Resumo Este estudo avaliou a acurácia de indicadores de obesidade abdominal (OA), definindo uma variável latente como padrão-ouro. Foram estudados 12.232 participantes do ELSA-Brasil de 35 a 74 anos. Avaliou-se três indicadores de OA, estratificados por sexo e raça/cor: circunferência da cintura (CC), razão cintura quadril (RCQ) e índice de conicidade (Índice C). Todos os grupos mostraram elevadas prevalências de OA, maiores entre os homens brancos (~70%) e mulheres pretas (~60%). Observou-se alta acurácia da CC para homens, RCQ e índice C entre homens e mulheres para discriminar OA latente. Identificou-se os seguintes pontos de corte para os indicadores de OA entre os homens brancos, pardos e pretos, respectivamente: CC: 89,9; 90,2 e 91,7cm; RCQ: 0,92; 0,92 e 0,90; índice C: 1,24; 1,24 e 1,24. Para as mulheres brancas, pardas e pretas, respectivamente, os pontos de corte identificados foram: CC: 80,4; 82,7 e 85,4cm; RCQ: 0,82; 0,83 e 0,84; índice C: 1,20; 1,22 e 1,19. A CC entre os homens e a RCQ e índice C entre homens e mulheres apresentaram alto poder para discriminar OA latente, sendo o índice C o melhor indicador.

Background: Studies evaluating dietary patterns, including the DASH diet, and their relationship with the metabolic syndrome and diabetes may help to understand the role of dairy products (low fat or full fat) in these conditions. Our aim is to identify dietary patterns in Brazilian adults and compare them with the (DASH) diet quality score in terms of their associations with metabolic syndrome and newly diagnosed diabetes in the Brazilian Longitudinal Study of Adult Health-the ELSA-Brasil study. Methods: The ELSA-Brasil is a multicenter cohort study comprising 15,105 civil servants, aged 35–74 years at baseline (2008–2010). Standardized interviews and exams were carried out, including an OGTT. We analyzed baseline data for 10,010 subjects. Dietary patterns were derived by principal component analysis. Multivariable logistic regression investigated associations of dietary patterns with metabolic syndrome and newly diagnosed diabetes and multivariable linear regression with components of metabolic syndrome. Results: After controlling for potential confounders, we observed that greater adherence to the Common Brazilian meal pattern (white rice, beans, beer, processed and fresh meats), was associated with higher frequencies of newly diagnosed diabetes, metabolic syndrome and all of its components, except HDL-C. Participants with greater intake of a Common Brazilian fast foods/full fat dairy/milk based desserts pattern presented less newly diagnosed diabetes. An inverse association was also seen between the DASH Diet pattern and the metabolic syndrome, blood pressure and waist circumference. Diet, light foods and beverages/low fat dairy pattern was associated with more prevalence of both outcomes, and higher fasting glucose, HDL-C, waist circumference (among men) and lower blood pressure. Vegetables/fruit dietary pattern did not protect against metabolic syndrome and newly diagnosed diabetes but was associated with lower waist circumference. Conclusions: The inverse associations found for the dietary pattern characterizing Brazilian fast foods and desserts, typically containing dairy products, with newly diagnosed diabetes, and for the DASH diet with metabolic syndrome, support previously demonstrated beneficial effects of dairy products in metabolism. The positive association with metabolic syndrome and newly diagnosed diabetes found for the pattern characterizing a typical Brazilian meal deserves further investigation, particularly since it is frequently accompanied by processed meat. Trial registration NCT02320461. Registered 18 December 2014

The relationship between 24-hr movement behavior and specific domains of cognitive function is unclear. The purpose of this study was to identify the joint association of daily time spent in light (light-intensity physical activity [LPA]) and moderate-to-vigorous physical activity (MVPA), sedentary behavior (SB), and sleep with cognitive function in middle-aged and older adults. Cross-sectional data from Wave 3 (2017–2019) of the Brazilian Longitudinal Study of Adult Health were analyzed. The study included adults aged 41–84 years old. Physical activity was assessed using a waist-worn accelerometer. Cognitive function was examined using standardized tests to assess memory, language, and Trail-Making test. Global cognitive function score was calculated by averaging domain-specific scores. Compositional isotemporal substitution models were performed to identify the association between the reallocation of time spent in LPA, MVPA, sleep, and SB with cognitive function. Participants (n = 8,608) were 55.9% female (mean age 58.9 [8.6] years). Reallocating time from SB to MVPA was associated with higher cognitive function: Reallocating 15 min to MVPA by reducing 5 min from each other behavior was associated with increased odds of better cognitive function in both insufficient (<7 hr/day; odds ratio [OR]: 0.64; 95% confidence interval [CI]: 0.54–0.77) and sufficient (≥7 hr/day; OR: 0.62; 95% CI: 0.58–0.67) sleep groups. Among those with insufficient sleep, reallocating time to MVPA and sleep from SB was associated with higher global cognitive performance. Small reductions in SB and increments in MVPA were associated with higher cognitive function in middle-aged and older adults.

Waist circumference (WC) has been incorporated in the definition of the metabolic syndrome (MetS) but the exact WC cut-off points across populations are not clear. The Joint Interim Statement (JIS) suggested possible cut-offs to different populations and ethnic groups. However, the adequacy of these cut-offs to Brazilian adults has been scarcely investigated. The objective of the study is to evaluate possible WC thresholds to be used in the definition of MetS using data from the Longitudinal Study of Adult Health (ELSA-Brasil), a multicenter cohort study of civil servants (35–74 years old) of six Brazilian cities. We analyzed baseline data from 14,893 participants (6772 men and 8121 women). A MetS was defined according to the JIS criteria, but excluding WC and thus requiring 2 of the 4 remaining elements. We used restricted cubic spline regression to graph the relationship between WC and MetS. We identified optimal cut-off points which maximized joint sensitivity and specificity (Youden’s index) from Receiver Operator Characteristic Curves. We also estimated the C-statistics using logistic regression. We found no apparent threshold for WC in restricted cubic spline plots. Optimal cut-off for men was 92 cm (2 cm lower than that recommended by JIS for Caucasian/Europids or Sub-Saharan African men), but 2 cm higher than that recommended for ethnic Central and South American. For women, optimal cut-off was 86, 6 cm higher than that recommended for Caucasian/Europids and ethnic Central and South American. Optimal cut-offs did not very across age groups and most common race/color categories (except for Asian men, 87 cm). Sex-specific cut-offs for WC recommended by JIS differ from optimal cut-offs we found for adult men and women of Brazil´s most common ethnic groups.

Perceived time constraints have been highlighted in sociological studies as representing a core issue in determining quality of life. The objective of this study was to test the hypothesis that gender inequalities regarding insufficiency of time play a role in the development of overweight and obesity in adults. The study used baseline data (2008–2010) from the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil), which monitors a cohort of 15,105 civil servants of 35 to 74 years of age. Insufficient time for personal care and leisure due to professional and domestic duties, as detailed in a structured questionnaire, comprised the main exposure variable. The outcome variable was overweight/obesity measured according to body mass index. Prevalence ratios were calculated using multinomial logistic regression. A greater proportion of women compared to men reported insufficient time for personal care and leisure (34.5% versus 23.8%, respectively). The prevalence of overweight was greater in men, while obesity was more common in women. Insufficient time for personal care and leisure was associated with overweight (PR = 1.29; 95% CI: 1.04–1.61) and obesity (PR = 1.65; 95% CI: 1.28–2.12) only in women working over 40 hours/week. No significant association was found for males. These results suggest that the length of the working week influences factors underlying weight gain, possibly issues linked to behavior and/or stress mechanisms. The fact that such an association was restricted to women suggests that the results originate from gender inequalities involving relationships between time and health. The findings of this study provide data on which to base public policies aimed at encouraging the redistribution of domestic responsibilities in the direction of gender equity, as well as macrosocial policies such as providing public schools for workers’ children.

This study aimed to predict dietary recommendations and compare the performance of algorithms based on collaborative filtering for making predictions of personalized dietary recommendations. We analyzed the baseline cross-sectional data (2008–2010) of 12,667 participants of the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). The participants were public employees of teaching and research institutions, aged 35–74 years, and 59% female. A semiquantitative Food Frequency Questionnaire (FFQ) was used for dietary assessment. The predictions of dietary recommendations were based on two machine learning (ML) algorithms—user-based collaborative filtering (UBCF) and item-based collaborative filtering (IBCF). The ML algorithms had similar precision (88–91%). The error metrics were lower for UBCF than for IBCF: with a root mean square error (RMSE) of 1.49 vs. 1.67 and a mean square error (MSE) of 2.21 vs. 2.78. Although all food groups were used as input in the system, the items eligible as recommendations included whole cereals, tubers and roots, beans and other legumes, oilseeds, fruits, vegetables, white meats and fish, and low-fat dairy products and milk. The algorithms’ performances were similar in making predictions for dietary recommendations. The models presented can provide support for health professionals in interventions that promote healthier habits and improve adherence to this personalized dietary advice.