Harris–Benedict equation estimations of energy needs as compared to measured 24-h energy expenditure by indirect calorimetry in people with early to mid-stage Huntington's disease

Introduction: Little is known about the energy needs in Huntington's disease (HD). The aims of this study are to analyze and compare the total energy expenditure (TEE) and energy balance (EB) in a representative sample of HD patients with healthy controls. Methods: This is an observational, case-control single-center study. Food caloric energy intake (EI) and TEE were considered for estimating EB. A dietary recall questionnaire was used to assess the EI. TEE was computed as the sum of resting energy expenditure (REE), measured by indirect calorimetry and physical activity (PA) monitored by an actigraph. Results: A total of 22 patients were included (36% men, mean age 50.3 ± 15.6 years, motor Unified Huntington's Disease Scale 27.9 ± 23.7, total functional capacity 11.0 (7.0-13.0), EI 38.6 ± 10.0 kcal/kg, PA 5.3 (3.0-7.4) kcal/kg, REE 30.9 ± 6.4 kcal/kg, TEE 2,023.4 (1,592.0-2,226.5) kcal/day) and 18 controls (50% men, mean age 47.4 ± 13.8 years, EI 38.6 ± 10.3 kcal/kg, PA 8.4 (5.0-13.8) kcal/kg, REE 30.8 ± 6.6 kcal/kg, TEE 2,281.0 (2,057.3-2,855.3) kcal/day). TEE was significantly lower in patients compared to controls (p = 0.03). PA was lower in patients compared to controls (p = 0.02). Conclusions: Although patients with HD appeared to have lower energy expenditure, mainly due to decreased voluntary PA, they were still able to maintain their energy needs with an adequate food intake.

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A Mobile Application for Smart Computer-Aided Self-Administered Testing of Cognition, Speech, and Motor Impairment

Sensors ◽

10.3390/s20113236 ◽

2020 ◽

Vol 20 (11) ◽

pp. 3236 ◽

Cited By ~ 1

Author(s):

Andrius Lauraitis ◽

Rytis Maskeliūnas ◽

Robertas Damaševičius ◽

Tomas Krilavičius

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Energy Expenditure ◽

Huntington's Disease ◽

Huntington’S Disease ◽

Mobile Application ◽

Speech Analysis ◽

Finger Tapping ◽

Hand Tremor ◽

Neural Impairment

We present a model for digital neural impairment screening and self-assessment, which can evaluate cognitive and motor deficits for patients with symptoms of central nervous system (CNS) disorders, such as mild cognitive impairment (MCI), Parkinson’s disease (PD), Huntington’s disease (HD), or dementia. The data was collected with an Android mobile application that can track cognitive, hand tremor, energy expenditure, and speech features of subjects. We extracted 238 features as the model inputs using 16 tasks, 12 of them were based on a self-administered cognitive testing (SAGE) methodology and others used finger tapping and voice features acquired from the sensors of a smart mobile device (smartphone or tablet). Fifteen subjects were involved in the investigation: 7 patients with neurological disorders (1 with Parkinson’s disease, 3 with Huntington’s disease, 1 with early dementia, 1 with cerebral palsy, 1 post-stroke) and 8 healthy subjects. The finger tapping, SAGE, energy expenditure, and speech analysis features were used for neural impairment evaluations. The best results were achieved using a fusion of 13 classifiers for combined finger tapping and SAGE features (96.12% accuracy), and using bidirectional long short-term memory (BiLSTM) (94.29% accuracy) for speech analysis features.

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Higher sedentary energy expenditure in patients with Huntington's disease

Annals of Neurology ◽

10.1002/1531-8249(200001)47:1<64::aid-ana11>3.0.co;2-s ◽

2000 ◽

Vol 47 (1) ◽

pp. 64-70 ◽

Cited By ~ 80

Author(s):

Richard E. Pratley ◽

Arline D. Salbe ◽

Eric Ravussin ◽

John N. Caviness

Keyword(s):

Energy Expenditure ◽

Huntington's Disease ◽

Huntington’S Disease

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Energy Needs Are Poorly Predicted in Critically Ill Elderly

Journal of Intensive Care Medicine ◽

10.1177/088506669701200106 ◽

1997 ◽

Vol 12 (1) ◽

pp. 45-49 ◽

Cited By ~ 3

Author(s):

R. Sheridan ◽

K. Prelack ◽

L. Yin ◽

Vincent Riggi

Keyword(s):

Energy Expenditure ◽

Elderly Patients ◽

Critically Ill ◽

Indirect Calorimetry ◽

Resting Energy Expenditure ◽

Apache Ii Score ◽

Mechanically Ventilated ◽

Energy Needs ◽

Expired Gas ◽

Resting Energy

Changes in energy expenditure with age have been described, but this physiology is not routinely considered when managing critically ill elderly patients. To allow us to avoid the potential problems associated with underfeeding or overfeeding the critically ill elderly population, with approval of the human studies committee and appropriate consent from legal guardians, 25 critically ill patients over 65 years of age requiring mechanical ventilation underwent expired gas indirect calorimetry. If they had a pulmonary artery catheter in place for clinical reasons, reverse-Fick indirect calorimetry was also performed. Data obtained by indirect calorimetry was compared with commonly applied equations for predicting energy expenditure by statistical methods of correlation and limits of agreement. These 25 patients had an average age of 74 ± 1.23 (standard error of the mean) and an average APACHE II score of 15. Predictive equations correlated poorly with measured resting energy expenditure, and although they showed reasonable bias, they were imprecise in their estimation of resting energy expenditure. These data suggest that energy expenditure in critically ill, mechanically ventilated elderly patients is highly variable. Although generally overestimating energy needs, currently available equations for predicting energy expenditure in this population are associated with significant bias and imprecision, which may lead to both overfeeding and underfeeding. Although these equations may be suitable as a basis of initiating nutritional support, energy provisions should ideally be guided by indirect calorimetry.

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