Background: Saturated fat ( SFA ), ω‐6 (n‐6) polyunsaturated fat ( PUFA ), and trans fat ( TFA ) influence risk of coronary heart disease ( CHD ), but attributable CHD mortalities by country, age, sex, and time are unclear. Methods and Results: National intakes of SFA , n‐6 PUFA , and TFA were estimated using a Bayesian hierarchical model based on country‐specific dietary surveys; food availability data; and, for TFA , industry reports on fats/oils and packaged foods. Etiologic effects of dietary fats on CHD mortality were derived from meta‐analyses of prospective cohorts and CHD mortality rates from the 2010 Global Burden of Diseases study. Absolute and proportional attributable CHD mortality were computed using a comparative risk assessment framework. In 2010, nonoptimal intakes of n‐6 PUFA , SFA , and TFA were estimated to result in 711 800 (95% uncertainty interval [UI] 680 700–745 000), 250 900 (95% UI 236 900–265 800), and 537 200 (95% UI 517 600–557 000) CHD deaths per year worldwide, accounting for 10.3% (95% UI 9.9%–10.6%), 3.6%, (95% UI 3.5%–3.6%) and 7.7% (95% UI 7.6%–7.9%) of global CHD mortality. Tropical oil–consuming countries were estimated to have the highest proportional n‐6 PUFA – and SFA ‐attributable CHD mortality, whereas Egypt, Pakistan, and Canada were estimated to have the highest proportional TFA ‐attributable CHD mortality. From 1990 to 2010 globally, the estimated proportional CHD mortality decreased by 9% for insufficient n‐6 PUFA and by 21% for higher SFA, whereas it increased by 4% for higher TFA , with the latter driven by increases in low‐ and middle‐income countries. Conclusions: Nonoptimal intakes of n‐6 PUFA , TFA , and SFA each contribute to significant estimated CHD mortality, with important heterogeneity across countries that informs nation‐specific clinical, public health, and policy priorities.

A avaliação do consumo alimentar na prática clínica é realizada com a finalidade de fornecer subsídios para o desenvolvimento e a implantação de planos nutricionais. Fatores como condições do estado geral do indivíduo/paciente, evolução da condição clínica e os motivos pelos quais o indivíduo necessita de orientação nutricional direcionam a escolha do método de avaliação do consumo alimentar. O método escolhido deve fornecer informações que permitam ao profissional orientar uma alimentação que vise promover a saúde, prevenir outras intercorrências e adequar o estado nutricional do paciente. Apesar de a literatura nacional disponibilizar informações abrangentes sobre métodos e técnicas para estimativa do consumo alimentar, o ambiente de atuação profissional ainda está permeado de dúvidas a respeito dos métodos mais adequados para essa avaliação na prática diária. O presente artigo se propôs a apresentar uma análise crítica, no contexto da aplicabilidade clínica, dos métodos disponíveis de inquéritos alimentares e suas características.

Research comparing the adherence to food-based dietary guidelines (FBDGs) across countries with different socio-economic status is lacking, which may be a concern for developing nutrition policies. The aim was to report on the adherence to FBDGs in high-income (HIC) and low-and-middle-income countries (LMIC). A systematic review with searches in six databases was performed up to June 2020. English language articles were included if they investigated a population of healthy children and adults (7–65 years), using an observational or experimental design evaluating adherence to national FBDGs. Findings indicate that almost 40% of populations in both HIC and LMIC do not adhere to their national FBDGs. Fruit and vegetables (FV) were most adhered to and the prevalence of adhering FV guidelines was between 7% to 67.3%. HIC have higher consumption of discretionary foods, while results were mixed for LMIC. Grains and dairy were consumed below recommendations in both HIC and LMIC. Consumption of animal proteins (>30%), particularly red meat, exceeded the recommendations. Individuals from HIC and LMIC may be falling short of at least one dietary recommendation from their country’s guidelines. Future health policies, behavioral-change strategies, and dietary guidelines may consider these results in their development.

Human milk microorganisms contribute not only to the healthy development of the immune system in infants, but also in shaping the gut microbiota. We evaluated the effect of the maternal diet during pregnancy and during the first month of lactation on the human milk microbiota in a cross-sectional study including 94 healthy lactating women. Microbiota composition was determined by 16S rDNA profiling and nutrient intake assessed through food questionnaires. Thirteen genera were present in at least 90% of all samples, with three genera present in all samples: Streptococcus, Staphylococcus, and Corynebacterium. Cluster analysis indicated two distinct compositions: one marked by a high abundance of Streptococcus (cluster 1), and other by a high abundance of Staphylococcus (cluster 2). A global association with milk microbiota diversity was observed for vitamin C intake during pregnancy (p = 0.029), which was higher for cluster 2 individuals (cluster 2 median = 232 mg/d; cluster 1 = 175 mg/d; p = 0.02). Positive correlations were found between Bifidobacterium in the milk and intake of polyunsaturated and linoleic fatty acids during the lactation period (p < 0.01). We show that maternal diet influences the human milk microbiota, especially during pregnancy, which may contribute in shaping the gut microbiota.

Introduction: Statistical methods such as Principal Component Analysis (PCA) and Factor Analysis (FA) are increasingly popular in Nutritional Epidemiology studies. However, misunderstandings regarding the choice and application of these methods have been observed. Objectives: This study aims to compare and present the main differences and similarities between FA and PCA, focusing on their applicability to nutritional studies. Methods: PCA and FA were applied on a matrix of 34 variables expressing the mean food intake of 1,102 individuals from a population-based study. Results: Two factors were extracted and, together, they explained 57.66% of the common variance of food group variables, while five components were extracted, explaining 26.25% of the total variance of food group variables. Among the main differences of these two methods are: normality assumption, matrices of variance-covariance/correlation and its explained variance, factorial scores, and associated error. The similarities are: both analyses are used for data reduction, the sample size usually needs to be big, correlated data, and they are based on matrices of variance-covariance. Conclusion: PCA and FA should not be treated as equal statistical methods, given that the theoretical rationale and assumptions for using these methods as well as the interpretation of results are different.