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Ultra-processed food and incident type 2 diabetes: studying the underlying consumption patterns to unravel the health effects of this heterogeneous food category in the prospective Lifelines cohort

Duan, Ming-Jie, Vinke, Petra C., Navis, Gerjan, Corpeleijn, Eva, Dekker, Louise H.·2022·BMC Medicine

Abstract

Background: The overall consumption of ultra-processed food (UPF) has previously been associated with type 2 diabetes. However, due to the substantial heterogeneity of this food category, in terms of their nutritional composition and product type, it remains unclear whether previous results apply to all underlying consumption patterns of UPF. Methods: Of 70,421 participants (35–70 years, 58.6% women) from the Lifelines cohort study, dietary intake was assessed with a food frequency questionnaire. UPF was identified according to the NOVA classification. Principal component analysis (PCA) was performed to derive UPF consumption patterns. The associations of UPF and adherence to UPF consumption patterns with incidence of type 2 diabetes were studied with logistic regression analyses adjusted for age, sex, diet quality, energy intake, alcohol intake, physical activity, TV watching time, smoking status, and educational level. Results: During a median follow-up of 41 months, a 10% increment in UPF consumption was associated with a 25% higher risk of developing type 2 diabetes (1128 cases; OR 1.25 [95% CI 1.16, 1.34]). PCA revealed four habitual UPF consumption patterns. A pattern high in cold savory snacks (OR 1.16 [95% CI 1.09, 1.22]) and a pattern high in warm savory snacks (OR 1.15 [95% CI 1.08, 1.21]) were associated with an increased risk of incident type 2 diabetes; a pattern high in traditional Dutch cuisine was not associated with type 2 diabetes incidence (OR 1.05 [95% CI 0.97, 1.14]), while a pattern high in sweet snacks and pastries was inversely associated with type 2 diabetes incidence (OR 0.82 [95% CI 0.76, 0.89]). Conclusions: The heterogeneity of UPF as a general food category is reflected by the discrepancy in associations between four distinct UPF consumption patterns and incident type 2 diabetes. For better public health prevention, research is encouraged to further clarify how different UPF consumption patterns are related to type 2 diabetes.

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  1. Saeedi P, Petersohn I, Salpea P, Malanda B, Karuranga S, Unwin N, et al. Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: results from the International Diabetes Federation Diabetes Atlas, 9th edition. Diabetes Res Clin Pract. 2019;157:107843. https://doi.org/10.1016/j.diabres.2019.107843 · DOI · Paper page
  2. Ardisson Korat A V, Willett WC, Hu FB, Hu F. Diet, lifestyle, and genetic risk factors for type 2 diabetes: a review from the Nurses’ Health Study, Nurses’ Health Study 2, and Health Professionals’ Follow-up. Curr Nutr Rep. 2014;3:345–54. https://doi.org/10.1007/s13668-014-0103-5 · DOI · Paper page
  3. Koloverou E, Esposito K, Giugliano D, Panagiotakos D. The effect of Mediterranean diet on the development of type 2 diabetes mellitus: a meta-analysis of 10 prospective studies and 136,846 participants. Metabolism. 2014;63(7):903–11. https://doi.org/10.1016/j.metabol.2014.04.010. · DOI · Paper page
  4. Vinke PC, Navis G, Kromhout D, Corpeleijn E. Socio-economic disparities in the association of diet quality and type 2 diabetes incidence in the Dutch Lifelines cohort. EClinicalMedicine. 2020;19:100252. https://doi.org/10.1016/j.eclinm.2019.100252. · DOI
  5. Levy RB, Rauber F, Chang K, Louzada ML da C, Monteiro CA, Millett C, et al. Ultra-processed food consumption and type 2 diabetes incidence: a prospective cohort study. Clin Nutr. 2021;40(5):3608–14. https://doi.org/10.1016/j.clnu.2020.12.018. · DOI
  6. Monteiro CA. Nutrition and health. The issue is not food, nor nutrients, so much as processing. Public Health Nutr. 2009;12(5):729–31. https://doi.org/10.1017/S1368980009005291. · DOI
  7. Monteiro CA, Cannon G, Levy R, Moubarac J-C, Jaime P, Parra APMDCMLD. NOVA. The star shines bright. World Nutr. 2016;7:28–38.
  8. Vandevijvere S, Jaacks LM, Monteiro CA, Moubarac JC, Girling-Butcher M, Lee AC, et al. Global trends in ultraprocessed food and drink product sales and their association with adult body mass index trajectories. Obes Rev. 2019;20(S2):10–9. https://doi.org/10.1111/obr.12860. · DOI
  9. Cordova R, Kliemann N, Huybrechts I, Rauber F, Vamos EP, Levy RB, et al. Consumption of ultra-processed foods associated with weight gain and obesity in adults: a multi-national cohort study. Clin Nutr. 2021;40(9):5079–88. https://doi.org/10.1016/j.clnu.2021.08.009. · DOI
  10. Rauber F, Chang K, Vamos EP, da Costa Louzada ML, Monteiro CA, Millett C, et al. Ultra-processed food consumption and risk of obesity: a prospective cohort study of UK Biobank. Eur J Nutr. 2021;60(4):2169–80. https://doi.org/10.1007/s00394-020-02367-1. · DOI
  11. Li M, Shi Z. Ultra-Processed Food Consumption Associated with Overweight / Obesity among Chinese Adults — Results from China Health and Nutrition Survey 1997-2011. Nutrients. 2021;13(8):2796. https://doi.org/10.3390/nu13082796. · DOI
  12. Srour B, Fezeu LK, Kesse-Guyot E, Allès B, Méjean C, Andrianasolo RM, et al. Ultra-processed food intake and risk of cardiovascular disease: Prospective cohort study (NutriNet-Santé). BMJ. 2019;365:l1451. https://doi.org/10.1136/bmj.l1451. · DOI · Paper page
  13. Du S, Kim H, Rebholz CM. Higher Ultra-Processed Food Consumption Is Associated with Increased Risk of Incident Coronary Artery Disease in the Atherosclerosis Risk in Communities Study. J Nutr. 2021:nxab285. https://doi.org/10.1093/jn/nxab285. · DOI
  14. Bonaccio M, Di Castelnuovo A, Costanzo S, De Curtis A, Persichillo M, Sofi F, et al. Ultra-processed food consumption is associated with increased risk of all-cause and cardiovascular mortality in the Moli-sani Study. Am J Clin Nutr. 2021;113(2):446–55. https://doi.org/10.1093/ajcn/nqaa299. · DOI · Paper page
  15. Rico-Campà A, Martínez-González MA, Alvarez-Alvarez I, De Deus MR, De La Fuente-Arrillaga C, Gómez-Donoso C, et al. Association between consumption of ultra-processed foods and all cause mortality: SUN prospective cohort study. BMJ. 2019;365:l1949. https://doi.org/10.1136/bmj.l1949. · DOI
  16. Romero Ferreiro C, Martín-Arriscado Arroba C, Cancelas Navia P, Lora Pablos D, Gómez de la Cámara A. Ultra-processed food intake and all-cause mortality: DRECE cohort study. Public Health Nutr. 2021:1–28. https://doi.org/10.1017/S1368980021003256. · DOI
  17. Zhong GC, Gu HT, Peng Y, Wang K, Le Wu YQ, Hu TY, et al. Association of ultra-processed food consumption with cardiovascular mortality in the US population: long-term results from a large prospective multicenter study. Int J Behav Nutr Phys Act. 2021;18(1):1–14. https://doi.org/10.1186/s12966-021-01081-3. · DOI
  18. Martínez Steele E, Juul F, Neri D, Rauber F, Monteiro CA, et al. Dietary share of ultra-processed foods and metabolic syndrome in the US adult population. Prev Med (Baltim). 2019;125:40–8.https://doi.org/10.1016/j.ypmed.2019.05.004 · DOI
  19. Nardocci M, Polsky JY, Moubarac JC. Consumption of ultra-processed foods is associated with obesity, diabetes and hypertension in Canadian adults. Can J Public Health. 2021;112(3):421–9. https://doi.org/10.17269/s41997-020-00429-9. · DOI
  20. Llavero-Valero M, Escalada-San Martín J, Martínez-González MA, Basterra-Gortari FJ, de la Fuente-Arrillaga C, Bes-Rastrollo M. Ultra-processed foods and type-2 diabetes risk in the SUN project: A prospective cohort study. Clin Nutr. 2021;40(5):2817–24. https://doi.org/10.1016/j.clnu.2021.03.039. · DOI
  21. Fardet A, Rock E. Ultra-processed foods: A new holistic paradigm? Trends Food Sci Technol. 2019;93:174–84. https://doi.org/10.1016/j.tifs.2019.09.016. · DOI
  22. Scholtens S, Smidt N, Swertz MA, Bakker SJL, Dotinga A, Vonk JM, et al. Cohort Profile: LifeLines, a three-generation cohort study and biobank. Int J Epidemiol. 2015;44(4):1172–80. https://doi.org/10.1093/ije/dyu229. · DOI · Paper page
  23. Stolk RP, Rosmalen JGM, Postma DS, De Boer RA, Navis G, Slaets JPJ, et al. Universal risk factors for multifactorial diseases: LifeLines: A three-generation population-based study. Eur J Epidemiol. 2008;23(1):67–74. https://doi.org/10.1007/s10654-007-9204-4. · DOI · Paper page
  24. Schofield WN. Predicting basal metabolic rate, new standards and review of previous work. Hum Nutr Clin Nutr. 1985;39(Suppl 1):5–41.
  25. Black AE. Critical evaluation of energy intake using the Goldberg cut-off for energy intake:basal metabolic rate. A practical guide to its calculation, use and limitations. Int J Obes (Lond). 2000;24(9):1119–30. https://doi.org/10.1038/sj.ijo.0801376. · DOI
  26. Siebelink E, Geelen A, de Vries JHM. Self-reported energy intake by FFQ compared with actual energy intake to maintain body weight in 516 adults. Br J Nutr. 2011;106(2):274–81. https://doi.org/10.1017/S0007114511000067. · DOI
  27. Vinke PC, Corpeleijn E, Dekker LH, Jacobs DR Jr, Navis G, Kromhout D. Development of the Lifelines Diet Score (LLDS) and its application in 129,369 Lifelines participants. Eur J Clin Nutr. 2018;72(8):1111–9. https://doi.org/10.1038/s41430-018-0205-z. · DOI
  28. Wendel-Vos GCW, Schuit AJ, Saris WHM, Kromhout D. Reproducibility and relative validity of the short questionnaire to assess health-enhancing physical activity. J Clin Epidemiol. 2003;56(12):1163–9. https://doi.org/10.1016/S0895-4356(03)00220-8. · DOI
  29. Von Eckardstein A, Schulte H, Assmann G. Risk for diabetes mellitus in middle-aged Caucasian male participants of the PROCAM study: Implications for the definition of impaired fasting glucose by the American Diabetes Association. J Clin Endocrinol Metab. 2000;85(9):3101–8. https://doi.org/10.1210/jcem.85.9.6773. · DOI
  30. Gibney MJ. Ultra-Processed Foods: Definitions and Policy Issues. Curr Dev Nutr. 2019;3(2):1–7. https://doi.org/10.1093/cdn/nzy077. · DOI
  31. Elizabeth L, Machado P, Zinöcker M, Baker P, Lawrence M. Ultra-processed foods and health outcomes: A narrative review. Nutrients. 2020;12(7). https://doi.org/10.3390/nu12071955. · DOI
  32. Aune D, Norat T, Romundstad P, Vatten LJ. Whole grain and refined grain consumption and the risk of type 2 diabetes: A systematic review and dose-response meta-analysis of cohort studies. Eur J Epidemiol. 2013;28(11):845–58. https://doi.org/10.1007/s10654-013-9852-5. · DOI · Paper page
  33. Juul F, Vaidean G, Parekh N. Ultra-processed Foods and Cardiovascular Diseases: Potential Mechanisms of Action. Adv Nutr. 2021;12(5):1673–80. https://doi.org/10.1093/advances/nmab049. · DOI · Paper page
  34. Almoraie NM, Saqaan R, Alharthi R, Alamoudi A, Badh L, Shatwan IM. Snacking patterns throughout the life span: potential implications on health. Nutr Res. 2021;91:81–94. https://doi.org/10.1016/j.nutres.2021.05.001. · DOI
  35. Hu FB, Malik VS. Sugar-sweetened beverages and risk of obesity and type 2 diabetes: Epidemiologic evidence. Physiol Behav. 2010;100(1):47–54. https://doi.org/10.1016/j.physbeh.2010.01.036. · DOI · Paper page
  36. Buijsse B, Boeing H, Drogan D, Schulze MB, Feskens EJ, Amiano P, et al. Consumption of fatty foods and incident type 2 diabetes in populations from eight European countries. Eur J Clin Nutr. 2015;69(4):455–61. https://doi.org/10.1038/ejcn.2014.249. · DOI · Paper page
  37. Macdonald IA. A review of recent evidence relating to sugars, insulin resistance and diabetes. Eur J Nutr. 2016;55(S2):17–23. https://doi.org/10.1007/s00394-016-1340-8. · DOI
  38. Wang JW, Light K, Henderson M, O’Loughlin J, Mathieu ME, Paradis G, et al. Consumption of added sugars from liquid but not solid sources predicts impaired glucose homeostasis and insulin resistance among youth at risk of obesity. J Nutr. 2014;144(1):81–6. https://doi.org/10.3945/jn.113.182519. · DOI · Paper page
  39. Janket SJ, Manson JAE, Sesso H, Buring JE, Liu S. A prospective study of sugar intake and risk of type 2 diabetes in women. Diabetes Care. 2003;26(4):1008–15. https://doi.org/10.2337/diacare.26.4.1008. · DOI
  40. Popkin BM, Barquera S, Corvalan C, Hofman KJ, Monteiro C, Ng SW, et al. Towards unified and impactful policies to reduce ultra-processed food consumption and promote healthier eating. Lancet Diabetes Endocrinol. 2021;9(7):462–70. https://doi.org/10.1016/S2213-8587(21)00078-4. · DOI
  41. Hu FB. Dietary pattern analysis: A new direction in nutritional epidemiology. Curr Opin Lipidol. 2002;13(1):3–9. https://doi.org/10.1097/00041433-200202000-00002. · DOI · Paper page
  42. Newby PK, Tucker KL. Empirically derived eating patterns using factor or cluster analysis: A review. Nutr Rev. 2004;62(5):177–203. https://doi.org/10.1111/j.1753-4887.2004.tb00040.x. · DOI · Paper page
  43. Dekker LH, Rijnks RH, Strijker D, Navis GJ. A spatial analysis of dietary patterns in a large representative population in the north of The Netherlands - the Lifelines cohort study. Int J Behav Nutr Phys Act. 2017;14(1):166. https://doi.org/10.1186/s12966-017-0622-8. · DOI
  44. Green MS, Symons MJ. A comparison of the logistic risk function and the proportional hazards model in prospective epidemiologic studies. J Chronic Dis. 1983;36(10):715–23. https://doi.org/10.1016/0021-9681(83)90165-0. · DOI
  45. Ngwa JS, Cabral HJ, Cheng DM, Pencina MJ, Gagnon DR, LaValley MP, et al. A comparison of time dependent Cox regression, pooled logistic regression and cross sectional pooling with simulations and an application to the Framingham Heart Study. BMC Med Res Methodol. 2016;16(1):1–12. https://doi.org/10.1186/s12874-016-0248-6. · DOI

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Metadata

Authors
Duan, M.-J., Vinke, P. C., Navis, G., Corpeleijn, E., & Dekker, L. H.
Venue
BMC Medicine
Published
2022-01-13
Volume
20
Issue
1
ISSN
1741-7015
Cited by
75
References
6
Language
en
Funders
h2020 marie skłodowska-curie actions (754425)

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