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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.

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Fig. 1: The electrode placements of bioelectrical impedance analysis.
Fig. 2: Bland–Altman plots depicting mean difference and 95% limits of agreement of Chun et al. and Nightingale and Gorgey prediction equations.
Fig. 3: Bland-Altman plots depicting mean difference and 95% limits of agreement of the newly developed REE prediction equations.

Data availability

The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

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.

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Authors and Affiliations

Authors

Contributions

YM: Software, Validation, Formal analysis, Investigation, Data Curation, Writing—Original Draft, Writing—Review & Editing, Visualization. SDG: Conceptualization, Methodology, Formal analysis, Data Curation, Writing—Review & Editing, Visualization, Supervision. DH: Software, Investigation, Writing—Review & Editing. WA: Resources. JA: Resources. PW: Formal analysis, Resources, Data Curation, Writing—Review & Editing, Visualization, Supervision. TJ: Formal analysis, Resources, Data Curation, Writing—Review & Editing, Visualization, Supervision.

Corresponding author

Correspondence to Yiming Ma.

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Competing interests

The authors declare no competing interests.

Ethical approval

This study was approved by the Medical Ethical Committee of Slotervaart hospital and Reade (NL64704.048.18).

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Ma, Y., de Groot, S., Hoevenaars, D. et al. Predicting resting energy expenditure in people with chronic spinal cord injury. Spinal Cord (2022). https://doi.org/10.1038/s41393-022-00827-5

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