Anthropometry to assess body fat in Indonesian adults

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.

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

Background

The 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.

Methods

Data 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.

Results

For 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'.

Conclusion

Machine 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%.

A Comparison of Equation Córdoba for Estimation of Body Fat (ECORE-BF) with Other Prediction Equations

There are multiple formulas for estimating the percentage of body fat (BF%). Clínica Universidad de Navarra-Body Adiposity Estimator (CUN-BAE) is one of the most used formulas because of its accuracy and its association with cardiovascular pathologies. Equation Córdoba for Estimation of Body Fat (ECORE-BF) was developed to simplify the calculation of BF% while maintaining a similar level of accuracy. The objective was to compare ECORE-BF in a large sample of Spanish workers using CUN-BAE as a reference. A cross-sectional study was carried out on 196,844 participants. The BF% was estimated using different formulas: relative fat mass (RFM), Palafolls, Deurenberg, and ECORE-BF. The accuracy of the estimation was determined using Lin’s concordance correlation coefficient (CCC) and the Bland–Altman method, using CUN-BAE as the reference method. ECORE-BF reached the highest concordance (CCC = 0.998). It also showed the lowest mean difference (−0.0077) and the tightest agreement limits (−0.9723, 0.9569) in the Bland–Altman test. In both analyses, it remained robust even when separating the analyses by sex, nutritional status, or age. ECORE-BF presented as the most straightforward and most accurate equation for the estimation of BF%, remaining robust regardless of population characteristics.