Association between gut microbiota and ultra-processed foods consumption among the patients with type 2 diabetes: a cross-sectional study

Sun H, Saeedi P, Karuranga S, et al. IDF Diabetes Atlas: global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045. Diabetes Res Clin Pract. 2022;183:109119. https://doi.org/10.1016/j.diabres.2021.109119.

Article  PubMed  Google Scholar 

Bommer C, Sagalova V, Heesemann E, et al. Global economic burden of diabetes in adults: projections from 2015 to 2030. Diabetes Care. 2018;41(5):963–70. https://doi.org/10.2337/dc17-1962.

Article  PubMed  Google Scholar 

American Diabetes Association Professional Practice Committee. pharmacologic approaches to glycemic treatment: standards of care in diabetes—2024. Diabetes Care. 2024;47:S158–78. https://doi.org/10.2337/dc24-S009.

Article  Google Scholar 

Evert AB, Dennison M, Gardner CD, et al. Nutrition therapy for adults with diabetes or prediabetes: a consensus report. Diabetes Care. 2019;42(5):731–54. https://doi.org/10.2337/dci19-0014.

Article  PubMed  PubMed Central  Google Scholar 

Wang L, Martínez Steele E, Du M, et al. Trends in consumption of ultraprocessed foods among US youths aged 2–19 years, 1999–2018. JAMA. 2021;326(6):519. https://doi.org/10.1001/jama.2021.10238.

Article  PubMed  PubMed Central  Google Scholar 

Vandevijvere S, Jaacks LM, Monteiro CA, 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.

Article  PubMed  Google Scholar 

Monteiro CA, Cannon G, Moubarac JC, Levy RB, Louzada MLC, Jaime PC. The UN decade of nutrition, the NOVA food classification and the trouble with ultra-processing. Public Health Nutr. 2018;21(1):5–17. https://doi.org/10.1017/S1368980017000234.

Article  PubMed  Google Scholar 

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.

Article  PubMed  Google Scholar 

Pant A, Gribbin S, Machado P, et al. Ultra-processed foods and incident cardiovascular disease and hypertension in middle-aged women. Eur J Nutr. 2024;63(3):713–25. https://doi.org/10.1007/s00394-023-03297-4.

Article  PubMed  Google Scholar 

Lane MM, Gamage E, Du S, et al. Ultra-processed food exposure and adverse health outcomes: umbrella review of epidemiological meta-analyses. BMJ. 2024. https://doi.org/10.1136/bmj-2023-077310.

Article  PubMed  PubMed Central  Google Scholar 

Visioli F, Del Rio D, Fogliano V, Marangoni F, Ricci C, Poli A. Ultra-processed foods and health: are we correctly interpreting the available evidence? Eur J Clin Nutr. 2024. https://doi.org/10.1038/s41430-024-01515-8.

Article  PubMed  Google Scholar 

Ross FC, Patangia D, Grimaud G, et al. The interplay between diet and the gut microbiome: implications for health and disease. Nat Rev Microbiol. 2024;22(11):671–86. https://doi.org/10.1038/s41579-024-01068-4.

Article  CAS  PubMed  Google Scholar 

Menafra D, Proganò M, Tecce N, Pivonello R, Colao A. Diet and gut microbiome: Impact of each factor and mutual interactions on prevention and treatment of type 1, type 2, and gestational diabetes mellitus. Human Nutr Metab. 2024. https://doi.org/10.1016/j.hnm.2024.200286.

Article  Google Scholar 

Atzeni A, Martínez MÁ, Babio N, et al. Association between ultra-processed food consumption and gut microbiota in senior subjects with overweight/obesity and metabolic syndrome. Front Nutr. 2022. https://doi.org/10.3389/fnut.2022.976547.

Article  PubMed  PubMed Central  Google Scholar 

Martínez Leo EE, Segura Campos MR. Effect of ultra-processed diet on gut microbiota and thus its role in neurodegenerative diseases. Nutrition. 2020. https://doi.org/10.1016/j.nut.2019.110609.

Article  PubMed  Google Scholar 

Hashimoto Y, Hamaguchi M, Kaji A, et al. Intake of sucrose affects gut dysbiosis in patients with type 2 diabetes. J Diabetes Investig. 2020;11(6):1623–34. https://doi.org/10.1111/jdi.13293.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Chen Z, Khandpur N, Desjardins C, et al. Ultra-processed food consumption and risk of type 2 diabetes: three large prospective U.S. cohort studies. Diabetes Care. 2023;46(7):1335–44. https://doi.org/10.2337/dc22-1993.

Article  PubMed  PubMed Central  Google Scholar 

Delpino FM, Figueiredo LM, Bielemann RM, et al. Ultra-processed food and risk of type 2 diabetes: a systematic review and meta-analysis of longitudinal studies. Int J Epidemiol. 2022;51(4):1120–41. https://doi.org/10.1093/ije/dyab247.

Article  PubMed  Google Scholar 

American Diabetes Association. Classification and diagnosis of diabetes: standards of medical care in diabetesd 2018. Diabetes Care. 2018;41:S13–27. https://doi.org/10.2337/dc18-S002.

Article  Google Scholar 

Hamaguchi M, Kojima T, Takeda N, et al. The metabolic syndrome as a predictor of nonalcoholic fatty liver disease. Ann Intern Med. 2005;143(10):722. https://doi.org/10.7326/0003-4819-143-10-200511150-00009.

Article  CAS  PubMed  Google Scholar 

Matsuo S, Imai E, Horio M, et al. Revised equations for estimated GFR from serum creatinine in Japan. Am J Kidney Dis. 2009;53(6):982–92. https://doi.org/10.1053/j.ajkd.2008.12.034.

Article  CAS  PubMed  Google Scholar 

Iwata M, Matsushita Y, Fukuda K, et al. Secretory units of islets in transplantation index is a useful predictor of insulin requirement in Japanese type 2 diabetic patients. J Diabetes Investig. 2014;5(5):570–80. https://doi.org/10.1111/jdi.12181.

Article  CAS  PubMed  Google Scholar 

Hashimoto Y, Kaji A, Sakai R, et al. Skipping breakfast is associated with glycemic variability in patients with type 2 diabetes. Nutrition. 2020. https://doi.org/10.1016/j.nut.2019.110639.

Article  PubMed  Google Scholar 

Kobayashi S, Murakami K, Sasaki S, et al. Comparison of relative validity of food group intakes estimated by comprehensive and brief-type self-administered diet history questionnaires against 16 d dietary records in Japanese adults. Public Health Nutr. 2011;14(7):1200–11. https://doi.org/10.1017/S1368980011000504.

Article  PubMed  Google Scholar 

Shinozaki N, Murakami K, Yuan X, et al. The association of highly processed food consumption with food choice values and food literacy in Japanese adults: a nationwide cross-sectional study. Int J Behav Nutr Phys Act. 2023. https://doi.org/10.1186/s12966-023-01538-7.

Article  PubMed  PubMed Central  Google Scholar 

Willett W, Howe G, Kushi L. Adjustment for total energy intake in epidemiologic studies. Am J Clin Nutr. 1997;65(4):1220S-1228S. https://doi.org/10.1093/ajcn/65.4.1220S.

Article  CAS  PubMed  Google Scholar 

Inoue R, Ohueekitano R, Tsukahara T, et al. Prediction of functional profiles of gut microbiota from 16S rRNA metagenomic data provides a more robust evaluation of gut dysbiosis occurring in Japanese type 2 diabetic patients. J Clin Biochem Nutr. 2017;61(3):217–21. https://doi.org/10.3164/jcbn.17744.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Takagi T, Naito Y, Inoue R, et al. The influence of longgterm use of proton pump inhibitors on the gut microbiota: an ageesexxmatched caseecontrol study. J Clin Biochem Nutr. 2018;62(1):100–5. https://doi.org/10.3164/jcbn.17778.

Article  CAS  PubMed  Google Scholar 

Nishino K, Nishida A, Inoue R, et al. Analysis of endoscopic brush samples identified mucosa-associated dysbiosis in inflammatory bowel disease. J Gastroenterol. 2018;53(1):95–106. https://doi.org/10.1007/s00535-017-1384-4.

Article  PubMed  Google Scholar 

Bolyen E, Rideout JR, Dillon MR, et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol. 2019;37(8):850–2. https://doi.org/10.1038/s41587-019-0190-3.

Article  CAS 

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