To investigate the association between dietary energy density (DED) and obesity in people with type 2 diabetes mellitus. Moreover, we compared the strength of the associations of DED with intake of energy and macronutrients in terms of obesity as well as nutritional factors that have long been used for medical nutritional therapy. Cross-sectionally investigated were 1615 outpatients with type 2 diabetes who attended 26 clinics nationwide with diabetes specialists. Odds ratios (ORs) were calculated for the association between obesity and DED, energy, and macronutrients by quintile categories and a 1 SD increment with adjustment for potential confounders. β coefficients were calculated for the association between body mass index (BMI) and each nutritional factor by 1 SD increments in nutritional values. Multi-adjusted OR for obesity between extreme quintiles of DED was 2.99 (95% confidence interval (95%CI): 2.01–3.12). Conversely, the ORs did not differ significantly according to the quintiles of other nutrient factors. Multi-adjusted β coefficient of BMI per 1 SD according to DED was far higher than those of other nutrient factors (β coefficient 0.65, 95% CI: 0.41–0.88). These findings indicated that DED in persons with type 2 diabetes was positively associated with BMI and the prevalence of obesity. DED was also much more potently associated with obesity and BMI than nutritional indicators such as intake of energy or macronutrients.

Background: In order to provide effective dietary guidance, it is necessary to consider dietary intake, which can change over time. This study analyzed changes in the diet of Japanese patients with type 2 diabetes over a 20-year period. Methods: We compared the results of two dietary surveys that used the food frequency questionnaire format. The first was conducted in 1996 by the Japan Diabetes Complications Study (JDCS) (n = 1509; males 53.3%), and the second in 2014–2018 by the Japan Diabetes Clinical Data Management Study (JDDM) (n = 1145; males 65.6%). Both are nationwide representative registries of outpatients with type 2 diabetes in Japan. Results: Over a 20-year period, both men and women with type 2 diabetes had a significant increase in body mass index (BMI). Nonetheless, there was only a small change in energy intake. Conversely, there was a significant increase in fat intake and thus in the fat-to-energy ratio. With regard to food groups, there was a significant increase in meat intake and a decrease in the intake of fish, soybeans/soy products, vegetables, and fruits, with a particularly significant decrease in vegetables. Conclusions: Even in Japan, an industrialized country with a stable socioeconomic environment, there were many significant changes in the dietary intake of patients with type 2 diabetes over the 20-year period.

Obesity is associated with deterioration of T2DM and its complications. Although low serum zinc levels are reportedly associated with obesity in nondiabetic people, clinical evidence regarding the association between dietary intake of zinc and obesity in diabetic individuals is sparse. Thus, we aimed to clarify this issue utilizing our nationwide registry of Japanese patients with diabetes. Analyzed were 1891 patients with T2DM (mean age, 62 y; men, 62%) whose data from a food frequency questionnaire were available. Obesity was defined as BMI of ≥25. The associations between obesity and intake of zinc were determined by multivariate regression as well as quintile analyses. Odds ratio (OR) for obesity was 0.74 (0.65, 0.84) per 1 mg increase in zinc intake. Quintile analysis revealed a stepwise decrease in ORs for obesity at higher quintiles. ORs in Q3-Q5 were significant compared to Q1 as the reference (Table). Food groups strongly related to zinc intake among our patients were meats (correlation coefficients, 0.64), fish (0.49), and vegetables (0.47). These results suggest close associations between zinc intake and obesity in patients with T2DM. This information would be clinically applicable for nutrition education. Disclosure R. Nedachi: None. K. Fujihara: None. M. Hatta: None. Y. Matsubayashi: None. Y. Takeda: None. D. Ishii: None. C. Horikawa: None. N. Kato: None. H. Maegawa: Research Support; Self; Antares Pharma, Boehringer Ingelheim Pharmaceuticals, Inc., Daiichi Sankyo Company, Limited, Mitsubishi Tanabe Pharma Corporation, Takeda Pharmaceutical Company Limited. Speaker's Bureau; Self; Astellas Pharma Inc., Boehringer Ingelheim Pharmaceuticals, Inc., Daiichi Sankyo Company, Limited, Merck Sharp & Dohme Corp., Mitsubishi Tanabe Pharma Corporation, Takeda Pharmaceutical Company Limited. H. Sone: Research Support; Self; Astellas Pharma Inc., Boehringer Ingelheim Pharmaceuticals, Inc., Daiichi Sankyo Company, Limited, Kowa Pharmaceutical Europe Co. Ltd., Kyowa Hakko Kirin Co., Ltd., Novo Nordisk Inc., Ono Pharmaceutical Co., Ltd., Taisho Pharmaceutical Co., Ltd., Takeda Pharmaceutical Company Limited, Teijin Pharma Limited.

An increase in screen time (ST) is reportedly associated with insulin resistance and obesity in children. However, most studies are of TV viewing but not smartphone use. Also, little is known of the relationship between ST and diet. In this study, we clarified the relationships among ST including smartphone use, lifestyle factors including dietary content, and obesity. Cross-sectionally analyzed were 1393 children between 11-15 years of age. ST (min/day) was classified into 4 groups: <120, 120-179, 180-239, ≥240. The associations of each group with dietary content and obesity were investigated. Smartphone usage time significantly increase and sleep duration significantly decreased as ST increased, which was not the case for physical activity. There was no significant difference in energy intake between the 4 groups of ST either for boys or girls, but intake of protein, vegetables green and yellow, other vegetables, and fish and shellfish decreased significantly as ST increased, as did increase in beverage intake. In addition, the association with obesity was significantly increased in the ≥240 ST group compared to the <120 ST group (1.93[1.14-3.28]). In conclusion, the increase in ST including smartphone usage was suggested to be associated with various unfavorable lifestyles including dietary habits that could be connected to obesity. Disclosure I. Ikeda: None. K. Fujihara: None. R. Nedachi: None. S.Y. Morikawa: None. H. Ishiguro: None. M.H. Yamada: None. Y. Matsubayashi: None. T. Yamada: None. H. Sone: Research Support; Self; Kyowa Hakko Kirin Co., Ltd., Novartis AG, Ono Pharmaceutical Co., Ltd., Taisho Pharmaceutical Co., Ltd., Takeda Pharmaceutical Co.

Although cardiorespiratory fitness (CRF) and muscular fitness (MF) are modifiable factors for metabolic risk, the role of weight status in these factors has not been clarified in the pediatric population. We examined associations between metabolic indices and CRF or MF according to weight categories and compared the prevalence of metabolic abnormalities among four groups stratified by combinations of PF and weight categories. Cross-sectionally analyzed were 1744 Japanese adolescents aged 13-14 years. The PF test included measurements of CRF (20-m shuttle run test), upper-limb strength (handgrip test), lower limb strength (standing long jump test), and muscular endurance (sit-ups). Participants were classified as non-overweight (non-OW) or overweight/obese (OW) according to BMI cutoffs by the International Obesity Task Force. Metabolic risk was defined as ≥1 SD of the clustered metabolic risk (estimated by summing standardized sex-specific Z scores of HbA1c, MAP, and non-HDL-C). After adjusting for BMI and other characteristics, linear regression analysis showed that non-HDL-C was inversely associated with CRF in the non-OW group (P <0.001) and with muscular endurance in the OW group (P = 0.002). In the OW group, coefficient of correlation showed that the clustered metabolic risk was slightly lower in those with higher sit-up scores even after adjusting for BMI and CRF (P = 0.06). As to the combination of weight status and PF, upper-limb strength or muscular endurance but not CRF were additively associated with metabolic risk; in comparison with the non-OW group in the second-lowest to highest quintiles, even the OW group in the second-lowest to highest quintiles of muscular endurance were 1.8 (1.1-2.8) times more likely to have metabolic risk while those in the lowest quintile had a 3.1 (1.7-5.7) greater likelihood. In summary, relationships of metabolic risk with components of PF differed according to weight status. In OW adolescents, MF might be more useful for stratifying metabolic risk than CRF. Disclosure S.Y. Morikawa: None. K. Fujihara: None. R. Nedachi: None. I. Ikeda: None. Y. Takeda: None. M. Takeuchi: None. M. Hatta: None. H. Ishiguro: None. T. Yamada: None. H. Sone: Research Support; Self; Kyowa Hakko Kirin Co., Ltd., Novartis AG, Ono Pharmaceutical Co., Ltd., Taisho Pharmaceutical Co., Ltd., Takeda Pharmaceutical Co.