AbstractSee Finger (doi: ) for a scientific commentary on this article.Abnormal eating behaviour and metabolic parameters including insulin resistance, dyslipidaemia and body mass index are increasingly recognized as important components of neurodegenerative disease and may contribute to survival. It has previously been established that behavioural variant frontotemporal dementia is associated with abnormal eating behaviour characterized by increased sweet preference. In this study, it was hypothesized that behavioural variant frontotemporal dementia might also be associated with altered energy expenditure. A cohort of 19 patients with behavioural variant frontotemporal dementia, 13 with Alzheimer’s disease and 16 (age- and sex-matched) healthy control subjects were studied using Actiheart devices (CamNtech) to assess resting and stressed heart rate. Actiheart devices were fitted for 7 days to measure sleeping heart rate, activity levels, and resting, active and total energy expenditure. Using high resolution structural magnetic resonance imaging the neural correlates of increased resting heart rate were investigated including cortical thickness and region of interest analyses. In behavioural variant frontotemporal dementia, resting ( P = 0.001), stressed ( P = 0.037) and sleeping heart rate ( P = 0.038) were increased compared to control subjects, and resting heart rate ( P = 0.020) compared to Alzheimer disease patients.
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Behavioural variant frontotemporal dementia was associated with decreased activity levels compared to controls ( P = 0.002) and increased resting energy expenditure ( P = 0.045) and total energy expenditure ( P = 0.035). Increased resting heart rate correlated with behavioural (Cambridge Behavioural Inventory) and cognitive measures (Addenbrooke’s Cognitive Examination). Increased resting heart rate in behavioural variant frontotemporal dementia correlated with atrophy involving the mesial temporal cortex, insula, and amygdala, regions previously suggested to be involved exclusively in social and emotion processing in frontotemporal dementia.
These neural correlates overlap the network involved in eating behaviour in frontotemporal dementia, suggesting a complex interaction between eating behaviour, autonomic function and energy homeostasis. As such the present study suggests that increased heart rate and autonomic changes are prevalent in behavioural variant frontotemporal dementia, and are associated with changes in energy expenditure.
An understanding of these changes and neural correlates may have potential relevance to disease progression and prognosis. , IntroductionTraditionally frontotemporal dementia (FTD) has been viewed as a syndrome characterized by behavioural and cognitive changes, although increasingly it is being recognized that there is involvement of networks that affect physiological processing including somatosensory processing including pain and temperature (, ), autonomic processing (; ), and neuroendocrine and metabolic changes (, ). In behavioural variant frontotemporal dementia (bvFTD), eating behavioural changes are common, including hyperphagia, increased sweet preference and changes in food preference that may be associated with increased body mass index (BMI), dyslipidaemia and insulin resistance.In several neurodegenerative diseases it is established that there are changes in metabolic parameters. One such condition is amyotrophic lateral sclerosis (ALS), which shares a clinical and pathological overlap with FTD and where consistent changes have been found , including increased resting energy expenditure in up to 50% of patients (; ).
It has also been suggested that the hypermetabolic state (defined as increased energy expenditure) is intrinsically linked to the process of neurodegeneration with several animal models of TAR DNA-binding protein 43 (TDP-43, encoded by TARDBP) and the C9orf72 gene expansion exhibiting hypermetabolism and weight loss (;;;; ).Given the significant overlap between FTD and ALS at a clinical, pathological and genetic level, it seems plausible that changes in energy expenditure may also be present in FTD. In addition, the changes in appetite appear to exceed the minor increments in BMI and weight gain seen in bvFTD, suggesting increased energy expenditure may also contribute to the body weight changes seen in patients (; ). This is also suggested by the fact that in studies measuring caloric intake in FTD, BMI does not correlate with caloric intake. Energy expenditure, which includes basal metabolic rate (amount of energy expended at rest) and active energy expenditure (a combination of activity levels and heart rate), has not been investigated in bvFTD.
Intrinsically linked with energy expenditure are potential alterations in autonomic activity including heart rate, heart rate variability, and sympathetic and parasympathetic drive. Changes in the autonomic nervous system, particularly in the sympathetic nervous system have also been proposed to affect glucose and fat metabolism (; ). Increases in heart rate have been found to predict an increase in metabolic rate and energy expenditure, via an increase in circulatory rate and oxygen consumption.Measurement of energy expenditure in free-living individuals is challenging. In this study, we used digital heart rate/activity monitoring (,; ) to examine changes in heart rate (rest, stressed and sleep), activity levels and energy expenditure and the potential correlations that this has to autonomic function in a large group of bvFTD patients, compared to a disease comparison group (Alzheimer’s disease) and healthy control subjects. This approach has been used previously in genetic obesity research but not in patients with neurodegeneration. The potential neurobiological underpinnings of increases in heart rate were further examined using structural MRI to provide insight into the neural correlates controlling energy expenditure and autonomic function in FTD.
Materials and methods ParticipantsThirty-two patients with dementia (19 bvFTD, 13 Alzheimer’s disease) were recruited from Forefront, Neuroscience Research Australia. These individuals were compared with 16 age- and sex-matched healthy controls.
All patients underwent neurological review, cognitive assessment and met current clinical diagnostic criteria for probable bvFTD or Alzheimer’s disease (,;; ). Disease severity was established using the Frontal Rating Scale (FRS). Controls were recruited from the Neuroscience Research Australia Volunteer database. Healthy controls scored 88/100 on the Addenbrooke’s Cognitive Examination-III and 0 on the sum of boxes score of the Clinical Dementia Rating scale.
Exclusion criteria for patients and controls included concurrent psychiatric disturbance, other neurodegenerative conditions or neurological disorders and/or history of substance abuse. Patient medical history and medicationsPatients medical records and list of medications were obtained from their general practitioner.
Given that the study measured heart rate variations, patients with a known cardiac rhythm disorder e.g. Atrial fibrillation, supraventricular tachycardia or conduction delays were excluded from the study (one bvFTD and one Alzheimer’s disease patient). Patients, their carers and control subjects were asked about their current smoking status and alcohol intake. EthicsThis study was approved by the South Eastern Sydney Local Health District and the University of New South Wales human ethics committees.
Written informed consent was obtained from each participant and/or their primary caregiver. Physiological measurements: Actiheart deviceParticipants were fitted with the Actiheart device (CamNtech), a compact device designed to quantify heart rate, activity and energy expenditure (,; ). The Actiheart has two clips, which are attached directly to standard ECG electrodes. One electrode was attached to V1 on the chest (fourth right intercostal space) and the second electrode was placed 10 cm away on the left mid-clavicular line. The number of R waves detected is recorded at 15-s epochs and from this the heart rate is derived.
An internal triaxial accelerometer senses the frequency and intensity of the participant’s torso movements including walking and pacing, from which activity counts are derived. When placed in short-term mode, the Actiheart measures heart rate variability, heart rate and interbeat interval (IBI) of the heart rate, with an epoch length of 15 s. Long term recordings in long-term mode provide mean heart rate and activity levels over an epoch of 15 s and recordings can be made for up to 11 days (,; ). Measurement of heart rateTo measure resting and stressed heart rate, participants wore the Actiheart in short-term recording mode for a period of 6 h following a standard protocol, described briefly. After arriving fasted (10 h) and an initial period of introduction patients were given a set breakfast that measured caloric intake.
No caffeine or tea was included in this breakfast to avoid their chronotropic effects. Participants were then asked to rest in a lounge environment and their resting heart rate was obtained over a period of 30 min. Following this, participants were taken into a room and took part in 2 h of cognitive testing.
The initial 30 min of this period was taken as the stressed heart rate. Resting and stressed heart rates and measures of autonomic function were obtained after entering the beat-to-beat RR interval data obtained by the Actiheart into Kubios HRV, an advanced and easy to use software for heart rate variability (HRV) analysis. The following measurements were obtained: the root mean squared of successive differences (RMSSD), which is a measure of vagal control of the heart ; the low frequency (LF), which corresponds to the 0.10 Hz slow fluctuations of arterial pressure and reflects sympathetic and parasympathetic tone; the high frequency (HF), which corresponds to ∼0.25 Hz fluctuations and is a measure of respiratory sinus arrhythmias and can be considered an index of vagal modulation; and the LF/HF ratio, which is used to indicate balance between sympathetic and parasympathetic tone. Long-term Actiheart recordingsFollowing the short term analyses, participants were fitted with the Actiheart in long-term mode and asked to wear this at home for 7 days to provide continuous heart rate and activity monitoring. The participants wore the device continuously and were allowed to shower with the device. Carers were given instructions to refit the device if it became loose or was removed.
From the long-term recording, sleeping heart rate and activity counts were calculated. Measurement of sleeping heart rateSleeping heart rate was calculated over a period of one night. For each participant, the one night was selected from the 7-day long-term recording, where a period of recording of uninterrupted sleep was obtained, between midnight and 5 am, where no activity was detected.
From this period, the average sleeping heart rate, minimum and maximum heart rate was calculated for each participant. Investigators were blinded to the patient ID and diagnoses for this selection. Measurement of activity over 24 hAn average activity count over 24 h was calculated for each participant. This was obtained by selecting a 24-h period in the 7-day long-term recording where continuous recording was available for each patient. This was found to be Day 2 in each patient as the Actiheart had been firmly attached and had not been removed, which tended to occur as the long-term recording proceeded. The average activity counts for each patient over these 24 h was obtained and expressed as an activity count per 24 h.
Significance levels between group means were examined after correcting for age and sex. Measurement of energy expenditureThe Actiheart measures heart rate and activity simultaneously and these data are transferred to the Actiheart software, which uses a validated branched model equation (, ) to derive active energy expenditure for each epoch using a combination of heart rate and activity. Using the branched model equation, group Cal JAP2007 (CamNtech) measures of daily energy expenditure were calculated for each participant every day for 7 days and an average over this period obtained. The following measures were obtained: Active Energy Expenditure (AEE) derived from the branched model equation, which includes a combination of heart rate and activity measures; resting energy expenditure (REE) derived from the Schofield equation, designed to measure basal metabolic rate adjusted for weight ; dietary induced thermogenesis (DIT) estimated as 10% of total energy expenditure (TEE). Total energy expenditure (TEE) = REE + AEE + DIT; physical activity level (PAL) = TEE/REE. Behavioural measurementsIn addition to measurements of heart rate variability, activity and energy expenditure, changes in eating behaviour were measured using caregiver-based questionnaires: the Appetite and Eating Habits Questionnaire (APEHQ) (; ) and the Cambridge Behavioural Inventory (CBI). These surveys were completed on the same day that the short-term recordings were obtained and were felt to be representative of intake over the proceeding 7 days when the long-term recordings were obtained.
The APEHQ provides measures of nicotine and alcohol consumption. Height and weight were measured barefoot and BMI calculated (weight in kilograms/height in metres squared). Imaging MRI acquisition and preprocessingAll participants underwent whole-brain structural MRI with a 3 T Phillips scanner using a standard 8-channel head coil. 3D high-resolution turbo field echo T 1-weighted sequences were acquired with the following parameters: coronal orientation, matrix 256 × 256, 200 slices, 1 mm 2 in-plane resolution, slice thickness 1 mm, echo time/repetition time 2.6/5.8 ms, flip angle α = 8°. MRI scans were obtained on the same day as the physiological and behavioural assessment.Before analyses, the two T 1 volumes were merged and averaged to increase the signal-to-noise ratio and the grey matter–white matter contrasts in brain structures.
FreeSurfer software, version 5.3.0 was used for surface-based cortical processing (; ) using standard methods. Cortical thickness was smoothed with a 20 mm full-width at half-height Gaussian kernel. This level of blurring kernel was chosen to reduce the impact of imperfect alignment between cortices and thereby improve the signal-to-noise ratio.In addition, the following subcortical structures were automatically segmented and extracted for both hemispheres: thalamus, caudate, putamen, globus pallidus, hippocampus, amygdala, and nucleus accumbens.
For these subcortical structures, measurements from both hemispheres were averaged and adjusted for total intracranial volume, in line with previous methodology.All the resulting images were visually inspected and manually corrected in the event of tissue segmentation errors. One patient with bvFTD and two healthy controls were excluded due to excessive surface or subcortical segmentation errors. Thus, 18 bvFTD, 13 Alzheimer’s disease and 14 healthy controls were included in the imaging analyses. Statistical analyses Demographic and physiological variablesAnalyses were conducted using IBM SPSS statistics (version 21.0). Kolmogorov-Smirnov tests were run to determine suitability of variables for parametric analyses.
ANOVA, followed by Tukey post hoc tests, were used to determine group differences in demographic and clinical variables (age, ACE-III). Categorical variables were analysed using chi-square analyses.
Independent t-tests were used to determine differences between bvFTD and Alzheimer’s disease for disease duration, abnormal behaviour (total CBI, CBI behavioural) and eating behaviour (APEHQ, CBI eating, and BMI) ( P ≤ 0.05 regarded as significant). Measurements of heart rate (resting and stressed), autonomic function (resting and stressed LF, HF, RMSSD after log transformation), activity counts and energy measures (REE, AEE, DIT, PAL) were also explored using ANOVA, followed by Tukey post hoc tests. The relationship between resting heart rate and disease duration, BMI, eating behaviour (APEHQ total score) cognitive status (ACE-III) and behavioural measures (CBI total, eating and behavioural) were further explored using Pearson rank correlations corrected for multiple comparisons ( P ≤ 0.01 regarded as significant). Imaging data analysesFor cortical thickness, sets of vertex-by-vertex analyses were performed using general linear models aimed to examine differences in cortical thickness between groups and then to estimate the neural correlates for the physiological variables where bvFTD showed significant differences with both Alzheimer’s disease and healthy controls (resting heart rate and stressed heart rare). In the first set of analyses, overall cortical thickness of both hemispheres was modelled including cortical thickness as a dependent variable and group (bvFTD, Alzheimer’s disease, healthy controls) as an independent variable. In the second set of analyses, we created separate linear models, one for each physiological variable under examination. Each model included the following repressors: group (bvFTD, Alzheimer’s disease, healthy controls), the physiological variable (resting heart rate, stressed heart rare) and the interaction between group and the physiological variable.
To determine physiological associations with cortical thickness specific to diagnostic group, we focused on the interaction effect between each diagnosis and each physiological variable. Correlations between physiological variables and cortical thickness were investigated first by combining all participants (behavioural variant FTD, Alzheimer’s disease, controls) and then in each patient group combined with controls, to identify neural correlates of resting and stressed heart rate to each patient group. As the groups were matched at baseline for age, sex and duration of disease, no covariates were included in the models. Statistical significance was set at P = 0.001 uncorrected for multiple comparisons. In addition, we used a conservative cluster extent threshold of k 50 mm 2.
This approach is designed to minimize type I error while balancing the risk of type II error.For the subcortical regions, group comparisons of subcortical volumes between bvFTD, Alzheimer’s disease and healthy controls were performed using ANOVA. Post hoc analyses were corrected for multiple comparisons using a Sidak adjustment. Next, to uncover the subcortical grey matter correlates of resting heart rate and stressed heart rate, correlations were investigated using the same approach outlined above.
Pearson two-tailed correlational analyses were conducted with Bonferroni correction for multiple comparisons ( P 0.365). Group differences were observed on measures of cognition (ACE-III), behavioural measures and eating behaviour and were in keeping with the known behaviour of the diagnostic groups. On the ACE-III as expected the patient groups scored lower than controls ( P. N = number of patients reporting alcohol consumption and regular smokers. All alcohol consumption was within normal guidelines (see text).
Medications, smoking and alcohol intakeA higher number of Alzheimer’s disease patients were on cholinesterase inhibitors (nine patients) compared to the bvFTD (two patients) and control (no patient) groups. There was no difference in the number of patients treated with a medication likely to affect heart rate and cardiac conduction (e.g. Beta blocker, calcium channel blocker) between the groups. One bvFTD patient was on stable and adequate thyroxine treatment.
Two bvFTD patients and two control subjects were current smokers. The carers of eight bvFTD patients reported alcohol consumption, less than weekly in four and several times per week in four. Ten control subjects reported alcohol consumption, four less than weekly and six several times a week. Five Alzheimer’s disease patients reported regular alcohol intake, two several times a week and three less than weekly. No patient or control participant stated that they consumed more alcohol than the recommended amount by the Australian National Health and Medical research council.
Eating behaviour and body mass indexOn both the APEHQ and CBI eating score the bvFTD group showed more severe eating disturbance based on caregiver surveys compared with Alzheimer’s disease patients (t = 4.1, P. Significant group differences were present for both resting (F = 8.6, P = 0.001) and stressed (F = 3.2, P = 0.047) heart rate measures. The bvFTD group (mean = 81.8 bpm) had an increased resting heart rate compared to both the control (mean = 68.5 bpm, P = 0.001) and Alzheimer’s disease groups (mean = 72.5 bpm, P = 0.020) and an increased stressed heart rate compared to the control group (bvFTD = 81.5 bpm, Alzheimer’s disease = 76.1 bpm, controls = 71.9 bpm; P = 0.037).
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