Food sources of sodium, saturated fat, and added sugar in the Physical Activity and Nutrition for Diabetes in Alberta (PANDA) trial

Why don't college freshmen meet the US dietary guidelines for added sugar, refined grains, sodium, and saturated fat?

OBJECTIVE

The objective of the study was to evaluate how well college students meet U.S. dietary guidelines by examining the mean intakes of nutrients to limit (sodium, sugar, refined grains, and saturated fat), and what factors lead to exceeding the dietary recommendations.

PARTICIPANTS

Participants were first-year undergraduate students (N = 269).

METHODS

Diet was assessed using a DHQ-III and estimated with food source composition tables. Multivariable logistic regression analysis was used to model variables associated with exceeding the recommendations. One-sample t-tests were performed to compare the cohort with national intakes.

RESULTS

One-third met added sugar guidelines; only 4% met daily refined grains requirements, Fewer than half met saturated fat guidelines, and slightly over half met recommended sodium guidelines. Level of physical activity, race/ethnicity, and living on campus were the important predictors for exceeding recommended intakes.

CONCLUSIONS

Most students do not adhere to the U.S. dietary guidelines for nutrients to limit.

Effectiveness and Acceptability of a Nutrition Intervention Targeting Chinese Adult Immigrants With Type 2 Diabetes in Canada: A Study Using Mixed-Methods Analysis

OBJECTIVES

Although culturally tailored diabetes treatment is recommended, there is a lack of relevant dietary resources for the Chinese population in Canada. In this study, we assessed the feasibility and efficacy of a culturally tailored menu plan combined with nutrition education on clinical outcomes, diet quality and qualitative outcomes among Chinese immigrants with type 2 diabetes.

METHODS

Participants were 17 Chinese immigrants living with type 2 diabetes in Edmonton, Alberta, Canada. The design was a 12-week, single-arm intervention that included weekly nutrition education supported by a culturally tailored menu plan with mixed-methods evaluation. Diet quality, clinical and other outcomes were assessed pre- and postintervention. One-on-one interviews were conducted postintervention to identify program feasibility and obstacles to adherence.

RESULTS

Waist circumference (mean ± standard deviation: -2.0±2.5 cm; p=0.004), total cholesterol (-21.4±28.2 mg/dL; p=0.007) and low-density lipoprotein cholesterol (-18.4±24.6 mg/dL; p=0.007) were decreased when compared with baseline. No significant change was detected in glycated hemoglobin. Postintervention, the Healthy Eating Index (p=0.01) and diabetes knowledge score (p=0.009) also increased. Participants reported that the program was culturally acceptable, easily understood and feasible to implement. Participants indicated the program helped them to improve their diabetes knowledge, adhere to the dietary guidelines, choose low glycemic index food and read food labels when shopping.

CONCLUSIONS

A flexible, culturally tailored menu plan was a feasible and effective tool for improving diabetes knowledge, diet quality and metabolic outcomes among Chinese immigrants with type 2 diabetes.

A Systematic Review of the Sources of Dietary Salt Around the World

Excess salt intake contributes to hypertension and increased cardiovascular disease risk. Efforts to implement effective salt-reduction strategies require accurate data on the sources of salt consumption. We therefore performed a systematic review to identify the sources of dietary salt around the world. We systematically searched peer-reviewed and gray literature databases for studies that quantified discretionary (salt added during cooking or at the table) and nondiscretionary sources of salt and those that provided information about the food groups contributing to dietary salt intake. Exploratory linear regression analysis was also conducted to assess whether the proportion of discretionary salt intake is related to the gross domestic product (GDP) per capita of a country. We identified 80 studies conducted in 34 countries between 1975 and 2018. The majority (n = 44, 55%) collected data on dietary salt sources within the past 10 y and were deemed to have a low or moderate risk of bias (n = 75, 94%). Thirty-two (40%) studies were judged to be nationally representative. Populations in Brazil, China, Costa Rica, Guatemala, India, Japan, Mozambique, and Romania received more than half of their daily salt intake from discretionary sources. A significant inverse correlation between discretionary salt intake and a country's per capita GDP was observed (P < 0.0001), such that for every $10,000 increase in per capita GDP, the amount of salt obtained from discretionary sources was lower by 8.7% (95% CI: 5.1%, 12%). Bread products, cereal and grains, meat products, and dairy products were the major contributors to dietary salt intake in most populations. There is marked variation in discretionary salt use around the world that is highly correlated with the level of economic development. Our findings have important implications for the type of salt-reduction strategy likely to be effective in a country.

Identification of Potential Type II Diabetes in a Large-Scale Chinese Population Using a Systematic Machine Learning Framework

BACKGROUND

An estimated 425 million people globally have diabetes, accounting for 12% of the world's health expenditures, and the number continues to grow, placing a huge burden on the healthcare system, especially in those remote, underserved areas.

METHODS

A total of 584,168 adult subjects who have participated in the national physical examination were enrolled in this study. The risk factors for type II diabetes mellitus (T2DM) were identified by p values and odds ratio, using logistic regression (LR) based on variables of physical measurement and a questionnaire. Combined with the risk factors selected by LR, we used a decision tree, a random forest, AdaBoost with a decision tree (AdaBoost), and an extreme gradient boosting decision tree (XGBoost) to identify individuals with T2DM, compared the performance of the four machine learning classifiers, and used the best-performing classifier to output the degree of variables' importance scores of T2DM.

RESULTS

The results indicated that XGBoost had the best performance (accuracy = 0.906, precision = 0.910, recall = 0.902, F-1 = 0.906, and AUC = 0.968). The degree of variables' importance scores in XGBoost showed that BMI was the most significant feature, followed by age, waist circumference, systolic pressure, ethnicity, smoking amount, fatty liver, hypertension, physical activity, drinking status, dietary ratio (meat to vegetables), drink amount, smoking status, and diet habit (oil loving).

CONCLUSIONS

We proposed a classifier based on LR-XGBoost which used fourteen variables of patients which are easily obtained and noninvasive as predictor variables to identify potential incidents of T2DM. The classifier can accurately screen the risk of diabetes in the early phrase, and the degree of variables' importance scores gives a clue to prevent diabetes occurrence.