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50 results on '"Dual X-ray absorptiometry"'

1. Relating QRS voltages to left ventricular mass and body composition in elite endurance athletes.

Author

De Bosscher R, Moeyersons J, Dausin C, Claeys M, Janssens K, Claus P, Goetschalckx K, Bogaert J, Van De Heyning CM, Paelinck B, Sanders P, Kalman J, Van Huffel S, Varon C, La Gerche A, Heidbuchel H, Claessen G, and Willems R

Subjects
Female, Humans, Male, Body Composition, Heart Ventricles, Magnetic Resonance Imaging, Electrocardiography methods, Hypertrophy, Left Ventricular pathology
Abstract

Purpose: Electrocardiogram (ECG) QRS voltages correlate poorly with left ventricular mass (LVM). Body composition explains some of the QRS voltage variability. The relation between QRS voltages, LVM and body composition in endurance athletes is unknown., Methods: Elite endurance athletes from the Pro@Heart trial were evaluated with 12-lead ECG for Cornell and Sokolow-Lyon voltage and product. Cardiac magnetic resonance imaging assessed LVM. Dual energy x-ray absorptiometry assessed fat mass (FM) and lean mass of the trunk and whole body (LBM). The determinants of QRS voltages and LVM were identified by multivariable linear regression. Models combining ECG, demographics, DEXA and exercise capacity to predict LVM were developed., Results: In 122 athletes (19 years, 71.3% male) LVM was a determinant of the Sokolow-Lyon voltage and product (β = 0.334 and 0.477, p < 0.001) but not of the Cornell criteria. FM of the trunk (β = - 0.186 and - 0.180, p < 0.05) negatively influenced the Cornell voltage and product but not the Sokolow-Lyon criteria. DEXA marginally improved the prediction of LVM by ECG (r = 0.773 vs 0.510, p < 0.001; RMSE = 18.9 ± 13.8 vs 25.5 ± 18.7 g, p > 0.05) with LBM as the strongest predictor (β = 0.664, p < 0.001). DEXA did not improve the prediction of LVM by ECG and demographics combined and LVM was best predicted by including VO 2 max (r = 0.845, RMSE = 15.9 ± 11.6 g)., Conclusion: LVM correlates poorly with QRS voltages with adipose tissue as a minor determinant in elite endurance athletes. LBM is the strongest single predictor of LVM but only marginally improves LVM prediction beyond ECG variables. In endurance athletes, LVM is best predicted by combining ECG, demographics and VO 2 max., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Published
2023
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48. The impact of body-weight components on forced spirometry in healthy Italians

Author

L Iacopino, Ehab I. Mohamed, N. Di Daniele, C. Maiolo, M Pepe, and A De Lorenzo

Subjects
Male, Adult, Pulmonary and Respiratory Medicine, Spirometry, medicine.medical_specialty, Adolescent, Correlation coefficient, Statistics as Topic, Vital Capacity, Peak Expiratory Flow Rate, Forced Expiratory Volume, Middle Aged, Female, Lung, Italy, Predictive Value of Tests, Body Weight, Body Composition, Humans, Age Factors, Body Mass Index, Sex Factors, Reference Values, Body Height, Bivariate analysis, FEV1/FVC ratio, Animal science, Internal medicine, Linear regression, medicine, Settore MED/49 - Scienze Tecniche Dietetiche Applicate, medicine.diagnostic_test, business.industry, respiratory system, Dual X-ray absorptiometry, respiratory tract diseases, Prediction equation, Endocrinology, Bone-free lean body mass, Predictive value of tests, Lean body mass, business, Body mass index
Abstract

Many studies have investigated lung function in relation to age and height among Caucasians, however, most of these studies did not consider the individual components of body weight. The objective of the present study was to study the effect of body weight components [bone-free lean body mass (BF-LBM), bone mineral content (BMC), and fat mass (FM)] measured by dual x-ray absorptiometry (DXA) on the lung-function variables (FVC, FEV1, and PEF) and to derive prediction equations for these variables in healthy adult Italians. Dynamic spirometric tests and body composition analysis by DXA were performed on 58 nonsmoking males, mean age ( +/-SE) 26.72 +/- 1.98 years and BMI 25.51 +/- 0.64 kg/m2, and 60 nonsmoking females matched for age and BMI (29.61 +/- 1.65 years and 26.45 +/- 1.05 kg/m2, respectively). Bivariate linear regression analysis showed the variables age, height, BF-LBM, BMC, and the interaction term BF-LBM*Height, but not weight and FM, to correlate significantly with lung-function variables for males and for females separately. Multiple linear regression analysis showed that sex, age, height, and BF-LBM*Height were significantly associated with FVC, FEV1, and PEF. The prediction equations developed for FVC, FEV1, and PEF on the basis of the independent variables i.e. sex, age (y), height (m), and BF-LBM*Height (kg. m) had a significantly higher cumulative correlation coefficient and a lower SEE compared with those based on age and height only. The present report suggests that the BF-LBM, expressed independently from height, can be considered for predicting lung-function variables.

Published
2002

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