sg:pub.10.1007/s00421-006-0325-3 - Springer Nature SciGraph

Article Info

DATE

2006-11-07

AUTHORS

Kiyoshi Sanada, Taishi Midorikawa, Tomohiro Yasuda, Charles F. Kearns, Takashi Abe

ABSTRACT

The present study developed nonexercise models for predicting maximal oxygen uptake \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$({\dot{{V}}}\hbox{O}_{\rm{2max}})$$\end{document} using skeletal muscle (SM) mass and cardiac dimensions and to investigate the validity of these equations in healthy Japanese young men. Sixty healthy Japanese men were randomly separated into two groups: 40 in the development group and 20 in the validation group. \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\dot{{V}}}\hbox{O}_{\rm{2max}}$$\end{document} during treadmill running was measured using an automated breath-by-breath mass spectrometry system. Left ventricular internal dimensions at end-diastole (LVIDD) and at end-systole (LVIDS) were measured using M-mode ultrasound with a 2.5 MHz transducer. Stroke volume (SV) was calculated based on the Pombo rule. SM mass was predicted by B-mode ultrasound muscle thickness. Correlations were observed between \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\dot{{V}}}\hbox{O}_{\rm{2max}}$$\end{document} and predicted thigh (r = 0.74, P < 0.001) and lower leg SM mass (r = 0.55, P < 0.001). Furthermore, there were correlations between \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\dot{{V}}}\hbox{O}_{\rm{2max}}$$\end{document} and LVIDD (r = 0.74, P < 0.001) and SV (r = 0.72, P < 0.001). Stepwise regression analysis was applied to thigh SM mass and SV for prediction of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\dot{{V}}}\hbox{O}_{\rm{2max}}$$\end{document} in the development group, and these parameters were closely correlated with absolute measured \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\dot{{V}}}\hbox{O}_{\rm{2max}}\ ({R}^{2}=0.72, P < 0.001)$$\end{document} by multiple regression analysis. When the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\dot{{V}}}\hbox{O}_{\rm{2max}}$$\end{document} prediction equations were applied to the validation group, significant correlations were also observed between the measured and predicted \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\dot{{V}}}\hbox{O}_{\rm{2max}}\ ({R}^{2}=0.83, P < 0.001).$$\end{document} These results suggested that nonexercise prediction of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\dot{{V}}}\hbox{O}_{\rm{2max}}$$\end{document} using thigh SM mass and cardiac dimension is a valid method to predict \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\dot{{V}}}\hbox{O}_{\rm{2max}}$$\end{document} in young Japanese adults. More... »

PAGES

143-148

References to SciGraph publications

2005-10-19. Prediction and validation of total and regional skeletal muscle mass by ultrasound in Japanese adults in EUROPEAN JOURNAL OF APPLIED PHYSIOLOGY

1953-05. A modified harvard step test for the evaluation of physical fitness in ARBEITSPHYSIOLOGIE; INTERNATIONALE ZEITSCHRIFT FÜR ANGEWANDTE PHYSIOLOGIE

2004-11-20. Peak oxygen uptake during running and arm cranking normalized to total and regional skeletal muscle mass measured by magnetic resonance imaging in EUROPEAN JOURNAL OF APPLIED PHYSIOLOGY

Journal

TITLE

European Journal of Applied Physiology

ISSUE

VOLUME

Subjects

FOR: Medical And Health Sciences

FOR: Clinical Sciences

MESH: Adult

MESH: Asians

MESH: Exercise Test

MESH: Heart

MESH: Heart Ventricles

MESH: Humans

MESH: Japan

MESH: Male

MESH: Models, Biological

MESH: Muscle, Skeletal

MESH: Organ Size

MESH: Oxygen Consumption

MESH: Predictive Value Of Tests

MESH: Reference Values

MESH: Reproducibility Of Results

MESH: Stroke Volume

MESH: Ultrasonography

Author Affiliations

Tokyo Metropolitan University, 1-1, Minami-Ohsawa, Hachioji, 192-0397, Tokyo, Japan

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00421-006-0325-3

DOI

http://dx.doi.org/10.1007/s00421-006-0325-3

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1041058342

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/17115179

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26	″	schema:description	The present study developed nonexercise models for predicting maximal oxygen uptake \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$({\dot{{V}}}\hbox{O}_{\rm{2max}})$$\end{document} using skeletal muscle (SM) mass and cardiac dimensions and to investigate the validity of these equations in healthy Japanese young men. Sixty healthy Japanese men were randomly separated into two groups: 40 in the development group and 20 in the validation group. \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\dot{{V}}}\hbox{O}_{\rm{2max}}$$\end{document} during treadmill running was measured using an automated breath-by-breath mass spectrometry system. Left ventricular internal dimensions at end-diastole (LVIDD) and at end-systole (LVIDS) were measured using M-mode ultrasound with a 2.5 MHz transducer. Stroke volume (SV) was calculated based on the Pombo rule. SM mass was predicted by B-mode ultrasound muscle thickness. Correlations were observed between \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\dot{{V}}}\hbox{O}_{\rm{2max}}$$\end{document} and predicted thigh (r = 0.74, P < 0.001) and lower leg SM mass (r = 0.55, P < 0.001). Furthermore, there were correlations between \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\dot{{V}}}\hbox{O}_{\rm{2max}}$$\end{document} and LVIDD (r = 0.74, P < 0.001) and SV (r = 0.72, P < 0.001). Stepwise regression analysis was applied to thigh SM mass and SV for prediction of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\dot{{V}}}\hbox{O}_{\rm{2max}}$$\end{document} in the development group, and these parameters were closely correlated with absolute measured \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\dot{{V}}}\hbox{O}_{\rm{2max}}\ ({R}^{2}=0.72, P < 0.001)$$\end{document} by multiple regression analysis. When the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\dot{{V}}}\hbox{O}_{\rm{2max}}$$\end{document} prediction equations were applied to the validation group, significant correlations were also observed between the measured and predicted \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\dot{{V}}}\hbox{O}_{\rm{2max}}\ ({R}^{2}=0.83, P < 0.001).$$\end{document} These results suggested that nonexercise prediction of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\dot{{V}}}\hbox{O}_{\rm{2max}}$$\end{document} using thigh SM mass and cardiac dimension is a valid method to predict \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\dot{{V}}}\hbox{O}_{\rm{2max}}$$\end{document} in young Japanese adults.
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32	″	schema:keywords	Japanese adults
33	″	″	Japanese men
34	″	″	Japanese young men
35	″	″	LVIDd
36	″	″	M-mode ultrasound
37	″	″	MHz transducer
38	″	″	SM mass
39	″	″	adults
40	″	″	analysis
41	″	″	breath
42	″	″	cardiac dimensions
43	″	″	correlation
44	″	″	development
45	″	″	development group
46	″	″	dimensions
47	″	″	end diastole
48	″	″	equations
49	″	″	group
50	″	″	healthy Japanese men
51	″	″	internal dimensions
52	″	″	left ventricular internal dimension
53	″	″	mass
54	″	″	mass spectrometry system
55	″	″	maximal oxygen uptake
56	″	″	men
57	″	″	method
58	″	″	model
59	″	″	multiple regression analysis
60	″	″	muscle mass
61	″	″	muscle thickness
62	″	″	nonexercise models
63	″	″	oxygen uptake
64	″	″	parameters
65	″	″	prediction
66	″	″	prediction equations
67	″	″	prediction model
68	″	″	present study
69	″	″	regression analysis
70	″	″	results
71	″	″	rules
72	″	″	running
73	″	″	significant correlation
74	″	″	skeletal muscle mass
75	″	″	spectrometry system
76	″	″	stepwise regression analysis
77	″	″	stroke volume
78	″	″	study
79	″	″	system
80	″	″	thickness
81	″	″	thigh
82	″	″	transducer
83	″	″	treadmill running
84	″	″	ultrasound
85	″	″	ultrasound muscle thickness
86	″	″	uptake
87	″	″	valid method
88	″	″	validation group
89	″	″	validity
90	″	″	ventricular internal dimension
91	″	″	volume
92	″	″	young Japanese adults
93	″	″	young men
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