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      Changes in nonnutritive sweetener intake in a cohort of preschoolers after the implementation of Chile's Law of Food Labelling and Advertising

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          An evaluation of Chile’s Law of Food Labeling and Advertising on sugar-sweetened beverage purchases from 2015 to 2017: A before-and-after study

          Background Chile’s Law of Food Labeling and Advertising, implemented in 2016, was the first national regulation to jointly mandate front-of-package warning labels, restrict child-directed marketing, and ban sales in schools of all foods and beverages containing added sugars, sodium, or saturated fats that exceed set nutrient or calorie thresholds. The objective of this study is to evaluate the impact of this package of policies on household beverage purchases. Method and findings In this observational study, monthly longitudinal data on packaged beverage purchases were collected from urban-dwelling households (n = 2,383) participating in the Kantar WordPanel Chile Survey from January 1, 2015, to December 31, 2017. Beverage purchases were linked to nutritional information at the product level, reviewed by a team of nutritionists, and categorized as “high-in” or “not high-in” according to whether they contained high levels of nutrients of concern (i.e., sugars, sodium, saturated fat, or energy) according to Chilean nutrient thresholds and were thus subject to the law’s warning label, marketing restriction, and school sales ban policies. The majority of high-in beverages were categorized as such because of high sugar content. We used fixed-effects models to compare the observed volume as well as calorie and sugar content of postregulation beverage purchases to a counterfactual based on preregulation trends, overall and by household-head educational attainment. Of households included in the study, 37% of household heads had low education (less than high school), 40% had medium education (graduated high school), and 23% had high education (graduated college), with the sample becoming more educated over the study period. Compared to the counterfactual, the volume of high-in beverage purchases decreased 22.8 mL/capita/day, postregulation (95% confidence interval [CI] −22.9 to −22.7; p < 0.001), or 23.7% (95% CI −23.8% to −23.7%). High-educated and low-educated households showed similar absolute reductions in high-in beverage purchases (approximately 27 mL/capita/day; p < 0.001), but for high-educated households this amounted to a larger relative decline (−28.7%, 95% CI −28.8% to −28.6%) compared to low-educated households (−21.5%, 95% CI −21.6% to −21.4%), likely because of the high-educated households’ lower level of high-in beverage purchases in the preregulation period. Calories from high-in beverage purchases decreased 11.9 kcal/capita/day (95% CI −12.0 to −11.9; p < 0.001) or 27.5% (95% CI −27.6% to −27.5%). Calories purchased from beverages classified as “not high-in” increased 5.7 kcal/capita/day (95% CI 5.7–5.7; p < 0.001), or 10.8% (10.8%–10.8%). Calories from total beverage purchases decreased 7.4 kcal/capita/day (95% CI −7.4 to −7.3; p < 0.001), or 7.5% (95% CI −7.6% to −7.5%). A key limitation of this study is the inability to assess causality because of its observational nature. We also cannot determine whether observed changes in purchases are due to reformulation or consumer behavioral change, nor can we parse out the effects of the labeling, marketing, and school sales ban policies. Conclusions Purchases of high-in beverages significantly declined following implementation of Chile’s Law of Food Labeling and Advertising; these reductions were larger than those observed from single, standalone policies, including sugar-sweetened-beverage taxes previously implemented in Latin America. Future research should evaluate the effects of Chile’s policies on purchases of high-in foods, dietary intake, and long-term purchasing changes.
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            Effects of Sweeteners on the Gut Microbiota: A Review of Experimental Studies and Clinical Trials

            ABSTRACT The consumption of sugar-free foods is growing because of their low-calorie content and the health concerns about products with high sugar content. Sweeteners that are frequently several hundred thousand times sweeter than sucrose are being consumed as sugar substitutes. Although nonnutritive sweeteners (NNSs) are considered safe and well tolerated, their effects on glucose intolerance, the activation of sweet taste receptors, and alterations to the composition of the intestinal microbiota are controversial. This review critically discusses the evidence supporting the effects of NNSs, both synthetic sweeteners (acesulfame K, aspartame, cyclamate, saccharin, neotame, advantame, and sucralose) and natural sweeteners (NSs; thaumatin, steviol glucosides, monellin, neohesperidin dihydrochalcone, and glycyrrhizin) and nutritive sweeteners (polyols or sugar alcohols) on the composition of microbiota in the human gut. So far, only saccharin and sucralose (NNSs) and stevia (NS) change the composition of the gut microbiota. By definition, a prebiotic is a nondigestible food ingredient, but some polyols can be absorbed, at least partially, in the small intestine by passive diffusion: however, a number of them, such as isomaltose, maltitol, lactitol, and xylitol, can reach the large bowel and increase the numbers of bifidobacteria in humans. Further research on the effects of sweeteners on the composition of the human gut microbiome is necessary.
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              A mixed-effects model approach for estimating the distribution of usual intake of nutrients: the NCI method.

              It is of interest to estimate the distribution of usual nutrient intake for a population from repeat 24-h dietary recall assessments. A mixed effects model and quantile estimation procedure, developed at the National Cancer Institute (NCI), may be used for this purpose. The model incorporates a Box-Cox parameter and covariates to estimate usual daily intake of nutrients; model parameters are estimated via quasi-Newton optimization of a likelihood approximated by the adaptive Gaussian quadrature. The parameter estimates are used in a Monte Carlo approach to generate empirical quantiles; standard errors are estimated by bootstrap. The NCI method is illustrated and compared with current estimation methods, including the individual mean and the semi-parametric method developed at the Iowa State University (ISU), using data from a random sample and computer simulations. Both the NCI and ISU methods for nutrients are superior to the distribution of individual means. For simple (no covariate) models, quantile estimates are similar between the NCI and ISU methods. The bootstrap approach used by the NCI method to estimate standard errors of quantiles appears preferable to Taylor linearization. One major advantage of the NCI method is its ability to provide estimates for subpopulations through the incorporation of covariates into the model. The NCI method may be used for estimating the distribution of usual nutrient intake for populations and subpopulations as part of a unified framework of estimation of usual intake of dietary constituents. Copyright © 2010 John Wiley & Sons, Ltd.
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                Author and article information

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                Journal
                Pediatric Obesity
                Pediatric Obesity
                Wiley
                2047-6302
                2047-6310
                July 2022
                January 27 2022
                July 2022
                : 17
                : 7
                Affiliations
                [1 ]Department of Nutrition, Gillings School of Global Public Health University of North Carolina at Chapel Hill Chapel Hill North Carolina USA
                [2 ]Institute of Nutrition and Food Technology (INTA) University of Chile Santiago Chile
                [3 ]Carolina Population Center University of North Carolina at Chapel Hill Chapel Hill North Carolina USA
                [4 ]Department of Epidemiology, Gillings School of Global Public Health University of North Carolina at Chapel Hill Chapel Hill North Carolina USA
                Article
                10.1111/ijpo.12895
                35088571
                5599818c-9fd2-49a5-9ba4-81b730f0f5a7
                © 2022

                http://onlinelibrary.wiley.com/termsAndConditions#vor

                http://doi.wiley.com/10.1002/tdm_license_1.1

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