RESUMO
This is the first detailed characterization of the microbiota and chemistry of different arid habitats from the State of Qatar. Analysis of bacterial 16S rRNA gene sequences showed that in aggregate, the dominant microbial phyla were Actinobacteria (32.3%), Proteobacteria (24.8%), Firmicutes (20.7%), Bacteroidetes (6.3%), and Chloroflexi (3.6%), though individual soils varied widely in the relative abundances of these and other phyla. Alpha diversity measured using feature richness (operational taxonomic units [OTUs]), Shannon's entropy, and Faith's phylogenetic diversity (PD) varied significantly between habitats (P = 0.016, P = 0.016, and P = 0.015, respectively). Sand, clay, and silt were significantly correlated with microbial diversity. Highly significant negative correlations were also seen at the class level between both classes Actinobacteria and Thermoleophilia (phylum Actinobacteria) and total sodium (R = -0.82 and P = 0.001 and R = -0.86, P = 0.000, respectively) and slowly available sodium (R = -0.81 and P = 0.001 and R = -0.8 and P = 0.002, respectively). Additionally, class Actinobacteria also showed significant negative correlation with sodium/calcium ratio (R = -0.81 and P = 0.001). More work is needed to understand if there is a causal relationship between these soil chemical parameters and the relative abundances of these bacteria. IMPORTANCE Soil microbes perform a multitude of essential biological functions, including organic matter decomposition, nutrient cycling, and soil structure preservation. Qatar is one of the most hostile and fragile arid environments on earth and is expected to face a disproportionate impact of climate change in the coming years. Thus, it is critical to establish a baseline understanding of microbial community composition and to assess how soil edaphic factors correlate with microbial community composition in this region. Although some previous studies have quantified culturable microbes in specific Qatari habitats, this approach has serious limitations, as in environmental samples, approximately only 0.5% of cells are culturable. Hence, this method vastly underestimates natural diversity within these habitats. Our study is the first to systematically characterize the chemistry and total microbiota associated with different habitats present in the State of Qatar.
RESUMO
OBJECTIVES: Body composition analysis is required for accurate assessment of nutritional status in patients with predialysis chronic kidney disease (CKD). The reference method for assessing body fat is dual-energy X-ray absorptiometry (DXA), but it is relatively expensive and often not available for widespread clinical use. There is only limited data on the utility of less expensive and easily available alternatives such as multifrequency bioimpedance assay (BIA) and skinfold thickness (SFT) measurements for assessing body fat in predialysis CKD. The study intends to assess the utility of BIA and SFT in measuring body fat compared to the reference method DXA in subjects with predialysis CKD. METHODS: Body composition analysis was done in 50 subjects with predialysis CKD using multifrequency BIA, SFT, and DXA. The agreement between the body fat percentages measured by reference method DXA and BIA/SFT was assessed by paired t-test, intraclass correlation coefficients (ICCs), regression, and Bland-Altman plots. RESULTS: Percentage of body fat measured by BIA was higher compared to the measurements by DXA, but the difference was not significant (30.44 ± 9.34 vs. 28.62 ± 9.00; P = .071). The ICC between DXA and BIA was 0.822 (confidence interval: 0.688, 0.899; P = .000). The mean values of body fat percentages measured by anthropometry (SFT) was considerably lower when compared to DXA (23.62 ± 8.18 vs. 28.62 ± 9.00; P = .000). The ICC between DXA and SFT was .851 (confidence interval: 0.739, 0.915; P = .000). Bland-Altman plots showed that BIA overestimated body fat by a mean of 1.8% (standard deviation, 6.98), whereas SFT underestimated body fat by 5% (standard deviation, 4.01). Regression plots showed a better agreement between SFT and DXA (R(2) = .79) than BIA (R(2) = .50). Overall, SFT showed better agreement with the DXA. Body mass index (BMI) showed a moderate positive correlation with body fat measured by DXA whereas serum albumin failed to show good correlation. CONCLUSIONS: SFT showed relatively better agreement with the reference method DXA, compared to BIA. SFT can be used as a tool for assessing nutritional status in predialysis patients with CKD.
