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In mammals, changes in the metabolic state, including obesity, fasting, cold challenge, and high-fat diets (HFDs), activate complex immune responses. In many strains of rodents, HFDs induce a rapid systemic inflammatory response and lead to obesity. Little is known about the molecular signals required for HFD-induced phenotypes. We studied the function of the receptor for advanced glycation end products (RAGE) in the development of phenotypes associated with high-fat feeding in mice. RAGE is highly expressed on immune cells, including macrophages. We found that high-fat feeding induced expression of RAGE ligand HMGB1 and carboxymethyllysine-advanced glycation end product epitopes in liver and adipose tissue. Genetic deficiency of RAGE prevented the effects of HFD on energy expenditure, weight gain, adipose tissue inflammation, and insulin resistance. RAGE deficiency had no effect on genetic forms of obesity caused by impaired melanocortin signaling. Hematopoietic deficiency of RAGE or treatment with soluble RAGE partially protected against peripheral HFD-induced inflammation and weight gain. These findings demonstrate that high-fat feeding induces peripheral inflammation and weight gain in a RAGE-dependent manner, providing a foothold in the pathways that regulate diet-induced obesity and offering the potential for therapeutic intervention.

BackgroundThe role of the microbiota in human health and disease has been increasingly studied, gathering momentum through the use of high-throughput technologies. Further identification of the roles of specific microbes is necessary to better understand the mechanisms involved in diseases related to microbiome perturbations. MethodsHere, we introduce a new microbiome-based group association testing method, optimal microbiome-based association test (OMiAT). OMiAT is a data-driven testing method which takes an optimal test throughout different tests from the sum of powered score tests (SPU) and microbiome regression-based kernel association test (MiRKAT). We illustrate that OMiAT efficiently discovers significant association signals arising from varying microbial abundances and different relative contributions from microbial abundance and phylogenetic information. We also propose a way to apply it to fine-mapping of diverse upper-level taxa at different taxonomic ranks (e.g., phylum, class, order, family, and genus), as well as the entire microbial community, within a newly introduced microbial taxa discovery framework, microbiome comprehensive association mapping (MiCAM).ResultsOur extensive simulations demonstrate that OMiAT is highly robust and powerful compared with other existing methods, while correctly controlling type I error rates. Our real data analyses also confirm that MiCAM is especially efficient for the assessment of upper-level taxa by integrating OMiAT as a group analytic method.ConclusionsOMiAT is attractive in practice due to the high complexity of microbiome data and the unknown true nature of the state. MiCAM also provides a hierarchical association map for numerous microbial taxa and can also be used as a guideline for further investigation on the roles of discovered taxa in human health and disease.Electronic supplementary materialThe online version of this article (doi:10.1186/s40168-017-0262-x) contains supplementary material, which is available to authorized users.

Women with HL may survive a subsequent diagnosis of BC, only to experience significant excesses of death from other primary cancers and cardiac disease. Greater awareness of screening for cardiac disease and subsequent primary cancers in patients with HL-BC is warranted.

For the well-known Fay-Herriot small area model, standard variance component estimation methods frequently produce zero estimates of the strictly positive model variance. As a consequence, an empirical best linear unbiased predictor of a small area mean, commonly used in the small area estimation, could reduce to a simple regression estimator, which typically has an overshrinking problem. We propose an adjusted maximum likelihood estimator of the model variance that maximizes an adjusted likelihood defined as a product of the model variance and a standard likelihood (e.g., profile or residual likelihood) function. The adjustment factor was suggested earlier by Carl Morris in the context of approximating a hierarchical Bayes solution where the hyperparameters, including the model variance, are assumed to follow a prior distribution. Interestingly, the proposed adjustment does not affect the mean squared error property of the model variance estimator or the corresponding empirical best linear unbiased predictors of the small area means in a higher order asymptotic sense. However, as demonstrated in our simulation study, the proposed adjustment has a considerable advantage in the small sample inference, especially in estimating the shrinkage parameters and in constructing the parametric bootstrap prediction intervals of the small area means, which require the use of a strictly positive consistent model variance estimate.

The feet of diabetic men had decreased populations of Staphylococcus species, increased populations of S. aureus, and increased bacterial diversity, compared with the feet of controls. These ecologic changes may affect the risk for wound infections.

ObjectiveSarcopenia might be associated with bone fragility in elderly individuals. This study aimed to investigate the prevalence of sarcopenia and its association with fragility fracture sites in elderly Chinese patients.MethodsPatients (322 men and 435 women) aged 65–94 years and with a history of fragility fractures in the ankle, wrist, vertebrae or hip, and healthy men (n = 1263) and women (n = 1057) aged 65–92 years without a history of fractures were enrolled. Whole-body dual energy X-ray absorptiometry was used to analyze skeletal muscle mass index (SMI), fat mass and bone mineral density. Sarcopenia was defined as SMI less than two standard deviations below the mean of a young reference group.ResultsSarcopenia occurrence varied with fracture location. Sarcopenia was more common in females with vertebral and hip fractures and in men with hip and ankle fractures than in the non-fracture group). Sarcopenia was significantly more prevalent in men with wrist, hip and ankle fractures than in women. SMI was correlated with BMD in different fracture groups. Logistic regression analyses revealed that lower SMI was associated with an increased risk of hip fracture both in men and women and ankle fracture in men.DiscussionSarcopenia may be an independent risk factor for hip and ankle fractures in men, and for hip fractures in women.