Predicting resting energy expenditure in people with chronic spinal cord injury

Yiming Ma; Sonja de Groot; Dirk Hoevenaars; Wendy Achterberg; Jacinthe Adriaansen; Peter J M Weijs; Thomas W J Janssen

doi:10.1038/s41393-022-00827-5

Predicting resting energy expenditure in people with chronic spinal cord injury

Yiming Ma
, Sonja de Groot
, Dirk Hoevenaars
, Wendy Achterberg
, Jacinthe Adriaansen
, Peter J M Weijs
, Thomas W J Janssen

Research output: Contribution to Journal › Article › Academic › peer-review

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Abstract

STUDY DESIGN: Cross-sectional study.

OBJECTIVES: The aims of this study were (1) to validate the two recently developed SCI-specific REE equations; (2) to develop new prediction equations to predict REE in a general population with SCI.

SETTING: University, the Netherlands.

METHODS: Forty-eight community-dwelling men and women with SCI were recruited (age: 18-75 years, time since injury: ≥12 months). Body composition was measured by dual-energy X-ray absorptiometry (DXA), single-frequency bioelectrical impedance analysis (SF-BIA) and skinfold thickness. REE was measured by indirect calorimetry. Personal and lesion characteristics were collected. SCI-specific REE equations by Chun et al. [1] and by Nightingale and Gorgey [2] were validated. New equations for predicting REE were developed using multivariate regression analysis.

RESULTS: Prediction equations by Chun et al. [1] and by Nightingale and Gorgey [2] significantly underestimated REE (Chun et al.: -11%; Nightingale and Gorgey: -11%). New equations were developed for predicting REE in the general population of people with SCI using FFM measured by SF-BIA and Goosey-Tolfrey et al. skinfold equation (R2 = 0.45-0.47; SEE = 200 kcal/day). The new equations showed proportional bias (p < 0.001) and wide limits of agreement (LoA, ±23%).

CONCLUSIONS: Prediction equations by Chun et al. [1] and by Nightingale and Gorgey [2] significantly underestimated REE and showed large individual variations in a general population with SCI. The newly developed REE equations showed proportional bias and a wide LoA (±23%) which limit the predictive power and accuracy to predict REE in the general population with SCI. Alternative methods for measuring REE need to be investigated.

Original language	English
Pages (from-to)	1100-1107
Number of pages	8
Journal	Spinal cord
Volume	60
Issue number	12
Early online date	2 Jul 2022
DOIs	https://doi.org/10.1038/s41393-022-00827-5
Publication status	Published - Dec 2022

Bibliographical note

Funding

We thank Amsterdam University of Applied Sciences (Amsterdam Nutritional Assessment Center, DR. MEURERHUIS) and the rehabilitation center Reade in Amsterdam which has contributed to the organization of measurements. We thank China Scholarship Council (CSC) which has supported the PhD career of the corresponding author. We appreciate the cooperation of all the participants, the master students who helped with the measurements and Drs. Robert G. Memelink for providing DXA training and guidance. We thank Amsterdam University of Applied Sciences (Amsterdam Nutritional Assessment Center, DR. MEURERHUIS) and the rehabilitation center Reade in Amsterdam which has contributed to the organization of measurements. We thank China Scholarship Council (CSC) which has supported the PhD career of the corresponding author. We appreciate the cooperation of all the participants, the master students who helped with the measurements and Drs. Robert G. Memelink for providing DXA training and guidance.

Funders
Amsterdam University of Applied Sciences
China Scholarship Council

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10.1038/s41393-022-00827-5

Predicting resting energy expenditure in people with chronic spinal cord injuryFinal published version, 1.25 MBLicence: Article 25fa Dutch Copyright Act

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@article{b7302e2043324818b6f63bd2f4e5414d,

title = "Predicting resting energy expenditure in people with chronic spinal cord injury",

abstract = "STUDY DESIGN: Cross-sectional study.OBJECTIVES: The aims of this study were (1) to validate the two recently developed SCI-specific REE equations; (2) to develop new prediction equations to predict REE in a general population with SCI.SETTING: University, the Netherlands.METHODS: Forty-eight community-dwelling men and women with SCI were recruited (age: 18-75 years, time since injury: ≥12 months). Body composition was measured by dual-energy X-ray absorptiometry (DXA), single-frequency bioelectrical impedance analysis (SF-BIA) and skinfold thickness. REE was measured by indirect calorimetry. Personal and lesion characteristics were collected. SCI-specific REE equations by Chun et al. [1] and by Nightingale and Gorgey [2] were validated. New equations for predicting REE were developed using multivariate regression analysis.RESULTS: Prediction equations by Chun et al. [1] and by Nightingale and Gorgey [2] significantly underestimated REE (Chun et al.: -11\%; Nightingale and Gorgey: -11\%). New equations were developed for predicting REE in the general population of people with SCI using FFM measured by SF-BIA and Goosey-Tolfrey et al. skinfold equation (R2 = 0.45-0.47; SEE = 200 kcal/day). The new equations showed proportional bias (p < 0.001) and wide limits of agreement (LoA, ±23\%).CONCLUSIONS: Prediction equations by Chun et al. [1] and by Nightingale and Gorgey [2] significantly underestimated REE and showed large individual variations in a general population with SCI. The newly developed REE equations showed proportional bias and a wide LoA (±23\%) which limit the predictive power and accuracy to predict REE in the general population with SCI. Alternative methods for measuring REE need to be investigated.",

author = "Yiming Ma and \{de Groot\}, Sonja and Dirk Hoevenaars and Wendy Achterberg and Jacinthe Adriaansen and Weijs, \{Peter J M\} and Janssen, \{Thomas W J\}",

note = "{\textcopyright} 2022. The Author(s), under exclusive licence to International Spinal Cord Society.",

year = "2022",

month = dec,

doi = "10.1038/s41393-022-00827-5",

language = "English",

volume = "60",

pages = "1100--1107",

journal = "Spinal cord",

issn = "1362-4393",

publisher = "Springer Nature",

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TY - JOUR

T1 - Predicting resting energy expenditure in people with chronic spinal cord injury

AU - Ma, Yiming

AU - de Groot, Sonja

AU - Hoevenaars, Dirk

AU - Achterberg, Wendy

AU - Adriaansen, Jacinthe

AU - Weijs, Peter J M

AU - Janssen, Thomas W J

PY - 2022/12

Y1 - 2022/12

N2 - STUDY DESIGN: Cross-sectional study.OBJECTIVES: The aims of this study were (1) to validate the two recently developed SCI-specific REE equations; (2) to develop new prediction equations to predict REE in a general population with SCI.SETTING: University, the Netherlands.METHODS: Forty-eight community-dwelling men and women with SCI were recruited (age: 18-75 years, time since injury: ≥12 months). Body composition was measured by dual-energy X-ray absorptiometry (DXA), single-frequency bioelectrical impedance analysis (SF-BIA) and skinfold thickness. REE was measured by indirect calorimetry. Personal and lesion characteristics were collected. SCI-specific REE equations by Chun et al. [1] and by Nightingale and Gorgey [2] were validated. New equations for predicting REE were developed using multivariate regression analysis.RESULTS: Prediction equations by Chun et al. [1] and by Nightingale and Gorgey [2] significantly underestimated REE (Chun et al.: -11%; Nightingale and Gorgey: -11%). New equations were developed for predicting REE in the general population of people with SCI using FFM measured by SF-BIA and Goosey-Tolfrey et al. skinfold equation (R2 = 0.45-0.47; SEE = 200 kcal/day). The new equations showed proportional bias (p < 0.001) and wide limits of agreement (LoA, ±23%).CONCLUSIONS: Prediction equations by Chun et al. [1] and by Nightingale and Gorgey [2] significantly underestimated REE and showed large individual variations in a general population with SCI. The newly developed REE equations showed proportional bias and a wide LoA (±23%) which limit the predictive power and accuracy to predict REE in the general population with SCI. Alternative methods for measuring REE need to be investigated.

AB - STUDY DESIGN: Cross-sectional study.OBJECTIVES: The aims of this study were (1) to validate the two recently developed SCI-specific REE equations; (2) to develop new prediction equations to predict REE in a general population with SCI.SETTING: University, the Netherlands.METHODS: Forty-eight community-dwelling men and women with SCI were recruited (age: 18-75 years, time since injury: ≥12 months). Body composition was measured by dual-energy X-ray absorptiometry (DXA), single-frequency bioelectrical impedance analysis (SF-BIA) and skinfold thickness. REE was measured by indirect calorimetry. Personal and lesion characteristics were collected. SCI-specific REE equations by Chun et al. [1] and by Nightingale and Gorgey [2] were validated. New equations for predicting REE were developed using multivariate regression analysis.RESULTS: Prediction equations by Chun et al. [1] and by Nightingale and Gorgey [2] significantly underestimated REE (Chun et al.: -11%; Nightingale and Gorgey: -11%). New equations were developed for predicting REE in the general population of people with SCI using FFM measured by SF-BIA and Goosey-Tolfrey et al. skinfold equation (R2 = 0.45-0.47; SEE = 200 kcal/day). The new equations showed proportional bias (p < 0.001) and wide limits of agreement (LoA, ±23%).CONCLUSIONS: Prediction equations by Chun et al. [1] and by Nightingale and Gorgey [2] significantly underestimated REE and showed large individual variations in a general population with SCI. The newly developed REE equations showed proportional bias and a wide LoA (±23%) which limit the predictive power and accuracy to predict REE in the general population with SCI. Alternative methods for measuring REE need to be investigated.

UR - https://www.scopus.com/pages/publications/85133218691

U2 - 10.1038/s41393-022-00827-5

DO - 10.1038/s41393-022-00827-5

M3 - Article

C2 - 35780202

SN - 1362-4393

VL - 60

SP - 1100

EP - 1107

JO - Spinal cord

JF - Spinal cord

IS - 12

ER -

Predicting resting energy expenditure in people with chronic spinal cord injury

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