Cargando…
DEXA Scan Body Fat Mass Distribution in Obese and Non-Obese Individuals and Risk of NAFLD—Analysis of 10,865 Individuals
Non-alcoholic fatty liver disease (NAFLD) is the most common chronic liver disease worldwide yet predicting non-obese NAFLD is challenging. Thus, this study investigates the potential of regional fat percentages obtained by dual-energy X-ray absorptiometry (DXA) in accurately assessing NAFLD risk. U...
Autores principales: | , , , , , , , , , , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9605163/ https://www.ncbi.nlm.nih.gov/pubmed/36294526 http://dx.doi.org/10.3390/jcm11206205 |
_version_ | 1784817996972163072 |
---|---|
author | Tan, Caitlyn Chan, Kai En Ng, Cheng Han Tseng, Michael Syn, Nicholas Tang, Ansel Shao Pin Chin, Yip Han Lim, Wen Hui Tan, Darren Jun Hao Chew, Nicholas Ong, Elden Yen Hng Koh, Teng Kiat Xiao, Jieling Chee, Douglas Valsan, Arun Siddiqui, Mohammad Shadab Huang, Daniel Noureddin, Mazen Wijarnpreecha, Karn Muthiah, Mark D. |
author_facet | Tan, Caitlyn Chan, Kai En Ng, Cheng Han Tseng, Michael Syn, Nicholas Tang, Ansel Shao Pin Chin, Yip Han Lim, Wen Hui Tan, Darren Jun Hao Chew, Nicholas Ong, Elden Yen Hng Koh, Teng Kiat Xiao, Jieling Chee, Douglas Valsan, Arun Siddiqui, Mohammad Shadab Huang, Daniel Noureddin, Mazen Wijarnpreecha, Karn Muthiah, Mark D. |
author_sort | Tan, Caitlyn |
collection | PubMed |
description | Non-alcoholic fatty liver disease (NAFLD) is the most common chronic liver disease worldwide yet predicting non-obese NAFLD is challenging. Thus, this study investigates the potential of regional fat percentages obtained by dual-energy X-ray absorptiometry (DXA) in accurately assessing NAFLD risk. Using the United States National Health and Nutrition Examination Survey (NHANES) 2011–2018, multivariate logistic regression and marginal analysis were conducted according to quartiles of regional fat percentages, stratified by gender. A total of 23,752 individuals were analysed. Males generally showed a larger increase in marginal probabilities of NAFLD development than females, except in head fat, which had the highest predictive probabilities of non-obese NAFLD in females (13.81%, 95%CI: 10.82–16.79) but the lowest in males (21.89%, 95%CI: 20.12–23.60). Increased percent of trunk fat was the strongest predictor of both non-obese (OR: 46.61, 95%CI: 33.55–64.76, p < 0.001) and obese NAFLD (OR: 2.93, 95%CI: 2.07–4.15, p < 0.001), whereas raised percent gynoid and leg fat were the weakest predictors. Ectopic fat deposits are increased in patients with non-obese NAFLD, with greater increases in truncal fat over gynoid fat. As increased fat deposits in all body regions can increase odds of NAFLD, therapeutic intervention to decrease ectopic fat, particularly truncal fat, may decrease NAFLD risk. |
format | Online Article Text |
id | pubmed-9605163 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-96051632022-10-27 DEXA Scan Body Fat Mass Distribution in Obese and Non-Obese Individuals and Risk of NAFLD—Analysis of 10,865 Individuals Tan, Caitlyn Chan, Kai En Ng, Cheng Han Tseng, Michael Syn, Nicholas Tang, Ansel Shao Pin Chin, Yip Han Lim, Wen Hui Tan, Darren Jun Hao Chew, Nicholas Ong, Elden Yen Hng Koh, Teng Kiat Xiao, Jieling Chee, Douglas Valsan, Arun Siddiqui, Mohammad Shadab Huang, Daniel Noureddin, Mazen Wijarnpreecha, Karn Muthiah, Mark D. J Clin Med Article Non-alcoholic fatty liver disease (NAFLD) is the most common chronic liver disease worldwide yet predicting non-obese NAFLD is challenging. Thus, this study investigates the potential of regional fat percentages obtained by dual-energy X-ray absorptiometry (DXA) in accurately assessing NAFLD risk. Using the United States National Health and Nutrition Examination Survey (NHANES) 2011–2018, multivariate logistic regression and marginal analysis were conducted according to quartiles of regional fat percentages, stratified by gender. A total of 23,752 individuals were analysed. Males generally showed a larger increase in marginal probabilities of NAFLD development than females, except in head fat, which had the highest predictive probabilities of non-obese NAFLD in females (13.81%, 95%CI: 10.82–16.79) but the lowest in males (21.89%, 95%CI: 20.12–23.60). Increased percent of trunk fat was the strongest predictor of both non-obese (OR: 46.61, 95%CI: 33.55–64.76, p < 0.001) and obese NAFLD (OR: 2.93, 95%CI: 2.07–4.15, p < 0.001), whereas raised percent gynoid and leg fat were the weakest predictors. Ectopic fat deposits are increased in patients with non-obese NAFLD, with greater increases in truncal fat over gynoid fat. As increased fat deposits in all body regions can increase odds of NAFLD, therapeutic intervention to decrease ectopic fat, particularly truncal fat, may decrease NAFLD risk. MDPI 2022-10-21 /pmc/articles/PMC9605163/ /pubmed/36294526 http://dx.doi.org/10.3390/jcm11206205 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Tan, Caitlyn Chan, Kai En Ng, Cheng Han Tseng, Michael Syn, Nicholas Tang, Ansel Shao Pin Chin, Yip Han Lim, Wen Hui Tan, Darren Jun Hao Chew, Nicholas Ong, Elden Yen Hng Koh, Teng Kiat Xiao, Jieling Chee, Douglas Valsan, Arun Siddiqui, Mohammad Shadab Huang, Daniel Noureddin, Mazen Wijarnpreecha, Karn Muthiah, Mark D. DEXA Scan Body Fat Mass Distribution in Obese and Non-Obese Individuals and Risk of NAFLD—Analysis of 10,865 Individuals |
title | DEXA Scan Body Fat Mass Distribution in Obese and Non-Obese Individuals and Risk of NAFLD—Analysis of 10,865 Individuals |
title_full | DEXA Scan Body Fat Mass Distribution in Obese and Non-Obese Individuals and Risk of NAFLD—Analysis of 10,865 Individuals |
title_fullStr | DEXA Scan Body Fat Mass Distribution in Obese and Non-Obese Individuals and Risk of NAFLD—Analysis of 10,865 Individuals |
title_full_unstemmed | DEXA Scan Body Fat Mass Distribution in Obese and Non-Obese Individuals and Risk of NAFLD—Analysis of 10,865 Individuals |
title_short | DEXA Scan Body Fat Mass Distribution in Obese and Non-Obese Individuals and Risk of NAFLD—Analysis of 10,865 Individuals |
title_sort | dexa scan body fat mass distribution in obese and non-obese individuals and risk of nafld—analysis of 10,865 individuals |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9605163/ https://www.ncbi.nlm.nih.gov/pubmed/36294526 http://dx.doi.org/10.3390/jcm11206205 |
work_keys_str_mv | AT tancaitlyn dexascanbodyfatmassdistributioninobeseandnonobeseindividualsandriskofnafldanalysisof10865individuals AT chankaien dexascanbodyfatmassdistributioninobeseandnonobeseindividualsandriskofnafldanalysisof10865individuals AT ngchenghan dexascanbodyfatmassdistributioninobeseandnonobeseindividualsandriskofnafldanalysisof10865individuals AT tsengmichael dexascanbodyfatmassdistributioninobeseandnonobeseindividualsandriskofnafldanalysisof10865individuals AT synnicholas dexascanbodyfatmassdistributioninobeseandnonobeseindividualsandriskofnafldanalysisof10865individuals AT tanganselshaopin dexascanbodyfatmassdistributioninobeseandnonobeseindividualsandriskofnafldanalysisof10865individuals AT chinyiphan dexascanbodyfatmassdistributioninobeseandnonobeseindividualsandriskofnafldanalysisof10865individuals AT limwenhui dexascanbodyfatmassdistributioninobeseandnonobeseindividualsandriskofnafldanalysisof10865individuals AT tandarrenjunhao dexascanbodyfatmassdistributioninobeseandnonobeseindividualsandriskofnafldanalysisof10865individuals AT chewnicholas dexascanbodyfatmassdistributioninobeseandnonobeseindividualsandriskofnafldanalysisof10865individuals AT ongeldenyenhng dexascanbodyfatmassdistributioninobeseandnonobeseindividualsandriskofnafldanalysisof10865individuals AT kohtengkiat dexascanbodyfatmassdistributioninobeseandnonobeseindividualsandriskofnafldanalysisof10865individuals AT xiaojieling dexascanbodyfatmassdistributioninobeseandnonobeseindividualsandriskofnafldanalysisof10865individuals AT cheedouglas dexascanbodyfatmassdistributioninobeseandnonobeseindividualsandriskofnafldanalysisof10865individuals AT valsanarun dexascanbodyfatmassdistributioninobeseandnonobeseindividualsandriskofnafldanalysisof10865individuals AT siddiquimohammadshadab dexascanbodyfatmassdistributioninobeseandnonobeseindividualsandriskofnafldanalysisof10865individuals AT huangdaniel dexascanbodyfatmassdistributioninobeseandnonobeseindividualsandriskofnafldanalysisof10865individuals AT noureddinmazen dexascanbodyfatmassdistributioninobeseandnonobeseindividualsandriskofnafldanalysisof10865individuals AT wijarnpreechakarn dexascanbodyfatmassdistributioninobeseandnonobeseindividualsandriskofnafldanalysisof10865individuals AT muthiahmarkd dexascanbodyfatmassdistributioninobeseandnonobeseindividualsandriskofnafldanalysisof10865individuals |