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

IF 3.9 2区医学 Q2 NUTRITION & DIETETICS Nutrition & Metabolism Pub Date : 2024-12-20 DOI:10.1186/s12986-024-00884-y

Takahiro Ichikawa, Yoshitaka Hashimoto, Yusuke Igarashi, Sayaka Kawai, Ayumi Kaji, Ryosuke Sakai, Takafumi Osaka, Ryo Inoue, Saori Kashiwagi, Katsura Mizushima, Kazuhiko Uchiyama, Tomohisa Takagi, Yuji Naito, Masahide Hamaguchi, Michiaki Fukui

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Abstract

Background: This study aimed to explore the relationship between ultra-processed foods (UPFs) consumption and gut microbiota in patients with type 2 diabetes (T2D).

Methods: This cross-sectional study included 362 participants with T2D. UPFs consumption was assessed using a brief-type self-administered diet history questionnaire, quantified as the density of UPFs intake (g/1000 kcal). Gut microbial composition was evaluated via 16S rRNA gene sequencing. We investigated the association between gut microbiota, previously identified as relevant to T2D, and the density of UPFs intake using Spearman rank correlation coefficients. Multiple regression analysis, adjusting for age, sex, BMI, smoking status, exercise, and medication use, was conducted to further investigate these associations.

Results: The mean age of participants was 68 (63-74) years. The density of UPFs intake showed significant associations with Bifidobacterium (r = 0.11, p = 0.031), Lactobacillus (r = 0.11, p = 0.046), Ruminococcus (r = -0.12, p = 0.019), Roseburia (r = 0.11, p = 0.045). After adjusting for covariates in multiple regression analysis, Ruminococcus and Roseburia showed modest negative (β = -0.11, p = 0.038) and positive (β = 0.12, p = 0.033) correlations, with the density of UPFs intake among participants with T2D, respectively.

Conclusions: The density of UPFs intake was modestly inversely associated with Ruminococcus among patients with T2D and modestly positively associated with Roseburia.

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2型糖尿病患者肠道微生物群与超加工食品消费之间的关系：一项横断面研究

背景：本研究旨在探讨2型糖尿病（T2D）患者超加工食品（upf）消费与肠道微生物群之间的关系。方法：本横断面研究纳入362例T2D患者。使用一份简短的自我管理饮食史问卷来评估UPFs的消耗，并将其量化为UPFs摄入密度（g/1000 kcal）。通过16S rRNA基因测序评估肠道微生物组成。我们使用Spearman秩相关系数研究了肠道微生物群（先前被确定与T2D相关）与UPFs摄入密度之间的关系。对年龄、性别、BMI、吸烟状况、运动和药物使用等因素进行调整后的多元回归分析，以进一步调查这些关联。结果：参与者的平均年龄为68岁（63-74）岁。upf摄入密度与双歧杆菌（r = 0.11, p = 0.031）、乳酸菌（r = 0.11, p = 0.046）、瘤胃球菌（r = -0.12, p = 0.019）、玫瑰菌（r = 0.11, p = 0.045）呈显著相关。在多元回归分析中调整协变量后，Ruminococcus和Roseburia分别与T2D参与者的upf摄入密度呈适度负相关（β = -0.11, p = 0.038）和正相关（β = 0.12, p = 0.033）。结论：在T2D患者中，upf摄入密度与Ruminococcus呈适度负相关，与Roseburia呈适度正相关。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Nutrition & Metabolism 医学-营养学

CiteScore

8.40

自引率

0.00%

发文量

审稿时长

4-8 weeks

期刊介绍： Nutrition & Metabolism publishes studies with a clear focus on nutrition and metabolism with applications ranging from nutrition needs, exercise physiology, clinical and population studies, as well as the underlying mechanisms in these aspects. The areas of interest for Nutrition & Metabolism encompass studies in molecular nutrition in the context of obesity, diabetes, lipedemias, metabolic syndrome and exercise physiology. Manuscripts related to molecular, cellular and human metabolism, nutrient sensing and nutrient–gene interactions are also in interest, as are submissions that have employed new and innovative strategies like metabolomics/lipidomics or other omic-based biomarkers to predict nutritional status and metabolic diseases. Key areas we wish to encourage submissions from include: -how diet and specific nutrients interact with genes, proteins or metabolites to influence metabolic phenotypes and disease outcomes; -the role of epigenetic factors and the microbiome in the pathogenesis of metabolic diseases and their influence on metabolic responses to diet and food components; -how diet and other environmental factors affect epigenetics and microbiota; the extent to which genetic and nongenetic factors modify personal metabolic responses to diet and food compositions and the mechanisms involved; -how specific biologic networks and nutrient sensing mechanisms attribute to metabolic variability.