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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
留言 (0)