Pragmatic Carbohydrate Quality Metrics in Relation to Glycemic Index, Glycemic Load, and Front-of-Pack Warning Labels in Grain Foods.

The challenges in the characterization of the nutritional quality of grain foods comprise obstacles to public health actions toward promotion of healthier grain-based foods. The present study investigated how carbohydrate metrics related to glycemic index (GI), glycemic load (GL), and warning labels of grain foods consumed by individuals living in São Paulo, Brazil. Information on intake of grain foods at individual level was obtained using 24 h recalls within a cross-sectional population-based survey conducted in 2015. There were 244 unique grain products reported by individuals in the survey, assessed through four metrics of carbohydrate quality, considering contents per 10 g of total carbohydrate: (1) ≥1 g fiber, (2) ≥1 g fiber and <1 g free sugars, (3) ≥1 g fiber and <2 g free sugars, and (4) ≥1 g fiber, and <2 g free sugars per 1 g of fiber. Outcomes included GI, GL, and inclusion of warning labels proposed by the Brazilian National Health Surveillance Agency (ANVISA), the Chilean Ministry of Health (1st and 3rd stages), and the Pan American Health Organization (PAHO). Metrics identified products with lower mean GI (-12.8 to -9.0 [p-values < 0.001]), and GL (-12.5 to -10.3 [p-values < 0.001]). Warning systems showed a certain degree of discrimination between products according to the metrics (p-value < 0.01 each); however, >50% of products with good nutritional quality according to the carbohydrate metrics still would receive warnings. Findings suggest that carbohydrate metrics identified products with lower GI and GL, and current warning labels may not adequately capture overall nutritional quality of grain foods.

Robust Deep Neural Network for Learning in Noisy Multi-Label Food Images.

Deep networks can facilitate the monitoring of a balanced diet to help prevent various health problems related to eating disorders. Large, diverse, and clean data are essential for learning these types of algorithms. Although data can be collected automatically, the data cleaning process is time-consuming. This study aims to provide the model with the ability to learn even when the data are not completely clean. For this purpose, we extend the Attentive Feature MixUp method to enable its learning on noisy multi-label food data. The extension was based on the hypothesis that during the MixUp phase, when a pair of images are mixed, the resulting soft labels should be different for each ingredient, being larger for ingredients that are mixed with the background because they are better distinguished than when they are mixed with other ingredients. Furthermore, to address data perturbation, the incorporation of the Laplace approximation as a post-hoc method was analyzed. The evaluation of the proposed method was performed on two food datasets, where a notable performance improvement was obtained in terms of Jaccard index and F1 score, which validated the hypothesis raised. With the proposed MixUp, our method reduces the memorization of noisy multi-labels, thereby improving its performance.

Misreporting of Energy Intake Is Related to Specific Food Items in Low-Middle Income Chilean Adolescents.

BACKGROUND: Misreporting of energy intake (EI) in self-reported dietary assessment is inevitable, and even less is known about which food items are misreported by low-middle income adolescents. We evaluated the prevalence of misreporting of energy intake and its relationship with nutrients and food intake. METHODS: We analyzed 24 h dietary recalls collected from 576 adolescents (52.08% boys) from southeastern Santiago. Anthropometrics measurements and information about sociodemographic characteristics were obtained during clinical visits. The method proposed by McCrory et al. was used to identify under-reporters (UnRs), over-reporters (OvRs), or plausible reporters (PRs). Food items were collapsed into 28 categories and every food item was expressed as a percentage of total EI. Logistic regression models were performed to investigate the factors associated with misreporting, and a two-part model was used to estimate the difference in the percentage of EI between UnRs versus PRs, and OvRs versus PRs in each food item. RESULTS: Half of the participants were classified as UnRs and 9% were OvRs. UnR was higher among boys (62%) and adolescents with overweight and obesity (72%). OvR was higher among adolescents with normal weight. UnRs had a lower intake of energy from cookies/cake, chocolate/confectionery, and a higher intake of vegetables and eggs than PRs. OvRs had a higher intake of cookies/cake, chocolate/confectionery, and a lower intake of fruit, white milk, and yogurt than PRs. CONCLUSIONS: A high frequency of UnR among boys and participants with excess weight was found in this study. Healthy and unhealthy foods are reported differently between UnRs and OvRs of energy intake, indicating that bias is specific for some food items that adolescents commonly eat.

Lower adherence to a prudent dietary pattern is associated with earlier age at menarche in adolescents from the Growth and Obesity Chilean Cohort Study.

Introduction: Early menarche is associated with obesity, and metabolic and mental health risks, among other diseases. Thus, it is relevant to identify modifiable risk factors of early menarche. Some nutrients and foods have been linked to pubertal timing, but how menarche relates to overall dietary patterns is unclear. Methods: The aim of this study was to analyze the association between dietary patterns and age at menarche in a prospective cohort of Chilean girls from low and middle-income families. We conducted a survival analysis of 215 girls (median = 12.7 years, IQR = 12.2-13.2) from the Growth and Obesity Cohort Study (GOCS) who had been followed prospectively since 4 years of age (2006). Age at menarche and anthropometric measurements were recorded every 6 months since 7 years of age while diet (24-hour dietary recall) was collected for 11 years. Dietary patterns were obtained from exploratory factor analysis. Accelerated Failure Time models adjusted for potential confounding variables were used to study the association between dietary patterns and age at menarche. Results: Girls' median age at menarche was 12.7 years. Three dietary patterns were identified: "Breakfast/Light Dinner," "Prudent" and "Snacking" which explained 19.5% of the diet variation. Girls in the lowest tertile of the "Prudent" pattern had menarche 3 months earlier than girls in the highest tertile (ß: 0.022; 95% CI: 0.003; 0.041). "Breakfast/Light Dinner" and "Snacking" patterns were not associated with age at menarche. Conclusion: Our results suggest that healthier dietary patterns during puberty might be associated with menarche timing. Nevertheless, further studies are required to confirm this result and to clarify the association between diet and puberty.

Misreporting of dietary energy intake obtained by 24 h recalls in older adults: a comparison of five previous methods using doubly labeled water.

BACKGROUND/OBJECTIVE: To test five different methods to detect misreporting in comparison to doubly labeled water in a sample of older adults. SUBJECTS/METHODS: A cross-sectional study with thirty-eight Brazilian community-dwelling older adults aged 60-84 years, who had their total energy expenditure measured by doubly labeled water (TEEDLW). Dietary data were collected by two 24 h recalls. Misreporting was compared with estimates obtained by the methods proposed by: Goldberg et al. [1, 2], Black [3], McCrory et al. [4], Huang et al [5], and Rennie et al [6]. Bland-Altman plots with 95% limits of agreement were constructed to assess the agreement between rEI and TEEDLW. Weighted kappa coefficients, sensitivity and specificity analyses, and area under the receiving operator characteristic curve (AUC) were used to test the performance of each method. RESULTS: The prevalence of under-reporters (UR) and over-reporters (OR) obtained by the reference (DLW) were 57.9% (n = 22) and 5.3% (n = 2), respectively. Black [3] presented the worst agreement and McCrory et al. [4] the best one to accurately classify individuals in the three categories of energy reporting. McCrory et al. [4] had the best performance in the sensitivity and specificity analyses detecting UR and plausible reporters. CONCLUSIONS: There was a high prevalence of misreporting, especially underreporting, in this sample of community-dwelling Brazilian older adults. The study showed a wide variation in the accuracy of predictive methods to handle misreporting, with none of the equations showing outstanding agreement with the reference. When DLW is not available, a valid method should be chosen to address energy intake reporting.

Opportunities for diet quality improvement: the potential role of staple grain foods.

OBJECTIVE: Given the high disease burden associated with the low intake of whole grains, modelling studies that estimate the impact of dietary strategies to increase more healthful grain foods consumption are essential to inform evidence-based and culturally specific policies. The current study investigated the potential nutritional impact of replacing staple grain foods with more healthful options. DESIGN: Based on the 2015 Health Survey of São Paulo, a cross-sectional, population-based study, we modelled the substitution of white rice and white bread with brown rice and whole-wheat bread. Outcomes included changes in more healthful grain foods, energy and nutrient intakes. SETTING: Urban area of São Paulo, Brazil. PARTICIPANTS: Participants aged over 12 years who completed a semi-structured questionnaire and one 24-h recall (n 1741). RESULTS: The substitution of all white rice and white bread with brown rice and whole-wheat bread, respectively, would result in more than 5 % increases in Zn (+9·1 %), Ca (+9·3 %), vitamin E (+18·8 %), dietary fibre (+27·0 %) and Mg (+52·9 %) intake, while more than a 5 % decrease would be seen for total carbohydrate (-6·1 %), folate (-6·6 %), available carbohydrate (-8·5 %), Fe (-8·6 %), vitamin B6 (-12·5 %), vitamin B2 (-17·4 %), and vitamin B1 (-20·7 %). A substantial increase in the amount of more healthful grain foods consumed would be seen (10 g/d to 220 g/d, or from 4 % to 69 % of total grain intake). CONCLUSIONS: Replacing white rice and white bread with their whole-grain versions has the potential to improve diet quality, suggesting they are prime targets for policy actions aiming at increasing intake of more healthful grain foods.

Dietary Patterns of Adolescents from the Chilean Growth and Obesity Cohort Study Indicate Poor Dietary Quality.

Diet during adolescence can have lasting effects on nutritional status, health, and development. We hypothesized that dietary patterns with low-quality nutrition are associated with overweightness. We collected data for 882 Chilean adolescents from the Growth and Obesity Cohort Study (mean age: 12 years). Dietary intake was assessed through 24-h recalls and weight status data were obtained during clinical visits. Dietary patterns were obtained through exploratory factor analysis. Multiple logistic regression models were used to examine cross-sectional associations between dietary patterns and overweight (BMI z-score ≥ 1SD). Four dietary patterns were identified: "Breakfast/Light dinner", "Natural foods", "Western", and "Snacking". "Breakfast/Light dinner", "Western", and "Snacking" patterns provided higher energy and excess nutrients (sodium, saturated fat, and added sugar). Moreover, adolescents with higher adherence to "Western" or " Snacking" patterns (third tertile) had higher odds of being classified as overweight (OR = 1.67; 95%CI: 1.103-2.522 and OR = 1.86; 95%CI: 1.235-2.792, respectively) than those with lower adherence (first tertile). "Natural foods" pattern was also associated with overweightness (OR = 1.83; 95%CI: 1.219-2.754). These dietary patterns were associated with overconsumption of nutrients of public health concern. Three of the four main dietary patterns were associated with overweightness. These results highlight the need of prioritizing adolescents on obesity prevention strategies.

Metabolic Syndrome and Mammographic Density in Premenopausal Chilean Women.

BACKGROUND: Metabolic syndrome (MetS) has been previously associated with an increased risk of breast cancer in postmenopausal women. Mammographic density (MD) is a marker of breast cancer risk. There is little evidence of an association between MetS and MD in premenopausal women. METHODS: Through a cross-sectional study, we evaluated 364 premenopausal Chilean women in which we measured anthropometric, blood pressure, and metabolic markers. MetS and its components were defined according to the National Cholesterol Education Program Adult Treatment Plan III criteria. We estimated MD by absolute dense volume (ADV, cm3), nondense volume (NDV, cm3), and percentage of dense volume (PDV, %). The relationship between MetS and MD was assessed by linear regression models. RESULTS: After adjusting for sociodemographic and gyneco-obstetrics variables, nonsignificant association was found between MetS and ADV (log ß = 0.10; 95%CI: -0.01, 0.21). However, abdominal obesity, high triglycerides, and number of components of MetS were directly related to higher ADV (P < 0.05). CONCLUSION: Our results showed no association between MetS and ADV; nevertheless, abdominal obesity and triglycerides were related to higher ADV. If MD could be modifiable through nutritional factors, it would open new perspectives for the prevention of breast cancer through obesity prevention strategies at population level.