Protocol of Energy balance estimates in Huntington's disease: an observational, case-control, multicenter pilot study. (Preprint)
Authors: Collazo Carla , Soto-Célix María , Raya-González Javier , Castillo-Alvira Daniel , Rivadeneyra-Posadas Jessica , Calvo Sara , Simón-Vicente Lucía , Rodríguez-Fernández Alejandro , Cubo Esther 
Published: August 2022

BACKGROUND

Huntington's disease (HD) is an inherited neurodegenerative disorder, autosomal dominant caused by an expanded triplet (CAG) repeat in HTT gene on chromosome 4p, with a prevalence of 14/100,000 people. Major clinical features are involuntary movements, psychiatric symptoms, and cognitive dysfunction [1, 2].
There is no curative treatment for HD, with only symptomatic pharmacological treatments available. Due to the progressive course of neurodegeneration, HD patients progressively lose their functional capacity with increased dependence and risk for cardiovascular events, sepsis, and death.[3]
Among the different clinical manifestations, weight loss and energy deficit (ED) have been described in HD, even in the early stages of the disease, contributing to the progression. The underlying mechanisms contributing to weight loss and the energy requirement in different stages of HD remain unknown.[4, 5] Surprisingly, HD patients often report an increased appetite with higher energy intake,[4, 6] and higher weight is associated with a slower rate of disease progression. [7] In this regard, the alterations in the brain and muscle metabolism and weight loss underscore that ED is likely an early phenomenon in the cascade of events leading to HD pathogenesis.[7] Biochemical studies support that mitochondrial dysfunction is a relevant factor in the alteration of ED in HD, and the hypothalamic dysfunction would contribute to weight loss.[5]
Calories are units that measure the energy in food and the energy produced, stored, and utilized by living organisms. One calorie is defined as the amount of heat energy required to raise 1 g of water by 1°C [8]. TEE is the amount of energy an individual uses to maintain essential body functions (respiration, circulation, digestion) as a result of physical activity (PA).[9] TEE is then the sum of several components: resting energy expenditure (REE; 60–80%), PA (20–40%), and diet-induced thermogenesis (7–10%).[10] Adequate EB is achieved when TEE equals energy intake, defined as the energy consumed via food and beverage [8]. Previous literature in HD suggests that EB is not adequate, either due to reduced food intake (HD patients frequently suffer from dysphagia), abnormal REE secondary to structural damage (hypothalamic, mitochondrial disturbances produced by the neurodegeneration process per se), or abnormal PA due to chorea, or decreased mobility.[5, 11]
Few studies on EB in neurodegenerative diseases, including HD, have comprehensively addressed and analyzed its contributing components.[12] Because most of these EB studies have been performed in controlled-laboratory research, there is a lack of knowledge about EB estimates in free-living environments. Valid measurement of PA in free-living environments is needed to describe and quantify PA and to assess the effectiveness of interventions and health promotion techniques.[13]
Since their introduction as an objective measure of free-living PA in the early 1980s,[14] accelerometers have become a staple of the PA assessment repertoire. They have been used extensively in validating self-reported PA surveys, as outcome measures in intervention studies, and in research designed to identify the psychosocial and environmental correlates of PA behaviors. Accelerometry has the potential to provide information about the intensity, duration, and frequency of PA. A challenge in accelerometry-based approaches is that the raw outputs (typically called counts) must be converted into meaningful and interpretable units.[13] In most calibration accelerometer studies, counts are converted to oxygen consumption, metabolic equivalents (METs), or caloric expenditure as meaningful and interpretable measurements of PA intensity. [15] However, to predict energy expenditure related to PA, we should differentiate community-based samples vs. disease-based samples and calibrate these accelerometers with accurate cut points to distinguish levels of PA (mild, moderate, or vigorous).
Sarcopenia defined as decreased muscle mass, is another important aspect of weight loss in HD and leads to higher dependency rates.[16] Overall, muscle mass can be quantified at different levels of body composition. Some methods, such as dual-energy X-ray absorptiometry (DXA), or Magnetic Resonance Imaging, have high validity but are complex and expensive.[17] In this regard, BIA is inexpensive and easy to perform in most settings, being the preferred method for clinical practice.[17] However, as in any other measurement, the accuracy of BIA in HD has not been established. Considering the lack of knowledge about EB in HD and its impact on body composition, it is important to conduct a study to validate different tools to calculate the EB and body composition in HD, and to provide preliminary data on EB in HD, which is the main objective. The present publication presents the protocol for this study.

OBJECTIVE

SPECIFIC GOALS
The main aim of this study is to calculate the EB in patients with HD. Specific objectives of this study are 1) to validate the use of accelerometers to quantify energy expenditure related to PA under controlled conditions for HD patients using Indirect Calorimetry (IC) as the gold standard; 2) to study the EB (i.e., EE and energy caloric intake) in HD patients and to compare it with healthy controls; 3) to validate the use of BIA for HD to study body composition using DXA as the gold standard; 4) to study the impact of EB on functional capacity, sarcopenia status, the severity of motor and non-motor symptoms, psychiatric, and cognitive manifestations, QoL, and caregiver burden in HD patients.

METHODS

MATERIALS AND METHODS
DESIGN
This study is a protocol of a cross-sectional observational, multicentre study of a cohort of patients with HD patients compared to age, gender matched-control subject. The protocol of this study followed the STROBE statement reporting criteria for protocol case-control reports (figure 1). This study is registered on clinicalTrials.gov Identifier, with the registration number NCT05250323. We will conduct this study at the Universidad Isabel I, in collaboration with the Hospital Universitario de Burgos (HUBU).

STUDY POPULATION
A convenience sample of symptomatic, ambulatory, manifest, and premanifest patients with a confirmed genetic mutation for HD with >36 CAG repeats in the HTT gene, able to walk with minimal support, males and non-pregnant females > 18 years old, will participate in this project. To minimize the effects of environmental conditions, age and gender matched non-HD controls, will be recruited from family members and/or acquaintances or people in the private environment of cases, with similar lifestyles. All participants must sign the informed consent. Patients and controls who had hospital admissions for 3 or more days in the last four weeks, with diabetes mellitus on pharmacological treatment, thyroid, other neurodegenerative, heart, pulmonary or skeletomuscular diseases, pregnancy or breastfeeding, active cancer, and medication known to affect metabolism/endocrine function will be excluded. TEE assessments in all participants will be obtained by fasting for at least 5 hours. All participants will be instructed to avoid vigorous exercise on the day before testing.

ETHICAL CONSIDERATIONS
This study will be conducted according to the standards for Good Clinical Practice, the fundamental ethical principles established in the Declaration of Helsinki and the Oviedo Convention, and the requirements established in Spanish legislation in the research field. This project has been approved by the Comité Ético Complejo Universitario Burgos y Soria (Certificate number: CEIM-2429, January 26th, 2021). In order to participate in the study, all participants must read the informed consent, be able to ask questions and sign the document. Two copies will be signed, both signed by participants, researchers, and guardians or legal representatives (if any).
The data's confidentiality and anonymity will be guaranteed throughout the study, available only to the researchers. The research documents will not be identified by the participant's name but will be assigned a code. The researchers will protect the participants' records, and the information on the codes, names, and addresses of the participants will remain confidential and available only to the researchers. The researchers will store all files in a secure location in a single folder dedicated to this study. The results of this research may be presented at meetings or publications; however, the participants' identities will not be revealed in these presentations.
The risks during the research are minimal or nonexistent, but the assessment procedures may cause discomfort due to the presence of involuntary movements. If discomfort is experienced, the data acquisition may be paused until medical interventions are performed, if necessary. However, if discomfort persists, the participant will be removed from the research, the data collected will be excluded from analysis, and the patient will not be reallocated to another group
Quality assurance: Investigator team
The investigator team will be composed of one movement disorders UHDRS certified, two occupational therapists, two PhD Scientists in Nutrition and Dietetics, one biostatistician (Hospital Universitario of Burgos, Spain), and three PhD Sport Scientists.

STUDY TIMETABLE
Overall, the study timetable includes several breaks due to Covid-19 pandemic issues.
1. Pre-start procedures: June-July 2021.
2. Evaluation: July 2021-July 2022.
3. Data analysis: August 2022-September 2022.
4. Report and publications: year 2023.

PROCEDURE
After signing the informed consent form, investigators will invite HD patients and controls to participate. There will be no personal expenses for the participant during the study. All the evaluations will be performed on the same day at Hospital Universitario Burgos, and at the Sports Science Laboratory, University Isabel I, Spain. Demographics such as gender, age, education background, HD duration, and pharmacological and non-pharmacological treatment data will be collected from the medical chart. A flow diagram will be included, providing information for participation during the study.
1.Study assessments and Outcomes
Disease severity: We will collect Sociodemographic information (gender, age, education background), and disease severity using standardized HD assessment tools: Unified Huntington's Disease Scale (UHDRS) and total functional capacity (TFC).[17] The UHDRS includes motor, behavioral, and cognitive evaluation, where low motor and behavioral scores and high cognitive scores denote better performance. TFC is derived from participant's and companion reports and quantifies a patient's ability to perform basic and instrumental activities (occupation, finances, housework, activities of daily living, and care level) ranging from 0 to 13, with higher scores indicating more intact functioning.[18] The severity of psychiatric symptoms will be assessed using the PBAs, with higher scores indicating greater severity.[19] Caregiver burden will be assessed using the Caregiver Burden Inventory,[20] with higher scores indicating higher caregiver burden and quality of life using the SF-12 Health Survey, with higher scores indicating higher quality of life.[21]
A 3-day dietary record will assess dietary intake and adherence to the traditional MeDi by a trained nutritionist at baseline.[22] In all cases, oral nutritional supplements or vitamin and mineral supplements will be considered. Food groups, macro-and micronutrients, current caffeine, and alcohol consumption (caffeinated coffee, caffeinated tea, and other caffeinated beverages), and calorie intake information will be analyzed using the software Alimentación y Salud, version 2.0. We computed the MeDi adherence according to previous publications.[23] Briefly, sex-specific medians of food group intake will be calculated. For beneficial components, such as cereals, vegetables, fruits, fish, legumes, and the ratio of MUFA/SFA, 1 point will be attributed if consumption was at or above the sex-specific median value. For components presumed to be detrimental, such as meats and dairy products, 1 point will be given if consumption is below the sex-specific median value. The MeDi adherence will be generated for each participant by adding the scores in the food categories (ranging 0-9); values 0-3 will be considered low adherence, and values 4-9 will be considered moderate/high adherence.
Energy Balance: To estimate EB, we will analyze EI and TEE. EI, macro, and micronutrients will be measured by using the Spanish validated questionary food consumption "Seguimiento Universidad de Navarra",[24] and a 3-day dietary record,[22] of 3 non-consecutive days. TEE will be calculated as the sum of REE, PA, and an estimated 10% diet-induced thermogenesis (Figure 2). REE will be measured by IC, using a gas analyzer (Medisoft Ergocard, Medisoft Group, Sorinnes, Belgium), and heart rate (Polar Electro V800, Kempele, Finland). The IC will be carried out in the morning after an overnight fast and not smoking, and REE will be obtained twice for 30 min after resting in bed in a quiet room and stabilizing respiratory quotient values for at least 5 min. Energy expenditure related to PA will be evaluated by IC, and two accelerometers simultaneously: Fitbit Charge 4® in the dominant hand and an ActiGraph wGT3X-BT® on the right hip. IC and both accelerometers will indirectly estimate energy expenditure using METs (Figure 3).
Test protocol: All participants will receive a familiarization session on the treadmill before the test. During this session, participants will be accustomed to walking at different speeds on the treadmill without using the handrails while breathing through the mouthpiece. They will be advised to stop at any time by giving an agreed signal or pressing the stop button. Energy expenditure related to PA will be evaluated using different activities: 1) daily living simulation activities such as combing and eating, and dressing (putting on and taking off a jacket), with a duration of 3 minutes each; and 2) treadmill walk (Cosmos Pulsar 4.0®, Cosmos Sports & Medical, Nussdorf-Traunstein, Germany) with different intensities (3.2-5.3 km/h) and constant slope (1%). The participant will be able to use the sidebars to hold on and facilitate adaptation to walking on the mat, with a previous familiarization of 2 minutes and using a harness throughout the test (risk protection). Walking at a constant intensity of 3.2 km/h with a constant gradient of 1%, will serve as a measure of the activity of daily living–walking, and at a constant intensity of 5.2 km/h with a constant gradient of 1% as a measure of moderate activity. We will allow a recovery of 3 minutes between each finished test. After 10 minutes of recovery, participants will perform a walking test for 6 minutes in a corridor to calculate the maximal length (meters) each person can achieve in 6 minutes.
Outdoor activity: participants with HD will be instructed to wear the Fitbit Charge 4® in the dominant hand and an ActiGraph wGT3X-BT® in the right hip for one week to quantify the total PA energy expenditure.
Sarcopenia, frailty and nutrition status: Anthropometry: Body mass index (BMI) will be calculated by the following formula: weight (kg)/height2 (m2). We will classify the BMI according to the International WHO standards with BMI≥18.5<25.0 kg/m2 normal, BMI<18.5 kg/m2 underweight, BMI≥25.0<30.0 kg/m2 overweight, and BMI≥30.0 obesity.[25] Waist circumference information will be estimated at the midpoint between the lower margin of the least palpable rib and the upper part of the iliac crest using stretch‐resistant tape. Measurements should be taken at the end of a normal expiration and repeated twice. The final measurement will be the average of both measurements. We will classify waist circumference associated with obesity as >102 cm for men and > 88 cm for women, and waist-to-height ratio associated with risk of abdominal obesity and chronic diseases as >0.5 cm.[26] The calf circumference for each leg will be used as a surrogate marker of muscle mass for diagnosing sarcopenia.[26]
The presence of sarcopenia will be evaluated following the sarcopenia European consensus (Figure 4),[28] based on 1) Muscle quantity: total body Skeletal Muscle Mass, as Appendicular Skeletal Muscle assessed by DXA (Prodigy. General Electric Healthcare, United States) as our gold standard. In addition, we will use a multiple frequency BIA (Body Composition Analyzer Seca mBCA 525 (Hamburg, Germany), which uses eight electrodes. Impedance will be measured with a current of 100 μA at frequencies of 1, 2, 5, 10, 20, 50, 100, 200, and 500 kHz and an impedance measuring range of 10 Ω to 1000 Ω 29. We will validate BIA estimates against DXA to estimate body composition. For muscle quantity, we will obtain the fat mass, fat-free mass defined as the lean mass plus the bone mineral content 30; fat mass index defined as fat-free mass/height2 (kg/m2). [30]
The following cutoff measurements will be used for men: <20 kg, appendicular skeletal mass/height2 <7.0 kg/m2, and for women <15 kg, appendicular skeletal mass/height2 < 5.5 kg/m2; [29, 31] 2) Muscle strength assessed by a handheld dynamometer (Jamar ® Plus hydraulic hand dynamometer) with a cutoff for men: < 27 kg, and for women: < 16 kg); [31] 3) Physical performance using the Short Physical Performance Battery as a multidimensional concept that involves muscles, central and peripheral nerve function and balance measuring gait speed, balance, and a chair test.[33] The maximum score is 12 points, and a score of < 8 indicates poor physical performance; 4) Sarcopenia screening questionnaires using SARC-F,[34] a 5-item self-report questionnaire as a screen for sarcopenia risk. Responses are based on the patient's perception of his or her limitations in strength, walking ability, rising from a chair, stair climbing, and experiences with falls.

STATISTICAL ANALYSIS
Sample Size
Considering the sample of previous validation of different tools and the low prevalence of HD,[35] and the low prevalence of the disease, we will include a convenient total sample of 20 patients with HD and 10 controls matched by age + 5 years and gender controls.
Analysis
Descriptive statistics for participants and main outcomes will be presented as the mean and standard deviation for continuous variables, the median, and the 25th–75th percentiles, interquartile ranges for non-normally distributed or ordinal data. The normality of the variables will be evaluated using the Shapiro-Wilk test. We will calculate the frequency distribution and percentages to describe categorical variables. Data will be analyzed using SPSS version 28 for Windows (SPSS Inc., Chicago, IL, USA) and Microsoft Excel. The level of significance will be set at p<0.05, two-tailed.
First Goal "Validation of accelerometers against IC": Comparisons of 1) METs assessments obtained from both accelerometers: the Fitbit Charge 4® and the ActiGraph wGT3X-BT® with IC (gold standard), and 2) muscle quantity assessments obtained from BIA with DXA, will be conducted using T-tests or the Mann-Whitney U test based on the normal distribution of the data. Validations will be carried out using the ICC, and Bland-Altman plots.[36]
Second Goal "EB estimates": EB calculated as the difference between EI minus TEE [PA free-living estimates (ActiGraph wGT3X-BT®) + RE (IC) + 10% diet-induced thermogenesis] will be compared between HD participants vs. healthy controls.
Third Goal "Validation of BIA against DXA": We will analyze the reliability between DXA and BIA using the ICC estimates and their 95% confidence intervals based on absolute-agreement and the two-way random-effects model. ICC values below 0.5 indicate poor reliability, between 0.5 and 0.75 moderate reliability, between 0.75 and 0.9 good reliability, and excellent reliability with values > 0.90. 32. We performed Bland-Altman plots for bias assessment with fat mass, fat-free mass.
Fourth Goal "Estimates of the impact of EB on HD severity, health-related quality of life (QoL), and caregiver burden": We will perform correlation analyses of 1) EB with functional capacity, sarcopenia status, UHDRS motor and cognitive scores, psychiatric and cognitive manifestations, SF-12, and caregiver burden in HD patients; and 2) TEE and RE with TFC, UHDRS motor and cognitive scores, PBA, SF-12, and caregiver burden scores. Finally, we will conduct a multivariate linear regression analysis, including EB as the dependent variable and demographics, UHDRS cognitive and motor subdomain, PBA scores, SF-12, and caregiver burden as the independent variables. The analyses will not attempt to estimate missing data.

RESULTS

We will report the results with tables and graphs. We will report the validation of accelerometers against IC, BIA estimates versus DXA, EB estimates and comparison between HD and controls, and estimates of the impact of EB on HD severity, health-related quality of life (QoL), and caregiver burden.

CONCLUSIONS

Besides the common existence of neuronal loss, neurodegenerative diseases are also associated with metabolic changes such as weight changes, fat mass loss, and altered feeding behavior. Importantly, preclinical research and clinical studies have demonstrated that altered energy homeostasis influences disease progression in HD, suggesting that identifying the pathways leading to perturbed EB might provide valuable therapeutic targets.[37] This study will analyze the benefits of measuring EB for patients with HD and understand the effects of EB on HD severity, QoL, and caregiver burden. In order to analyze EB adequately, especially energy expenditure related to PA, it is recommended to conduct these studies in a free-living setting to replicate the regular patient motor behavior. Therefore, we will conduct this study at different stages: The first stage will determine the accuracy of accelerometers for estimating energy expenditure related to PA, and BIA for estimating body composition in HD. The second stage will determine and compare EB in HD patients vs. controls. The third stage will analyze the impact of EB on motor, and cognition severity caregiver burden, patient's quality of life, and frailty in terms of body composition and sarcopenia estimates in HD.
To date, very few studies have studied EB in HD.[12, 38, 39] In a previous study conducted by our group, patients with HD appeared to have lower TEE mainly due to decreased PA but were still able to maintain their weight with an adequate food intake.[12] Decreased PA has also been seen in other neurodegenerative diseases, such as Parkinson's disease, suggesting that mobility problems or impaired cognition could also be a determinant factor for decreased energy expenditure related to PA.[12]
Maintaining a neutral EB prevents malnutrition and its complications and might improve physical functioning, quality of life, and survival. [40] EB is a complex, multifactorial, poorly understood clinical problem requiring integrated multidisciplinary research and approach development. For this reason, one of the most important public health challenges would be to identify cost-effective interventions to prevent EB deficit, improve health status, and prevent disability in patients with neurodegenerative diseases.
The main limitation of this laboratory-based study will be the small sample size of HD patients and controls given a limited budget and, consequently, lack of statistical power, selection bias, and lack of extrapolation to the general HD community. However, the results of this study will provide the scientific rationality to understand this complex problem and facilitate the integration of pharmacological and non-pharmacological strategies to prevent EB deficit. The results of this study should be confirmed in a real-life environment. Likewise, the results of this study could be used to calculate sample sizes in further studies. In terms of clinical utility, these results could be used to develop non-pharmacological strategies, providing adequate nutrition counseling to counteract a deficit in EB and prevent weight loss.[41, 43] Likewise, adequate PA counseling could improve functional health and EB, reducing sarcopenia and frailty in HD. [43]
It should be noted that it would be interesting to carry out this study with a larger sample of both patients and controls. This would facilitate obtaining greater statistical power.
In conclusion, this study is a challenging and original initiative designed to provide scientific background to provide therapeutic strategies to prevent EB deficit in HD. We hope this project will provide important information about EB estimates, the accuracy of wearable devices to quantify EE related to PA in a free-living setting, BIA to estimate body composition, and increase the knowledge about the clinical impact of EB on HD. All results from the study will be communicated by publication without any restriction.

 
ACKNOWLEDGMENTS

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