Predict Percent Body Fat Research Articles

Predicted body fat percentage, fat mass and lean body mass in relation to risk of prostate cancer: Results from the NHANES 1999 to 2010

Predicted body fat percentage, fat mass and lean body mass in relation to risk of prostate cancer: Results from the NHANES 1999 to 2010

Associations of body adiposity index, body mass index, waist circumference, and percentage of body fat in young female Emirati adults.

Body Adiposity Index (BAI), which relies on an individual's hip circumference and height, was proposed as an alternative anthropometric measurement to Body Mass Index (BMI). Although this measure has been validated across different populations, its accuracy in predicting percent body fat (%BF) in the United Arab Emirates has not yet been assessed. The objective of this study was to examine the association between BAI, BMI, Waist Circumference (WC), and %BF in young female Emirati adults and determine the relative accuracy of BAI when predicting %BF. A retrospective cross-sectional study was conducted among 95 Emirati women between the ages of 17 and 27. The %BF was measured using the dual-energy X-ray absorption (DXA) scanner. Anthropometric measurements were collected, and BMI and BAI were calculated. BMI and %BF (r = 0.823, p <0.001) showed a greater association than that between BAI and %BF (r = 0.702, p <0.001). A linear regression analysis revealed that BMI was the single best predictor of %BF in the sample (r2 = 0.678, p<0.001). The variation around the regression line for BAI comparisons with %BF (standard error of estimate = 4.879) was greater than BMI comparisons (standard error of estimate = 3.889). BAI was found to significantly underestimate %BF at higher adiposity levels (mean difference = 8.7%). The ROC curve analysis demonstrated that BMI had a higher discriminatory capacity (AUC = 0.891) over WC and BAI. The results demonstrated that BMI was a better predictor of %BF in the sample than BAI and WC. Thus, BMI may be more useful in assessing adiposity in young female Emirati adults than BAI. However, the potential of BAI as an alternative measure of adiposity should continue to be examined.

Heterogeneous body compositions and all-cause mortality in acute coronary syndrome patients: a ten-year retrospective cohort study.

The association of different body components, including lean mass and body fat, with the risk of death in acute coronary syndrome (ACS) patients are unclear. We enrolled adults diagnosed with ACS at our center between January 2011 and December 2012 and obtained follow-up outcomes via telephone questionnaires. We used restricted cubic splines (RCS) with the Cox proportional hazards model to analyze the associations between body mass index (BMI), predicted lean mass index (LMI), predicted body fat percentage (BF), and the value of LMI/BF with 10-year mortality. We also examined the secondary outcome of death during hospitalization. During the maximum 10-year follow-up of 1398 patients, 331 deaths (23.6%) occurred, and a U-shaped relationship was found between BMI and death risk (P nonlinearity = 0.03). After adjusting for age and history of diabetes, the overweight group (24 ≤ BMI < 28 kg/m2) had the lowest mortality (HR = 0.53, 95% CI: 0.29-0.99). Predicted LMI and LMI/BF had an inverse linear relationship with a 10-year death risk (P nonlinearity = 0.24 and P nonlinearity = 0.38, respectively), while an increase in BF was associated with increased mortality (P nonlinearity = 0.64). During hospitalization, 31 deaths (2.2%) were recorded, and the associations of the indicators with in-hospital mortality were consistent with the long-term outcome analyses. Our study provides new insight into the "obesity paradox" in ACS patients, highlighting the importance of considering body composition heterogeneity. Predicted LMI and BF may serve as useful tools for assessing nutritional status and predicting the prognosis of ACS, based on their linear associations with all-cause mortality.

The validity of predicted body fat percentage from body mass index and from impedance in samples of five European populations

The validity of predicted body fat percentage from body mass index and from impedance in samples of five European populations

Predicting body fat percentage at 36 weeks of postmenstrual age in infants born preterm: A diagnostic accuracy study.

Current standards for assessing body composition can be costly and technically challenging. There is a need for a predictive equation that combines multiple clinical and anthropometric factors to predictbody composition outcomes at 36 weeks of postmenstrual age (PMA) or discharge. To develop a widely applicable equation that predicts body fat percentage in preterm infants, we analyzed anthropometric data collected prospectively from a cohort of infants born very preterm between 2017 and 2018. We integrated clinical variables significantly associated with adiposity into a predictive equation using Bayesian linear regression models and leave-one-out cross-validation. We analyzed data from 86 infants born at 32 weeks of gestation or less (median gestational age, 30 weeks; mean birthweight, 1471 ± 270 g). Weight gain and increase in length per week from birth to 36 weeks of PMA, midarm circumference at 36 weeks of PMA, male sex, and higher enteral fluid intake (>180 ml/kg/day) were the strongest predictors of body fat percentage in the model with the highest predictive value (R2 = 0.65). The correlation between actual and predicted body fat percentage using this Bayesian model was high (r = 0.82). Weight gain and increase in length per week from birth to 36 weeks of PMA, midarm circumference at 36 weeks of PMA, male sex, and enteral fluid intake are significant predictors of body fat percentage at 36 weeks of PMA in very preterm infants.

Predicting Body Fat Percentage from Anthropometric Measurements in Asian Athletes

Predicting Body Fat Percentage from Anthropometric Measurements in Asian Athletes

Using A-Mode Ultrasound to Assess the Body Composition of Soccer Players: A Comparative Study of Prediction Formulas.

For elite athletes, monitoring body composition is important for maximizing performance without health risks. Amplitude (A)-mode ultrasound (AUS) has attracted increasing attention as an alternative to skinfold thickness measurements commonly used for assessing the amount of body fat in athletes. AUS accuracy and precision, however, depend on the formula used to predict body fat percentage (%BF) from subcutaneous fat layer thicknesses. Therefore, this study evaluates the accuracy of the 1-point biceps (B1), 9-sites Parrillo, 3-sites Jackson and Pollock (JP3), and 7-sites Jackson and Pollock (JP7) formulas. Relying on the previous validation of the JP3 formula in college-aged male athletes, we took AUS measurements in 54 professional soccer players (aged 22.9 ± 3.83 y, mean ± SD) and compared the results given by different formulas. The Kruskal-Wallis test indicated significant differences (p < 10-6), and Conover's post hoc test revealed that the JP3 and JP7 data come from the same distribution, whereas the data given by B1 and P9 differ from all the others. Lin's concordance correlation coefficients for B1 vs. JP7, P9 vs. JP7, and JP3 vs. JP7 were 0.464, 0.341, and 0.909, respectively. The Bland-Altman analysis indicated mean differences of -0.5 %BF between JP3 and JP7, 4.7 %BF between P9 and JP7, and 3.1 %BF between B1 and JP7. This study suggests that JP7 and JP3 are equally valid, whereas P9 and B1 overestimate %BF in athletes.

Predicting body fat using a novel fuzzy-weighted approach optimized by the whale optimization algorithm

Obesity, termed as excessive body fat, is a major public health problem. Being able to accurately predict body fat percentage provides an opportunity for making appropriate prevention and diagnosis. Although various assessment techniques have been applied for body fat estimation, high-cost tests are required using special equipment, and the performance of standard machine learning models are typically impacted by noise data. In this paper, we propose a novel fuzzy-weighted approach for improving body fat prediction using easily accessed body measurements in a cost-effective manner. Given the noise data problem, we design a novel fuzzy weighted operation by extending the minimum and maximum values for calculating the fuzzy weights to cover more possibilities. The fuzzy weights are incorporated into the error constraints of a relative error support vector machine model to improve its robustness to noise data. In addition, the Whale Optimization Algorithm (WOA) is applied in our proposed model for parameter optimization. Our proposed fuzzy-weighted approach delivers excellent results for body fat prediction by outperforming other well-known machine learning models. Further, the statistical test based on the 20 runs of experiments not only confirms that our proposed model could significantly outperform the models under comparison, but also show the fuzzy weighted operation and WOA are effective to improve the proposed approach. This offers a viable alternative for medical decision makers to make appropriate prevention, early diagnosis, and early treatment of body fat related diseases when high-cost measurement techniques are not available.

Which anthropometric equation to predict body fat percentage is more strongly associated with maximum oxygen uptake in adolescents? A cross-sectional study.

Identifying the relationship between maximum consumption of oxygen and body fat percentage is important due to increased cardiovascular risk factors. This study aimed to verify the association between body fat percentage determined by three predictive equations using anthropometric measures (Lohman, Boileau, and Slaughter) and maximum oxygen uptake (VO2max). We also aimed to estimate the capacity of these equations for explaining VO2max variations in adolescents according to sex. This was a cross-sectional study conducted in high schools in São José, Southern Brazil. This study included 879 adolescents (14-19 years) from Southern Brazil. Aerobic fitness was assessed using the modified Canadian Aerobic Fitness Test. The independent variable was body fat percentage predicted by the Lohman, Boileau, and Slaughter equations. Analyses adjusted for sociodemographic variables, physical activity level, and sexual maturation were performed with P value < 0.05. All anthropometric prediction equations used to estimate body fat percentage explained VO2max variations in adolescents. In male adolescents, both regression models based on the Boileau et al.12 and Lohman10 equations revealed higher explanatory power for VO2max (20%) compared with that based on the Slaughter et al.13 equation (19%). In female adolescents, the model based on the anthropometric equation of Slaughter et al.13 showed the greatest explanatory power for VO2max (18%). The inverse relationship between VO2max and body fat intensifies the need for effective intervention programs that prioritize maintenance of appropriate body fat and aerobic fitness levels because inadequate levels of both factors result in negative health consequences.

Body fat percentage assessment using skinfold thickness agrees with measures obtained by DXA scan in African American and Caucasian American women

Dual-energy x-ray absorptiometry (DXA) is considered to have high accuracy in estimating fat mass; however, DXA is not always available. We hypothesized that the equations most commonly used for predicting body fat percentage (BF%) using skinfold thickness agree with direct measures of BF% obtained by DXA scan in African American (AA) and Caucasian American (CA) women. Data from 42 women from Alabama who were 21 to 45 years of age, who self-identify as AA (n=20) or CA (n=22) were included. BF% was estimated using DXA scan and through 6 different skinfold thickness equations. Agreement between DXA-BF% and BF% based on the skinfold thickness equations was assessed following the Bland-Altman method (bias and agreement limits). Agreement analysis showed in both AA and CA women that the BF%-Siri equation reflects better agreement and lower mean differences (bias) with BF%-DXA than the BF%-Brozek equation after applying 4 body density (BD) equations. Limits showed that BF%-Siri and BF%-Brozek predictive equations overestimate BF% compared with DXA-BF% in both AA and CA women. In AAs, equations that overestimated less were Wilmore and Behnke-Siri (by 1.81%) and Durnin and Womersley-Siri (by 2.5%) equations. Regarding CAs, equations that overestimated less were Durnin and Womersley-Siri (by 2.74%) and Wilmore and Behnke-Siri (by 3.11%) equations. The results of this study show that the BF%-Siri equation is a more accurate alternative than the BF%-Brozek equation for the calculation of BF%. In the calculation of BD, the Wilmore and Behnke equation in AA women and Durnin and Womersley in CA women were those that overestimated BF% to a lesser degree.

Using bioelectrical impedance analysis for modeling regression and predicting body fat accumulation in dogs in East Thailand.

Background and Aim:Obesity in dogs leads to several health problems, such as premature death, and contributes to other diseases. Recently, body fat percentage has been considered to represent the body condition of dogs, and bioelectrical impedance analysis (BIA) is the most effective method for accurately measuring body fat in dogs. In Thailand, information on the body condition of dogs is limited, and there is no standard body fat level for Thai or mongrel dogs. This study was designed to evaluate and analyze the body fat percentage in dogs through BIA using a handheld instrument. The results of this study can help enhance the quality of life and health of dogs and aid in setting a standard body fat level for Thai or mongrel dogs.Materials and Methods:The body fat percentage of 340 Thai and mongrel dogs in East Thailand was measured in the standing position, and the body condition score (BCS) (range, 1–5), sex, sterilization status, age, type of diet, and lifestyle were recorded. A linear regression model was developed to compare the variables and the predicted body fat percentage, and multiple linear regressions were used to analyze the factors for body fat increment.Results:The linear regression model used to estimate the percentage of body fat (y) for each BCS (x) was y = 0.84 + 8.36x (R2 = 0.7219; p < 0.0001); the average body fat percentage was 27.52% for all studied dogs; specifically, 24.83% for the Thai Bangkaew, 26.42% for the Thai Ridgeback, and 27.65% for mongrels. The median body fat percentage was significantly higher in female than in male dogs. We found that as age increases, body fat percentage also increases; this increasing trend begins at the age of 5 years. However, increasing the level of activity and decreasing meal frequency leads to an increase in body fat percentage in neutered male dogs.Conclusion:The average body fat percentage of dogs in East Thailand is 27.52% and this value is expected to increase when these dogs reach the age of 5 years. BIA is a valid and effective measurement tool for detecting the body fat percentage in dogs.

Tri-ponderal mass index and body mass index for estimating body fat in children in Surakarta, Indonesia

Background: The body mass index parameter is currently used to determine obesity (excess body fat) in children. Using this measure has limitations in predicting body fat, particularly in adolescents. An alternative parameter to predict body fat is the tri-ponderal mass index (TMI).Objective: The study aims to investigate the different patterns between body mass index and triponderal mass index in children and to determine the correlation of body mass index (BMI) or TMI as a predictor of body fat.Methods: This cross-sectional study of 351 subjects aged 6-18 in Surakarta (241 females; 110 males). All subjects underwent anthropometric measurements (height and weight) and body fat percentage measurements. We used equations for predicting the percent body fat in boys and girls based on their TMI and BMI.Results: TMI and BMI overall means were 13.3±2.9 vs 20.2±4.7. Among subjects less and more than 12 years, the TMI and BMI means were 13.3±2.1 vs 17.9±3.6. and 13.3±3.1 vs 20.7±4.8. The adjusted R2 of equations for predicting percent body fat based TMI and BMI were 0.76 vs 0.33 in boys and 0.89 vs 0.87 in girls).Conclusions: TMI showed greater stability with age than BMI. TMI was a better predictor for body fat percentage than BMI for both sexes, especially in boys. It is worth considering replacing body mass index with triponderal mass index to estimate body fat percentage in boys.

Generalized Equations for Predicting Percent Body Fat from Anthropometric Measures Using a Criterion Five-Compartment Model.

Anthropometric-based equations are used to estimate percent body fat (%BF) when laboratory methods are impractical or not available. However, because these equations are often derived from two-compartment models, they are prone to error because of the assumptions regarding fat-free mass composition. The purpose of this study was to develop a new anthropometric-based equation for the prediction of %BF, using a five-compartment (5C) model as the criterion measure. A sample of healthy adults (52.2% female; age, 18 to 69 yr; body mass index, 15.7 to 49.5 kg·m-2) completed hydrostatic weighing, dual-energy x-ray absorptiometry, and bioimpedance spectroscopy measurements for calculation of 5C %BF (%BF5C), as well as skinfolds and circumferences. %BF5C was regressed on anthropometric measures using hierarchical variable selection in a random sample of subjects (n = 279). The resulting equation was cross-validated in the remaining participants (n = 78). New model performance was also compared with several common anthropometric-based equations. The new equation [%BFNew = 6.083 + (0.143 × SSnew) - (12.058 × sex) - (0.150 × age) - (0.233 × body mass index) + (0.256 × waist) + (0.162 × sex × age)] explained a significant proportion of variance in %BF5C (R2 = 0.775, SEE = 4.0%). Predictors included sum of skinfolds (SSnew, midaxillary, triceps, and thigh) and waist circumference. The new equation cross-validated well against %BF5C when compared with other existing equations, producing a large intraclass correlation coefficient (0.90), small mean bias and limits of agreement (0.4% ± 8.6%), and small measures of error (SEE = 2.5%). %BFNew improved on previous anthropometric-based equations, providing better overall agreement and less error in %BF estimation. The equation described in this study may provide an accurate estimate of %BF5C in healthy adults when measurement is not practical.

Fat mass prediction equations and reference ranges for Saudi Arabian Children aged 8-12 years using machine technique method.

BackgroundThe number of children with obesity has increased in Saudi Arabia, which is a significant public health concern. Early diagnosis of childhood obesity and screening of the prevalence is needed using a simple in situ method. This study aims to generate statistical equations to predict body fat percentage (BF%) for Saudi children by employing machine learning technology and to establish gender and age-specific body fat reference range.MethodsData was combined from two cross-sectional studies conducted in Saudi Arabia for 1,292 boys and girls aged 8–12 years. Body fat was measured in both studies using bio-electrical impedance analysis devices. Height and weight were measured and body mass index was calculated and classified according to CDC 2,000 charts. A total of 603 girls and 374 boys were randomly selected for the learning phase, and 153 girls and 93 boys were employed in the validation set. Analyses of different machine learning methods showed that an accurate, sensitive model could be created. Two regression models were trained and fitted with the construction samples and validated. Gradient boosting algorithm was employed to achieve a better estimation and produce the equations, then the root means squared error (RMSE) equation was performed to decrease the error. Body fat reference ranges were derived for children aged 8–12 years.ResultsFor the gradient boosting models, the predicted fat percentage values were more aligned with the true value than those in regression models. Gradient boosting achieved better performance than the regression equation as it combined multiple simple models into a single composite model to take advantage of that weak classifier. The developed predictive model archived RMSE of 3.12 for girls and 2.48 boys. BF% and Fat mass index charts were presented in which cut-offs for 5th, 75th and 95th centiles are used to define ‘under-fat’, ‘normal’, ‘overfat’ and ‘subject with obesity’.ConclusionMachine learning models could represent a significant advancement for investigators studying adiposity-related issues in children. These models and newly developed centile charts could be useful tools for the estimation and classification of BF%.

Assessment of a proposed BMI formula in predicting body fat percentage among Filipino young adults

Body mass index (BMI), while routinely used in evaluating adiposity, cannot distinguish between fat and lean mass, and thus can misclassify weight status particularly among athletic, physically active, and tall- and short-statured individuals, whose lean-to-fat ratios and body proportions vary considerably from average individuals. Believing that the traditional BMI formula divides weight by too much with short people and by too little with tall people, University of Oxford professor L. N. Trefethen proposed a modified formula in computing BMI. This study was conducted among a sample of Filipino young adults (n = 190) to assess the performance of the modified BMI formula against the traditional one in: (1) predicting body fat percentage (%BF) measured using bioelectric impedance analysis, and (2) diagnosing overweight/obesity. Using robust polynomial regression analysis (covariates: age, waist circumference, smoking history and alcohol intake), the BMI quadratic models had the highest adjusted R2 and the lowest AIC and BIC for both sexes compared to the linear models. The AuROCs of the traditional BMI were higher than those of the proposed BMI, albeit nonsignificant. In conclusion, both traditional and modified BMIs significantly predicted %BF, as well as adequately discriminated between %BF-defined normal and overweight-obese states using optimal BMI cutoff values.

The nutritional status of people with alkaptonuria: An exploratory analysis suggests a protein/energy dilemma.

BackgroundAlkaptonuria (AKU) is a disorder of tyrosine/protein metabolism leading to accumulation of homogentisic acid. Clinical management historically recommended reducing dietary protein intake, especially in childhood, which has since been discredited in the literature. For the first time, analysis of baseline cross‐sectional nutritional surveillance data from a large cohort of AKU patients is presented, which has clinical implications in all aspects of treatment planning.MethodSeventy‐four patients (mean 55 years) admitted to the National Alkaptonuria Centre (NAC), underwent a global nutritional assessment, which included objective anthropometry, bioimpedance measures, habitual nutritional intake using a 7‐day food diary and key nutritional biomarkers, including 24 hours urinary nitrogen, serum albumin, total protein and total 25‐hydroxy vitamin D. All data was compared with cohort norms or recommended nutrient intakes for health (RNI). The potential beneficial impact of protein and anti‐inflammatory nutrients such as vitamin C, selenium, and zinc were statistically interrogated against the AKU severity score index (AKUSSI)—a validated measure of disease progression stratified by age.ResultsFifty percent of AKU patients reported some level of protein restriction at some point in their lives. In comparison with national data sets, AKU patients present with significantly lower than predicted mid‐upper arm circumference, grip strength, BMI, total energy and protein intake, and higher than predicted percentage body fat. They therefore meet the ESPEN criteria as “clinically undernourished.” Severity fluctuates over the life course. No statistical association is identified between protein intake, expressed as %RNI or g/kg, or anti‐inflammatory nutrients, including vitamin C as a high dose supplement on the severity of the disease, when correlated against the validated AKUSSI score.ConclusionAKU patients are at risk of protein depletion associated with a “perfect storm” of risk factors: historical, poorly evidenced recommendations to reduce total protein intake; limited mobility as the condition progresses, compromising muscle integrity; frequent hospital admissions for major surgery associated with multiple joint replacements, creating pinch points of high metabolic demand and the potential impact of the disease itself. As this is the first time this risk has been identified, the authors consider the dietetic implications of nitisinone treatment, which requires dietary protein control to manage the acquired tyrosinaemia. The lack of statistically significant evidence to support dietary manipulation of any kind to impede disease progression in AKU is demonstrated.

A new anthropometric index to predict percent body fat in young adults.

To propose a new anthropometric index that can be employed to better predict percent body fat (PBF) among young adults and to compare with current anthropometric indices. Cross-sectional. All measurements were taken in a controlled laboratory setting in Seoul (South Korea), between 1 December 2015 and 30 June 2016. Eighty-seven young adults (18-35 years) who underwent dual-energy x-ray absorptiometry (DXA) were used for analysis. Multiple regression analyses were conducted to develop a body fat index (BFI) using simple demographic and anthropometric information. Correlations of DXA measured PBF (DXA_PBF) with previously developed anthropometric indices and the BFI were analysed. Receiver operating characteristic curve analyses were conducted to compare the ability of anthropometric indices to identify obese individuals. BFI showed a strong correlation with DXA_PBF (r = 0·84), which was higher than the correlations of DXA_PBF with the traditional (waist circumference, r = 0·49; waist to height ratio, r = 0·68; BMI, r = 0·36) and alternate anthropometric indices (a body shape index, r = 0·47; body roundness index, r = 0·68; body adiposity index, r = 0·70). Moreover, the BFI showed higher accuracy at identifying obese individuals (area under the curve (AUC) = 0·91), compared with the other anthropometric indices (AUC = 0·71-0·86). The BFI can accurately predict DXA_PBF in young adults, using simple demographic and anthropometric information that are commonly available in research and clinical settings. However, larger representative studies are required to build on our findings.

Development and validation of body fat prediction models in American adults.

SummaryIntroductionCommonly used statistical models to predict body fat percentage currently rely on skinfold measures, anthropometric measures, or some combination of the two but do not account for the wide ranges of age and body mass index (BMI) present in the American adult population. The objective of this study was to develop a statistical regression model to predict in vivo body fat percentage (dual energy X‐ray) in men and women across significant age and obesity ranges.MethodsThis study included 228 adults between the ages of 21 and 70, with BMI between 18.5 and 40.0 kg m−2. The study population was split into training (n = 163) and validation (n = 65) groups, which were used to develop and validate the prediction models. The models were developed on the training group using a backwards stepwise regression analysis, with the initial predictors including age, BMI, and several anthropometric and skinfold measurements.ResultsThe final statistical regression models included age, BMI, anthropometric measures, and skinfold measures with significant effects following the stepwise process. The models predicted body fat percentage in the testing group with average errors of less than 0.10% body fat in males and females, while the four previously existing methods (Durnin, Hodgdon, Jackson, and Woolcott) significantly underestimated or overestimated body fat in both genders, with errors ranging between 2% and 10%.ConclusionsThe final models included hand thickness, and the female model was dependent on waist circumference and two of the skinfold measures, while the male model used hip and thigh circumferences, along with three skinfold measures. By including the skinfold measurements separately, instead of only as sums like previous models have done, these models can account for the different relative contributions of each site to total body fat.

The influence of Body Roundness Index on sensorial block level of spinal anaesthesia for elective caesarean section: an observational study

In this prospective trial, we investigate the effectiveness of maternal Body Roundness Index in predicting the spread of spinal anaesthesia and vasopressor requirement in parturients receiving spinal anaesthesia during the elective caesarean section. We prospectively enrolled 175 parturients. Spinal anaesthesia performed with 10 mg 0.5% hyperbaric bupivacaine at the L3–L4 intervertebral space and the optimal cut-off points of the BRI evaluated as 6.59 by receiver operating characteristic analysis calculating area under the curve. Parturients were divided into two groups with BRI <6.59 and BRI ≥6.59 for analyses. Multivariate logistic regression analysis was used to test for a relationship between variables and maximum sensory block level and vasopressor requirement. BRI was found as an independent risk factor associated with maximum sensory block level (OR = 1.378, 95% CI: 1.125–1.687, p = 0.002). Hypotension and bradycardia events after spinal anaesthesia was not associated with BRI and other variables. The present study indicates that BRI was a practical tool to predict spinal drug distribution in term parturients undergoing caesarean delivery.Impact statementWhat is already known on this subject? Spinal anaesthesia is a commonly used anaesthetic technique for the caesarean section. However, the spinal drug distribution is highly unpredictable. Anthropometric variables may predict the intrathecal drug distribution in parturients. Body Roundness Index (BRI) captures body circumference regarding height to predict body fat percentage, consider the shape of the human body as an ellipse. An ellipsoid body shape might affect the spread of spinal anaesthesia.What do the results of this study add? Our results show that the BRI was as an independent risk factor associated with maximum sensory block level in term parturients undergoing caesarean delivery.What are the implications of these findings for future clinical practice and/or further research? A future study would present the possibility to design a formula for the exact amount of local anaesthetic to be used in spinal anaesthesia with the aid of maternal BRI.

Long-term status of predicted body fat percentage, body mass index and other anthropometric factors with risk of colorectal carcinoma: Two large prospective cohort studies in the US.

Anthropometric measurements, such as body mass index (BMI), waist circumference, and body fat percentage, have been used as indicators of obesity. Despite evidence that excess body fat is a risk factor for colorectal carcinoma (CRC), the magnitude of the association of BMI and other obesity indicators with the long-term risk of CRC remains unclear. Utilizing a Cox proportional hazards regression model, we examined differential associations between predicted body fat percentage and BMI with the risk of CRC (n = 2,017). The associations between CRC incidence and different adiposity measurements were also assessed. Predicted body fat percentage had a similar increased risk of CRC risk as BMI. In multivariable-adjusted analyses, the hazard ratio for CRC in the second to fifth quintiles (compared to the first quintile) of predicted body fat percentage were 1.32, 1.31, 1.53 and 2.09 for men (ptrend < 0.001) and 0.91, 0.90, 0.98 and 1.15 for women (ptrend = 0.03). Among various anthropometric measurements, predicted fat mass and waist circumference were slightly more strongly associated with CRC risk than BMI. In conclusion, the novel anthropometric prediction equations provided further evidence that a greater amount of body fat might contribute to CRC risk in both sexes. An innovative approach enabled us to estimate the susceptibilities of specific body composition with CRC risk, in an inexpensive and minimally invasive manner. Furthermore, the typically used measures of BMI and waist circumference are robust measures of adiposity to predict cancer risk in a relatively healthy population.