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Late eating and shortened fasting are associated with higher ultra-processed food intake across all age groups: a population-based study

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Abstract

Purpose

Global dietary patterns are increasingly driven by ultra-processed foods–cheap, highly palatable, and ready-to-eat options. Exploring time-related eating patterns and its association with ultra-processed foods could help in intervention efforts, but knowledge on this topic is still limited. This study assessed the association of time-related eating patterns with unprocessed/minimally processed and ultra-processed food consumption across different life stages.

Methods

Two 24-hour food recalls from a nationally representative sample in Brazil (Brazilian Household Budget Survey, POF, 2017–2018; n = 46,164) were used to estimate tertiles of first and last intake times, eating midpoint, caloric midpoint time, and night fasting (independent variables). All consumed foods were classified according to the Nova classification system, and the outcomes of interest were consumption of unprocessed/minimally processed and ultra-processed foods. Multiple linear regression models were performed for all individuals and stratified for each age group: adolescents (10–19 years, n = 8,469), adults (20–59 years, n = 29,332), and older individuals (≥ 60 years, n = 8,322).

Results

The later tertile of first food intake time, last food intake time, caloric midpoint, and eating midpoint were positively associated with consumption of ultra-processed foods (β = 3.69, 95%CI = 3.04, 4.34; β = 1.89, 95%CI = 1.32, 2.47; β = 5.20, 95%CI = 4.60, 5.81; β = 3.10, 95%CI = 2.49, 3.71, respectively) and negatively associated with consumption of unprocessed or minimally processed foods (β=-2.79, 95%CI=-3.37; -2.22; β=-1.65, 95%CI=-2.24, -1.05; β=-3.94, 95%CI=-4.44, -3.44; β=- 2.35, 95%CI=-2.93, -1.78, respectively) compared to the first “earlier” tertile (reference). An inverse association was found for night fasting (β=-1.74, 95%CI=-2.28, -1.22 and β = 1.52, 95%CI = 0.98, 2.06 for ultra-processed and unprocessed/minimally processed foods, respectively). These associations were consistent across all age groups.

Conclusion

Chrononutrition patterns characterized by late intake timing and shortened overnight fasting were associated with higher consumption of ultra-processed foods and lower intake of unprocessed/minimally processed foods across all age groups.

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Data availability

The datasets analyzed in the current study are freely available on the IBGE website (https://www.ibge.gov.br/estatisticas/sociais/saude/24786-pesquisa-de-orcamentos-familiares-2.html).

References

  1. Monteiro CA, Cannon G, Lawrence M, Costa Louzada ML, Pereira Machado P (2019) Ultra-processed foods, diet quality, and health using the NOVA classification system. Rome, FAO

  2. Baker P, Machado P, Santos T, Sievert K, Backholer K, Hadjikakou M, Russell C, Huse O, Bell C, Scrinis G et al (2020) Ultra-processed foods and the nutrition transition: global, regional and National trends, food systems transformations and political economy drivers. Obes Rev 21(12):e13126

    Article  PubMed  Google Scholar 

  3. Martini D, Godos J, Bonaccio M, Vitaglione P, Grosso G (2021) Ultra-processed foods and nutritional dietary profile: a meta-analysis of nationally representative samples. Nutrients 13:3390. https://doi.org/10.3390/nu13103390

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Suksatan W, Moradi S, Naeini F, Bagheri R, Mohammadi H, Talebi S et al (2021) Ultra-processed food consumption and adult mortality risk: a systematic review and dose-response meta-analysis of 207,291 participants. Nutrients 14:174. https://doi.org/10.3390/NU14010174

    Article  PubMed  PubMed Central  Google Scholar 

  5. Lane MM, Davis JA, Beattie S, Gómez-Donoso C, Loughman A, O’Neil A et al (2021) Ultraprocessed food and chronic noncommunicable diseases: a systematic review and meta-analysis of 43 observational studies. Obes Rev 22:e13146. https://doi.org/10.1111/obr.13146

    Article  PubMed  Google Scholar 

  6. Askari M, Heshmati J, Shahinfar H, Tripathi N, Daneshzad E (2020) Ultra-processed food and the risk of overweight and obesity: a systematic review and meta-analysis of observational studies. Int J Obes 44:2080–2091. https://doi.org/10.1038/s41366-020-00650-z

    Article  Google Scholar 

  7. Dashti HS, Gómez-Abellán P, Qian J, Esteban A, Morales E, Scheer FAJL, Garaulet M (2021) Late eating is associated with cardiometabolic risk traits, obesogenic behaviors, and impaired weight loss. Am J Clin Nutr 113(1):154–161

    Article  PubMed  Google Scholar 

  8. Crispim CA, Rinaldi AE, Azeredo CM, Skene DJ, Moreno CR (2023) Is time of eating associated with BMI and obesity? A population-based study. Eur J Nutr. 1–11

  9. Oike H, Oishi K, Kobori M (2014) Nutrients clock genes, and chrononutrition. Curr Nutr Rep 3:204–212

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Panda S (2016) Circadian physiology of metabolism. Science 354(6315):1008–1015

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Morgan L, Hampton S, Gibbs M, Arendt J (2003) Circadian aspects of postprandial metabolism. Chronobiol Int 20(5):795–808

    Article  CAS  PubMed  Google Scholar 

  12. Bonham MP, Kaias E, Zimberg I, Leung GK, Davis R, Sletten TL et al (2019) Effect of night time eating on postprandial triglyceride metabolism in healthy adults: a systematic literature review. J Biol Rhythms 34(2):119–130

    Article  PubMed  Google Scholar 

  13. Rani R, Dharaiya CN, Singh B (2021) Importance of not skipping breakfast: A review. Int J Food Sci Technol 56(1):28–38

    Article  CAS  Google Scholar 

  14. Teixeira GP, Guimarães KC, Soares AGNS, Marqueze EC, Moreno CRC, Mota MC, Crispim CA (2022) Role of chronotype in dietary intake, meal timing, and obesity: a systematic review. Nutr Rev 81(1):75–90

    Article  PubMed  Google Scholar 

  15. Bonaccio M, Ruggiero E, Di Castelnuovo A, Martínez CF, Esposito S, Costanzo S et al (2023) Association between Late-Eating pattern and higher consumption of Ultra-Processed food among Italian adults: findings from the INHES study. Nutrients 15(6):1497

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Instituto Brasileiro de Geografia e Estatística (2019) Pesquisa de Orçamentos familiares: Primeiros resultados: 2017–2018. IBGE, Rio de Janeiro

    Google Scholar 

  17. Conway JM, Ingwersen LA, Vinyard BT, Moshfegh AJ (2003) Effectiveness of the US department of agriculture 5-step multiple-pass method in assessing food intake in obese and Nonobese women. Am J Clin Nutr 77(5):1171–1178. https://doi.org/10.1093/ajcn/77.5.1171

    Article  CAS  PubMed  Google Scholar 

  18. Instituto Brasileiro de Geografia e Estatística (2020) Diretoria de Pesquisas, coordenação de Trabalho e rendimento. Pesquisa de Orçamentos familiares 2017–2018: análise do Consumo alimentar pessoal no Brasil. IBGE, Rio de Janeiro

    Google Scholar 

  19. Brazilian Table of Food Composition (TBCA). University of São Paulo (USP). Food Research Center (FoRC). Version 7.2. São Paulo: USP (2023) Available at: http://www.fcf.usp.br/tbca. [Accessed on: December 27, 2024]

  20. International Network of Food Data Systems (INFOODS) (2012) Guidelines for food matching. Version 1.2. Food and Agriculture Organization - FAO, Rome, p 26

    Google Scholar 

  21. International Network of Food Data Systems (INFOODS) (2019) Standards and guidelines. Food and Agriculture Organization - FAO, Rome

    Google Scholar 

  22. Gill S, Panda S (2015) A smartphone app reveals erratic diurnal eating patterns in humans that can be modulated for health benefits. Cell Metab 22:789–798

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Loy SL, Wee PH, Colega MT, Cheung YB, Aris IM, Chan JKY et al (2017) Maternal night-fasting interval during pregnancy is directly associated with neonatal head circumference and adiposity in girls but not boys. J Nutr 147(7):1384–1391

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. De Castro JM (2004) The time of day of food intake influences overall intake in humans. J Nutr 134(1):104–111

    Article  CAS  PubMed  Google Scholar 

  25. Teixeira GP et al (2020) The association between chronotype, food craving and weight gain in pregnant women. J Hum Nutr Diet 33(3):342–350

    Article  CAS  PubMed  Google Scholar 

  26. Bonnell EK et al (2017) Influences on dietary choices during day versus night shift in shift workers: a mixed methods study. Nutrients 9(3):193

    Article  PubMed  PubMed Central  Google Scholar 

  27. Matsui K et al (2021) A Cross-Sectional study of evening hyperphagia and nocturnal ingestion: core constituents of night eating syndrome with different background factors. Nutrients 13(11):4179

    Article  PubMed  PubMed Central  Google Scholar 

  28. Klinenberg E (2016) Social isolation, loneliness, and living alone: identifying the risks for public health. Am J Public Health 106:786–787. https://doi.org/10.2105/AJPH.2016.303166

    Article  PubMed  PubMed Central  Google Scholar 

  29. Cummins S, Macintyre S (2006) Food environments and obesity—neighbourhood or nation? Int J Epidemiol 35(1):100–104

    Article  PubMed  Google Scholar 

  30. de Oliveira MF, Martins CA, Ribeiro de Castro IR (2022) The (scarce and circumscribed) culinary content in food-based dietary guidelines around the world: 1991–2021. Public Health Nutr 25(12):3559–3567

    Article  Google Scholar 

  31. Adam TC, Epel ES (2007) Stress, eating and the reward system. Physiol Behav 91(4):449–458

    Article  CAS  PubMed  Google Scholar 

  32. Burns RJ, Rothman AJ (2015) Offering variety: A subtle manipulation to promote healthy food choice throughout the day. Health Psychol 34(5):566

    Article  PubMed  Google Scholar 

  33. Lukomskyj N et al (2021) Associations between breakfast consumption from childhood to adulthood and cardiometabolic health: A systematic review. Nutr Dietetics 78(1):6–23

    Article  Google Scholar 

  34. de Sousa JR et al (2019) Nutritional quality of breakfast consumed by the low-income population in Brazil: A nationwide cross-sectional survey. Nutrients 11(6):1418

    Article  PubMed  PubMed Central  Google Scholar 

  35. Teixeira GP, Barreto ACF, Mota MC, Crispim CA (2019) Caloric midpoint is associated with total calorie and macronutrient intake and body mass index in undergraduate students. Chronobiol Int 36(10):1418–1428

    Article  CAS  PubMed  Google Scholar 

  36. Gontijo CA et al (2020) Higher energy intake at night effects daily energy distribution and contributes to excessive weight gain during pregnancy. Nutrition 74:110756

    Article  CAS  PubMed  Google Scholar 

  37. da Cunha NB, Teixeira GP, Rinaldi AEM, Azeredo CM, Crispim CA (2023) Late meal intake is associated with abdominal obesity and metabolic disorders related to metabolic syndrome: A chrononutrition approach using data from NHANES 2015–2018. Clin Nutr 42(9):1798–1805

    Article  Google Scholar 

  38. Bo S et al (2014) Consuming more of daily caloric intake at dinner predisposes to obesity. A 6-year population-based prospective cohort study. PLoS ONE 9(9):e108467

    Article  PubMed  PubMed Central  Google Scholar 

  39. Palla L, Almoosawi S (2019) Diurnal patterns of energy intake derived via principal component analysis and their relationship with adiposity measures in adolescents: results from the National diet and nutrition survey RP (2008–2012). Nutrients 11(2):422

    Article  PubMed  PubMed Central  Google Scholar 

  40. Ha K, Song Y (2019) Associations of meal timing and frequency with obesity and metabolic syndrome among Korean adults. Nutrients. 11

  41. Eicher-Miller HA, Khanna N, Boushey CJ, Gelfand SB, Delp EJ (2016) Temporal dietary patterns derived among the adult participants of the National health and diet. Associations of meal timing and frequency with obesity and metabolic syndrome among Korean adults. 116:283–291

  42. Spiegel K, Leproult R, L’Hermite-Balériaux M, Copinschi G, Penev PD, Van Cauter E (2004) Leptin levels are dependent on sleep duration: relationships with sympathovagal balance, carbohydrate regulation, cortisol, and Thyrotropin. J Clin Endocr 89(11):5762–5771

    Article  CAS  PubMed  Google Scholar 

  43. Guimarães KC, Silva CM, Latorraca CDOC, Oliveira RDA, Crispim CA (2022) Is self-reported short sleep duration associated with obesity? A systematic review and meta-analysis of cohort studies. Nutr Rev 80(5):983–1000

    Article  PubMed  Google Scholar 

  44. Gomes S et al (2023) Sleep patterns, eating behavior and the risk of noncommunicable diseases. Nutrients 15(11):2462

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Drager LF, Pachito DV, Morihisa R, Carvalho P, Lobao A, Poyares D (2022) Sleep quality in the Brazilian general population: a cross-sectional study. Sleep Epidemiology 2 (2022): 100020

  46. Marot LP, Lopes TDVC, Balieiro LCT, Crispim CA, Moreno CRC (2023) Impact of nighttime food consumption and feasibility of fasting during night work: a narrative review. Nutrients 15(11):2570

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Jankovic N et al (2022) Changes in chronotype and social jetlag during adolescence and their association with concurrent changes in BMI-SDS and body composition, in the DONALD study. Eur J Clin Nutr 76(5):765–771

    Article  PubMed  Google Scholar 

  48. Shaw ME (1998) Adolescent breakfast skipping: an Australian study. Adolescence 33(132):851–861

    CAS  PubMed  Google Scholar 

  49. Louzada FM, Silva AGT, Tardelli C, Mennabarreto L (2008) The adolescence sleep phase delay: causes, consequences and possible interventions. Sleep Sci 1:49–53

    Google Scholar 

  50. El-Ammari A et al (2020) Social-ecological influences on unhealthy dietary behaviours among Moroccan adolescents: a mixed-methods study. Public Health Nutr 23(6):996–1008

    Article  PubMed  PubMed Central  Google Scholar 

  51. Alves FR et al (2020) Sleep duration and daytime sleepiness in a large sample of Brazilian high school adolescents. Sleep Med 66:207–215

    Article  PubMed  Google Scholar 

  52. Gruber R et al (2010) Short sleep duration is associated with poor performance on IQ measures in healthy school-age children. Sleep Med 11(3):289–294

    Article  PubMed  Google Scholar 

  53. Fischer D, Lombardi DA, Marucci-Wellman H, Roenneberg T (2017) Chronotypes in the US–influence of age and sex. PLoS ONE 12(6):e0178782

    Article  PubMed  PubMed Central  Google Scholar 

  54. National Institutes of Health (2004) National Cancer institute. 24-hour dietary recall (24HR) at a glance. The Dietary Assessment Primer

  55. Wang X, Cheng Z (2020) Cross-sectional studies: strengths, weaknesses, and recommendations. Chest 158(1):S65–S71

    Article  PubMed  Google Scholar 

  56. Shim JS, Oh K, Kim HC (2014) Dietary assessment methods in epidemiologic studies. Epidemiol Health 36:e2014009

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank the Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) and the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq; CAC and CRCM are CNPq fellows; CAC: #401761/2022-3 and 312309/2020-1; CRCM: #307875/2022-9) for financial support and the scholarship, and the University of Surrey for the IAS Fellowship to CAC in 2022.

Funding

This work was supported by the Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP), the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) and the Institute of Advanced Studies (IAS) of the University of Surrey. CAC and CRCM are CNPq fellows - CAC: #401761/2022-3 and 312309/2020-1; CRCM: #311278/2019; CAC was awarded the IAS Fellowship by the University of Surrey in 2022.

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CAC, CRCM and DJS participated in the planning, interpretation of results and writing of the manuscript. CAC performed the statistical analysis. CMA and AEMR participated in the interpretation of results, support on the statistical analysis and writing of the manuscript.

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Correspondence to Cibele A. Crispim.

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Crispim, C.A., Azeredo, C.M., Rinaldi, A.E. et al. Late eating and shortened fasting are associated with higher ultra-processed food intake across all age groups: a population-based study. Eur J Nutr 64, 134 (2025). https://doi.org/10.1007/s00394-025-03633-w

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  1. Ana E.M. Rinaldi
  2. Claudia R.C. Moreno