Clínica Universidad De Navarra-Body Adiposity Estimator Research Articles

Association of the CUN-BAE body adiposity estimator and other obesity indicators with cardiometabolic multimorbidity: a cross-sectional study

Cardiometabolic multimorbidity (CM), defined as the coexistence of two or three cardiometabolic disorders, is one of the most common and deleterious multimorbidities. This study aimed to investigate the association of Clínica Universidad de Navarra-Body Adiposity Estimator (CUN-BAE), body mass index (BMI), waist circumference (WC), and waist-to-height ratio (WHtR) with the prevalence of CM. The data were obtained from the 2021 health checkup database for residents of the Electronic Health Management Center in Xinzheng, Henan Province, China. 81,532 participants aged ≥ 60 years were included in this study. Logistic regression models were used to estimate the odd ratios (ORs) and 95% confidence intervals (CIs) for CUN-BAE, BMI, WC, and WHtR in CM. The area under the receiver operating characteristic curve (AUC) was used to compare the discriminatory ability of different anthropometric indicators for CM. The multivariable-adjusted ORs (95% CIs) (per 1 SD increase) of CM were 1.799 (1.710–1.893) for CUN-BAE, 1.329 (1.295–1.364) for BMI, 1.343 (1.308–1.378) for WC, and 1.314 (1.280–1.349) for WHtR, respectively. Compared with BMI, WC and WHtR, CUN-BAE had the highest AUC in both males and females (AUC: 0.642; 95% CI 0.630–0.653 for males, AUC: 0.614; 95% CI 0.630–0.653 for females). CUN-BAE may be a better measure of the adverse effect of adiposity on the prevalence of CM than BMI, WC, and WHtR.

The relation of body adiposity to the outcomes of thoracoscopic lobectomy for lung cancer - a single-center cohort study.

The outcomes of lung cancer surgery depend on the patients' nutritional status. Body fat percentage (BF%) is one of the indicators of body composition and nutritional status. Direct measurement of BF% is complicated, requires significant resources and is rarely performed. The CUN-BAE (Clínica Universidad de Navarra - Body Adiposity Estimator) index has been shown to accurately predict BF% is several clinical settings, but its relation to the outcomes of lung surgery has not been reported so far. To determine the relation of the BF% to the outcomes of thoracoscopic lobectomy. This retrospective study included 1,183 patients who underwent thoracoscopic lobectomy for non-small cell lung cancer between June 1999, and September 2019 at one department. BF% was calculated according to the Clínica Universidad de Navarra - Body Adiposity Estimator equation. The primary endpoints were postoperative complications and long-term survival. Univariate analysis showed that higher BF% was related to lower incidence of complications (p = 0.001), including prolonged air leak (p < 0.001), atelectasis (p < 0.05), psychosis (p < 0.001), reoperations (p < 0.05), and shorter chest drainage (p = 0.001) and hospitalization duration (p < 0.001). Multivariate analysis showed that higher BF% was correlated with lower risk of complications (p = 0.005; OR = 0.964; 95% CI: 0.940 to 0.989), including prolonged air leak (p < 0.001; OR = 0.923; 95% CI: 0.886 to 0.962), and shorter duration of chest drainage (p < 0.001; B = -0.046; 95% CI: -0.069 to -0.023) and hospitalization (p < 0.001; B = -0.112; 95% CI: -0.176 to -0.048). Cox proportional hazards regression analysis showed that BF% was not related to long-term survival. Body fat percentage is a valuable tool that can help predict the short-term outcomes of minimally lobectomy for lung cancer.

Association of the Clínica Universidad de Navarra-Body Adiposity Estimator With Type 2 Diabetes: A Retrospective Cohort Study.

Objectives: Clínica Universidad de Navarra-Body Adiposity Estimator (CUN-BAE) is considered to be a more accurate indicator of body fat estimation. We aimed to investigate the association of CUN-BAE with the risk of type 2 diabetes mellitus (T2DM) and to compare the strength of the association between CUN-BAE, body mass index (BMI), waist circumference (WC), waist-to-height ratio (WHtR) and T2DM. Methods: The data were obtained from the annual health checkup database of residents in Xinzheng, China. From January 2011 to December 2021, 80,555 subjects aged ≥45years met the inclusion criteria. Cox proportional hazard regression models were used to estimate the hazard ratios (HRs) and 95% confidence intervals (CIs) for CUN-BAE, BMI, WC, and WHtR in T2DM. Results: During a mean follow-up of 6.26years, T2DM occurred in 12,967 subjects. The multivariable-adjusted HRs (95% CIs) of T2DM (highest vs. reference group) were 1.994 (1.811-2.196) for CUN-BAE, 1.751 (1.665-1.842) for WC, 1.715 (1.631-1.804) for WHtR, and 1.510 (1.436-1.588) for BMI, respectively. In addition, the risk of T2DM increased with baseline CUN-BAE (HR: 1.374; 95% CI: 1.328, 1.421), WC (HR: 1.236; 95% CI: 1.215, 1.256), WHtR (HR: 1.228; 95% CI: 1.208, 1.248), and BMI (HR: 1.175; 95% CI: 1.156, 1.195). Conclusion: Compared to BMI, WC or WHtR, CUN-BAE may more adequately reflect the adverse effects of adiposity on the risk of T2DM.

The relationship between the CUN-BAE body fatness index and incident diabetes: a longitudinal retrospective study

BackgroundThe Clínica Universidad de Navarra-Body Adiposity Estimator (CUN-BAE) index has been recommended as an ideal indicator of body fat and exhibited significant correlation with cardiometabolic risk factors. However, whether the CUN-BAE index correlates with incident diabetes in Asian populations is unknown. Therefore, this longitudinal study was designed to evaluate the association between baseline CUN-BAE index and type 2 diabetes mellitus (T2DM).MethodsThis retrospective longitudinal study involved 15,464 participants of 18–79 years of age in the NAGALA (NAfld in the Gifu Area Longitudinal Analysis) study over the period of 2004–2015. Cox proportional hazards regression was performed to test the relationship between the baseline CUN-BAE index and diabetes incidence. Further stratification analysis was conducted to ensure that the results were robust. The diagnostic utility of the CUN-BAE index was tested by the receiver operating characteristic (ROC) curve.ResultsOver the course of an average follow-up of 5.4 years, 373 (2.41%) participants developed diabetes. A higher diabetes incidence was associated with higher CUN-BAE quartiles (P for trend< 0.001). Each 1 unit increase in CUN-BAE index was associated with a 1.08-fold and 1.14-fold increased risk of diabetes after adjustment for confounders in males and females, respectively (both P < 0.001). Stratification analysis demonstrated a consistent positive correlation between baseline CUN-BAE and diabetes incidence. Moreover, based on ROC analysis, CUN-BAE exhibited a better capacity for diabetes prediction than both body mass index (BMI) and waist circumference (WC) in both sexes.ConclusionsThe baseline CUN-BAE level was independently related to the incidence of diabetes. Increased adiposity determined by CUN-BAE could be used as a strong nonlaboratory predictor of incident diabetes in clinical practice.

Is There a Link between Obesity Indices and Skin Autofluorescence? A Response from the ILERVAS Project.

There is controversial information about the accumulation of advanced glycation end-products (AGEs) in obesity. We assessed the impact of total and abdominal adiposity on AGE levels via a cross-sectional investigation with 4254 middle-aged subjects from the ILERVAS project. Skin autofluorescence (SAF), a non-invasive assessment of subcutaneous AGEs, was measured. Total adiposity indices (BMI and Clínica Universidad de Navarra-Body Adiposity Estimator (CUN-BAE)) and abdominal adiposity (waist circumference and body roundness index (BRI)) were assessed. Lean mass was estimated using the Hume index. The area under the receiver operating characteristic (ROC) curve was evaluated for each index. Different cardiovascular risk factors (smoking, prediabetes, hypertension and dyslipidemia) were evaluated. In the study population, 26.2% showed elevated SAF values. No differences in total body fat, visceral adiposity and lean body mass were detected between patients with normal and high SAF values. SAF levels showed a very slight but positive correlation with total body fat percentage (estimated by the CUN-BAE formula) and abdominal adiposity (estimated by the BRI). However, none of them had sufficient power to identify patients with high SAF levels (area under the ROC curve &lt;0.52 in all cases). Finally, a progressive increase in SAF levels was observed in parallel with cardiovascular risk factors in the entire population and when patients with normal weight, overweight and obesity were evaluated separately. In conclusion, total obesity and visceral adiposity are not associated with a greater deposit of AGE. The elevation of AGE in obesity is related to the presence of cardiometabolic risk.

Body adiposity estimated with CUN-BAE predicts atrial fibrillation: the SUN cohort

Abstract Background Obesity is widely recognised as a strong risk factor for atrial fibrillation and recent interest has focused on epicardial fat. Although body mass index is commonly used in the clinical practice, it doesn't precisely estimate adiposity. The Clínica Universidad de Navarra-Body Adiposity Estimator (CUN-BAE) is an equation developed for body fat calculation, that has been previously associated with cardiometabolic disease. Our objective was to assess the association between body fat, as captured by the CUN-BAE index, and the incidence of atrial fibrillation in a prospective cohort. Methods The SUN project is a dynamic, prospective cohort of Spanish university graduates. A total of 20.136 participants, free of atrial fibrillation at baseline, were followed-up for a median time of 12 years, with a retention proportion of 91%. CUN-BAE was calculated for each participant taking into account only sex, age and body mass index. Incident cases of atrial fibrillation were confirmed by a cardiologist according to a prespecified protocol. Multivariable Cox regression models were used to estimate hazard ratios (HR) of atrial fibrillation according to calculated body fat. Results During follow-up, we identified 128 incident cases of atrial fibrillation. There was a strong direct association between a higher CUN-BAE index at baseline and incident atrial fibrillation during follow-up. In comparison to participants with a lower body fat category (median 21.3%), those with a higher category of body fat (median 34.8%) exhibited a significant 118% higher relative risk of incident atrial fibrillation (adjusted HR=2.18; 95% CI: 1.22, 3.90). For each 2-unit increase in the CUN-BAE index, atrial fibrillation risk significantly increased by 10%. The risk of incident atrial fibrillation remained significantly associated with adiposity even after further adjustment for obesity (body mass index ≥30 kg/m2) and in repeated measures analysis of the CUN-BAE index every other year. Conclusions Increased body adiposity, as captured by CUN-BAE index, was strongly and independently associated with a higher risk of atrial fibrillation in a Spanish cohort. These findings support the hypothesis that adiposity is closely related with arrhythmogenic mechanisms that could lead to atrial fibrillation and may be predicted using an equation to estimate body fat. Funding Acknowledgement Type of funding sources: Public grant(s) – EU funding. Main funding source(s): Spanish Ministry of Health and European Regional Development Fund (FEDER)

Anthropometric indices and cut-off points for screening of metabolic syndrome among South African taxi drivers.

BackgroundDetecting the early onset of metabolic syndrome (MetS) allows for quick intervention which may slow progression to a variety of health consequences, hence, determining the best measurement to detect MetS is essential.AimThis research aimed at examining the MetS predictive power of anthropometric indices, such as body mass index (BMI), waist circumference (WC), waist-to-height ratio (WHtR), body shape index (ABSI), body roundness index (BRI), percentage body fat (%BF), conicity index (CI), and Clínica Universidad de Navarra-body adiposity estimator (CUN-BAE) to determine the cut-off points to identify male South African taxi drivers with MetS.MethodA cross-sectional study was conducted among 185 male taxi drivers. Their weight, height, WC, blood lipid profile were measured. International Diabetes Federation (IDF) definition was used to define MetS. Receiver Operating Characteristic (ROC) curves were used to compare the predictive ability of Anthropometric indices to detect MetS.ResultsThe mean age of the participants was 39.84 years. Overall, 41.6% (N = 77) of the participants presented with MetS. The mean values for BMI, WC, WHtR, %BF, BRI, CUN-BAE, ABSI and CI were 28.60 ± 6.20 kg/m2, 99.13 ± 17.59 cm, 0.58 ± 0.10, 27.28 ± 8.28%, 5.09 ± 2.33, 27.78 ± 8.34, 0.08 ± 0.01 and 1.70 ± 0.19, respectively. The mean values for these indices were significantly (p < 0.001) higher in participants with MetS. The highest area under the curve (AUC) outcomes for screening MetS were for the %BF and CUN-BAE, followed by the BMI and WHtR, and lastly the BRI. All these anthropometric indices had outstanding discriminatory powers for predicting MetS with AUCs and sensitivity values above 80%. The BMI, WHtR, %BF, BRI, and CUN-BAE, had cut-off points for detection of metS in South African men at 28.25 kg/m2, 0.55, 25.29%, 4.55, and 27.10, respectively. Based on the logistic regression models abnormal BMI, WHtR, %BF, BRI, CUN-BAE, TG, FBG, systolic BP, diastolic BP and WC showed increased risk of MetS.ConclusionWhile the %BF, CUN-BAE, BMI, WC, WHtR, BRI, CI and CUN-BAE could predict MetS among South African male taxi drivers, these indices were less effective in predicting the individual MetS risk factors such as TG, BP, and FBG.

Increased Adiposity Appraised with CUN-BAE Is Highly Predictive of Incident Hypertension. The SUN Project.

Overweight and obesity are growing worldwide and strongly associated with hypertension. The Clínica Universidad de Navarra-Body Adiposity Estimator (CUN-BAE) index is proposed as an optimal indicator of body fatness. We aimed to investigate the association of body fat as captured by the CUN-BAE index with incident hypertension in a Mediterranean population. We assessed 15,950 participants of the SUN (Seguimiento Universidad de Navarra) prospective cohort (63.7% women) initially free of hypertension. Participants completed follow-up questionnaires biennially. A validated 136-item food-frequency questionnaire was administered at baseline. We used Cox models adjusted for multiple confounders. Among 12.3 years of median follow-up (interquartile range: 8.3, 15.0 years), 2160 participants reported having received a diagnosis of hypertension. We observed a strong direct association between progressively higher the CUN-BAE index at baseline and incident hypertension during follow-up in multivariable-adjusted models for men and women, even after further adjustment for BMI ≥ 30 kg/m2, showing a significant association also in non-obese participants. For each 2-unit increase in the CUN-BAE index, hypertension risk increased by 27% and 29% in men and women, respectively. The results remained significant when considering longitudinal repeated measures of changes in body fat assessed with the CUN-BAE index among the different biennial follow-up questionnaires. Our results emphasize the importance of reducing and maintaining a low body fat to prevent hypertension.

Clinical Usefulness of Anthropometric Indices to Predict the Presence of Prediabetes. Data from the ILERVAS Cohort.

Prediabetes is closely related to excess body weight and adipose distribution. For this reason, we aimed to assess and compare the diagnostic usefulness of ten anthropometric adiposity indices to predict prediabetes. Cross-sectional study with 8188 overweight subjects free of type 2 diabetes from the ILERVAS project (NCT03228459). Prediabetes was diagnosed by levels of glycated hemoglobin (HbA1c). Total body adiposity indices [BMI, Clínica Universidad de Navarra-Body Adiposity Estimator (CUN-BAE) and Deurenberg’s formula] and abdominal adiposity (waist and neck circumferences, conicity index, waist to height ratio, Bonora’s equation, A body shape index, and body roundness index) were calculated. The area under the receiver-operating characteristic (ROC) curve, the best cutoff and the prevalence of prediabetes around this value were calculated for every anthropometric index. All anthropometric indices other than the A body adiposity were higher in men and women with prediabetes compared with controls (p < 0.001 for all). In addition, a slightly positive correlation was found between indices and HbA1c in both sexes (r ≤ 0.182 and p ≤ 0.026 for all). None of the measures achieved acceptable levels of discrimination in ROC analysis (area under the ROC ≤ 0.63 for all). Assessing BMI, the prevalence of prediabetes among men increased from 20.4% to 36.2% around the cutoff of 28.2 kg/m2, with similar data among women (from 29.3 to 44.8% with a cutoff of 28.6 kg/m2). No lonely obesity index appears to be the perfect biomarker to use in clinical practice to detect individuals with prediabetes.

Anthropometric indices and cut-off points in the diagnosis of metabolic disorders.

Identifying metabolic disorders at the earliest phase of their development allows for an early intervention and the prevention of serious consequences of diseases. However, it is difficult to determine which of the anthropometric indices of obesity is the best tool for diagnosing metabolic disorders. The aims of this study were to evaluate the usefulness of selected anthropometric indices and to determine optimal cut-off points for the identification of single metabolic disorders that are components of metabolic syndrome (MetS). Cross-sectional study. We analyzed the data of 12,328 participants aged 55.7±5.4 years. All participants were of European descent. Four MetS components were included: high glucose concentration, high blood triglyceride concentration, low high-density lipoprotein cholesterol concentration, and elevated blood pressure. The following obesity indices were considered: waist circumference (WC), body mass index (BMI), waist-to-height ratio (WHtR), body fat percentage (%BF), Clínica Universidad de Navarra-body adiposity estimator (CUN-BAE), body roundness index (BRI), and a body shape index (ABSI). The following indices had the highest discriminatory power for the identification of at least one MetS component: CUN-BAE, BMI, and WC in men (AUC = 0.734, 0.728, and 0.728, respectively) and WHtR, CUN-BAE, and WC in women (AUC = 0.715, 0.714, and 0.712, respectively) (p<0.001 for all). The other indices were similarly useful, except for the ABSI. For the BMI, the optimal cut-off point for the identification of metabolic abnormalities was 27.2 kg/m2 for both sexes. For the WC, the optimal cut-off point was of 94 cm for men and 87 cm for women. Prospective studies are needed to identify those indices in which changes in value predict the occurrence of metabolic disorders best.

Is urinary sodium excretion related to anthropometric indicators of adiposity in adults?

Background:Although increasing salt intake is associated with greater odds of obesity, little is known about its relationship with body fat. We investigated the relation of urinary sodium (UNa) with obesity indices, including Clínica Universidad de Navarra–Body Adiposity Estimator (CUN-BAE), a body shape index (ABSI), body mass index (BMI), waist circumference (WC), and waist-to-height ratio (WHtR).Materials and Methods:A total of 508 free-living adults aged ≥ 19 years were selected through stratified multistage random method as a representative of general population from central parts of Iran and were included in this cross-sectional study. Dietary sodium intake was measured using 24-h UNa (24-UNa) excretion. Weight, height, and WC were measured using standard protocols and calibrated equipment and used to measure obesity indicators, including BMI, WHtR, ABSI, and CUN-BAE. Adjusted univariate multiple logistic regression was used to assess the risk of having greater obesity measures across the tertiles of 24-UNa.Results:Individuals in the top tertile of 24-UNa in comparison with those in the first tertile had greater body weight (72.02 ± 1.00 vs. 66.02 ± 0.89 kg; P < 0.0001), BMI (26.14 ± 0.33 vs. 24.82 ± 0.29 kg/m2; P = 0.007), and CUN-BAE (29.89 ± 0.42 vs. 28.38 ± 0.78; P = 0.036). There was a trend toward an increment in WC by increasing sodium intake (P = 0.073). After controlling for potential confounders, individuals with greater sodium consumption had greater chance for overweight (odds ratio [OR]: 1.004, 95% confidence interval [CI]: 1.001–1.007; P = 0.015), abdominal obesity (OR: 1.004, 95% CI: 1.00–1.008; P = 0.031), and more body fat (OR: 1.007, 95% CI: 1.003–1.01; P = 0.001). No significant association was found for sodium and WHtR and ABSI.Conclusion:Greater 24-UNa excretion was associated with greater means of body weight, BMI, WC, and CUN-BAE. Although changes in obesity indices per each additional 24-UNa excretion were small, our findings are relevant because of the rising obesity epidemic.

Equation Córdoba: A Simplified Method for Estimation of Body Fat (ECORE-BF).

Background: Many methods for measuring body fat have been developed, but applications in clinical settings are limited. For this reason, researchers have tried to identify different formulas for its estimation but most of are hard to incorporate into daily work due to the variability in population and difficulty of use. The aim of this study was to develop and validate a new equation for the simplified estimation of body fat using the Clínica Universidad de Navarra – Body Adiposity Estimator (CUN-BAE) as a reference. Methods: This research was conducted in two phases. In the first, the new body fat estimation equation was developed. The developed equation was validated in the second phase. Pearson’s linear correlation, raw and adjusted linear regressions, the intraclass correlation coefficient, and Bland–Altman graphs were used. Results: The variables that best adjusted the body fat percentage were age, sex, and the Napierian logarithm of Body Mass Index (LnBMI), forming the Equation Córdoba for Estimation of Body Fat (ECORE-BF) model. In its validation, the model presented correlation values of 0.994, an intraclass correlation coefficient of 0.960, with the Bland–Altman graph indicating means differences of 1.82 with respect to the estimation with the CUN-BAE. Nevertheless, although the aim was to simplify the CUN-BAE, the main limitation of this study is that a gold standard, such as air displacement plethysmography (ADP) or dual-energy X-ray absorptiometry (DXA), was not used. Conclusions: The proposed equation (ECORE-BF) simplified the CUN-BAE and provided a precise method, respecting the principle of parsimony, for the calculation of body fat.

Relationship of body composition measured by DEXA with lifestyle and satisfaction with body image in university students

Introduction: body mass index (BMI) can under-diagnose overweight and obesity as it is a surrogate measure of central adiposity, making it necessary to define more adequate variables for its correct diagnosis. Objective: to compare the categorization of overweight and obesity according to the percentage of fat measured with dual-energy X-ray absorptiometry (DEXA) with the BMI and the Clínica Universidad de Navarra - Body Adiposity Estimator (CUN-BAE) formula, as well as their relationship with adherence to the Mediterranean diet, physical activity and satisfaction with body image. Subjects and methods: cross-sectional study conducted in 64 medical students. We determined weight, height, waist circumference, percentage of fat measured with DEXA, adherence to the Mediterranean diet, level of physical activity and degree of satisfaction with body image. Results: BMI underdiagnoses overweight with respect to the percentage of fat measured by DEXA while CUN-BAE has a good correlation. Percentage of fat is inversely associated with satisfaction with body image more strongly than the BMI. Conclusion: the limitations of BMI to diagnose excess body fat pose the need for new tools to distinguish patients with normal BMI whose percentage of body fat is high. It would be useful to assess whether the use of a questionnaire of satisfaction with body image in people classified as normal weight could distinguish individuals with a higher probability of excess of fat, and thus, employ more accurate study methods in this group.

Short- and long-term effectiveness of a smartphone application for improving measures of adiposity: A randomised clinical trial – EVIDENT II study

Evidence on the efficacy of smartphone applications (apps) for reducing body weight and other measurements of adiposity sustainably is not conclusive. To evaluate the effect of adding an app for 3 months to traditional counselling on physical activity (PA) and a heart-healthy diet for the modification of measurements of adiposity at 3 and 12 months after intervention. This randomised clinical trial included 833 subjects. The counselling and app group (IG) had 415 subjects, while 418 were included in the counselling only group (CG). The primary outcome was adiposity measurements at 3 and 12 months after intervention. The secondary outcome was the effect of the intervention by sex. Counselling on a heart-healthy diet and PA was given to both groups. The IG also received training in the use of a smartphone app designed to promote a heart-healthy diet and PA, and this group was given access to this application for 3 months. Outcome measurements included waist circumference (WC), body mass index (BMI) and Clínica Universidad de Navarra - body adiposity estimator (CUN-BAE). In the IG at 12 months, the following decreased: WC -0.72 cm (95% confidence interval [CI]: -2.35 to -0.02) and CUN-BAE -0.35 (95% CI: -0.63 to -0.06). These decreases were only observed in women. After baseline adjustment, the beneficial effect was maintained in the IG compared to the CG at 12 months in terms of WC (-0.67; 95% CI: -0.29 to -0.02) and CUN-BAE (-0.57; 95% CI: -1.10 to -0.04), but only in women. An intervention of nutritional counselling and PA plus the smartphone app with personalised recommendations compared to CG showed beneficial results in terms of reduction of abdominal obesity and the percentage of body fat in women, but not in men.

Comparison of body mass index (BMI) with the CUN-BAE body adiposity estimator in the prediction of hypertension and type 2 diabetes.

BackgroundObesity is a world-wide epidemic whose prevalence is underestimated by BMI measurements, but CUN-BAE (Clínica Universidad de Navarra - Body Adiposity Estimator) estimates the percentage of body fat (BF) while incorporating information on sex and age, thus giving a better match. Our aim is to compare the BMI and CUN-BAE in determining the population attributable fraction (AFp) for obesity as a cause of chronic diseases.MethodsWe calculated the Pearson correlation coefficient between BMI and CUN-BAE, the Kappa index and the internal validity of the BMI. The risks of arterial hypertension (AHT) and diabetes mellitus (DM) and the AFp for obesity were assessed using both the BMI and CUN-BAE.Results3888 white subjects were investigated. The overall correlation between BMI and CUN-BAE was R2 = 0.48, which improved when sex and age were taken into account (R2 > 0.90). The Kappa coefficient for diagnosis of obesity was low (28.7 %). The AFp was 50 % higher for DM and double for AHT when CUN-BAE was used.ConclusionsThe overall correlation between BMI and CUN-BAE was not good. The AFp of obesity for AHT and DM may be underestimated if assessed using the BMI, as may the prevalence of obesity when estimated from the percentage of BF.

Accuracy of three novel predictive methods for measurements of fat mass in healthy older subjects

This study evaluated the agreement of novel anthropometric equations and established indirect methods (skinfold thickness and bioimpedance analysis) with reference methods [dual X-ray absorptiometry (DXA) and air displacement plethysmography (ADP)] for fat mass assessment (FM) in older subjects. Forty subjects (M/F = 15/25, age = 61-84 years, BMI = 18-37 kg/m(2)) were recruited. The agreement of the following predictive equations was evaluated: body adiposity index (BAI), BAI-Fels and Clínica Universidad de Navarra-body adiposity estimator (CUN-BAE). BAI estimates were comparable to DXA (Δ ± 2SD = 0.4 ± 6.0 kg, p > 0.05) but not to ADP (Δ ± 2SD = -2.8 ± 7.2 kg, p < 0.001); BAI-Fels estimates were comparable to DXA (Δ ± 2SD = 0.8 ± 5.5 kg, p > 0.05) but not to ADP (Δ ± 2SD = -4.0 ± 6.9 kg, p < 0.001). The difference between CUN-BAE and ADP was not significant (Δ ± 2SD = -0.4 ± 5.6 kg, p > 0.05), whereas it significantly overestimated DXA (Δ ± 2SD = 2.8 ± 5.4 kg, p < 0.001). ADP significantly overestimated FM compared to DXA (Δ ± 2SD = 3.2 ± 5.4 kg, p < 0.001) and the measurement bias was significantly correlated with BMI in men (p = 0.004). The accuracy of the three anthropometric indexes is dependent on the choice of the reference method. The variability of the FM estimates was large and these indexes cannot be recommended for the assessment of FM in older subjects.

Clinical Usefulness of a New Equation for Estimating Body Fat

OBJECTIVETo assess the predictive capacity of a recently described equation that we have termed CUN-BAE (Clínica Universidad de Navarra-Body Adiposity Estimator) based on BMI, sex, and age for estimating body fat percentage (BF%) and to study its clinical usefulness.RESEARCH DESIGN AND METHODSWe conducted a comparison study of the developed equation with many other anthropometric indices regarding its correlation with actual BF% in a large cohort of 6,510 white subjects from both sexes (67% female) representing a wide range of ages (18–80 years) and adiposity. Additionally, a validation study in a separate cohort (n = 1,149) and a further analysis of the clinical usefulness of this prediction equation regarding its association with cardiometabolic risk factors (n = 634) was carried out.RESULTSThe mean BF% in the cohort of 6,510 subjects determined by air displacement plethysmography was 39.9 ± 10.1%, and the mean BF% estimated by the CUN-BAE was 39.3 ± 8.9% (SE of the estimate, 4.66%). In this group, BF% calculated with the CUN-BAE showed the highest correlation with actual BF% (r = 0.89, P < 0.000001) compared with other anthropometric measures or BF% estimators. Similar agreement was found in the validation sample. Moreover, BF% estimated by the CUN-BAE exhibits, in general, better correlations with cardiometabolic risk factors than BMI as well as waist circumference in the subset of 634 subjects.CONCLUSIONSCUN-BAE is an easy-to-apply predictive equation that may be used as a first screening tool in clinical practice. Furthermore, our equation may be a good tool for identifying patients at cardiovascular and type 2 diabetes risk.