Hostname: page-component-8678677fb7-wnmm5 Total loading time: 0 Render date: 2026-10-11T03:42:54.600Z Has data issue: false hasContentIssue false
  • English
  • Français

Reproducibility and validity of dietary polyphenols derived from 24-h dietary recalls in the Anhui Lifestyle Validation Study

Published online by Cambridge University Press:  04 August 2026

Guobing Sun ,
Meiling Li ,
Jiawei Zhang ,
Junyi Zhang ,
Hongjuan Cao ,
Young Lyu ,
Xinghao Ma ,
Yu Han ,
Xinsheng Qin  and
Zhuang Zhang
...Show all authors
Guobing Sun
Affiliation:
Department of Nutrition, School of Public Health, Anhui Medical University, People’s Republic of China
Meiling Li
Affiliation:
Department of Nutrition, School of Public Health, Anhui Medical University, People’s Republic of China
Jiawei Zhang
Affiliation:
Department of General Surgery, First Affiliated Hospital of Anhui Medical University, People’s Republic of China
Junyi Zhang
Affiliation:
Department of Nutrition, School of Public Health, Anhui Medical University, People’s Republic of China
Hongjuan Cao
Affiliation:
Department of Chronic Non-communicable Diseases Prevention and Control, Lu’an Municipal Center for Disease Control and Prevention, People’s Republic of China
Young Lyu
Affiliation:
Department of Chronic Non-communicable Diseases Prevention and Control, Lu’an Municipal Center for Disease Control and Prevention, People’s Republic of China
Xinghao Ma
Affiliation:
Department of Clinical Nutrition, Lu’an Hospital, Lu’an, People’s Republic of China
Yu Han
Affiliation:
Department of Physical Examination Center, Lu’an Hospital, Lu’an, People’s Republic of China
Xinsheng Qin
Affiliation:
Department of Nutrition, School of Public Health, Anhui Medical University, People’s Republic of China
Zhuang Zhang
Affiliation:
Department of Nutrition, School of Public Health, Anhui Medical University, People’s Republic of China
Wanshui Yang*
Affiliation:
Department of Nutrition, School of Public Health, Anhui Medical University, People’s Republic of China
*
Corresponding author: Wanshui Yang; Email wanshuiyang@ahmu.edu.cn
Get access Rights & Permissions [Opens in a new window]

Abstract

While 24-h dietary recalls (24HDR) are increasingly used to assess polyphenol–disease associations, their performance for measuring total dietary polyphenols and their subclasses remains unknown. In the Anhui Lifestyle Validation Study, 432 community-dwelling adults completed 12-d dietary recalls, structured as four seasonal sets of three consecutive 24HDR (2 weekdays and 1 weekend day each) between July 2021 and July 2022. Reproducibility of dietary total polyphenols, flavonoids, lignans and phenolic acids assessed by different number of non-consecutive 24HDR was evaluated by intraclass correlation coefficient (ICC). Relative validity was evaluated by comparing estimates from different numbers of 24HDR with the internal reference using deattenuated Spearman’s correlation coefficients (rc). The reproducibility from two 24HDR was weak for these four polyphenol categories (ICC: 0·10–0·39), while three 24HDR yielded a reasonable reproducibility for total polyphenols (ICC: 0·40, 95 % CI 0·31–0·50), flavonoids (ICC: 0·42, 95 % CI 0·33–0·51) and phenolic acids (ICC: 0·45, 95 % CI 0·16–0·66), but not for lignans (ICC: 0·11, 95 % CI 0·01–0·26). Increasing days up to 4–6 seemed not to appreciably improve the reproducibility (ICC range: 0·13–0·17 for lignans, 0·41–0·56 for others). The relative validity against the internal reference of these four polyphenol categories was moderate for two 24HDR (rc: 0·50–0·74) and improved for ≥ 3 24HDR (rc: 0·76–1·00). Older age, female sex and weekend administration were associated with lower reproducibility of three 24HDR-based assessments of phenolic acids or flavonoids. Overall, our results suggest that three non-consecutive 24HDR may have acceptable performance in measuring most dietary polyphenol intake except for lignans.

Keywords

Reproducibilityvaliditypolyphenolspolyphenol subclasses24-h dietary recall

Information

Type
Research Article
Information
British Journal of Nutrition , First View , pp. 1 - 9
Check for updates
Copyright
© The Author(s), 2026. Published by Cambridge University Press on behalf of The Nutrition Society

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

a

These authors contributed equally as co-first authors for this article.

b

These authors contributed equally as co-corresponding authors for this article.

References

Carnauba, RA, Sarti, FM, Hassimotto, NMA, et al. (2023) Estimated polyphenol intake and major food sources of the Brazilian population: changes between 2008–2009 and 2017–2018. Br J Nutr 130, 147–154.Google Scholar
Durazzo, A, Lucarini, M, Souto, EB, et al. (2019) Polyphenols: a concise overview on the chemistry, occurrence, and human health. Phytother Res : PTR 33, 2221–2243.Google Scholar
Moorthy, M, Sundralingam, U & Palanisamy, UD (2021) Polyphenols as prebiotics in the management of high-fat diet-induced obesity: a systematic review of animal studies. Foods (Basel, Switzerland) 10, 299.Google Scholar
Maleki, SJ, Crespo, JF & Cabanillas, B (2019) Anti-inflammatory effects of flavonoids. Food Chem 299, 125124.Google Scholar
Gardeazabal, I, Romanos-Nanclares, A, Martínez-González, M, et al. (2019) Total polyphenol intake and breast cancer risk in the Seguimiento Universidad de Navarra (SUN) cohort. Br J Nutr 122, 542–551.Google Scholar
Fike, LT, Munro, H, Yu, D, et al. (2022) Dietary polyphenols and the risk of colorectal cancer in the prospective southern community cohort study. Am J Clin Nutr 115, 1155–1165.Google Scholar
Li, Y, Xu, Y, Ma, X, et al. (2023) (Poly)phenol intake, plant-rich dietary patterns and cardiometabolic health: a cross-sectional study. Food Funct 14, 4078–4091.Google Scholar
Rubín-García, M, Vitelli-Storelli, F, Álvarez-Álvarez, L, et al. (2022) Association Among polyphenol intake, uric acid, and hyperuricemia: a cross-sectional analysis in a population at high cardiovascular risk. J Am Heart Assoc 11, e026053.Google Scholar
Chen, Z, Qian, F, Hu, Y, et al. (2023) Dietary phytoestrogens and total and cause-specific mortality: results from 2 prospective cohort studies. Am J Clin Nutr 117, 130–140.Google Scholar
Mérida, DM, Vitelli-Storelli, F, Moreno-Franco, B, et al. (2023) Polyphenol intake and mortality: a nationwide cohort study in the adult population of Spain. Clin Nutr (Edinburgh, Scotland) 42, 1076–1085.Google Scholar
Hertog, MG, Sweetnam, PM, Fehily, AM, et al. (1997) Antioxidant flavonols and ischemic heart disease in a Welsh population of men: the Caerphilly study. Am J Clin Nutr 65, 1489–1494.Google Scholar
Godos, J, Vitale, M, Micek, A, et al. (2019) Dietary polyphenol intake, blood pressure, and hypertension: a systematic review and meta-analysis of observational studies. Antioxidants (Basel, Switzerland) 8.Google Scholar
Xu, Y, Le Sayec, M, Roberts, C, et al. (2021) Dietary assessment methods to estimate (poly)phenol intake in epidemiological studies: a systematic review. Adv Nutr (Bethesda, Md) 12, 1781–1801.Google Scholar
Burkholder-Cooley, NM, Rajaram, SS, Haddad, EH, et al. (2017) Validating polyphenol intake estimates from a food-frequency questionnaire by using repeated 24-h dietary recalls and a unique method-of-triads approach with 2 biomarkers. Am J Clin Nutr 105, 685–694.Google Scholar
Hoge, A, Guillaume, M, Albert, A, et al. (2019) Validation of a food frequency questionnaire assessing dietary polyphenol exposure using the method of triads. Free Radic Biol Med 130, 189–195.Google Scholar
Kent, K & Charlton, K (2017) Relative validity and reproducibility of an interviewer administered 14-item FFQ to estimate Flavonoid intake among older adults with mild-moderate dementia. J Nutr Gerontol Geriatr 36, 134–147.Google Scholar
Yue, Y, Petimar, J, Willett, WC, et al. (2020) Dietary flavonoids and flavonoid-rich foods: validity and reproducibility of FFQ-derived intake estimates. Public Health Nutr 23, 3295–3303.Google Scholar
Okada, E, Nakade, M, Hanzawa, F, et al. (2023) National nutrition surveys applying dietary records or 24-h dietary recalls with questionnaires: a scoping review. Nutrients 15.Google Scholar
Lanuza, F, Zamora-Ros, R, Bondonno, NP, et al. (2023) Dietary polyphenols, metabolic syndrome and cardiometabolic risk factors: an observational study based on the DCH-NG subcohort. Nutr Metab Cardiovasc Dis 33, 1167–1178.Google Scholar
Zamora-Ros, R, Knaze, V, Rothwell, JA, et al. (2016) Dietary polyphenol intake in Europe: the European prospective investigation into Cancer and nutrition (EPIC) study. Eur J Nutr 55, 1359–1375.Google Scholar
Zhao, Z, Gao, W, Ding, X, et al. (2023) The association between dietary intake of flavonoids and its subclasses and the risk of metabolic syndrome. Front Nutr 10, 1195107.Google Scholar
Huang, K, Zhao, L, Fang, H, et al. (2022) A Preliminary study on a form of the 24-h recall that balances survey cost and accuracy, based on the NCI method. Nutrients 14.Google Scholar
Willett, W (2012) Nutritional Epidemiology. Oxford University Press.Google Scholar
Peterson, L, Lee, H, Huybrechts, I, et al. (2023) Reliability estimates for assessing meal timing derived from longitudinal repeated 24-hour dietary recalls. Am J Clin Nutr 117, 964–975.Google Scholar
Ma, Y, Olendzki, BC, Pagoto, SL, et al. (2009) Number of 24-hour diet recalls needed to estimate energy intake. Ann Epidemiol 19, 553–559.Google Scholar
Stote, KS, Radecki, SV, Moshfegh, AJ, et al. (2011) The number of 24 h dietary recalls using the US Department of Agriculture’s automated multiple-pass method required to estimate nutrient intake in overweight and obese adults. Public Health Nutr 14, 1736–1742.Google Scholar
Rankin, D, MacIntyre, UE, Hanekom, SM, et al. (2012) Reproducibility of two, three, four and five 24-hour recalls in peri-urban African adolescents in the North West province. S Afr J Clin Nutr 25, 27–32.Google Scholar
Foster, E, Lee, C, Imamura, F, et al. (2019) Validity and reliability of an online self-report 24-h dietary recall method (Intake24): a doubly labelled water study and repeated-measures analysis. J Nutr Sci 8, e29.Google Scholar
Li, M, Sun, G, Han, Y, et al. (2026) Reproducibility and validity of healthy dietary indices derived from 24-hour dietary recalls. Front Nutr 13, 1730154.Google Scholar
Li, M, Li, X, Yin, K, et al. (2025) Reproducibility and validity of low-carbohydrate diet and low-fat diet scores derived from 24-hour dietary recalls. Am J Epidemiol 194, 2724–2732.Google Scholar
Huang, Y, Zhang, J, Zhang, Y, et al. (2024) Red meat intake, faecal microbiome, serum trimethylamine N-oxide and hepatic steatosis among Chinese adults. Liver Int 44, 1142–1153.Google Scholar
Pan, Y, Yang, Y, Peng, Z, et al. (2025) Gut microbiota may modify the association between dietary polyphenol intake and serum concentrations of hippuric acid: results from a 1-year longitudinal study in China. Am J Clin Nutr 121, 654–662.Google Scholar
Zhang, JY, Liang, SX, Sun, GB, et al. (2025) Validation of a food frequency questionnaire for measuring dietary intake of isoflavones and lignans using the method of triads. Am J Clin Nutr 121, 1157–1164.Google Scholar
Rhodes, DG, Murayi, T, Clemens, JC, et al. (2013) The USDA Automated Multiple-Pass method accurately assesses population sodium intakes. Am J Clin Nutr 97, 958–964.Google Scholar
Shrout, PE & Fleiss, JL (1979) Intraclass correlations: uses in assessing rater reliability. Psychol Bull 86, 420–428.Google Scholar
Beaton, GH, Milner, J, McGuire, V, et al. (1983) Source of variance in 24-hour dietary recall data: implications for nutrition study design and interpretation. Carbohydrate sources, vitamins, and minerals. Am J Clin Nutr 37, 986–995.Google Scholar
Rosner, B & Willett, WC (1988) Interval estimates for correlation coefficients corrected for within-person variation: implications for study design and hypothesis testing. Am J Epidemiol 127, 377–386.Google Scholar
Al-Shaar, L, Yuan, C, Rosner, B, et al. (2021) Reproducibility and validity of a semiquantitative food frequency questionnaire in men assessed by multiple methods. Am J Epidemiol 190, 1122–1132.Google Scholar
Schober, P, Boer, C & Schwarte, LA (2018) Correlation coefficients: appropriate Use and Interpretation. Anesth Analg 126, 1763–1768.Google Scholar
Ocké, MC & Kaaks, RJ (1997) Biochemical markers as additional measurements in dietary validity studies: application of the method of triads with examples from the European Prospective Investigation into Cancer and Nutrition. Am J Clin Nutr 65, 1240s–1245s.Google Scholar
Moghames, P, Hammami, N, Hwalla, N, et al. (2016) Validity and reliability of a food frequency questionnaire to estimate dietary intake among Lebanese children. Nutr J 15, 4.Google Scholar
Zhou, BF, Cooperative Meta-Analysis Group of the Working Group on Obesity in C (2002) Predictive values of body mass index and waist circumference for risk factors of certain related diseases in Chinese adults – study on optimal cut-off points of body mass index and waist circumference in Chinese adults. Biomed Environ Sci 15, 83–96.Google Scholar
Zheng, X, Zhang, X & Zeng, F (2025) Biological functions and health benefits of flavonoids in fruits and vegetables: a contemporary review. Foods (Basel, Switzerland) 14.Google Scholar
Kumar, N & Goel, N (2019) Phenolic acids: natural versatile molecules with promising therapeutic applications. Biotechnol Rep (Amsterdam, Netherlands) 24, e00370.Google Scholar
St George, SM, Van Horn, ML, Lawman, HG, et al. (2016) Reliability of 24-hour dietary recalls as a measure of diet in African-American youth. J Acad Nutr Diet 116, 1551–1559.Google Scholar
Singh, R, Verest, MTE & Salathé, M (2025) Minimum days estimation for reliable dietary intake information: findings from a digital cohort. Eur J Clin Nutr 79, 1007–1017.Google Scholar
Bailey, RL (2021) Overview of dietary assessment methods for measuring intakes of foods, beverages, and dietary supplements in research studies. Curr Opin Biotechnol 70, 91–96.Google Scholar
Freedman, LS, Schatzkin, A, Midthune, D, et al. (2011) Dealing with dietary measurement error in nutritional cohort studies. J Natl Cancer Inst 103, 1086–1092.Google Scholar
Giraudeau, B & Mary, JY (2001) Planning a reproducibility study: how many subjects and how many replicates per subject for an expected width of the 95 per cent confidence interval of the intraclass correlation coefficient. Stat Med 20, 3205–3214.Google Scholar
Gibson, RS, Charrondiere, UR & Bell, W (2017) Measurement errors in dietary assessment using self-reported 24-hour recalls in low-income countries and strategies for their prevention. Adv Nutr (Bethesda, Md) 8, 980–991.Google Scholar
Quandt, SA, Vitolins, MZ, Smith, SL, et al. (2007) Comparative validation of standard, picture-sort and meal-based food-frequency questionnaires adapted for an elderly population of low socio-economic status. Public Health Nutr 10, 524–532.Google Scholar
Borrelli, R, Simonetti, MS & Fidanza, F (1992) Inter- and intra-individual variability in food intake of elderly people in Perugia (Italy). Br J Nutr 68, 3–10.Google Scholar
Dehghan, M, Akhtar-Danesh, N & Merchant, AT (2011) Factors associated with fruit and vegetable consumption among adults. J Hum Nutr Diet 24, 128–134.Google Scholar
Küçük, N, Urak, F, Bilgic, A, et al. (2023) Fruit and vegetable consumption across population segments: evidence from a national household survey. J Health Popul Nutr 42, 54.Google Scholar
Shahar, DR, Yerushalmi, N, Lubin, F, et al. (2001) Seasonal variations in dietary intake affect the consistency of dietary assessment. Eur J Epidemiol 17, 129–133.Google Scholar
Esposito, F, Sanmarchi, F, Marini, S, et al. (2022) Weekday and Weekend Differences in Eating Habits, Physical Activity and Screen Time Behavior among a Sample of Primary School Children: The “Seven Days for My Health” Project. Int J Environ Res Public Health 19, 4215.Google Scholar
Say, YH, Nordin, MS & Ng, ALO (2025) The effects of chrononutrition, chronotype and sleep behavior variabilities on adiposity traits and appetite sensations among students and staff of a Malaysian university. Chronobiol Int 42, 581–594.Google Scholar
Dashti, HS, Scheer, F, Saxena, R, et al. (2019) Timing of food intake: identifying contributing factors to design effective interventions. Adv Nutr (Bethesda, Md) 10, 606–620.Google Scholar
National Cancer Institute (2025) Validation Using Imperfect Reference Instruments (Comparative or Relative Validation). https://epi.grants.cancer.gov/dietary-assessment-primer/concepts/validation/imperfect.html (accessed June, 2026).Google Scholar
Supplementary material: File

Sun et al. supplementary material

Sun et al. supplementary material
Download Sun et al. supplementary material(File)
File 53.1 KB