Hypertension is a major risk factor for developing cardiovascular disease, stroke, and kidney disease. To lower blood pressure (BP), several lifestyl

The consumption of dairy products is encouraged at all life stages as a nutrient-rich component of the diet. However, many milk and yogurt products, particularly flavored varieties, may contain large amounts of free sugar. The aim of this paper was to evaluate the availability and sugar content of flavored milks and yogurts in supermarkets across 3 countries: Australia, England, and South. Nutrition information for flavored milks and yogurts was collected by trained researchers and supplemented by crowd-sourced data from a smartphone application. Data were extracted in April 2018 and 3724 milk and yogurt products were available for analysis. Mean sugar concentrations were compared across countries with the use of ANOVA followed by Tukey's post-hoc pairwise comparisons. Sugar concentrations were compared with the UK's “green” traffic-light classifications. Approximately 74% (n = 2753) of all products were flavored. Flavored products contained nearly twice the average total sugar content of unflavored products, with substantial variability: mean total sugar was 9.1 g/100 mL (range: 4.3–15.0 g/100 mL) and 11.5 g/100 g (range: 0.1–22.6 g/100 g) for flavored milks and yogurts, respectively. Free sugars contributed an estimated 41% and 42% of total sugar in milks and yogurts, respectively. Flavored milks in England had ∼0.7 g/100 mL higher total sugar on average compared with Australia and South Africa (P ≤ 0.04), whereas flavored yogurts in South Africa had the lowest average total sugar (∼2 g/100 g lower than England and Australia; P < 0.001). Less than 4% of flavored products would receive a “green” rating under the UK traffic-light labeling scheme. In Australia, England, and South Africa, flavored milks and yogurts are highly prevalent in the food supply and contain significantly higher concentrations of total and added sugars than unflavored products.

While sodium and potassium are individually important for blood pressure (BP) regulation, the relative contribution of sodium to potassium intake has not been sufficiently investigated. This study aimed to evaluate the association between urinary sodium to potassium ratio (UNa: K) and systolic and diastolic BP in adults. A systematic review (PROSPERO; CRD42016035296) was conducted and was reported according to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Three scientific databases (MEDLINE, Scopus, Web of Science) were searched to March 2020 while reference lists of included articles were further hand-searched. Randomized controlled trials (RCT), cohort and cross-sectional studies that assessed 24-h urinary excretion in adults were included. Data from eligible studies were extracted and summarized. Random effects meta-analysis was conducted on RCT data to assess standardized mean differences (SMD) in systolic and diastolic BP according to 24-h UNa: K. Thirty-nine studies were included. Meta-analysis of 5 RCTs found a lower UNa: K ratio to be associated with a significantly greater reduction in systolic and diastolic BP compared with a higher UNa: K ratio [SMD: -1.09 (95% CI: -1.91, -0.28) mmHg and -1.42 (95% CI: -2.24, -0.59) mmHg, respectively]. Heterogeneity between RCTs was observed in systolic and diastolic BP (I2 = 97%, P < 0.0001 and I2 = 98%, P < 0.0001, respectively). The current body of evidence demonstrates that a lower 24-h UNa: K ratio is associated with lower BP in adults. Dietary strategies to achieve an increase in potassium while at the same time lowering sodium would be beneficial in lowering BP.

Dietary guidelines recommend limiting the intake of added sugars. However, despite the public health importance, most countries have not mandated the labeling of added-sugar content on packaged foods and beverages, making it difficult for consumers to avoid products with added sugar, and limiting the ability of policymakers to identify priority products for intervention. The aim was to develop a machine learning approach for the prediction of added-sugar content in packaged products using available nutrient, ingredient, and food category information. The added-sugar prediction algorithm was developed using k-nearest neighbors (KNN) and packaged food information from the US Label Insight dataset (n = 70,522). A synthetic dataset of Australian packaged products (n = 500) was used to assess validity and generalization. Performance metrics included the coefficient of determination (R2), mean absolute error (MAE), and Spearman rank correlation (ρ). To benchmark the KNN approach, the KNN approach was compared with an existing added-sugar prediction approach that relies on a series of manual steps. Compared with the existing added-sugar prediction approach, the KNN approach was similarly apt at explaining variation in added-sugar content (R2 = 0.96 vs. 0.97, respectively) and ranking products from highest to lowest in added-sugar content (ρ = 0.91 vs. 0.93, respectively), while less apt at minimizing absolute deviations between predicted and true values (MAE = 1.68 g vs. 1.26 g per 100 g or 100 mL, respectively). KNN can be used to predict added-sugar content in packaged products with a high degree of validity. Being automated, KNN can easily be applied to large datasets. Such predicted added-sugar levels can be used to monitor the food supply and inform interventions aimed at reducing added-sugar intake.