Roedmartin9613

Obesity and osteoporosis are health problems with high impact on the morbidity and mortality rate. While the association between BMI and bone density is known, the combined effects of obesity and metabolic components on bone health have not yet been revealed. The objectives of this study were to determine the association between bone health and different phenotypes of obesity in an elderly population.

This cross-sectional study was conducted on the data collected in the Bushehr Elderly Health Program (BEHP). The participants were classified in four groups based on the metabolic phenotypes of obesity (metabolic healthy obese (MHO), metabolic non-healthy non-obese (MNHNO), metabolic non-healthy obese (MNHO), and metabolic healthy non-obese (MHNO)). The association between osteoporosis and TBS and the metabolic phenotypes of obesity were assessed using multiple variable logistic regression models.

Totally, 2378 people (1227 women) were considered for analyses. The prevalence of MHNO, MHO, MNHNO, and MNHO were 902 (39.9%), 138 (6.1%), 758 (33.5%), and 464 (20.5%), respectively. In the multivariate logistic regression models, those with MHO (OR 0.22; 95% CI 0.12-0.36), MNHNO (OR 0.52; 95% CI 0.4-0.66), and MNHO phenotypes (OR 0.22; 95% CI 0.16-0.3) had a significantly lower risk of osteoporosis. Likewise, those having MHO (OR 2.38; 95% CI 1.51-3.76), MNHNO (OR 1.49; 95% CI 1.11-2), and MNHO (OR 2.50; 95% CI 1.82-3.42) phenotypes were found to had higher risk of low bone quality as confirmed by TBS.

The obese subjects have lower bone quality, regardless of their obesity phenotype.

The obese subjects have lower bone quality, regardless of their obesity phenotype.There was no difference in Trabecular Bone Score (TBS) comparing White and Black women after adjusting for body mass index (BMI) and diabetes status. Japanese women had lower TBS than White women. Our results diverge from established differences in fracture rates by race/ethnicity.

The TBS was developed as an indirect measure of vertebral bone microarchitecture derived from texture analysis of lumbar spine DXA scans. There is little information on race/ethnic differences in TBS.

We compared TBS in 656 White, 492 Black, and 268 Japanese pre- and early perimenopausal women. We used a beta version of TBS that accounts for tissue thickness using DXA measured soft tissue thickness rather than BMI. The relation between BMI and tissue thickness corrected TBS differed by BMI; we used a three-segment linear spline to adjust for BMI.

The women were, on average, 46.5years of age; 50% were premenopausal. In BMI and diabetes adjusted models, there was no difference in TBS between White and Black women. TBS was modestly (2%) lower in the Japanese women compared to White women, p = 0.04. In a sensitivity analysis, restricting the analysis to those with BMI 24-31kg/m

, results were similar.

TBS was similar in Black and White women after accounting for tissue thickness and adjusting for BMI, diabetes, and other covariates. The Japanese women had modestly lower TBS. These results diverge from established race/ethnic differences in fracture rates and areal bone mineral density, underscoring the need for further studies.

TBS was similar in Black and White women after accounting for tissue thickness and adjusting for BMI, diabetes, and other covariates. The Japanese women had modestly lower TBS. These results diverge from established race/ethnic differences in fracture rates and areal bone mineral density, underscoring the need for further studies.In order to obtain the typical soil physical properties of reclaimed land more quickly and accurately, the South Dump of the China Coal's Antaibao Open-Pit Mine in Pingshuo was focussed on in this paper, and ground penetrating radar (GPR) technology was utilized to detect the soil physical properties of reclaimed land in the mining area. The soil profile sampling and GPR detection methods were used to acquire the data. The gravel content of surface soil was analyzed by counting the number of isolated gravel signals in GPR images. The change of effective soil thickness was analyzed by establishing the fitting relationship between calibration depth and GPR image depth. The Topp's model was validated by comparing its inversion with the measured soil volumetric water content. And the Topp's model was further validated by the soil volumetric water content obtained from the Topp's model and which obtained from the wave velocity inversion. The results are as follows (1) Based on the number of isolated gravel signalse difference was 2%. In summary, the benefits of using GPR to detect soil physical properties of reclaimed land in mining area are as follows (1) GPR can be used to detect soil layer thickness and surface gravel content faster and more accurately. (2) Topp model can also be used to calculate the soil moisture content of non-natural deposits such as reclaimed land in mining area.The objective of this study was to evaluate the accuracy of genomic predictions of growth traits in Nellore cattle. Data from 5064 animals belonging to farms that participate in the Conexão DeltaGen and PAINT breeding programs were used. Genotyping was performed with the Illumina BovineHD BeadChip (777,962 SNPs). After quality control of the genomic data, 412,993 SNPs were used. SAG agonist ic50 Deregressed EBVs (DEBVs) were calculated using the estimated breeding values (EBVs) and accuracies of birth weight (BW), weight gain from birth to weaning (GBW), postweaning weight gain (PWG), yearling height (YH), and cow weight (CW) provided by GenSys. Three models were used to estimate marker effects genomic best linear unbiased prediction (GBLUP), BayesCπ, and improved Bayesian least absolute shrinkage and selection operator (IBLASSO). The prediction ability of genomic estimated breeding value (GEBVs) was estimated by the average Pearson correlation between DEBVs and GEBVs, predicted with the different methodologies in the validation populations. The regression coefficients of DEBVs on GEBVs in the validation population were calculated and used as indicators of prediction bias of GEBV. In general, the Bayesian methods provided slightly more accurate predictions of genomic breeding values than GBLUP. The BayesCπ and IBLASSO were similar for all traits (BW, GBW, PWG, and YH), except for CW. Thus, there does not seem to be a more suitable method for the estimation of SNP effects and genomic breeding values. Bayesian regression models are of interest for future applications of genomic selection in this population, but further improvements are needed to reduce deflation of their predictions.