Estimated LBW could be used to determine the contrast material dose and rate during MDCT. The aim of this study is to test the accuracy of a technique for estimation of lean body weight (LBW) from a single multi-detec...Estimated LBW could be used to determine the contrast material dose and rate during MDCT. The aim of this study is to test the accuracy of a technique for estimation of lean body weight (LBW) from a single multi-detector row computed tomographic (MDCT) abdominal image, using a bioelectrical body composition analyzer scale as the reference standard. CT images of 21 patients with previously measured LBW (mLBW) were processed using computer-assisted, vendor-specific software (Advantage Windows 4.2;GE Healthcare, Inc). For each transverse image, a fat-fraction was automatically measured as the number of fat pixels (-200 to -50 HU) divided by the total number of pixels having an attenuation value ≥-200 HU. Estimated LBW (eLBW) of five single contiguous sections was calculated in each of three abdominal regions (upper abdomen, mid abdomen and pelvis) by multiplying TBW by (1 – fat-fraction). Bland-Altman plot with limits of agreement was used to assess agreement between mLBW and eLBW. The mean mLBW for all patients was 56 kg (range, 39 - 75 kg). Mean differences and limits of agreement between mLBW and eLBW measurements for the upper abdomen, mid abdomen and pelvis reported were -8.9 kg (-25.6 kg, +7.5 kg), -10.6 kg (-27.7 kg, +6.4 kg), and +0.5 kg (-12.8 kg, +13.8 kg) respectively. eLBW deriving directly from a transverse CT image of the pelvis can accurately predict mLBW.展开更多
Background: Basal Metabolic Rate (BMR) is the quantum of calories needed for optimum body function when at rest. This has long been an indicator of one’s health and the basis for determining the metabolic age of indi...Background: Basal Metabolic Rate (BMR) is the quantum of calories needed for optimum body function when at rest. This has long been an indicator of one’s health and the basis for determining the metabolic age of individuals. Many scholastic projects have led to the establishment of mathematical models and inventions that measure the BMR and other body composition parameters. However, existing computations have limitations as they do not offer accurate results for Ghanaians. Aim: The purpose of the study was to model BMR metrics that are most suitable for Ghanaians and to investigate the effect of caloric difference on weight, Lean Body Mass (LBM) and % fat composition that can be implemented with Information Technology. Research Methods and Procedures: This was an experimental study that adopted a quantitative approach. BMR and body composition were measured in a sample of 242 Ghanaian adults (141 males and 101 females) from 19 to 30 years of age. Body composition was measured using bioelectrical impendence analysis (BIA) in all participants. Each participant was under study for 7 days. A simple linear regression model was used to examine associations between BMR/calorie intake and total body weight and LBM. Results: There was a significant statistical relation between BMR and LBM and between BMR and weight of both men and women. Equations for BMR and weight were established for males and females. Furthermore, caloric intake differences affected changes in total weight as well as differences in % fat composition. Caloric intake however did not affect the difference in LBM. Conclusion: Caloric difference had an impact on total body weight and Lean Body Mass. The model derived from the study predicts weight change and BMR of Ghanaians from 19 to 30 years of age. It is termed the Health and Age Monitoring System (HAMS).展开更多
旨在使用加权一步法全基因组关联分析方法,充分利用群体的表型、系谱和基因型信息,探索与大白猪眼肌面积、估计瘦肉率和背膘厚相关的候选基因。本研究收集21754头大白猪眼肌面积、估计瘦肉率和背膘厚的表型记录数据,其中基因型数据个体...旨在使用加权一步法全基因组关联分析方法,充分利用群体的表型、系谱和基因型信息,探索与大白猪眼肌面积、估计瘦肉率和背膘厚相关的候选基因。本研究收集21754头大白猪眼肌面积、估计瘦肉率和背膘厚的表型记录数据,其中基因型数据个体共1259头。通过方差分析和加权一步法全基因组关联分析,确定性状显著相关的数量性状基因座(quantitative trait locus,QTL)。并进行基因注释、GO(gene ontology)功能和KEGG(kyoto encyclopedia of genes and genomes)通路富集分析。计算结果表明,大白猪眼肌面积、估计瘦肉率和背膘厚的遗传力分别为0.4506±0.0173、0.4968±0.0174和0.4758±0.0172。利用加权一步法全基因组关联分析,定位到与眼肌面积显著相关的候选QTL区域10个,与估计瘦肉率显著相关的候选QTL区域8个,与背膘厚显著相关的候选QTL区域12个。后续基因注释、GO功能和KEGG通路富集分析显示,与眼肌面积相关的候选基因36个,共富集到7个条目;与估计瘦肉率相关的候选基因29个,共富集到2个条目;与背膘厚相关的候选基因41个,共富集到11个条目。分析这些条目中所包含的基因,结果显示其中部分候选基因与生长发育、肌肉脂肪和骨骼发育有关。如ADAL基因影响脂肪细胞分化等活动;FGF 9基因被报道参与骨骼发育等过程。本研究通过加权一步法关联分析定位到与大白猪生长性状相关的重要候选基因,结果扩展了大白猪生长性状的相关研究,并为今后该品种生长性状的基因组遗传改良提供重要分子标记。展开更多
文摘Estimated LBW could be used to determine the contrast material dose and rate during MDCT. The aim of this study is to test the accuracy of a technique for estimation of lean body weight (LBW) from a single multi-detector row computed tomographic (MDCT) abdominal image, using a bioelectrical body composition analyzer scale as the reference standard. CT images of 21 patients with previously measured LBW (mLBW) were processed using computer-assisted, vendor-specific software (Advantage Windows 4.2;GE Healthcare, Inc). For each transverse image, a fat-fraction was automatically measured as the number of fat pixels (-200 to -50 HU) divided by the total number of pixels having an attenuation value ≥-200 HU. Estimated LBW (eLBW) of five single contiguous sections was calculated in each of three abdominal regions (upper abdomen, mid abdomen and pelvis) by multiplying TBW by (1 – fat-fraction). Bland-Altman plot with limits of agreement was used to assess agreement between mLBW and eLBW. The mean mLBW for all patients was 56 kg (range, 39 - 75 kg). Mean differences and limits of agreement between mLBW and eLBW measurements for the upper abdomen, mid abdomen and pelvis reported were -8.9 kg (-25.6 kg, +7.5 kg), -10.6 kg (-27.7 kg, +6.4 kg), and +0.5 kg (-12.8 kg, +13.8 kg) respectively. eLBW deriving directly from a transverse CT image of the pelvis can accurately predict mLBW.
文摘Background: Basal Metabolic Rate (BMR) is the quantum of calories needed for optimum body function when at rest. This has long been an indicator of one’s health and the basis for determining the metabolic age of individuals. Many scholastic projects have led to the establishment of mathematical models and inventions that measure the BMR and other body composition parameters. However, existing computations have limitations as they do not offer accurate results for Ghanaians. Aim: The purpose of the study was to model BMR metrics that are most suitable for Ghanaians and to investigate the effect of caloric difference on weight, Lean Body Mass (LBM) and % fat composition that can be implemented with Information Technology. Research Methods and Procedures: This was an experimental study that adopted a quantitative approach. BMR and body composition were measured in a sample of 242 Ghanaian adults (141 males and 101 females) from 19 to 30 years of age. Body composition was measured using bioelectrical impendence analysis (BIA) in all participants. Each participant was under study for 7 days. A simple linear regression model was used to examine associations between BMR/calorie intake and total body weight and LBM. Results: There was a significant statistical relation between BMR and LBM and between BMR and weight of both men and women. Equations for BMR and weight were established for males and females. Furthermore, caloric intake differences affected changes in total weight as well as differences in % fat composition. Caloric intake however did not affect the difference in LBM. Conclusion: Caloric difference had an impact on total body weight and Lean Body Mass. The model derived from the study predicts weight change and BMR of Ghanaians from 19 to 30 years of age. It is termed the Health and Age Monitoring System (HAMS).
文摘旨在使用加权一步法全基因组关联分析方法,充分利用群体的表型、系谱和基因型信息,探索与大白猪眼肌面积、估计瘦肉率和背膘厚相关的候选基因。本研究收集21754头大白猪眼肌面积、估计瘦肉率和背膘厚的表型记录数据,其中基因型数据个体共1259头。通过方差分析和加权一步法全基因组关联分析,确定性状显著相关的数量性状基因座(quantitative trait locus,QTL)。并进行基因注释、GO(gene ontology)功能和KEGG(kyoto encyclopedia of genes and genomes)通路富集分析。计算结果表明,大白猪眼肌面积、估计瘦肉率和背膘厚的遗传力分别为0.4506±0.0173、0.4968±0.0174和0.4758±0.0172。利用加权一步法全基因组关联分析,定位到与眼肌面积显著相关的候选QTL区域10个,与估计瘦肉率显著相关的候选QTL区域8个,与背膘厚显著相关的候选QTL区域12个。后续基因注释、GO功能和KEGG通路富集分析显示,与眼肌面积相关的候选基因36个,共富集到7个条目;与估计瘦肉率相关的候选基因29个,共富集到2个条目;与背膘厚相关的候选基因41个,共富集到11个条目。分析这些条目中所包含的基因,结果显示其中部分候选基因与生长发育、肌肉脂肪和骨骼发育有关。如ADAL基因影响脂肪细胞分化等活动;FGF 9基因被报道参与骨骼发育等过程。本研究通过加权一步法关联分析定位到与大白猪生长性状相关的重要候选基因,结果扩展了大白猪生长性状的相关研究,并为今后该品种生长性状的基因组遗传改良提供重要分子标记。
