出口 香菜子

Background: Studies outside Japan have linked sugar-sweetened beverage (SSB) intake with weight gain; however, evidence in Japanese adults is scarce, and no study has examined beverage-derived energy in relation to anthropometric indices and handgrip strength. Methods: The participants were employees of Fujita Health University aged 20-39 years (n = 76; male n = 35, average age: 29.97 ± 4.67 years; female n = 41, average age: 27.29 ± 4.53 years). Energy from beverage intake was assessed via the Brief Beverage Intake Questionnaire-15, and energy from alcoholic drinks, milk, SSBs, and total beverages was calculated. The associations of energy from different beverages with nutrient intake, BMI, skeletal muscle mass index (SMI), and handgrip strength were analyzed via ordinary least squares (OLS) regression; quantile regression (QR) and the generalized additive model (GAM) were used for sensitivity analyses. Results: Increased SSB intake was associated with increased BMI (standardized β = 0.35, 95% CI 0.12-0.58, p(OLS) < 0.001; p(QR) = 0.23; p(GAM) < 0.001) and was nonlinearly associated with increased SMI (standardized β = 0.21, 95% CI 0.043-0.37, p(OLS) = 0.02; p(QR) = 0.11; p(GAM) = 0.02), even after adjustment for total energy intake. Modest milk intake was linked to higher protein intake and a higher SMI without a higher BMI (standardized β = 0.18, 95% CI 0.020-0.35, p(OLS) = 0.03; p(QR) = 0.39; p(GAM) = 0.03). Conclusions: A positive association was found between SSB intake and both BMI and SMI and between MILK intake and SMI. Clarification in larger, diverse Japanese populations will be necessary.

Type 1 diabetes mellitus is a major risk factor for both sarcopenia and osteoporosis, primarily due to the body's inability to utilize glucose as a result of insulin deficiency. Impairments in insulin and glucose signaling can accelerate the decline in muscle and bone health. To investigate this interaction, we examined whether insulin deficiency exacerbates muscle and bone deterioration in Chrebp knockout (KO) mice. Male wild-type (WT) and KO mice, aged 18 weeks, were intraperitoneally treated with 200 mg/kg BW streptozotocin (STZ), which selectively destroys pancreatic beta cells, thereby inducing insulin deficiency. Two weeks after STZ administration, compared with STZ-treated WT mice, STZ-treated KO mice presented significantly greater reductions in body weight and gastrocnemius muscle weight (BW: WT-vehicle vs. WT-STZ; 2.58 [-1.23, 6.39] (p = 0.21); KO-vehicle vs. KO-STZ: 8.03 [5.23, 10.82]; GA muscle: WT vehicle vs. WT STZ: 0.084 [0.047, 0.12], p < 0.0001; KO vehicle vs. KO STZ: 0.084, [0.047, 0.12], p < 0.0001). The decrease in grip strength caused by STZ administration was greater in the KO mice than in the WT mice (mean differences [95% CIs]: WT vehicle-WT STZ, 49.6. [0.9, 98.4], p = 0.046; WT STZ-KO STZ: 71.40 [29.1, 113.7], p = 0.0059; KO vehicle-KO STZ: 84.3 [51.9, 116.8], p = 0.0003). Consistent with these findings, STZ administration reduced IGF-1 expression and increased atrogin mRNA levels, with the highest levels in STZ-treated KO mice. In skeletal muscle, the changes in IGF-1 and Atrogen induced by STZ administration were significantly greater in the KO group than in the WT group (IGF-1: WT vehicle-WT STZ: 0.19 [-0.072, 0.46], p = 0.17; KO vehicle-KO STZ: 0.79 [0.53, 1.06], p < 0.0001; Atrogen: WT vehicle-WT STZ: -2.7 [-3.01, -2.29], p < 0.0001; KO vehicle-KO STZ: -3.35 [-3.71, -2.99], p < 0.0001). The BMD in the Chrebp-deficient group was greater than that in the wild-type group (WT vehicle-KO vehicle: -5.2 [-8.4, -1.9], p = 0.0014); however, the administration of STZ significantly decreased the BMD only in the KO group (WT vehicle-WT STZ: p = 0.45, KO vehicle-KO STZ: 7.2 [3.9, 10.4], p < 0.0001). These results suggest that Chrebp deficiency combined with insulin deficiency aggravates sarcopenia and osteoporosis risk. Therefore, insulin and glucose signals are important for maintaining muscle and bone mass and function. However, further studies are needed to elucidate the mechanisms by which ChREBP deletion and insulin deficiency cause osteosarcopenia.

Background and Aim: Underweight young adult women are vulnerable to health risks such as menstrual disorders and vitamin deficiencies. Because few seek medical care for low body weight, the underlying causes remain unclear. This study aimed to examine the associations of body type with dietary patterns and gut microbiota diversity in young women. Methods: We enrolled 40 women aged 20-39 years who visited a nutrition evaluation clinic with a BMI < 17.5 at their first consultation (underweight group) and 40 age-matched women with 18.5 ≤ BMI < 25 (control group). Some women in the underweight group were no longer underweight at the time of analysis but were classified based on their initial BMI. Dietary patterns were assessed based on ten major food categories (meat, fish, eggs, dairy products, soybeans, green and yellow vegetables, seaweed, fruit, tubers, and fats and oil) based on the Food Frequency Questionnaire based on Food Groups. Gut microbiota α-diversity was evaluated using the Shannon, Simpson, and Pielou indices, while β-diversity was analyzed by nonmetric multidimensional scaling (NMDS) and redundancy analysis (RDA). Genera contributing to group differences were identified by RDA and ANOVA-Like Differential Expression tool (ALDEx2). Results: Underweight women had significantly lower gut microbiota α-diversity, while no difference was observed in dietary patterns. NMDS revealed significant β-diversity differences in gut microbiota (PERMANOVA: R2 = 0.064, F = 5.31, p = 0.0001) but not in dietary patterns (p = 0.99). RDA showed that body type explained 4.5% of variance (adjusted R2 = 0.032, F = 3.65, p = 0.0005). Bacteroides, Bifidobacterium, Enterocloster, and Erysipelatoclostridium were enriched in underweight women, whereas Fusicatenibacter, Agathobacter, Dorea, and Prevotella were enriched in controls. AldEx2 confirmed increases in Bacteroides, Enterocloster, and Erysipelatoclostridium and a decrease in Dorea. Conclusions: Underweight women demonstrated reduced gut microbiota diversity and enrichment of taxa associated with inflammatory tendencies. Dietary therapies involving not only prebiotics but also probiotics may beneficially modulate gut microbiota and contribute to the management of low body weight.

Background/Aim: To date, there have been no data regarding the reproducibility or agreement of meal duration when a test meal is eaten. To confirm the reproducibility and agreement of the meal duration, number of chews, chewing tempo, and number of bites of a test meal, we performed a prospective observation study. Methods: We measured the duration, number of chews, chewing tempo, and number of bites of a test meal (salmon bento) among 33 participants (male: 15; female: 18) aged 20–60 years who ate twice at 2-week intervals to verify the agreement (by Bland-Altman (BA) analysis) and reproducibility (intraclass correlation coefficient (ICC)) by sex. Results: The meal duration (s) and number of bites (times) were significantly greater in the female group (560.4 (128.7) and 731.9 (266.3), p = 0.023; 17.1 (9.9) vs. 26.4 (13.7), p = 0.036), and the number of chews tended to be greater in the female group (752.5 (203.3) vs. 938.1 (375.9), p = 0.083). Meal duration was positively associated with the number of chews (0.64 [0.53, 0.74], p &lt; 0.001) and bites (10.4 [5.1, 15.8], p &lt; 0.001). For both sexes, the % error calculated via BA analysis was high for meal duration, the number of chews, and the chewing tempo (21.4 and 13.4%; 16.5 and 18.5%; and 6.8 and 5.3%, respectively) and low for the number of bites (37.9 and 68.9%). The ICCs were high for meal duration (0.73 and 0.90), the number of chews (0.76 and 0.89), and the chewing tempo (0.76 and 0.90), and low for the number of bites (0.84 and 0.69). Moreover, systematic and proportional errors were found only for the number of bites in the female group (median_difference with 95% CI: −9.00 (−13.00, −2.00); −0.320 (−0.45, −0.093)). Conclusions: Although the sample size was small due to the exploratory nature of the study, meal duration, number of chews, and chewing tempo had high reproducibility and agreement, at least when this test meal was consumed. These measures may indicate individual-specific eating behavior.

Background/Objectives: Dietary patterns vary with age and sex. The aim of this study was to clarify the differences in dietary patterns among young and middle-aged Japanese individuals by age group and sex via statistical methods such as alpha diversity and beta diversity analyses. Methods: Using data from a dietary survey of 10 food items during health checkups of 2743 Fujita Health University employees, we examined the effects of age and sex on alpha diversity (Shannon index) and beta diversity (nonmetric multidimensional scaling (NMDS) and RDA). Unlike principal component analysis which assumes linear relationships, redundancy analysis (RDA) incorporates explanatory variables to directly assess how external factors shape multivariate patterns. Results: The Shannon index increased with age and was greater in males across age groups. Type III ANOVA revealed significant main effects of age (p < 0.001) and sex (p < 0.001), and the effect of the interaction between age and sex approached significance (p = 0.08). Visualization of the NMDS data revealed that women aged 20-29 years and women aged 30 years and older and men aged 20-39 years and men aged 50-59 years have different dietary patterns. The RDA model accounted for 2.01% of the variance (adjusted R2 = 1.94%), with age and sex contributing 56.7% and 43.3%, respectively. RDA1 and RDA2 were correlated with age (r = 0.26, -0.14) and sex (r = 0.15, 0.21). The RDA1 values increased with age and were greater in females, whereas the RDA2 values decreased with age and were greater in females. RDA1 (1.41% of the total variance in food group intake, 70.1% of the constrained variance) was positively associated with fruits, milk, and seaweed and negatively associated with meat and eggs. In RDA2 (0.60% of total variance, 29.9% contribution), fruits, potatoes, and vegetables had positive effects, whereas fish had negative effects. Conclusions: Dietary patterns vary by age and sex, with meat, fish, eggs, and fruit as key determinants. Nutritional guidance must account for variations in dietary patterns influenced by age and sex.