This cross-sectional study aimed to clarify the characteristic gut microbiota of Japanese patients with type 2 diabetes (T2DM) using t-distributed stochastic neighbor embedding analysis and the k-means method and to clarify the relationship with background data, including dietary habits. The gut microbiota data of 383 patients with T2DM and 114 individuals without T2DM were classified into red, blue, green, and yellow groups. The proportions of patients with T2DM in the red, blue, green, and yellow groups was 86.8% (112/129), 69.8% (81/116), 76.3% (90/118), and 74.6% (100/134), respectively; the red group had the highest prevalence of T2DM. There were no intergroup differences in sex, age, or body mass index. The red group had higher percentages of the Bifidobacterium and Lactobacillus genera and lower percentages of the Blautia and Phascolarctobacterium genera. Higher proportions of patients with T2DM in the red group used α-glucosidase inhibitors and glinide medications and had a low intake of fermented soybean foods, including miso soup, than those in the other groups. The gut microbiota pattern of the red group may indicate characteristic changes in the gut microbiota associated with T2DM in Japan. These results also suggest that certain diabetes drugs and fermented foods may be involved in this change. Further studies are needed to confirm the relationships among traditional dietary habits, the gut microbiota, and T2DM in Japan.

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(1) Background: From the perspective of patient-centered care, it is important for medical professionals involved in diabetes care to know the role of choice behavior when individuals with type 2 diabetes mellitus select their meals at home. In Japan, online meal management applications are widely used to help individuals to prepare healthy, colorful, and tasty meals. (2) Objective: To assess menu selection from an online diet management application in individuals with type 2 diabetes mellitus over a period of 12 months. (3) Method: The saved data of the selected food menus on the online diet management application were analyzed. We identified specific nutritional groups of the food menus, called nutritional clusters, by clustering the multidimensional data of the nutrients after de-dimensioning them. Then, we analyzed the constitutional nutrients of each nutritional cluster with the highest and lowest frequencies of selection by the users of the application. (4) Results: In all, 9674 food menus made by 3164 people were included in the analysis, and 24 nutritional clusters were identified. Low-carbohydrate and low-calorie food clusters showed the highest selection frequency. The average caloric value of 149.7 kcal and average carbohydrate ratio of 47% in the cluster with the highest selection frequency were significantly lower than the average caloric value of 435.2 kcal and carbohydrate ratio of 63% in the cluster with the lowest selection frequency (p < 0.001, respectively). (5) Conclusion: Individuals with type 2 diabetes in this population preferred to select lower-carbohydrate and lower-calorie food menus at home using online diet management applications. To improve sustained self-management and quality of life, medical professionals may consider incorporating preferred dietary behaviors into medical management of type 2 diabetes mellitus.