scholarly journals Epidemiological study of body fat percentage, lean body mass, and total body water for Asian patients with chronic kidney disease

2020 ◽  
Vol 7 (2) ◽  
pp. 139
Author(s):  
Song-Hui Kim ◽  
Yu-Il Bang ◽  
Yong Ri ◽  
Gum-Hak Choe
Keyword(s):  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Epidemiological Study ◽  
Lean Body Mass ◽  
Body Fat ◽  
Total Body Water ◽  
Body Fat Percentage ◽  
Fat Percentage ◽  
Asian Patients ◽  
Total Body

scholarly journals Anthropometry-based Body Fat Percentage Predicts High hs-CRP in Chronic Kidney Disease Patients

2018 ◽  
Vol 10 (2) ◽  
pp. 184-91
Author(s):  
Mochammad Thaha ◽  
Maulana Antiyan Empitu ◽  
Ika Nindya Kadariswantiningsih ◽  
Cahyo Wibisono Nugroho ◽  
Nurina Hasanatuludhhiyah ◽  
...  
Keyword(s):  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Body Fat ◽  
High Sensitivity ◽  
Low Grade ◽  
Body Fat Percentage ◽  
Fat Percentage ◽  
Cross Sectional ◽  
Significant Difference ◽  
Hs Crp

BACKGROUND: Obesity is an important cardiovascular risk factor and associated with low grade inflammation in chronic kidney disease (CKD) patients. This study aims to assess the association between body fat with serum high sensitivity C-reactive protein (hs-CRP) level in CKD patients.METHODS: A cross-sectional study was performed in 71 CKD patients. Anthropometric measurements included body weight, height, body mass index (BMI), body fat percentage (BFP), skinfold thickness (SKF) of triceps and biceps were performed by trained physician. BFP was calculated using Kwok’s Formula and hs-CRP was measured by Particle enhanced Turbidimetry.RESULTS: The averaged BMI of our subjects was 25.8±4.4. There was no significant difference in BMI between pre-dialysis and hemodialysis CKD patients. Positive correlation was found between BFP and hs-CRP (r=0.266; p<0.05), while there was no significant correlation between BMI and hs-CRP.CONCLUSION: Body fat percentage was associated with hs-CRP. Hence, it will be more beneficial to assess nutritional status in CKD using BFP rather than BMI alone since it was demonstrated to correlate with hs-CRP in our studyKEYWORDS: CKD, obesity, inflammation, body fat, hs-CRP

Comparison of the Effect of Soy protein and Whey protein on Body Composition: A Meta-Analysis of Randomized Clinical Trials

2021 ◽  
pp. 1-27
Author(s):  
Masoome Piri Damaghi ◽  
Atieh Mirzababaei ◽  
Sajjad Moradi ◽  
Elnaz Daneshzad ◽  
Atefeh Tavakoli ◽  
...  
Keyword(s):  
Body Composition ◽  
Whey Protein ◽  
Lean Body Mass ◽  
Body Mass ◽  
Body Fat ◽  
Fat Mass ◽  
Soy Protein ◽  
Protein Supplementation ◽  
Body Fat Percentage ◽  
Fat Percentage

Abstract Background: Essential amino acids (EAAs) promote the process of regulating muscle synthesis. Thus, whey protein that contains higher amounts of EAA can have a considerable effect on modifying muscle synthesis. However, there is insufficient evidence regarding the effect of soy and whey protein supplementation on body composition. Thus, we sought to perform a meta-analysis of published Randomized Clinical Trials that examined the effect of whey protein supplementation and soy protein supplementation on body composition (lean body mass, fat mass, body mass and body fat percentage) in adults. Methods: We searched PubMed, Scopus, and Google Scholar, up to August 2020, for all relevant published articles assessing soy protein supplementation and whey protein supplementation on body composition parameters. We included all Randomized Clinical Trials that investigated the effect of whey protein supplementation and soy protein supplementation on body composition in adults. Pooled means and standard deviations (SD) were calculated using random-effects models. Subgroup analysis was applied to discern possible sources of heterogeneity. Results: After excluding non-relevant articles, 10 studies, with 596 participants, remained in this study. We found a significant increase in lean body mass after whey protein supplementation weighted mean difference (WMD: 0.91; 95% CI: 0.15, 1.67. P= 0.019). Subgroup analysis, for whey protein, indicated that there was a significant increase in lean body mass in individuals concomitant to exercise (WMD: 1.24; 95% CI: 0.47, 2.00; P= 0.001). There was a significant increase in lean body mass in individuals who received 12 or less weeks of whey protein (WMD: 1.91; 95% CI: 1.18, 2.63; P<0.0001). We observed no significant change between whey protein supplementation and body mass, fat mass, and body fat percentage. We found no significant change between soy protein supplementation and lean body mass, body mass, fat mass, and body fat percentage. Subgroup analysis for soy protein indicated there was a significant increase in lean body mass in individuals who supplemented for 12 or less weeks with soy protein (WMD: 1.48; 95% CI: 1.07, 1.89; P< 0.0001). Conclusion: Whey protein supplementation significantly improved body composition via increases in lean body mass, without influencing fat mass, body mass, and body fat percentage.

scholarly journals Body mass index and waist circumference are better predictors of insulin resistance than total body fat percentage in middle-aged and elderly Taiwanese

Medicine ◽  
2017 ◽  
Vol 96 (39) ◽  
pp. e8126 ◽  
Author(s):  
Yiu-Hua Cheng ◽  
Yu-Chung Tsao ◽  
I-Shiang Tzeng ◽  
Hai-Hua Chuang ◽  
Wen-Cheng Li ◽  
...  
Keyword(s):  
Insulin Resistance ◽  
Body Mass Index ◽  
Waist Circumference ◽  
Body Mass ◽  
Body Fat ◽  
Body Fat Percentage ◽  
Middle Aged ◽  
Fat Percentage ◽  
Total Body Fat ◽  
Total Body

Assessment of Total Body Fat Percentage From Regional Spine and Femur DXA Measurements Among Chinese Women and Men

2007 ◽  
Vol 10 (1) ◽  
pp. 55-64 ◽  
Author(s):  
Tian-Min Zhang ◽  
Hao Xu ◽  
Zhong-Man Yuan ◽  
Jia-Xuan Chen ◽  
Jian Gong ◽  
...  
Keyword(s):  
Body Fat ◽  
Chinese Women ◽  
Body Fat Percentage ◽  
Fat Percentage ◽  
Total Body Fat ◽  
Total Body

scholarly journals Causal Effects of Life Course Adiposity on Chronic Kidney Disease: A Mendelian Randomization Study

2021 ◽  
Author(s):  
Wanchun Yang ◽  
Hongchun Chen ◽  
Qiuyun Yuan ◽  
Yang Zou
Keyword(s):  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Life Course ◽  
Causal Relationship ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Sensitivity Analyses ◽  
Body Fat Percentage ◽  
Fat Percentage ◽  
Hip Circumference

Abstract Background Obesity is reported to be tightly correlated the development of chronic kidney disease (CKD). However, whether there exists causation is unknown, and it remains controversial about the role of obesity in CKD is protective or destructive. In this study, we try to infer the causal relationship between life course adiposity and CKD, to provide a rationale for obesity management in CKD patients.Methods A two-sample Mendelian randomization (MR) analysis was conducted to explore the causal relationship of life course adiposity traits including including body mass index (BMI), childhood BMI, body fat percentage (BF), birth weight (BW), waist circumference, hip circumference and waist-to-hip ratio (WHR) to CKD. Significant single nucleotide polymorphisms from genome-wide association study on human adiposity traits were utilized as exposure instruments, and summary statistics of CKD as outcome. The causal relationship was evaluated by inverse variance weighted, MR Egger regression and weighted median methods, and further verified by extensive sensitivity analyses.Results Genetically determined one standard deviation increase in adult BMI was associated with higher risk of CKD in all four MR methods. And other indexes including childhood BMI, body fat percentage, and waist/hip circumference also have a causal effect on the risk of CKD. The results were robust under all sensitivity analyses.Conclusions There exist causal effect of life course adiposity on the risk of CKD. A genetic predisposition to higher adult BMI may increase the risk of CKD.

scholarly journals L Score as a Novel Anthropometric Measure for Obesity Screening in Adult Individuals: An Exploratory Study

2020 ◽  
Vol 8 (E) ◽  
pp. 60-64
Author(s):  
Devi Prasad Mohapatra ◽  
Jaya Prakash Sahoo ◽  
Madhusmita Mohanty Mohaptra ◽  
Sitanshu Sekhar Kar ◽  
Sridharan Kalyani ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Body Fat ◽  
Tertiary Care ◽  
Primary Objective ◽  
Overweight And Obesity ◽  
Rapid Screening ◽  
Body Fat Percentage ◽  
Fat Percentage ◽  
Secondary Objective ◽  
Total Body

BACKGROUND: Obesity is one of today’s most neglected public health problems, affecting every region of the world. Early identification of increased weight gain among the population is paramount to prevent the attendant complications associated with obesity. OBJECTIVES: The primary objective of this study was to measure the distribution of L score in the representative population and the secondary objective was to identify an association between L score values and other measures of obesity such as body mass index, waist circumference, waist-to-height ratio, neck circumference (NC), and total body fat percentage. METHODS: This study was conducted in the departments of plastic surgery and endocrinology of a tertiary care institute. The L score (a measure of fullness of the lateral retromalleolar fossa in the lower limb) was assessed in all the participating individuals. Statistical analysis was performed using the Statistical Package for the Social Sciences version 19.0. p < 0.05 was considered as statistically significant in statistical analysis. RESULTS: Among the 50 participants taken in this study, 24 had L score 0, 15 had score 1, and 11 had score 2. The participants with L score 1 and 2 had higher obesity, higher NC, and more body fat percentage compared to those having score 0. All the participants with L score 2 were overweight and had central obesity. CONCLUSIONS: The L score measure has a potential for simple and rapid screening of at-risk population for overweight and obesity.

scholarly journals Effects of KidQuest on body mass index, lean body mass and body fat percentage among children ages 11 to 14 years in Nebraska (808.14)

2014 ◽  
Vol 28 (S1) ◽  
Author(s):  
Weiwen Chai ◽  
Jean Ann Fischer ◽  
Shinya Takahashi ◽  
Melissa Wallinga ◽  
Mindy Anderson‐Knott ◽  
...  
Keyword(s):  
Body Mass Index ◽  
Lean Body Mass ◽  
Body Mass ◽  
Body Fat ◽  
Body Fat Percentage ◽  
Fat Percentage

scholarly journals Development and Cross-Validation of Anthropometric Predictive Equations to Estimate Total Body Fat Percentage in Adult Women in Sri Lanka

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Nirmala Rathnayake ◽  
Gayani Alwis ◽  
Janaka Lenora ◽  
Sarath Lekamwasam
Keyword(s):  
Body Fat ◽  
Cross Validation ◽  
Body Fat Percentage ◽  
Adult Women ◽  
Fat Percentage ◽  
Predictive Equations ◽  
Total Body Fat ◽  
Group A ◽  
Total Body ◽  
Group B

Attempts have been made to estimate body fat using anthropometry, and most of them are country-specific. This study was designed to develop and cross-validate anthropometric predictive equations to estimate the total body fat percentage (TBFP) of Sri Lankan adult women. A cross-sectional study was conducted in Galle, Sri Lanka, with two groups: Group A (group for equation development) and Group B (cross-validation group) (n = 175 each) of randomly selected healthy adult women aged 30–60 years. TBFP (%) was quantified with total body DXA (TBFPDXA). Height (m), weight (kg), and skinfold thickness (SFT, mm) at six sites and circumferences (cm) at five sites were measured. In the first step, four anthropometric equations were developed based on the data obtained from multiple regression analyses (TBFPDXA = dependent variable and anthropometric measurements and age = independent variables) with Group A. They were developed on the basis of circumferences (TBFP1), SFTs (TBFP2), circumferences and SFTs (TBFP3), and highly significant circumferences and SFTs (r ≥ 0.6) (TBFP4). In the second step, the newly developed equations were cross-validated using Group B. Three equations (TBFP1, TBFP2, and TBFP4) showed the agreement with cross-validation criteria. There were no differences between TBFPDXA and TBFP estimated by these equations (p>0.05). They showed higher measurement concordance with TBFPDXA; correlation between measured TBFP with DXA and estimated with TBFP1, TBFP2, and TBFP4, respectively, was 0.80 (R2 = 0.65, SEE = 3.10), 0.83 (R2 = 0.69, SEE = 2.93), and 0.84 (R2 = 0.72, SEE = 2.78). Three anthropometric measurements based on predictive equations were developed and cross-validated to satisfactorily estimate the TBFP in adult women.

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