Literature DB >> 24447263

Predicting muscle mass from anthropometry using magnetic resonance imaging as reference: a systematic review.

Yasmin Y Al-Gindan1, Catherine R Hankey, Wilma Leslie, Lindsay Govan, Michael E J Lean.   

Abstract

Identification and management of sarcopenia are limited by lack of reliable simple approaches to assess muscle mass. The aim of this review is to identify and evaluate simple methods to quantify muscle mass/volume of adults. Using Cochrane Review methodology, Medline (1946-2012), Embase (1974-2012), Web of Science (1898-2012), PubMed, and the Cochrane Library (to 08/2012) were searched for publications that included prediction equations (from anthropometric measurements) to estimate muscle mass by magnetic resonance imaging (MRI) in adults. Of 257 papers identified from primary search terms, 12 studies met the inclusion criteria. Most studies (n = 10) assessed only regional/limb muscle mass/volume. Many studies (n = 9) assessed limb circumference adjusted for skinfold thickness, which limits their practical applications. Only two included validation in separate subject-samples, and two reported relationships between whole-body MRI-measured muscle mass and anthropometry beyond linear correlations. In conclusion, one simple prediction equation shows promise, but it has not been validated in a separate population with different investigators. Furthermore, it did not incorporate widely available trunk/limb girths, which have offered valuable prediction of body composition in other studies.
© 2014 International Life Sciences Institute.

Entities:  

Keywords:  anthropometry; magnetic resonance imaging; muscle mass; validation

Mesh:

Year:  2014        PMID: 24447263     DOI: 10.1111/nure.12096

Source DB:  PubMed          Journal:  Nutr Rev        ISSN: 0029-6643            Impact factor:   7.110


  6 in total

1.  Derivation and validation of simple anthropometric equations to predict adipose tissue mass and total fat mass with MRI as the reference method.

Authors:  Yasmin Y Al-Gindan; Catherine R Hankey; Lindsay Govan; Dympna Gallagher; Steven B Heymsfield; Michael E J Lean
Journal:  Br J Nutr       Date:  2015-10-05       Impact factor: 3.718

2.  Precise Prediction of Total Body Lean and Fat Mass From Anthropometric and Demographic Data: Development and Validation of Neural Network Models.

Authors:  Simon Lebech Cichosz; Nicklas Højgaard Rasmussen; Peter Vestergaard; Ole Hejlesen
Journal:  J Diabetes Sci Technol       Date:  2020-11-16

3.  Is adductor pollicis muscle thickness a good predictor of lean mass in adults?

Authors:  Renata Moraes Bielemann; Bernardo Lessa Horta; Silvana Paiva Orlandi; Thiago Gonzalez Barbosa-Silva; Maria Cristina Gonzalez; Maria Cecília Assunção; Denise Petrucci Gigante
Journal:  Clin Nutr       Date:  2015-08-07       Impact factor: 7.324

4.  Posttransplant muscle mass measured by urinary creatinine excretion rate predicts long-term outcomes after liver transplantation.

Authors:  Suzanne P Stam; Maryse C J Osté; Michele F Eisenga; Hans Blokzijl; Aad P van den Berg; Stephan J L Bakker; Vincent E de Meijer
Journal:  Am J Transplant       Date:  2018-06-03       Impact factor: 8.086

5.  Pre-Disease and Pre-Surgery BMI, Weight Loss and Sarcopenia Impact Survival of Resected Lung Cancer Independently of Tumor Stage.

Authors:  Philippe Icard; Olivier Schussler; Mauro Loi; Antonio Bobbio; Audrey Mansuet Lupo; Marie Wislez; Antonio Iannelli; Ludovic Fournel; Diane Damotte; Marco Alifano
Journal:  Cancers (Basel)       Date:  2020-01-22       Impact factor: 6.639

6.  Derivation and validation of simple equations to predict total muscle mass from simple anthropometric and demographic data.

Authors:  Yasmin Y Al-Gindan; Catherine Hankey; Lindsay Govan; Dympna Gallagher; Steven B Heymsfield; Michael E J Lean
Journal:  Am J Clin Nutr       Date:  2014-08-13       Impact factor: 7.045

  6 in total

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