score, blood glucose (BG), triglycerides (TG), and high-density lipoprotein cholesterol (HDL-c). Multilevel mixed-effect linear regression models were applied to examine the association between (poly)phenol quintiles and cardiometabolic health parameters. Compared to the lowest quintile, adolescents in the highest quintile of total (poly)phenol intake had lower WC Z-scores, mean arterial pressure Z-scores, and HDL-c after multivariable adjustment. The WC Z-scores and HDL-c were lower in the highest quintile of flavonoid intake compared to the lowest quintile. The highest quintile of phenolic acid intake was associated with a lower WC Z-score and TG levels, and the highest quintile of stilbene intake with lower BG and TG, and with higher HDL-c compared to the lowest quintile. A higher intake of (poly)phenols, especially flavonoids, phenolic acids, and stilbenes, was associated with better cardiometabolic parameters in adolescents.

Keywords

PhytochemicalResveratrolLignanFlavan-3-olsAnthocyaninCardiovascular healthTeenagers

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References

[1]

J. Steinberger, S.R. Daniels, N. Hagberg, et al., Cardiovascular health promotion in children: challenges and opportunities for 2020 and beyond: a scientific statement from the American heart association, Circulation 134(2016) e236-e255. https://doi.org/10.1161/CIR.0000000000000441.

Crossref Google Scholar
[2]

J.A. Morrison, L.A. Friedman, P. Wang, et al., Metabolic syndrome in childhood predicts adult metabolic syndrome and type 2 diabetes mellitus 25 to 30 years later, J. Pediatr. 152(2008) 201-206. https://doi.org/10.1016/j.jpeds.2007.09.010.

Crossref Google Scholar
[3]

K.J. Penczynski, C. Herder, D. Krupp, et al., Flavonoid intake from fruit and vegetables during adolescence is prospectively associated with a favourable risk factor profile for type 2 diabetes in early adulthood, Eur. J. Nutr. 58(2019) 1159-1172. https://doi.org/10.1007/s00394-018-1631-3.

Crossref Google Scholar
[4]

A. Tresserra-Rimbau, E.B. Rimm, A. Medina-Remón, et al., Inverse association between habitual polyphenol intake and incidence of cardiovascular events in the PREDIMED study, Nutr. Metab. Cardiovas. 24(2014) 639-647. https://doi.org/10.1016/j.numecd.2013.12.014.

Crossref Google Scholar
[5]

S. Castro-Barquero, A. Tresserra-Rimbau, F. Vitelli-Storelli, et al., Dietary polyphenol intake is associated with HDL-cholesterol and a better profile of other components of the metabolic syndrome: A PREDIMED-Plus sub-study, Nutrients 12(2020) 689. https://doi.org/10.3390/nu12030689.

Crossref Google Scholar
[6]

N. Kardum, M. Glibetic, Polyphenols and their interactions with other dietary compounds: implications for human health, Adv. Food Nutr. Res. 84(2018) 103-144. https://doi.org/10.1016/bs.afnr.2017.12.001.

Crossref Google Scholar
[7]

A. Tresserra-Rimbau, A. Medina-Remón, J. Pérez-Jiménez, et al., Dietary intake and major food sources of polyphenols in a Spanish population at high cardiovascular risk: the PREDIMED study, Nutr. Metab. Cardiovas. 23(2013) 953-959. https://doi.org/10.1016/j.numecd.2012.10.008.

Crossref Google Scholar
[8]

K.M. Crowe-White, L.W. Evans, G.G.C. Kuhnle, et al., Flavan-3-ols and cardiometabolic health: a guideline recommendation by the academy of nutrition and dietetics, Adv. Nutr. 10(2022) S308-S319. https://doi.org/10.1093/advances/nmac105.

Crossref Google Scholar
[9]

R.W. Wisnuwardani, S. de Henauw, O. Androutsos, et al., Estimated dietary intake of polyphenols in European adolescents: the HELENA study, Eur. J. Nutr. 58(2019) 2345-2363. https://doi.org/10.1007/s00394-018-1787-x.

Crossref Google Scholar
[10]

R.W. Wisnuwardani, S. de Henauw, M. Forsner, et al., Polyphenol intake and metabolic syndrome risk in European adolescents: the HELENA study, Eur. J. Nutr. 59(2020) 801-812. https://doi.org/10.1007/s00394-019-01946-1.

Crossref Google Scholar
[11]

S.L. Ramírez-Garza, E.P. Laveriano-Santos, C. Arancibia-Riveros, et al., Urinary nitric oxide levels are associated with blood pressure, fruit and vegetable intake and total polyphenol excretion in adolescents from the SI! Program, Antioxidants 11(2022) 2140. https://doi.org/10.3390/antiox11112140.

Crossref Google Scholar
[12]

E.P. Laveriano-Santos, C. Arancibia-Riveros, I. Parilli-Moser, et al., Total urinary polyphenols and ideal cardiovascular health metrics in Spanish adolescents enrolled in the SI Program: a cross-sectional study, Sci. Rep. 12(2022). https://doi.org/10.1038/S41598-022-19684-6.

Crossref Google Scholar
[13]

E.P. Laveriano-Santos, C. Arancibia-Riveros, A. Tresserra-Rimbau, et al., Flavonoid intake from cocoa-based products and adiposity parameters in adolescents in Spain, Front. Nutr. 9(2022). https://doi.org/10.3389/FNUT.2022.931171.

Crossref Google Scholar
[14]

E.P. Laveriano-Santos, P. Quifer-Rada, M. Marhuenda-Muñoz, et al., Microbial phenolic metabolites in urine are inversely linked to certain features of metabolic syndrome in Spanish adolescents, Antioxidants 11(2022) 2191. https://doi.org/10.3390/antiox11112191.

Crossref Google Scholar
[15]

E.P. Laveriano-Santos, I. Parilli-Moser, S.L. Ramírez-Garza, et al., Polyphenols in urine and cardiovascular risk factors: a cross-sectional analysis reveals gender differences in spanish adolescents from the SI! program, Antioxidants 9(2020) 910. https://doi.org/10.3390/antiox9100910.

Crossref Google Scholar
[16]

R.W. Wisnuwardani, S. de Henauw, L. Béghin, et al., Changes in (poly) phenols intake and metabolic syndrome risk over ten years from adolescence to adulthood, Nutr. Metab. Cardiovas. Dis. 32(2022) 1830-1840. https://doi.org/10.1016/j.numecd.2022.04.015.

Crossref Google Scholar
[17]

R. Fernandez-Jimenez, G. Santos-Beneit, A. Tresserra-Rimbau, et al., Rationale and design of the school-based SI! Program to face obesity and promote health among Spanish adolescents: a cluster-randomized controlled trial, Am. Heart J. 215(2019) 27-40. https://doi.org/10.1016/j.ahj.2019.03.014.

Crossref Google Scholar
[18]

W.C. Willett, G.R. Howe, L.H. Kushi, Adjustment for total energy intake in epidemiologic studies, Am. J. Clin. Nutr. 65(1997) 1220S-1228S. https://doi.org/10.1093/ajcn/65.4.1220S.

Crossref Google Scholar
[19]

C. Juton, S. Castro-Barquero, R. Casas, et al., Reliability and concurrent and construct validity of a food frequency questionnaire for pregnant women at high risk to develop fetal growth restriction, Nutrients 13(2021) 1629. https://doi.org/10.3390/nu13051629.

Crossref Google Scholar
[20]

P. Zimmet, G. Alberti, F. Kaufman, et al., The metabolic syndrome in children and adolescents, The Lancet 369(2007) 2059-2061. https://doi.org/10.1016/S0140-6736(07)60958-1.

Crossref Google Scholar
[21]

A.K. Sharma, D.L. Metzger, C. Daymont, et al., LMS tables for waist-circumference and waist-height ratio Z-scores in children aged 5–19 y in NHANES Ⅲ: association with cardio-metabolic risks, Pediatr. Res. 78(2015) 723-729. https://doi.org/10.1038/pr.2015.160.

Crossref Google Scholar
[22]

C.L. Ogden, R.J. Kuczmarski, K.M. Flegal, et al., Centers for disease control and prevention 2000 growth charts for the united states: improvements to the 1977 national center for health statistics version, Pediatrics 109(2002) 45-60. https://doi.org/10.1542/peds.109.1.45.

Crossref Google Scholar
[23]
National High Blood Pressure Education Program Working Group on High Blood Pressure in Children and Adolescents, The fourth report on the diagnosis, evaluation, and treatment of high blood pressure in children and adolescents., Pediatrics 114(2004) 555-576. http://www.ncbi.nlm.nih.gov/pubmed/15286277(accessed June 18, 2022).
[24]

D.G. Rowland, H.P. Gutgesell, Use of mean arterial pressure for noninvasive determination of left ventricular end-systolic wall stress in infants and children, Am. J. Cardiol. 74(1994) 98-99. https://doi.org/10.1016/0002-9149(94)90503-7.

Crossref Google Scholar
[25]

S.S. Franklin, V.A. Lopez, N.D. Wong, et al., Single versus combined blood pressure components and risk for cardiovascular disease, Circulation 119(2009) 243-250. https://doi.org/10.1161/CIRCULATIONAHA.108.797936.

Crossref Google Scholar
[26]
O. Moreiras, H. Carvajal, L. Cabrera, et al., Tablas de composición de alimentos, 2019.
[27]
J. Mataix, L. García, M. Mañas, et al., Tabla de composición de alimentos, Granada, 2003.
[28]
UNESCO, International Standard Classification of Education (ISCED) 2011, UNESCO Institute for Statistics, 2012. DOI: 10.15220/978-92-9189-123-8-en.
[29]
INE: Instituto Nacional de Estadística, Encuesta anual de estructura salarial. https://www.ine.es/jaxiT3/Datos.htm?t=9948(accessed June 19, 2022).
[30]

J.L. Chandler, K. Brazendale, M.W. Beets, et al., Classification of physical activity intensities using a wrist-worn accelerometer in 8-12-year-old children, Pediatr. Obes. 11(2016) 120-127. https://doi.org/10.1111/ijpo.12033.

Crossref Google Scholar
[31]

N. Barbosa, C.E. Sanchez, J.A. Vera, et al., A physical activity questionnaire: reproducibility and validity, J. Sports Sci. Med. 6(2007) 505-518.

Google Scholar
[32]

J.M. Tanner, R.H. Whitehouse, Clinical longitudinal standards for height, weight, height velocity, weight velocity, and stages of puberty, Arch. Dis. Child 51(1976) 170-179. https://doi.org/10.1136/adc.51.3.170.

Crossref Google Scholar
[33]

J.A. Rothwell, J. Perez-Jimenez, V. Neveu, et al., Phenol-Explorer 3.0: a major update of the Phenol-Explorer database to incorporate data on the effects of food processing on polyphenol content, Database 2013(2013) bat070–bat070. https://doi.org/10.1093/database/bat070.

Crossref Google Scholar
[34]

N. Ziauddeen, A. Rosi, D. del Rio, et al., Dietary intake of (poly)phenols in children and adults: cross-sectional analysis of UK National Diet and Nutrition Survey Rolling Programme (2008–2014), Eur. J. Nutr. 58(2019) 3183-3198. https://doi.org/10.1007/s00394-018-1862-3.

Crossref Google Scholar
[35]

G. Grosso, U. Stepaniak, A. Micek, et al., Dietary polyphenols are inversely associated with metabolic syndrome in Polish adults of the HAPIEE study, Eur. J. Nutr. 56(2017) 1409-1420. https://doi.org/10.1007/s00394-016-1187-z.

Crossref Google Scholar
[36]

M. Vitale, O. Vaccaro, M. Masulli, et al., Polyphenol intake and cardiovascular risk factors in a population with type 2 diabetes: the TOSCA. IT study, Clin. Nutr. 36(2017) 1686-1692. https://doi.org/10.1016/j.clnu.2016.11.002.

Crossref Google Scholar
[37]

K. Liu, M. Luo, S. Wei, The bioprotective effects of polyphenols on metabolic syndrome against oxidative stress: evidences and perspectives, Oxid. Med. Cell Longev. 2019(2019) 1-16. https://doi.org/10.1155/2019/6713194.

Crossref Google Scholar
[38]

M. Kosmalski, A. Pękala-Wojciechowska, A. Sut, et al., Dietary intake of polyphenols or polyunsaturated fatty acids and its relationship with metabolic and inflammatory state in patients with type 2 diabetes mellitus, Nutrients 14(2022) 1083. https://doi.org/10.3390/nu14051083.

Crossref Google Scholar
[39]

X. Wang, Y. Qi, H. Zheng, Dietary polyphenol, gut microbiota, and health benefits, Antioxidants 11(2022) 1212. https://doi.org/10.3390/antiox11061212.

Crossref Google Scholar
[40]

G. Sohrab, S. Hosseinpour-Niazi, J. Hejazi, et al., Dietary polyphenols and metabolic syndrome among Iranian adults, Int. J. Food Sci. Nutr. 64(2013) 661-667. https://doi.org/10.3109/09637486.2013.787397.

Crossref Google Scholar
[41]

P. Mena, R. Domínguez-Perles, A. Gironés-Vilaplana, et al., Flavan-3-ols, anthocyanins, and inflammation, IUBMB Life 66(2014) 745-758. https://doi.org/10.1002/iub.1332.

Crossref Google Scholar
[42]

L.M. Cercato, J.P. Oliveira, M.T.S. Souza, et al., Effect of flavonoids in preclinical models of experimental obesity, PharmaNutrition 16(2021) 100260. https://doi.org/10.1016/j.phanu.2021.100260.

Crossref Google Scholar
[43]

C.L. Cheatham, D.C. Nieman, A.P. Neilson, et al., Enhancing the cognitive effects of flavonoids with physical activity: is there a case for the gut microbiome? Front. Neurosci. 16(2022) 96. https://doi.org/10.3389/FNINS.2022.833202/BIBTEX.

Crossref Google Scholar
[44]

T. Fujimaki, C. Sato, R. Yamamoto, et al., Isolation of phenolic acids and tannin acids from Mangifera indica L. kernels as inhibitors of lipid accumulation in 3T3-L1 cells, Biosci. Biotechnol. Biochem. 86(2022) 665-671. https://doi.org/10.1093/bbb/zbac030.

Crossref Google Scholar
[45]

O.B. Ibitoye, T.O. Ajiboye, Dietary phenolic acids reverse insulin resistance, hyperglycaemia, dyslipidaemia, inflammation and oxidative stress in high-fructose diet-induced metabolic syndrome rats, Arch. Physiol. Biochem. 124(2018) 410-417. https://doi.org/10.1080/13813455.2017.1415938.

Crossref Google Scholar
[46]

S. Ramírez-Garza, E. Laveriano-Santos, M. Marhuenda-Muñoz, et al., Health effects of resveratrol: results from human intervention trials, Nutrients 10(2018) 1892. https://doi.org/10.3390/nu10121892.

Crossref Google Scholar
[47]

Arcila-Agudelo, Ferrer-Svoboda, Torres-Fernàndez, et al., Determinants of adherence to healthy eating patterns in a population of children and adolescents: evidence on the mediterranean diet in the city of Mataró (Catalonia, Spain), Nutrients 11 (2019) 854. https://doi.org/10.3390/nu11040854.

Crossref Google Scholar
[48]

L. Jones, A. Ness, P. Emmett, Misreporting of energy intake from food records completed by adolescents: associations with sex, body image, nutrient, and food group intake, Front. Nutr. 8(2021) 970. https://doi.org/10.3389/fnut.2021.749007.

Crossref Google Scholar
[49]

Y. Probst, V. Guan, K. Kent, A systematic review of food composition tools used for determining dietary polyphenol intake in estimated intake studies, Food Chem. 238(2018) 146-152. https://doi.org/10.1016/j.foodchem.2016.11.010.

Crossref Google Scholar
[50]

E.B. Hill, A.J. Kennedy, K.M. Roberts, et al., Considerations for use of the phenol-explorer database to estimate dietary (poly)phenol intake, J. Acad. Nutr. Diet. 121(2021) 833-834. https://doi.org/10.1016/j.jand.2021.02.010.

Crossref Google Scholar
Food Science and Human Wellness
Volume 13 Issue 6,
November 2024
Pages 3381-3390
DOI: 10.26599/FSHW.2023.9250023
Cite this article:
Laveriano-Santos EP, Castro-Barquero S, Arancibia-Riveros C, et al. Dietary (poly)phenol intake is associated with cardiometabolic health parameters in adolescents. Food Science and Human Wellness, 2024, 13(6): 3381-3390. https://doi.org/10.26599/FSHW.2023.9250023
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General characteristics of the study population according to quintiles of energy-adjusted total (poly)phenol intake (mg/day).

Characteristics Total Q1 (< 438.0) Q2 (438.0-570.1) Q3 (570.2-699.9) Q4 (700.0-875.0) Q5 (> 875.0) P P-for trend
Note: Values are expressed as frequencies and percentages for categorical variables or means ± SDs for continuous variables. ANOVA was used for continuous variables and the x2 or Fisher’s exact test for categorical variables. P and P-for trend values < 0.05 are considered statistically significant.
N (total) 944 189 189 189 189 188
Female, n (%) 455 (48) 76 (40) 88 (47) 91 (48) 98 (52) 102 (54) 0.066 0.004
Age (years) 12.0 ± 0.4 12.0 ± 0.4 12.0 ± 0.5 12.0 ± 0.4 12.0 ± 0.4 12.0 ± 0.4 0.620 0.543
WC (cm) 71.9 ± 10.1 72.0 ± 10.5 71.8 ± 9.7 72.9 ± 10.6 71.1 ± 9.5 71.7 ± 10.3 0.527 0.546
SBP (mm Hg) 104.9 ± 10.7 103.7 ± 11.0 104.4 ± 10.7 105.5 ± 11 105.7 ± 10.4 105.1 ± 10.3 0.334 0.086
DBP (mm Hg) 61.8 ± 8.6 61.6 ± 8.5 61.3 ± 8.5 62.9 ± 8.7 61.6 ± 8.7 61.7 ± 8.8 0.392 0.798
MAP (mm Hg) 76.2 ± 8.3 75.6 ± 8.1 75.7 ± 7.7 77.1 ± 8.2 76.3 ± 8.0 76.1 ± 8.1 0.433 0.345
BG (mg/dL) 102.9 ± 11.9 102.5 ± 12.2 103.7 ± 11.5 103.1 ± 11.4 101.9 ± 12.9 103.3 ± 11.3 0.221 0.908
TG (mg/dL) 77.9 ± 37.5 74.8 ± 38.8 76.4 ± 35.1 80.5 ± 38.8 80.0 ± 36.8 78.1 ± 38.0 0.508 0.237
HDL-c (mg/dL) 62.9 ± 16.2 65.3 ± 18.2 63.0 ± 16.3 61.6 ± 15.1 62.1 ± 15.9 62.8 ± 15.1 0.229 0.113
MVPA (min/day) 74.4 ± 25.5 75.4 ± 23.6 74.3 ± 24.8 73.6 ± 25.4 76.5 ± 28.4 72.3 ± 24.9 0.559 0.495
Smoking
No smoking experience 878 (93) 174 (92) 169 (90) 176 (93) 182 (96) 177 (94) 0.420 0.072
Non-smokers with smoking experience 48 (5) 11 (6) 15 (8) 10 (5) 4 (2) 8 (4)
Smokers 17 (2) 4 (2) 4 (2) 3 (2) 3 (2) 3 (2)
Parental educational level 0.374 0.374
Low/medium 676 (74) 135 (73) 134 (73) 129 (71) 136 (73) 142 (78)
High 240 (26) 49 (27) 49 (27) 52 (29) 50 (27) 40 (22)
Household income 0.488 0.153
Low 257 (29) 51 (29) 44 (25) 51 (29) 55 (30) 56 (31)
Medium 267 (30) 45 (26) 61 (35) 50 (29) 51 (28) 60 (34)
High 366 (41) 80 (45) 71 (40) 73 (42) 79 (42) 63 (35)

Contribution (%) of (poly)phenol subclasses to total (poly)phenol intake and food sources.

(Poly)phenol subclassesMean ± SD (mg/day)(% of total polyphenols)Median (mg/day)(25th; 75th percentile)Food sources (% of contribution)
Total (poly)phenols 683.2 ± 379.4 598.6 (441.7; 854.8)
Flavonoids 530.1 ± 331.3 (77.6) 459.7 (314.9; 664.2)
Anthocyanins 57.6 ± 75.9 (8.4) 31.9 (19.5; 96.0) Cherries (43.3), strawberries (40.3), grape (12.0), olives (2.6), and other foods (1.8)
Dihydrochalcones 1.20 ± 1.14 (0.2) 1.2 (0.4; 1.4) Apples (79.5), fruit juices from concentrate (20.4)
Flavanols 328.0 ± 245.7 (48.0) 275.2 (162.6; 410.6)
Catechins 27.5 ± 19.0 (4.0) 23.6 (15.9; 34.7)Cocoa powder (30.5), apples (11.4), strawberries (7.8), peaches (7.1), cherries (6.7), fruit juices from concentrate (6.1), dark chocolate > 70% cocoa (5.0), grapes (5.1), chocolate (5.0), other foods (15.3)
Proanthocyanidins 300.5 ± 230.6 (44.0) 252.0 (147.4; 380.4)Cocoa powder (36.8), strawberries (15.4), dark chocolate > 70% cocoa (13.8), cherries (9.0), apples (8.4), chocolates (5.8), grapes (4.6), other foods (4.1)
Theaflavins 0.11 ± 0.82 (0.02) 0.0 (0;0) Tea (100)
Flavanones 70.4 ± 76.9 (10.3) 53.9 (20.6; 92.9) Oranges (72.9), fruit juices from concentrate (14.2), natural orange juice (10.9), other foods (2.0)
Flavones 41.5 ± 28.5 (6.1) 35.5 (21.7; 51.7) Bread (27.6), oranges (26.9), whole-grain bread (17.8), cookies, pastries, and bakery products (12.9), natural orange juice (5.8), artichoke (3.8), other foods (5.2)
Flavonols 29.5 ± 20.8 (4.3) 25.3 (15.1; 38.0) Spinach (27.9), onions (23.5), lettuce (12.8), green beans (4.3), cabbage (3.8), asparagus (3.6), apples (3.5), other foods (17.5)
Isoflavonoids 1.8 ± 8.5 (0.3) 0.0 (0; 0) Soy milk (100)
Phenolic acids 97.8 ± 64.5 (14.3) 81.9 (59.8; 118.4)
Hydroxybenzoic acids 6.7 ± 4.8 (1.0) 5.7 (3.7; 8.3) Strawberries (27.1), olives (15.2), walnuts (12.8), chickpeas (11.7), banana (4.8), Swiss chard (4.3), other foods (22.6)
Hydroxycinnamic acids 90.7 ± 62.0 (13.3) 75.6 (54.9; 109.6) Potatoes (13.5), plums (11.2), cherries (11.0), seeds (9.0), apples (7.4), coffee (6.1), white bread (5.6), fruit juices from concentrate (5.2), other foods (21.4)
Hydroxyphenylacetic acids 0.3 ± 0.3 (< 0.01) 0.1 (0.0; 0.2) Olives (97.2), extra virgin olive oil (1.8), other foods (1.0)
Hydroxyphenylpropanoic acids 0.2 ± 0.2 (< 0.01) 0.1 (0.0; 0.2) Olives (100)
Stilbenes 0.2 ± 0.3 (< 0.01) 0.1 (0.0; 0.3) Strawberries (55.0), grapes (29.7), dark chocolate > 70% cocoa (1.0), other foods (14.2)
Lignans 3.8 ± 5.1 (0.6) 2.4 (1.2; 4.1) Seeds (71.5), oranges (2.8), peaches (2.3), extra virgin olive oil (1.9), other foods (21.4)
Other (poly)phenols 51.3 ± 34.4 (7.5) 598.6 (441.5; 854.8)
Alkylphenols 22.8 ± 27.3 (3.3) 18.9 (4.7; 35.5) Breakfast cereals (74.5), whole grain bread (10.4), cookies, pastries, and bakery products (6.6), pasta (4.4), other foods (4.1)
Furanocoumarins 0.4 ± 0.4 (0.1) 0.3 (0.1; 0.5) Celery stalks (60.6), parsley (36.1), grapefruit juice (3.3)
Hydroxybenzaldehydes 0.1 ± 0.2 (< 0.01) 0.0 (0.01; 0.05) Walnuts (25.5), cocoa powder (4.1), olives (2.4), other foods (67.5)
Naphtoquinones 0.3 ± 0.5 (< 0.01) 0.2 (0; 0.2) Walnuts (100)
Tyrosols 21.5 ± 14.6 (3.1) 17.5 (11.6; 27.6) Olives (40.0), extra virgin olive oil (24.6), refined olive oil (11.7), other foods (23.7)
Curcuminoids 4.1 ± 12.2 (0.6) 0.0 (0; 3.2) Spices (100)
Other 1.8 ± 2.8 (0.3) 0.8 (0.4; 2.2) Orange juice (51.6), other fruit juices (34.7), pears (6.8), other foods (6.9)

Cardiometabolic parameters according to quintiles of energy-adjusted total (poly)phenol intake.

Q1 (< 438.0 mg/day) Q2 (438.0-570.1 mg/day) Q3 (570.2-699.9 mg/day) Q4 (700.0-875.0 mg/day) Q5 (> 875.0 mg/day) P-for trend
Note: Unstandardized coefficient (β). Multilevel linear mixed-effect models. Model 1: sex and age. Model 2: Model 1 plus pubertal status, physical activity, body mass index Z-score (except for WC), fasting, high parental educational level, and household income. Model 3: Model 2 plus energy, fiber, added sugars, animal-based protein, and saturated fatty acid intake. Region and schools were considered random effects. SBP, DBP, and MAP were additionally adjusted for sodium intake. *P < 0.05, **P < 0.01, and ***P < 0.001. P and P-for trend values across quintiles of TPI < 0.05 are considered significant.
WC(Z-score) Model 1, β (95% CI) Reference 0.05 (-0.03; 0.14) 0.13 (0.02; 0.24)* 0.02 (-0.01; 0.05) 0.05 (0.04; 0.07)*** 0.284
Model 2, β (95% CI) Reference 0.10 (-0.01; 0.20) 0.12 (0.05; 0.19)** 0.07 (0.03; 0.10)** 0.03 (-0.01; 0.07) 0.370
Model 3, β (95% CI) Reference 0.05 (-0.01; 0.11) 0.02 (-0.12; 0.16) -0.04 (-0.16; 0.08) -0.09 (-0.09; -0.08)*** 0.002
SBP(Z-score) Model 1, β (95% CI) Reference -0.001 (-0.18; 0.18) 0.09 (-0.05; 0.23) 0.12 (0.02; 0.23)* 0.07 (-0.03; 0.18) < 0.001
Model 2, β (95% CI) Reference -0.02 (-0.22; 0.18) 0.05 (-0.22; 0.32) 0.13 (-0.08; 0.34) 0.06 (-0.15; 0.27) 0.223
Model 3, β (95% CI) Reference 0.01 (-0.20; 0.21) 0.10 (-0.19; 0.39) 0.15 (-0.07; 0.36) 0.04 (-0.19; 0.27) 0.346
DBP(Z-score) Model 1, β (95% CI) Reference -0.06 (-0.12; 0.001) 0.05 (-0.08; 0.19) -0.04 (-0.28; 0.20) -0.03 (-0.06; 0.001) 0.532
Model 2, β (95% CI) Reference -0.08 (-0.10; -0.07)*** 0.02 (-0.16; 0.20) -0.05 (-0.30; 0.20) -0.06 (-0.09; -0.02)** 0.345
Model 3, β (95% CI) Reference -0.10 (-0.10; -0.09)*** -0.01 (-0.14; 0.13) -0.10 (-0.27; 0.07) -0.14 (-0.29; 0.02) < 0.001
MAP(Z-score) Model 1, β (95% CI) Reference -0.04 (-0.17; 0.10) 0.16 (-0.02; 0.34) 0.07 (-0.25; 0.40) 0.05 (0.03; 0.07)*** 0.060
Model 2, β (95% CI) Reference -0.06 (-0.18; 0.04) 0.11 (-0.16; 0.39) 0.07 (-0.31; 0.44) 0.01 (-0.09; 0.11) 0.553
Model 3, β (95% CI) Reference -0.06 (-0.15; 0.03) 0.10 (-0.15; 0.35) 0.002 (-0.30; 0.31) -0.11 (-0.12; -0.09)*** 0.206
BG(mg/dL) Model 1, β (95% CI) Reference 1.74 (0.07; 3.42)* 0.91 (0.64; 1.18)*** 0.58 (-2.06; 3.22) 1.33 (-0.95; 3.61) < 0.001
Model 2, β (95% CI) Reference 1.27 (-1.13; 3.68) 1.17 (0.93; 1.41)*** 0.40 (-2.10; 2.90) 1.44 (-1.80; 4.68) 0.013
Model 3, β (95% CI) Reference 1.29 (-0.62; 3.19) 1.17 (0.63; 1.71)*** 0.10 (-2.83; 3.03) 0.80 (-2.43; 4.03) 0.616
TG(mg/dL) Model 1, β (95% CI) Reference 0.95 (0.86; 1.04)*** 5.22 (2.77; 7.68)*** 4.82 (4.27; 5.38)*** 2.63 (-6.86; 12.12) 0.358
Model 2, β (95% CI) Reference 0.67 (-2.44; 3.78) 2.93 (-2.60; 8.46) 4.47 (4.00; 4.94)*** 2.17 (-8.10; 12.43) 0.373
Model 3, β (95% CI) Reference 0.22 (-1.51; 1.95) 2.24 (-0.49; 4.97) 3.16 (1.04; 5.27)* 0.55 (-7.08; 8.18) 0.489
HDL-c(mg/dL) Model 1, β (95% CI) Reference -1.88 (-5.26; 1.49) -3.19 (-5.99; -0.39)* -2.04 (-3.14; -0.94)*** -1.68 (-2.10; -1.26)*** < 0.001
Model 2, β (95% CI) Reference -1.60 (-2.91; -0.29)* -3.18 (-5.10; -1.25)** -1.89 (-3.93; 0.16) -1.55 (-1.73; -1.37)*** < 0.001
Model 3, β (95% CI) Reference -1.43 (-2.39; -0.47)** -3.01 (-5.54; -0.47)* -1.72 (-3.98; 0.54) -1.52 (-2.90; -0.14)* 0.108