Ultra-processed Food Intake and Risk of Type 2 Diabetes in Korean Adults.

BACKGROUND: Several studies from the United States and European countries reported a positive association between ultra-processed food intake and diabetes risk. However, little is known about the association in Asian populations. It is also unknown about the individual ultra-processed food items that are most unfavorably associated with diabetes risk. OBJECTIVE: We examined the associations of ultra-processed food intake (combined, as well as individual ultra-processed food items) with the risk of type 2 diabetes. METHODS: This prospective analysis included 7438 participants aged 40-69 y from the Korean Genome and Epidemiology Study Ansan-Ansung cohort. Dietary intake was assessed at baseline using a 103-item semiquantitative food-frequency questionnaire. Ultra-processed foods were classified using the Nova definition. Incident type 2 diabetes cases were identified via follow-up interviews and health examination. Multivariable Cox proportional hazard models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs), adjusting for potential confounders. RESULTS: During the follow-up (2001-2019; median: 15 y), a total of 1187 type 2 diabetes cases were identified. Compared with the lowest quartile of ultra-processed food intake, the highest quartile was positively associated with diabetes risk [HR (95% CI) = 1.34 (1.13, 1.59), P-trend = 0.002]. The association did not change after additional adjustment for diet quality or BMI. Among individual ultra-processed food items, a higher consumption of ham/sausage [per 1% increase in the weight ratio: HR (95% CI) = 1.40 (1.05, 1.86)], instant noodles [1.07 (1.02, 1.11)], ice cream [1.08 (1.03, 1.13)], and carbonated beverages [1.02 (1.00, 1.04)] were associated with an increased risk of type 2 diabetes, whereas a higher intake of candy/chocolate was associated with a decreased risk [0.78 (0.62, 0.99)]. CONCLUSIONS: Our data suggest that the high intake of ultra-processed foods, particularly ham/sausage, instant noodles, ice cream, and carbonated beverages, is associated with an increased risk of type 2 diabetes in Korean adults.

Smartphone Usage Patterns and Dietary Risk Factors in Adolescents.

BACKGROUND: Whereas earlier studies have shown that traditional screen time such as TV watching influences dietary behaviors in adolescents, little is known about the effects of modern screen time such as smartphone use. OBJECTIVES: We examined the associations of smartphone usage duration and content type with dietary risk factors among adolescents. METHODS: We conducted a cross-sectional analysis using a nationally representative sample of 54,601 middle- and high-school students (aged 12-18 y; 26,928 boys and 27,673 girls) in the Korea Youth Risk Behavior Web-Based Survey 2017. Smartphone use (average duration and most frequently used content type) and dietary intakes [frequent breakfast skipping; less frequent intakes of fruits and vegetables; more frequent intakes of instant noodles, fast food, chips/crackers, and sugar-sweetened beverages (SSBs)] were self-reported via an online-based questionnaire. Multivariable logistic regression was performed to estimate ORs and 95% CIs for the associations of smartphone usage duration and content type with prevalence of dietary risk factors, adjusting for potential confounders. All analyses accounted for complex survey sampling. RESULTS: Prolonged smartphone use (≥300 compared with 1-59 min/d) was associated with (OR; 95% CI) higher prevalence of frequent breakfast skipping (1.60; 1.45, 1.76); frequent intakes of instant noodles (1.65; 1.47, 1.84), fast food (1.36; 1.20, 1.53), and SSBs (1.92; 1.75, 2.11); and less frequent intakes of fruits (1.44; 1.30, 1.59) and vegetables (1.32; 1.18, 1.47). Adjusting for usage duration, using smartphones mainly for messenger/e-mail, social networking service (SNS)/forum, and games were associated with less frequent intakes of fruits (OR: 1.21; 95% CI: 1.12, 1.31; OR: 1.28; 95% CI: 1.17, 1.40; and OR: 1.20; 95% CI: 1.10, 1.32, respectively) and vegetables (OR: 1.24; 95% CI: 1.12, 1.37; OR: 1.26; 95% CI: 1.13, 1.40; and OR: 1.25; 95% CI: 1.12, 1.40, respectively) than was using smartphones for education/information search. Using smartphones mainly for messenger/e-mail (OR: 1.24; 95% CI: 1.14, 1.35) and SNS/forum (OR: 1.18; 95% CI: 1.08, 1.29) were also associated with more frequent intake of SSBs. CONCLUSIONS: Our data suggest that both duration and content type of smartphone use are independently associated with dietary risk factors among adolescents.

Early-life body mass index and risks of breast, endometrial, and ovarian cancers: a dose-response meta-analysis of prospective studies.

BACKGROUND: The evidence for the associations between early-life adiposity and female cancer risks is mixed. Little is known about the exact shape of the relationships and whether the associations are independent of adult adiposity. METHODS: We conducted dose-response meta-analyses of prospective studies to summarise the relationships of early-life body mass index (BMI) with breast, endometrial, and ovarian cancer risks. Pubmed and Embase were searched through June 2020 to identify relevant studies. Using random-effects models, the summary relative risks (RRs) and 95% confidence intervals (CIs) were estimated per 5-kg/m2 increase in BMI at ages ≤ 25 years. A nonlinear dose-response meta-analysis was conducted using restricted cubic spline analysis. RESULTS: After screening 33,948 publications, 37 prospective studies were included in this analysis. The summary RRs associated with every 5-kg/m2 increase in early-life BMI were 0.84 (95% CI = 0.81-0.87) for breast, 1.40 (95% CI = 1.25-1.57) for endometrial, and 1.15 (95% CI = 1.07-1.23) for ovarian cancers. For breast cancer, the association remained statistically significant after adjustment for adult BMI (RR = 0.80, 95% CI = 0.73-0.87). For premenopausal breast, endometrial, and ovarian cancers, the dose-response curves suggested evidence of nonlinearity. CONCLUSIONS: With early-life adiposity, our data support an inverse association with breast cancer and positive associations with ovarian and endometrial cancer risks.