Anthropometric Equations Research Articles

Anthropometric equation has sufficient diagnostic capacity to identify sarcopenia in women with breast cancer

BackgroundSarcopenia is a common condition in women with breast cancer, however still presents limitations for an effective diagnosis. This study aimed to evaluate the agreement and diagnostic accuracy of an anthropometric equation in diagnosing sarcopenia in women with breast cancer based on different constructs.MethodsCross-sectional study carried out with women with breast cancer aged ≥ 20 years. Sarcopenia was identified according to the European Working Group on Sarcopenia in Older People 2 (EWGSOP2) criteria. Muscle strength was obtained by the handgrip strength test (HGS) and muscle mass (MM) by dual-energy x-ray absorptiometry (DXA) and by the anthropometric predictive equation. For the diagnosis of sarcopenia, eight constructs were proposed based on the MM assessment method (equation or DXA) and different cutoff points. Agreement analyses using Cohen’s Kappa test and diagnostic performance measures were performed. The significance level for all tests was 5%.ResultsA total of 122 women, with a mean age of 55.3 ± 11.4 years, were evaluated. There was a predominance of brown participants (50.8%), insufficiently active (57.4%), with diagnosis time ≤ 3 months (54.1%), and with invasive breast carcinoma (69.7%). The prevalence of sarcopenia ranged from 3.3 to 8.2%, depending on the construct used. The constructs determined from the cutoff points < 16.0 kg/< 7.58 m² and < 23.0 kg/< 7.58 m² were the ones that showed the best ability to detect sarcopenia.ConclusionThe anthropometric equation showed sufficient diagnostic capacity to be used as an alternative in identifying sarcopenia in women with breast cancer.

Development and validation of a new anthropometric equation to predict fat mass percentage in a white Spanish population.

(1) To develop a new regression equation for estimating fat mass percentage (%FM) from anthropometric measurements in a heterogeneous Caucasian population and (2) to compare it with the Durnin and Womersley equation, which is one of the most used anthropometric equations for fat mass assessment. Body mass, stature, and four skinfolds (biceps, triceps, subscapular, and supracrestal) were assessed by an accredited anthropometrist, according to the International Society for Advancement in Kinanthropometry. Participants completed a dual-energy X-ray absorptiometry (DXA) whole-body scan to determine their %FM. A new anthropometric equation to estimate %FM was developed using multiple forward regression analyses with DXA as the reference method. Tests for the accuracy of the different equations included mean differences, coefficient of determination, standard error of the estimate (SEE), concordance correlation coefficient (CCC), and Bland-Altman plots. Spain. Two hundred and eighteen healthy Caucasian participants aged 18-65 years participated in this cross-sectional study. Our proposed equation explained 89.9% of the variance in the DXA-derived %FM, with a low random error (SEE = 3.00%), a very strong agreement (CCC = 0.93), no fixed or proportional bias, and a relatively low individual variability (5.84%). However, the Durnin and Womersley equations obtained a fixed bias of -3.65% when compared to DXA and a greater individual variability (6.74%). The proposed equation can accurately estimate %FM in a heterogeneous Caucasian population with a wide age range (18-65 years). Additionally, the Durnin and Womersley equation was inadequate when applied to our participants.

Forensic alcohol calculations in individuals under the age of 18.

Alcohol calculations are carried out in many forensic case types. On occasion, individuals under the age of 18 are involved, and alcohol calculations may be beneficial. To date, there are no studies that have determined the best way to estimate total body water (TBW) or alcohol elimination rates in juveniles for alcohol calculations. We utilized a data set of 207 females and 218 males, aged 3-18 years, from a variety of ethnic backgrounds, where TBW had been empirically measured, to evaluate the best anthropometric equation to use to estimate TBW in juveniles. For males aged 3-15 years and females aged 2-13 years, the equation of Wells etal. was the most accurate and precise (RMSE of ≤10.4% in males and ≤9.9% in females). For males aged 16+ years and females aged 14+ years, the equation of Watson etal. was more appropriate (RMSE ≤11.5% and ≤12.4%, respectively). Based on published studies where the alcohol elimination rate was determined in 43 juveniles (aged 10-17 years) who were hospitalized due to alcohol consumption, amean alcohol elimination rate of 16 mg/100 mL/h should be used. Therecommended range being 9-25 mg/100 mL (5th-95th percentile). This study provides evidence that there are valid anthropometric equations to determine TBW and alcohol elimination rates that can be used for alcohol calculations in juveniles between the ages of 10 and 17.

Anthropometric and Body Composition Changes during Pre-Season of Spanish Professional Female Soccer Players According to Playing Position.

In professional soccer, body composition analysis is crucial to assess preparation and optimize performance. Different playing positions have different physical demands, which can lead to variations in body composition. However, there are few studies on women's soccer that consider the playing position. This study aims to fill that gap by examining position-specific differences in anthropometric and body composition characteristics among Spanish professional female soccer players at the beginning and end of pre-season. Furthermore, it investigates the possible changes during the pre-season period between positions and correlates the data obtained from anthropometric equations with bioimpedance (BIA) measurements. Thirty-four female soccer players: 8 midfielders, 12 defenders, 11 forwards, and 3 goalkeepers (age: 23.06 ± 4.29 years, height: 164.15 ± 5.84 cm, weight: 58.39 ± 6.62 kg, and ∑6 skinfolds: 74.57 ± 18.48 mm) completed the study that lasted 4 weeks (pre-season) where they were measured anthropometrically and by bioimpedance twice. Goalkeepers showed greater wingspan (176.60 ± 7.06 p < 0.05) compared to other positions. Regarding differences during pre-season, midfielders had the greatest decrease in ∑6 skinfolds compared to other positions (∆ -12.10 ± 5.69 p < 0.05). There was a correlation of % fat between Faulkner's equation and BIA (Pearson's r = 0.817). It seems that there are no significant differences in terms of positions and body composition, except for the wingspan and ankle diameter. During pre-season, midfielders are the ones who improve their body composition the greatest. The anthropometric equation for body fat that shows the highest correlation with BIA is Faulkner's equation, followed by Durnin's equation.

Neural network model for prediction of possible sarcopenic obesity using Korean national fitness award data (2010–2023)

Sarcopenic obesity (SO) is characterized by concomitant sarcopenia and obesity and presents a high risk of disability, morbidity, and mortality among older adults. However, predictions based on sequential neural network SO studies and the relationship between physical fitness factors and SO are lacking. This study aimed to develop a predictive model for SO in older adults by focusing on physical fitness factors. A comprehensive dataset of older Korean adults participating in national fitness programs was analyzed using sequential neural networks. Appendicular skeletal muscle/body weight was defined as SO using an anthropometric equation. Independent variables included body fat (BF, %), waist circumference, systolic and diastolic blood pressure, and various physical fitness factors. The dependent variable was a binary outcome (possible SO vs normal). We analyzed hyperparameter tuning and stratified K-fold validation to optimize a predictive model. The prevalence of SO was significantly higher in women (13.81%) than in men, highlighting sex-specific differences. The optimized neural network model and Shapley Additive Explanations analysis demonstrated a high validation accuracy of 93.1%, with BF% and absolute grip strength emerging as the most influential predictors of SO. This study presents a highly accurate predictive model for SO in older adults, emphasizing the critical roles of BF% and absolute grip strength. We identified BF, absolute grip strength, and sit-and-reach as key SO predictors. Our findings underscore the sex-specific nature of SO and the importance of physical fitness factors in its prediction.

Prediction of body fat percentage: development and validation of new anthropometric equations

Prediction of body fat percentage: development and validation of new anthropometric equations

The Negative Impacts of Sarcopenia on Primary Total Knee Arthroplasty under the Enhanced Recovery after Surgery Protocol.

Sarcopenia, as an emerging public health concern, has been associated with postoperative adverse outcomes in various surgical procedures. However, the evidence regarding the impacts of sarcopenia on total knee arthroplasty (TKA) remained limited. This study aimed to assess the impacts of sarcopenia on primary TKA based on the enhanced recovery after surgery (ERAS) protocol. This retrospective study included 291 patients who received unilateral TKA from October 2017 to May 2018 in our institution. Sarcopenia was diagnosed based on the algorithm of Asian Working Group for Sarcopenia 2019. The handgrip strength was measured using a handheld dynamometer and the muscle mass was estimated by a previously validated anthropometric equation. Patients were classified into sarcopenia group and non-sarcopenia group. The outcomes included complications, postoperative length of stay (LOS), total hospitalization cost, operative time, total estimated blood loss, blood transfusion rate, and the 12-item forgotten joint score (FJS-12) at the follow-up. The propensity score matching (PSM) was used to adjust confounding factors. We compared continuous variables using Student's t-test and the Wilcoxon Mann-Whitney U test for normal and non-normal distributions, respectively, and categorical variables with chi-square tests. Of the 291 patients, 58 (19.9%) patients were identified as having sarcopenia. After PSM, each group matched 42 patients. All matched patients were followed-up at least 5 years. Patients with sarcopenia had higher rates of surgical complications compared to the non-sarcopenia group (p = 0.019), and no significant difference was observed in 30-day readmission, and periprosthetic joint infection. The sarcopenia group had significantly longer LOS (p = 0.038), higher total hospitalization (p = 0.015) than the non-sarcopenia group. For the FJS-12 scores at follow-up, patients with sarcopenia had significantly higher scores than the non-sarcopenia group (p = 0.024). Our findings indicated sarcopenia may be a risk factor for postoperative complications, prolonged LOS, increased hospitalization cost and reduced patient satisfaction.

Skeletal muscle mass obtained by anthropometric equation and presence of sarcopenia in postmenopausal women.

To analyze the amount of muscle and the presence of sarcopenia in postmenopausal women using different methods, verifying the agreement between them as to skeletal muscle mass (SMM). This cross-sectional observational study was conducted with postmenopausal women aged ≥ 50 years. SMM was obtained from a predictive equation, Bioelectrical Impedance (BIA), and Dual Energy X-Ray Absorptiometry (DXA). The skeletal muscle mass index (SMI) and the appendicular skeletal muscle mass index (ASMI) were calculated. The cut-off point of SMI was determined for the population itself. The agreement between the SMI obtained using the different methods was verified. Sarcopenia was diagnosed according to the criteria proposed by the European Working Group on Sarcopenia in Older People 2 (EWGSOP2). The significance level adopted for all tests was 5.0%. A total of 112 women were evaluated, with an average age of 66.1 ± 5.65 years. Among them, 51.8% were sufficiently active and 43.8% were overweight and obese. The SMI cut-offs were 6.46 kg/m2 for the predictive equation and 7.66 kg/m2 for BIA, with high sensitivity and specificity. There was an excellent agreement in the identification of SMM by the predictive equation (0.89 [0.824-0.917], p < 0.001) and BIA (0.92 [0.883-0.945], p < 0.001), in reference to DXA. The prevalence of sarcopenia was 0.9%, 1.8%, and 2.7% according to BIA, DXA, and the predictive equation, respectively. The predictive equation showed the expected agreement in estimating skeletal muscle mass in postmenopausal women, offering a viable and accurate alternative.

Prediction of fat-free mass from body surface area in young basketball players

BackgroundFat Free Mass (FFM) is an important and essential indicator in various sports populations, since greater muscle and bone mass generates greater strength, endurance and speed in athletes.ObjectiveThe purpose of the study was to validate Body Surface Area (BSA) as an anthropometric indicator to estimate FFM in young basketball players.MethodsA descriptive cross-sectional study was carried out in 105 male basketball players of the Brazilian Basketball Confederation of Sao Paulo (Campinas), Brazil. The age range was 11 to 15 years. Weight and height were evaluated. BSA, body mass index (BMI) and maturity status (MS) were calculated. Total body scanning was performed by dual X-ray absorptiometry (DXA). The components were extracted: Fat mass (FM), Fat free mass (FFM), percentage of fat mass (%FM) and bone mass (BM). The data were analyzed using the correlation coefficient of concordance (CCC) in terms of precision and accuracy.ResultsThree regression equations were generated: equation 1 had age and body weight as predictors [FFM= -30.059+(2.926*age)+(0.625*Weight)] (R2 = 92%, precision = 0.96 and accuracy = 0.99), equation 2 used age and BSA [FFM=-45.719+(1.934*age)+(39.388*BSA)] (R2 = 94%, precision = 0.97 and accuracy = 0.99) and equation 3 was based on APHV and BSA [FFM=-15.284+(1.765*APHV)+(37.610*(BSA)] (R2 = 94%, precision = 0.96 and accuracy = 0.99).ConclusionsThe results suggest the use of anthropometric equation using decimal age and BSA to estimate FFM in young basketball players. This new method developed can be used to design, evaluate and control training programs and monitor the weight status of athletes.

Study of the f-cell ratio using plasma dilution and albumin mass kinetics

BackgroundThe f-cell ratio of 0.91 is a conversion factor between the hematocrit measured in peripheral blood and the hematocrit obtained by separate measurements of the red blood cell mass and plasma volume. The physiological background of the f-cell ratio is unclear. MethodsData were retrieved from 155 intravenous infusion experiments where 15–25 mL/kg of crystalloid fluid diluted the blood hemoglobin and plasma albumin concentrations. The hemodilution was converted to plasma dilution using the peripheral hematocrit, and the volume of distribution of exogenous albumin was calculated in 41 volunteers who received 20 % or 5 % albumin by intravenous infusion. Finally, the kinetics of plasma albumin was studied during 98 infusion experiments with 20 % albumin. ResultsPlasma dilution based on hemoglobin and albumin showed a median difference of −0.001 and a mean difference of 0.000 (N = 2184), which demonstrates that these biomarkers indicate the same expandable vascular space. In contrast, exogenous albumin occupied a volume that was 10 % larger than the plasma volume indicated by the anthropometric equations of Nadler et al. and Retzlaff et al. The kinetic analysis identified a secondary compartment that was 450 mL in size and rapidly exchanged albumin with the circulating plasma. ConclusionsThe results suggest that the f-cell ratio is due to rapid exchange of albumin between the plasma and a non-expandable compartment located outside the circulating blood (possibly the liver sinusoids). This means that the hematocrit measured in peripheral blood correctly represents the ratio between the red cell volume and the circulating plasma volume.

Association between dietary knowledge and muscle mass in Chinese older adults: a cross-sectional and longitudinal study

ObjectivesThis study aims to explore the possible association between dietary knowledge and muscle mass in a Chinese population aged 60 years and above.DesignCross-sectional and longitudinal studies.SettingData from the 2006 and...

Sex differences in the associations between relative fat mass and all-cause and cardiovascular mortality: A population-based prospective cohort study

Background and aimsThe novel sex-specific anthropometric equation relative fat mass (RFM) is a new estimator of whole-body fat %. The study aimed to investigate the predictive role of RFM in cardiometabolic abnormalities, cardiovascular disease (CVD), all-cause and cardiovascular mortality, and explored potential sex differences. Methods and resultsThe study analyzed data from 26,754 adults in NHANES 1999–2010, with a median follow-up of 13.8 years. The correlation between RFM and body composition as well as fat distribution assessed by dual-energy X-ray absorptiometry was investigated. Weighted multivariable generalized linear models, Cox proportional hazards models and restricted cubic spline were applied to investigate the predictive role of RFM in metabolic markers, cardiovascular risk factors, CVD, all-cause and cardiovascular mortality. RFM exhibited a robust correlation with both whole-body fat % and trunk fat %. Higher RFM exhibited a stronger association with impaired glucose homeostasis, serum lipids, the incidence of hypertension, and coronary heart disease in males, while a stronger association with C-reactive protein in females. A U-shaped association between RFM and all-cause mortality was observed only in males. The hazard ratio (HR) of all-cause and cardiovascular mortality in males increased rapidly when RFM exceeded 30. However, in females, the HR of all-cause and cardiovascular mortality fluctuated until RFM exceeded 45, after which it increased rapidly. ConclusionRFM was a sex-specific estimator for both general and central obesity, sex-specific differences in predicting cardiometabolic abnormalities and adverse events using RFM might be partially attributed to differences in body composition and fat distribution between sexes.

Prediction of body fat percentage: Development and validation of new anthropometric equations

The aim of this study was to test the validity of existing equations, retrieved from the literature, in the Algerian adult population. To develop, and validate, new predictive equations for body fat percentage (%BF) using simple and easy-to-measure anthropometric parameters. This is a cross-sectional study including 877 Algerian adults who underwent a body composition assessment by the direct segmental multi-frequency bioelectrical impedance technique (Inbody-770). Participants were randomly divided into two groups: the development group (n=577) and the validation group (n=300). To develop the equations, multiple linear regression models were analyzed. The predictive performance of the developed equations was compared with the direct technique. The following validation tests were used: Student's t-test for paired samples, correlation, and Bland-Altman diagram. Diagnostic accuracy has also been assessed. Four existing equations were tested, and all showed statically significant bias. Four new equations were developed; all had satisfactory predictive performance, with a correlation coefficient ranging from 0.72 to 0.94 in men and 0.87 to 0.93 in women. The best-fitting equation was based on body mass index, waist-to-hip ratio, and chest circumference. The diagnostic accuracy of this equation was 96.7% in men and 95.3% in women. The newly developed equations based on anthropometric parameters can serve as a simple tool for the accurate prediction of BF% in adult subjects, at both individual and epidemiological levels.

Estimating fat-free mass in recreationally resistance-trained young men: Longitudinal and cross-sectional validation of different methods

Several techniques exist to measure fat-free mass (FFM). Accordingly, this study is based on data from our recent trial comparing the sensitivity of the main field methods available with that of dual-energy X-ray absorptiometry (DXA) as reference and analyzing the cross-sectional accuracy of these field methods in recreationally resistance-trained males. We hypothesized that the use of these techniques would lead to varying estimates of FFM compared with DXA. Participants (N=23; 21.4 ± 3.3 years) completed a 10-week resistance training plus diet intervention designed to optimize hypertrophy. FFM was determined by bioelectrical impedance analysis (BIA), 23 anthropometric equations, and DXA. After the intervention, FFM increased significantly according to BIA and most anthropometric estimates, but this increase was not detected by 2 anthropometric equations or by DXA. Only 1 of these 2 equations showed significant correlation with DXA and no standardized or significant differences to this reference method, although it did display significant heteroscedasticity. In our cross-sectional analysis, only 1 anthropometric equation gave rise to good accuracy as confirmed by DXA. Our findings indicate that the use of different techniques to assess FFM gains in response to a hypertrophic intervention yields different results. BIA with general embedded equations should not be used to monitor a young male adult's body composition. To monitor FFM over time, we would recommend the Dunne etal. equation (2) as the most sensitive field method, and to assess FFM cross-sectionally, equation (1) of these authors is the most accurate field method.

Association of predicted fat mass and lean body mass with diabetes: a longitudinal cohort study in an Asian population.

The relationship between body composition fat mass (FM) and lean body mass (LBM) and diabetes risk is currently debated, and the purpose of this study was to examine the association of predicted FM and LBM with diabetes in both sexes. The current study was a secondary analysis of data from the NAGALA (NAfld in the Gifu Area, Longitudinal Analysis) cohort study of 15,463 baseline normoglycemic participants. Predicted LBM and FM were calculated for each participant using anthropometric prediction equations developed and validated for different sexes based on the National Health and Nutrition Examination Survey (NHANES) database, and the outcome of interest was diabetes (types not distinguished) onset. Multivariate Cox regression analyses were applied to estimate the hazard ratios (HRs) and 95% confidence intervals (CIs) for the associations of predicted FM and LBM with diabetes risk and further visualized their associations using a restricted cubic spline function. The incidence density of diabetes was 3.93/1000 person-years over a mean observation period of 6.13 years. In women, predicted LBM and FM were linearly associated with diabetes risk, with each kilogram increase in predicted LBM reducing the diabetes risk by 65% (HR 0.35, 95%CI 0.17, 0.71; P < 0.05), whereas each kilogram increase in predicted FM increased the diabetes risk by 84% (HR 1.84, 95%CI 1.26, 2.69; P < 0.05). In contrast, predicted LBM and FM were non-linearly associated with diabetes risk in men (all P for non-linearity < 0.05), with an L-shaped association between predicted LBM and diabetes risk and a saturation point that minimized the risk of diabetes was 45.4 kg, while predicted FM was associated with diabetes risk in a U-shape pattern and a threshold point with the lowest predicted FM-related diabetes risk was 13.76 kg. In this Asian population cohort, we found that high LBM and low FM were associated with lower diabetes risk according to anthropometric equations. Based on the results of the non-linear analysis, we believed that it may be appropriate for Asian men to keep their LBM above 45.4 kg and their FM around 13.76 kg.

Prediction equations to estimate muscle mass using anthropometric data: a systematic review.

Reduced muscle mass is linked to poor outcomes in both inpatients and outpatients, highlighting the importance of muscle mass assessment in clinical practice. However, laboratory methods to assess muscle mass are not yet feasible for routine use in clinical practice because of limited availability and high costs. This work aims to review the literature on muscle mass prediction by anthropometric equations in adults or older people. The following databases were searched for observational studies published until June 2022: MEDLINE, Embase, Scopus, SPORTDiscus, and Web of Science. Of 6437 articles initially identified, 63 met the inclusion criteria for this review. Four independent reviewers, working in pairs, selected and extracted data from those articles. Two studies reported new equations for prediction of skeletal muscle mass: 10 equations for free-fat mass and lean soft tissue, 22 for appendicular lean mass, 7 for upper-body muscle mass, and 7 for lower-body muscle mass. Twenty-one studies validated previously proposed equations. This systematic review shows there are numerous equations in the literature for muscle mass prediction, and most are validated for healthy adults. However, many equations were not always accurate and validated in all groups, especially people with obesity, undernourished people, and older people. Moreover, in some studies, it was unclear if fat-free mass or lean soft tissue had been assessed because of an imprecise description of muscle mass terminology. This systematic review identified several feasible, practical, and low-cost equations for muscle mass prediction, some of which have excellent accuracy in healthy adults, older people, women, and athletes. Malnourished individuals and people with obesity were understudied in the literature, as were older people, for whom there are only equations for appendicular lean mass. PROSPERO registration number CRD42021257200.

390 Comparison of Body Composition Measurements in Patients With Chronic Kidney Disease Receiving Maintenance Hemodialysis Using Bioelectrical Impedance Analysis and Anthropometric Equations

390 Comparison of Body Composition Measurements in Patients With Chronic Kidney Disease Receiving Maintenance Hemodialysis Using Bioelectrical Impedance Analysis and Anthropometric Equations

Gurka vs Slaughter equations to estimate the fat percentage in children with cerebral palsy from all subtypes and levels of the Gross Motor Function Classification System

BackgroundBody composition assessment in children with cerebral palsy (CP) is a challenge, specially the fat percentage. There are different methods that can be used to estimate the fat percentage in this population, such as anthropometric equations, but there is still a need to determine which is the best and most accurate. The purpose of the study was to determine the method that best estimates the fat percentage in children from all CP subtypes and levels of the Gross Motor Function Classification System (GMFCS).MethodsAnalytical cross-sectional study in which 108 children with CP diagnosed by a pediatric neurologist were included with any type of dysfunction and from all levels of the GFMCS. Slaughter equation, Gurka equation and Bioelectrical impedance analysis (BIA) as reference method, were used. Groups were stratified by sex, CP subtypes, GMFCS level and Tanner stage. Median differences, Kruskal–Wallis, Mann–Whitney U test, Spearman's correlation coefficients and simple regressions were used, also multivariate models were performed.ResultsThe Slaughter equation differed from the other methods in the total population and when it was compared by sex, CP subtypes, gross motor function and Tanner stage. The Gurka equation showed significant differences by sex and gross motor function. Gurka equation correlated positively and significantly with BIA to estimate the fat percentage in all the CP subtypes and levels of the GMFCS. Tricipital skinfold (TSF), arm fat area (AFA) and weight for age index (W/A) showed the highest variability with respect to fat percentage.ConclusionGurka equation is more appropriate and accurate than Slaughter equation to estimate the fat percentage in children with CP from all subtypes and levels of the GMFCS.

Prognostic significance of sarcopenia diagnosed based on the anthropometric equation for progression-free survival and overall survival in patients with colorectal cancer.

The purpose of this study was to investigate the prognostic significance of sarcopenia diagnosed based on anthropometric equations for progression-free survival (PFS) and overall survival (OS) in patients with colorectal cancer (CRC). A total of 1,441 CRC patients who underwent surgical treatment between January 2012 and December 2016 were enrolled in this study. Sarcopenia was diagnosed according to validated anthropometric equations. The Kaplan-Meier method with the log-rank test was used to estimate the survival curve. Cox proportional hazards regression models with forward selection were used to evaluate risk factors affecting the prognosis of CRC patients. R package "survival" was used to build the prognostic nomograms to predict 1-5 years of PFS and OS in CRC patients. The concordance index (C-index) and calibration curve were used to evaluate the prognostic accuracy of the prognostic nomogram. Two hundred and seventy-one patients (18.8%) were diagnosed with sarcopenia. Sarcopenia was significantly associated with advanced age, large tumor size, and high mortality. Compared with the non-sarcopenia patients, the PFS of sarcopenia patients was worse (5-year PFS, 48.34 vs. 58.80%, p = 0.003). Multivariate survival analysis showed that patients with sarcopenia had a higher risk (23.9%) of adverse PFS (HR, 1.239; 95%CI: 1.019-1.505, p = 0.031) than patients without sarcopenia. The OS of patients with sarcopenia was significantly worse than that of patients without sarcopenia (5-year OS: 50.92 vs. 61.62%, p = 0.001). In CRC patients, sarcopenia was independently associated with poor OS (HR: 1.273, 95%CI: 1.042-1.556, p < 0.001). Moreover, sarcopenia effectively differentiated the OS of CRC patients in the normal carcinoembryonic antigen (CEA) subgroup but not in the high CEA subgroup. Notably, sarcopenia can provide effective prognostic stratification in CRC patients at different pathological stages. Nomograms that integrated prognostic features were built to predict the risk of adverse outcomes in CRC patients. The C-index and calibration curves showed that these nomograms had good prediction accuracy. Internal validation confirmed that our nomogram has wide application potential. Sarcopenia diagnosed based on anthropometric equations is an independent risk factor for PFS and OS in CRC patients.

Anthropometric equations to predict visceral adipose tissue in European and American youth.

To investigate whether prediction equations including a limited but selected number of anthropometrics that consider differences in subcutaneous abdominal adipose tissue may improve prediction of the visceral adipose tissue (VAT) in youth. Anthropometrics and abdominal adipose tissue by MRI were available in 7-18years old youth with overweight or obesity: 181 White Europeans and 186 White and Black Americans. Multivariable regressions were performed to develop and validate the VAT anthropometric predictive equations in a cross-sectional study. A model with both waist circumference (WaistC) and hip circumference (HipC) (VAT=[1.594×WaistC] - [0.681×HipC]+[1.74×Age] - 48.95) more strongly predicted VAT in girls of White European ethnicity (R2=50.8%; standard error of the estimate [SEE] = 13.47cm2), White American ethnicity (R2=41.9%; SEE, 15.63cm2), and Black American ethnicity (R2=25.1%; SEE, 16.34cm2)(P<.001), than WaistC or BMI. In boys, WaistC was the strongest predictor of VAT; HipC did not significantly improve VAT prediction. A model including both WaistC and HipC that considers differences in subcutaneous abdominal adipose tissue more accurately predicts VAT in girls and is superior to commonly measured anthropometrics used individually. In boys, other anthropometric measures did not significantly contribute to the prediction of VAT beyond WaistC alone. This demonstrates that selected anthropometric predictive equations for VAT can be an accessible, cost-effective alternative to imaging methods that can be used in both clinics and research.