Literature DB >> 30143785

Estimated skeletal muscle mass and density values measured on computed tomography examinations in over 1000 living kidney donors.

Jeroen L A van Vugt1, Yordi van Putten2, Irma M van der Kall3, Stefan Buettner2, Frank C H D'Ancona4, Helena M Dekker5, Hendrikus J A N Kimenai2, Ron W F de Bruin2, Michiel C Warlé3, Jan N M IJzermans2.   

Abstract

BACKGROUND/
OBJECTIVES: Currently, there are no widely accepted cut-off points to categorize patients as sarcopenic (low skeletal muscle mass) or myosteatotic based on computed tomography (CT) measurements. Moreover, little is known about skeletal muscle mass in healthy subjects, particularly in a Western-European population. SUBJECTS/
METHODS: Skeletal muscle mass (skeletal muscle index, cm2/m2) and density (Hounsfield units, HU) at the level of the third lumbar vertebra were measured on contrast-enhanced CT images in live kidney donors with an age range of 18-86 years, who may be considered as healthy subjects, from 2010 to 2015. Differences between sex, body mass index (BMI), age groups, and American Society of Anesthesiologists (ASA) classification were assessed. Mann-Whitney U and Kruskal-Wallis tests were used to compare groups.
RESULTS: Of the 1073 included patients, 499 (46.5%) were male and the median age and BMI were 51 years and 25.4 kg/m2, respectively. Male gender, increased age, and increased BMI were significantly associated with both skeletal muscle mass and density. Nomograms including these parameters were developed to calculate the estimated skeletal muscle mass and density of a healthy subject and the lower bound of the 90% prediction interval (p5) values were provided.
CONCLUSIONS: Skeletal muscle density and mass were significantly associated with sex, age, and BMI in a large cohort of healthy Western-European subjects. The newly developed nomograms may be used to calculate the estimated healthy skeletal muscle mass for individuals in patient populations.

Entities:  

Mesh:

Year:  2018        PMID: 30143785     DOI: 10.1038/s41430-018-0287-7

Source DB:  PubMed          Journal:  Eur J Clin Nutr        ISSN: 0954-3007            Impact factor:   4.016


  6 in total

1.  Body composition and outcome in patients undergoing resection of colorectal liver metastases.

Authors:  M G van Vledder; S Levolger; N Ayez; C Verhoef; T C K Tran; J N M Ijzermans
Journal:  Br J Surg       Date:  2012-01-13       Impact factor: 6.939

2.  Adipose tissue volume determinations in women by computed tomography: technical considerations.

Authors:  H Kvist; L Sjöström; U Tylén
Journal:  Int J Obes       Date:  1986

Review 3.  Human body composition: advances in models and methods.

Authors:  S B Heymsfield; Z Wang; R N Baumgartner; R Ross
Journal:  Annu Rev Nutr       Date:  1997       Impact factor: 11.848

4.  Cancer cachexia in the age of obesity: skeletal muscle depletion is a powerful prognostic factor, independent of body mass index.

Authors:  Lisa Martin; Laura Birdsell; Neil Macdonald; Tony Reiman; M Thomas Clandinin; Linda J McCargar; Rachel Murphy; Sunita Ghosh; Michael B Sawyer; Vickie E Baracos
Journal:  J Clin Oncol       Date:  2013-03-25       Impact factor: 44.544

5.  Validity of self-reported height and weight for measuring prevalence of obesity.

Authors:  Noori Akhtar-Danesh; Mahshid Dehghan; Anwar T Merchant; James A Rainey
Journal:  Open Med       Date:  2008-08-26

6.  A comparative study of software programmes for cross-sectional skeletal muscle and adipose tissue measurements on abdominal computed tomography scans of rectal cancer patients.

Authors:  Jeroen L A van Vugt; Stef Levolger; Arvind Gharbharan; Marcel Koek; Wiro J Niessen; Jacobus W A Burger; Sten P Willemsen; Ron W F de Bruin; Jan N M IJzermans
Journal:  J Cachexia Sarcopenia Muscle       Date:  2016-11-22       Impact factor: 12.910

  6 in total
  3 in total

1.  Impact of Body Composition in Overweight and Obese Patients With Localised Renal Cell Carcinoma.

Authors:  Tiffany Darbas; Geraud Forestier; Sophie Leobon; Julia Pestre; Pierre Jesus; Denis Lachatre; Nicole Tubiana-Mathieu; Aurelien Descazeaud; Elise Deluche
Journal:  In Vivo       Date:  2020 Sep-Oct       Impact factor: 2.155

2.  Deep learning-based muscle segmentation and quantification at abdominal CT: application to a longitudinal adult screening cohort for sarcopenia assessment.

Authors:  Peter M Graffy; Jiamin Liu; Perry J Pickhardt; Joseph E Burns; Jianhua Yao; Ronald M Summers
Journal:  Br J Radiol       Date:  2019-06-24       Impact factor: 3.039

3.  Defining sarcopenia and myosteatosis: the necessity for consensus on a technical standard and standardized cut-off values.

Authors:  Lisa B Westenberg; Marcel Zorgdrager; Alain R Viddeleer; Robert A Pol
Journal:  J Cachexia Sarcopenia Muscle       Date:  2022-02-26       Impact factor: 12.910

  3 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.