Sleep disorders are increasingly being characterized in modern society as contributing to a host of serious medical problems, including obesity and metabolic syndrome. Changes to the microbial community in the human gut have been reportedly associated with many of these cardiometabolic outcomes. In this study, we investigated the impact of sleep length on the gut microbiota in a large cohort of 655 participants of African descent, aged 25–45, from Ghana, South Africa (SA), Jamaica, and the United States (US). The sleep duration was self-reported via a questionnaire. Participants were classified into 3 sleep groups: short (<7hrs), normal (7-<9hrs), and long (≥9hrs). Forty-seven percent of US participants were classified as short sleepers and 88% of SA participants as long sleepers. Gut microbial composition analysis (16S rRNA gene sequencing) revealed that bacterial alpha diversity negatively correlated with sleep length (p<0.05). Furthermore, sleep length significantly contributed to the inter-individual beta diversity dissimilarity in gut microbial composition (p<0.01). Participants with both short and long-sleep durations exhibited significantly higher abundances of several taxonomic features, compared to normal sleep duration participants. The predicted relative proportion of two genes involved in the butyrate synthesis via lysine pathway were enriched in short sleep duration participants. Finally, co-occurrence relationships revealed by network analysis showed unique interactions among the short, normal and long duration sleepers. These results suggest that sleep length in humans may alter gut microbiota by driving population shifts of the whole microbiota and also specific changes in Exact Sequence Variants abundance, which may have implications for chronic inflammation associated diseases. The current findings suggest a possible relationship between disrupted sleep patterns and the composition of the gut microbiota. Prospective investigations in larger and more prolonged sleep researches and causally experimental studies are needed to confirm these findings, investigate the underlying mechanism and determine whether improving microbial homeostasis may buffer against sleep-related health decline in humans.

Long-chain omega-3 PUFAs, specifically eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), are of increasing interest because of their favorable effect on cardiometabolic risk. This study explores the association between omega 6 and 3 fatty acids intake and cardiometabolic risk in four African-origin populations spanning the epidemiological transition. Data are obtained from a cohort of 2500 adults aged 25–45 enrolled in the Modeling the Epidemiologic Transition Study (METS), from the US, Ghana, Jamaica, and the Seychelles. Dietary intake was measured using two 24 h recalls from the Nutrient Data System for Research (NDSR). The prevalence of cardiometabolic risk was analyzed by comparing the lowest and highest quartile of omega-3 (EPA+ DHA) consumption and by comparing participants who consumed a ratio of arachidonic acid (AA)/EPA + DHA and >4:1. Data were analyzed using multiple variable logistic regression adjusted for age, gender, activity, calorie intake, alcohol intake, and smoking status. The lowest quartile of EPA + DHA intake is associated with cardiometabolic risk 2.16 (1.45, 3.2), inflammation 1.59 (1.17, 2.16), and obesity 2.06 (1.50, 2.82). Additionally, consuming an AA/EPA + DHA ratio of >4:1 is also associated with cardiometabolic risk 1.80 (1.24, 2.60), inflammation 1.47 (1.06, 2.03), and obesity 1.72 (1.25, 2.39). Our findings corroborate previous research supporting a beneficial role for monounsaturated fatty acids in reducing cardiometabolic risk.

Background: The Pulvers’ silhouette showcards provide a non-invasive, easy-to-use, and possibly cross-culturally acceptable way of assessing an individual’s perception of their body size. This study examined, in three different populations: 1) the relationship between silhouettes and body mass index (BMI), 2) the predictive performance of silhouettes to predict dichotomous adiposity categories, and 3) whether silhouette ranking performed similarly in predicting BMI, waist circumference (WC), and waist-to-height ratio (WHR). Methods: This study included 751 participants of African-origin from the United States of America (USA), the Republic of Seychelles, and Ghana, from the ongoing cohort Modeling the Epidemiological Transition Study. We assessed the mean BMI for each silhouette rank by country and sex and performed a least-squares linear regression for the silhouette’s performance by country and sex. The performance of the silhouettes to predict overweight and obesity (BMI ³ 25 kg/m2), and obesity alone (BMI ³ 30 kg/m2) was examined through a receiver operator curve (ROC) analysis with corresponding sensitivities and specificities. Finally, a ROC analysis area under the curve (AUC) was also performed for the detection of elevated waist circumference (men ≥ 94 cm; women ≥ 80 cm) and waist-to-height ratio (> 0.5) by country and sex.Results: Mean measured BMI (kg/m2) in men/women differed largely across countries: 28.9/35.8 in the USA, 28.3/30.5 in Seychelles, and 23.9/28.5 in Ghana. The slope of the relation between silhouette ranking and BMI (i.e., linear regression coefficient and 95% confidence intervals) was similar between sexes of the same country but differed between countries: 3.65 [95% CI: 3.34-3.97 BMI units/silhouette unit] in the USA, 3.23 [2.93-3.74] in Seychelles, and 1.99 [1.72-2.26] in Ghana. Different silhouette cut-offs predicted dichotomous adiposity categories differently in the three countries. For example, a silhouette ³ 5 had sensitivity/specificity of 77.3%/90.6% to predict BMI ≥ 25 kg/m2 in the USA, but 77.8%/85.9% in Seychelles and 84.9%/71.4% in Ghana. Finally, silhouettes predicted BMI, WC, and WHR similarly, within each country and sex, based on Spearman correlations coefficients (continuous scale) and c-statistic (dichotomous classification).Conclusion: Our data suggest that Pulvers’ silhouette showcards can be a useful tool to objectively predict different adiposity measures in different populations when direct measurement cannot be performed. However, population-specific differences in the slopes of the associations, which possibly partly reflect differences in perceptions of one’s body size according to country adiposity prevalence, stress the need to calibrate silhouette showcards when using them as a survey tool.

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The Pulvers’ silhouette showcards provide a non-invasive and easy-to-use way of assessing an individual’s body size perception using nine silhouette shapes. However, their utility across different populations has not been examined. This study aimed to assess: 1) the relationship between silhouette perception and measured anthropometrics, i.e., body mass index (BMI), waist circumference (WC), waist-height-ratio (WHtR), and 2) the ability to predict with silhouette showcards anthropometric adiposity measures, i.e., overweight and obesity (BMI ≥ 25 kg/m2), obesity alone (BMI ≥ 30 kg/m2), elevated WC (men ≥ 94 cm; women ≥ 80 cm), and WHtR (> 0.5) across the epidemiological transition. 751 African-origin participants, aged 20–68 years old, from the United States (US), Seychelles, and Ghana, completed anthropometrics and selected silhouettes corresponding to their perceived body size. Silhouette performance to anthropometrics was examined using a least-squares linear regression model. A receiver operator curve (ROC) was used to investigate the showcards ability to predict anthropometric adiposity measures. The relationship between silhouette ranking and BMI were similar between sexes of the same country but differed between countries: 3.65 [95% CI: 3.34–3.97] BMI units/silhouette unit in the US, 3.23 [2.93–3.74] in Seychelles, and 1.99 [1.72–2.26] in Ghana. Different silhouette cutoffs predicted obesity differently in the three countries. For example, a silhouette ≥ five had a sensitivity/specificity of 77.3%/90.6% to predict BMI ≥ 25 kg/m2 in the US, but 77.8%/85.9% in Seychelles and 84.9%/71.4% in Ghana. Ultimately, silhouettes predicted BMI, WC, and WHtR similarly within each country and sex but not across countries. Our data suggest that Pulvers’ silhouette showcards may be a helpful tool to predict anthropometric and adiposity measures in different populations when direct measurement cannot be performed. However, no universal silhouette cutoff can be used for detecting overweight or obesity status, and population-specific differences may stress the need to calibrate silhouette showcards when using them as a survey tool in different countries.