Objective: The aim of this study was to review the existence and types of correlations between body composition densitometric parameters and laboratory values associated to cardiometabolic risk. Methods: We retrospectively analyzed data from 316 individuals in the weight range from normality to super-obesity, submitted to total body dual-energy x-ray absorptiometry (DXA) scans and routine biochemistry at S.Orsola-Malpighi Hospital from June 2010 to March 2014. The study included 182 women, 45.8 ± 13.4 y of age, with a body mass index (BMI) of 31.5 (± 11) kg/m2 (group F) and 134 men, 45.4 ± 13.6 y of age, with a BMI of 27.6 (± 7.8) kg/m2 (group M). All patients underwent whole-body scan (Lunar iDXA, GE Healthcare, Madison, WI, USA) and laboratory analysis (blood fasting glucose, total cholesterol, high-density lipoprotein cholesterol, tricylglycerides [TGs], aspartate aminotransferase, and alanine aminotransferase). Correlation between laboratory values and total body and regional fat mass (including visceral adipose tissue [VAT] and subcutaneous adipose tissue in the android region), and lean mass parameters were analyzed with linear and stepwise regressions analysis (significance limit, P < 0.05). Receiver operating characteristic curves were performed to assess the accuracy of the best-fit DXA parameter (VAT) to identify at least one laboratory risk factor. Results: In both groups, BMI and densitometric parameters showed a linear correlation with fasting blood glucose and TG levels and an inverse correlation with high-density lipoprotein cholesterol (P < 0.05), whereas no correlation was observed with total cholesterol levels. The only densitometric parameter retained in the final model of stepwise multiple regression was VAT for fasting blood glucose (group F: β = 0.4627, P < 0.0001; group M: β = 0.6221, P < 0.0001) and TG levels (group F: β = 0.4931, P < 0.0001; group M: β = 0.1990, P < 0.0261) independently of BMI. The optimal cutoff points of VAT to identify the presence of at least one laboratory risk factor were >1395 g and >1479 cm3 for men and >1281 g and >1357 cm3 for women. Conclusions: DXA analysis of VAT is associated with selected laboratory parameters used for the evaluation of cardiometabolic risk and could be per se a helpful parameter in the assessment of clinical risk.

Correlation between DXA and laboratory parameters in normal weight, overweight, and obese patients / Aparisi Gómez, Mp; Ponti, F; Mercatelli, D; Gasperini, C; Napoli, A; Battista, G; Cariani, S; Marchesini, G; Bazzocchi, A. - In: NUTRITION. - ISSN 0899-9007. - 61:(2019), pp. 143-150. [10.1016/j.nut.2018.10.023]

Correlation between DXA and laboratory parameters in normal weight, overweight, and obese patients

Napoli A;
2019

Abstract

Objective: The aim of this study was to review the existence and types of correlations between body composition densitometric parameters and laboratory values associated to cardiometabolic risk. Methods: We retrospectively analyzed data from 316 individuals in the weight range from normality to super-obesity, submitted to total body dual-energy x-ray absorptiometry (DXA) scans and routine biochemistry at S.Orsola-Malpighi Hospital from June 2010 to March 2014. The study included 182 women, 45.8 ± 13.4 y of age, with a body mass index (BMI) of 31.5 (± 11) kg/m2 (group F) and 134 men, 45.4 ± 13.6 y of age, with a BMI of 27.6 (± 7.8) kg/m2 (group M). All patients underwent whole-body scan (Lunar iDXA, GE Healthcare, Madison, WI, USA) and laboratory analysis (blood fasting glucose, total cholesterol, high-density lipoprotein cholesterol, tricylglycerides [TGs], aspartate aminotransferase, and alanine aminotransferase). Correlation between laboratory values and total body and regional fat mass (including visceral adipose tissue [VAT] and subcutaneous adipose tissue in the android region), and lean mass parameters were analyzed with linear and stepwise regressions analysis (significance limit, P < 0.05). Receiver operating characteristic curves were performed to assess the accuracy of the best-fit DXA parameter (VAT) to identify at least one laboratory risk factor. Results: In both groups, BMI and densitometric parameters showed a linear correlation with fasting blood glucose and TG levels and an inverse correlation with high-density lipoprotein cholesterol (P < 0.05), whereas no correlation was observed with total cholesterol levels. The only densitometric parameter retained in the final model of stepwise multiple regression was VAT for fasting blood glucose (group F: β = 0.4627, P < 0.0001; group M: β = 0.6221, P < 0.0001) and TG levels (group F: β = 0.4931, P < 0.0001; group M: β = 0.1990, P < 0.0261) independently of BMI. The optimal cutoff points of VAT to identify the presence of at least one laboratory risk factor were >1395 g and >1479 cm3 for men and >1281 g and >1357 cm3 for women. Conclusions: DXA analysis of VAT is associated with selected laboratory parameters used for the evaluation of cardiometabolic risk and could be per se a helpful parameter in the assessment of clinical risk.
2019
absorptiometry, photon; body composition; cardiometabolic risk; obesity; visceral fat
01 Pubblicazione su rivista::01a Articolo in rivista
Correlation between DXA and laboratory parameters in normal weight, overweight, and obese patients / Aparisi Gómez, Mp; Ponti, F; Mercatelli, D; Gasperini, C; Napoli, A; Battista, G; Cariani, S; Marchesini, G; Bazzocchi, A. - In: NUTRITION. - ISSN 0899-9007. - 61:(2019), pp. 143-150. [10.1016/j.nut.2018.10.023]
File allegati a questo prodotto
File Dimensione Formato  
Aparisi Gomez_Correlation between DXA_2019.pdf

solo gestori archivio

Tipologia: Versione editoriale (versione pubblicata con il layout dell'editore)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 901.39 kB
Formato Adobe PDF
901.39 kB Adobe PDF   Contatta l'autore

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1683635
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 13
  • ???jsp.display-item.citation.isi??? 12
social impact