Abstract
Background:
Despite the clearly recognized progressive functional decline of Huntington’s disease (HD), detailed investigations of factors associated with the rate of functional progression are limited.
Objective:
Understanding factors associated with functional decline through examination of existing HD clinical databases may improve efforts to mitigate it.
Methods:
We analyzed data from 2CARE, a randomized clinical trial with up to 5 years of follow-up, to assess potential risk factors for more rapid functional decline in HD.
Results:
Variables associated with faster functional decline included worse motor performance, worse cognitive test scores, female sex, lower weight and body mass index, and a higher CAG repeat length, especially in younger people.
Conclusion:
While our data are limited to the structured environment and homogeneity of a clinical trial, attention to several of the identified risk factors may be useful towards managing functional decline over time. The observation that women progress faster than men, while potentially confounded by an association between sex and weight, deserves further study.
Keywords: Huntington’s disease, clinical trial, functional decline, total functional capacity
INTRODUCTION
Huntington’s disease (HD) is a progressive, fatal neurodegenerative disorder involving motor, cognitive, and behavioral abnormalities [1]. These cardinal disease features gradually worsen, with a cumulative impact on daily function [2]. Once present, functional decline is progressive, as demonstrated across multiple studies [3-5]. While the importance of preserving function is generally accepted as an aim of clinical care [6-9], it is not entirely clear from prior research which demographic and lifestyle factors are most related to functional progression over time. In general, CAG repeat length is associated with earlier age of onset, but not rate of clinical progression [10]; understanding additional variables that are associated with functional decline may be of value towards improving research and clinical management strategies.
The Unified Huntington’s Disease Rating Scale (UHDRS), a commonly used tool for quantifying disease features and their progression, includes the Total Functional Capacity scale (TFC) [11]. The TFC is a broad assay of functional state with established interrater reliability and validity, and has been the primary outcome measure for numerous clinical trials in HD. It consists of five domains reflecting major lifestyle elements (capacity for work, finances, domestic chores, activities of daily living, home status) [5, 12-15]. Its value is driven by a practical relevance to patient-reported capability across usual activities in life (maintaining employment, managing personal responsibilities) apart from observable phenomena measured elsewhere in the UHDRS (performance with discrete motor tasks, cognitive assessments, etc.). The TFC has a synthetic quality in reflecting how motor, cognitive, and behavioral elements of HD combine to influence a person’s day-to-day experience, and is known to be affected by the core features of the disease: TFC decline is predicted by UHDRS motor, cognitive, and depressive symptoms [16]. It is less well understood if other characteristics apart from core disease features may be associated with functional decline.
Using data from 2CARE [5], a large randomized clinical trial that included up to 5 years of follow-up of participants, we sought to identify baseline clinical, demographic and lifestyle factors that are associated with functional decline as measured by the TFC.
MATERIALS AND METHODS
In the 2CARE trial, 609 participants were enrolled at 48 sites in the United States, Canada, and Australia between 2008 and 2012 and randomized to receive either coenzyme Q10 2400 mg/day or matching placebo. Participants were scheduled to be followed for 5 years with evaluations at baseline, Months 1, 3, and 6, and every 6 months thereafter. Because the trial was halted for futility in July 2014, only 34% of participants had completed 5 years of follow-up at the time of study termination, although 91% had completed at least 1 year of follow-up and 69% had completed 3 years [5]. Details regarding the design and results of the trial have been published [5, 17].
The outcome variable in this secondary analysis of the 2CARE database was the rate of change over time (slope) for the TFC score. Potential baseline risk factors (demographic variables as well as core disease features) for more rapid functional decline were examined using linear mixed effects models that included TFC score as the outcome variable and the risk factor, time (continuous, expressed as the number of years since randomization), and the interaction between the risk factor and time as independent variables. These models included random intercept and slope coefficients, and they appropriately account for missing data under the missing at random assumption [18]. The analyses of risk factors that were continuous were repeated after categorizing them using pre-determined cut points (quartiles) for purposes of easier description. Interactions between CAG repeat length and age, total motor score and sex, age and sex, and marital status and employment status were examined by adding the appropriate interaction terms to the model. The assumption of a linear trajectory over time was checked by including a quadratic term for time in the model; this resulted in minimal change to the model, so only a linear term was included. All statistical analyses were performed using SAS software, Version 9.4.
Standard protocol approvals, registrations, patient consents and data availability
Procedures and activities from the 2CARE clinical trial (ClinicalTrials.gov identifier number NCT00608881) were done in accord with the ethical standards of the Committees on Human Experimentation of the institutions in which the trial was conducted and in accord with the Helsinki Declaration of 1975. The 2CARE study from which the present analysis was derived was approved by the institutional review boards at 48 sites in the United States, Canada, and Australia. All participants provided written informed consent. The NINDS-appointed independent Data and Safety Monitoring Board (DSMB) monitored the progress of the trial. De-identified 2CARE participant data of the type used in the present manuscript are available on dbGaP (https://www.ncbi.nlm.nih.gov/gap/).
RESULTS
As previously described [5], the 2CARE cohort enrolled participants with early-stage HD (TFC score ≥9). The mean (standard deviation) age at randomization was 50.6 (11.7), the mean age at symptom onset was 45.4 (11.6), and 49% were men. The mean CAG repeat length was 44.0 (4.0) (range 36–70). The average TFC score was 10.9 (1.5) and the average Total Motor Score was 27.8 (13.6). The mean annual rate of change over time on the TFC was −0.83 points/year (95% confidence interval [CI] −0.90 to −0.77).
The associations between continuous baseline variables and annual rate of change in TFC score are found in Table 1. Age at randomization, age at symptom onset, and age at diagnosis were not associated with the rate of functional decline. Higher CAG repeat length, lower body mass index, higher (worse) Total Motor Score, and worse cognitive test results (verbal fluency, Symbol Digit Modalities Test, Stroop Interference tasks) were all associated with an increased rate of functional decline. There was evidence of an interaction between CAG repeat length and age at randomization (described below). Worse baseline functional status, as measured by the TFC, Functional Assessment, and Independence Scale, were also associated with faster annual rate of decline in TFC score. Behavioral Frequency × Severity scores were not associated with the rate of functional decline.
Table 1.
Associations between continuous baseline variables and rate of change in Total Functional Capacity score
| Characteristic | Slope Change | 95% CI | p |
|---|---|---|---|
| Age† | 0.001 | (−0.05, 0.05) | 0.96 |
| Age at symptom onset† | 0.03 | (−0.03, 0.08) | 0.32 |
| Age at HD diagnosis† | 0.01 | (−0.05, 0.06) | 0.79 |
| CAG repeat length‡ | −0.20 | (−0.28, −0.12) | <0.0001 |
| Body mass index (kg/m2)‡ | 0.08 | (0.02, 0.14) | 0.006 |
| Weight (kg)† | 0.09 | (0.05, 0.13) | <0.0001 |
| Total motor score‡ | −0.10 | (−0.12, −0.08) | <0.0001 |
| Verbal Fluency Test‡ | 0.09 | (0.06, 0.11) | <0.0001 |
| Symbol Digit Modalities Test‡ | 0.11 | (0.09, 0.14) | <0.0001 |
| Stroop Interference Test – Color Naming‡ | 0.07 | (0.05, 0.09) | <0.0001 |
| Stroop Interference Test – Word Reading‡ | 0.06 | (0.05, 0.08) | <0.0001 |
| Stroop Interference Test – Interference‡ | 0.10 | (0.08, 0.13) | <0.0001 |
| Behavioral Frequency×Severity score | 0.004 | (−0.001, 0.008) | 0.14 |
| Functional Assessment score‡ | 0.32 | (0.18, 0.46) | <0.0001 |
| Independence Scale score‡ | 0.06 | (0.03, 0.10) | 0.0003 |
| Total Functional Capacity score | 0.05 | (0.009, 0.09) | 0.02 |
| CAG repeat length×age interaction□ | −0.13 | (−0.20, −0.07) | <0.0001 |
Values are the change in slope associated with a 10-unit (†) or a 5-unit (‡) increase in the variable.
Value is the change in the slope change associated with a 10-year increase in age that is associated with a 5-unit increase in CAG repeat length.
Estimates of the mean annual rate of change in TFC score by categorized baseline variables are described in Table 2. When age was categorized by quartiles (less than 43 years, 43–51 years, 52–58 years, greater than 58), the mean functional decline was faster in the youngest (−0.92, 95% CI −1.05 to −0.80) and oldest (−0.92, 95% CI −1.04 to −0.80) groups, suggesting a nonlinear association. Age at symptom onset and age at diagnosis demonstrated similar patterns of association. Relationships between mean functional decline and other variables were generally monotone, including those with CAG repeat length, Total Motor Score (TMS), cognitive test scores, and baseline functional status. The nature of the interaction between CAG repeat length and age is described in Table 2 and Fig. 1. The association between CAG repeat length and rate of decline (greater decline with higher CAG repeat length) is more pronounced for those who are younger. There was also evidence of a possible interaction between baseline TMS and sex, with the association between TMS and rate of decline in the TFC being somewhat more pronounced in women than in men (Table 2).
Table 2.
Estimates of rate of change (slope) in Total Functional Capacity score by categorized baseline variables
| Characteristic | Slope | 95% CI | p |
|---|---|---|---|
| Age (years) | 0.03 | ||
| <43 | −0.92 | (−1.05, −0.80) | |
| 43–51 | −0.80 | (−0.92, −0.68) | |
| 52–58 | −0.70 | (−0.82, −0.58) | |
| >58 | −0.92 | (−1.04, −0.80) | |
| Age at symptom onset (years) | 0.07 | ||
| ≤38 | −0.99 | (−1.12, −0.86) | |
| 39–45 | −0.78 | (−0.91, −0.65) | |
| 46–54 | −0.78 | (−0.90, −0.66) | |
| >54 | −0.86 | (−1.00, −0.72) | |
| Age at HD diagnosis (years) | 0.27 | ||
| ≤40 | −0.91 | (−1.03, −0.78) | |
| 41–48 | −0.83 | (−0.96, −0.71) | |
| 49–56 | −0.74 | (−0.87, −0.62) | |
| >56 | −0.88 | (−1.01, −0.76) | |
| CAG repeat length | 0.0001 | ||
| <42 | −0.68 | (−0.81, −0.56) | |
| 42 | −0.74 | (−0.88, −0.60) | |
| 43–44 | −0.80 | (−0.92, −0.69) | |
| >44 | −1.04 | (−1.16, −0.93) | |
| Body mass index (kg/m2) | 0.08 | ||
| ≤22.9 | −0.91 | (−1.03, −0.78) | |
| 22.9–25.5 | −0.94 | (−1.06, −0.81) | |
| 25.5–29.0 | −0.78 | (−0.91, −0.66) | |
| >29.0 | −0.74 | (−0.86, −0.62) | |
| Weight (kg) | 0.0002 | ||
| ≤65.0 | −0.98 | (−1.10, −0.85) | |
| 65.0–75.0 | −0.89 | (−1.01, −0.77) | |
| 75.0–86.6 | −0.86 | (−0.99, −0.74) | |
| >86.6 | −0.61 | (−0.73, −0.49) | |
| Total motor score | <0.0001 | ||
| ≤18 | −0.55 | (−0.66, −0.44) | |
| 19–26 | −0.73 | (−0.84, −0.62) | |
| 27–36 | −0.94 | (−1.07, −0.82) | |
| >36 | −1.17 | (−1.30, −1.06) | |
| Verbal Fluency Test | <0.0001 | ||
| ≤18 | −1.14 | (−1.25, −1.02) | |
| 19–25 | −0.86 | (−0.98, −0.74) | |
| 26–32 | −0.76 | (−0.89, −0.64) | |
| >32 | −0.53 | (−0.64, −0.42) | |
| Symbol Digit Modalities Test | <0.0001 | ||
| ≤21 | −1.13 | (−1.24, −1.02) | |
| 22–28 | −0.99 | (−1.10, −0.87) | |
| 29–36 | −0.70 | (−0.81, −0.58) | |
| >36 | −0.46 | (−0.58, −0.34) | |
| Stroop Interference Test – Color Naming | <0.0001 | ||
| ≤40 | −1.17 | (−1.29, −1.06) | |
| 41–50 | −0.89 | (−1.01, −0.77) | |
| 51–63 | −0.67 | (−0.79, −0.56) | |
| >63 | −0.55 | (−0.67, −0.43) | |
| Stroop Interference Test – Word Reading | <0.0001 | ||
| ≤51 | −1.13 | (−1.24, −1.02) | |
| 52–64 | −0.86 | (−0.98, −0.74) | |
| 65–77 | −0.79 | (−0.91, −0.67) | |
| >77 | −0.51 | (−0.63, −0.39) | |
| Stroop Interference Test – Interference | <0.0001 | ||
| ≤22 | −1.23 | (−1.35, −1.12) | |
| 23–29 | −0.82 | (−0.93, −0.70) | |
| 30–37 | −0.67 | (−0.80, −0.55) | |
| >37 | −0.57 | (−0.69, −0.45) | |
| Behavioral Frequency×Severity score | 0.01 | ||
| ≤1 | −0.94 | (−1.05, −0.82) | |
| 2–6 | −0.73 | (−0.86, −0.61) | |
| 7–18 | −0.92 | (−1.04, −0.80) | |
| >18 | −0.71 | (−0.84, −0.58) | |
| Functional Assessment score | <0.0001 | ||
| ≤22 | −1.05 | (−1.15, −0.94) | |
| 23 | −0.66 | (−0.80, −0.53) | |
| 24 | −0.71 | (−0.85, −0.58) | |
| >24 | −0.76 | (−0.87, −0.64) | |
| Independence Scale score | 0.02 | ||
| ≤80 | −0.96 | (−1.08, −0.84) | |
| 81–90 | −0.91 | (−1.07, −0.76) | |
| 90–99 | −0.77 | (−0.89, −0.65) | |
| 100 | −0.74 | (−0.85, −0.63) | |
| Total Functional Capacity score | 0.05 | ||
| ≤10 | −0.99 | (−1.12, −0.85) | |
| 11 | −0.81 | (−0.95, −0.68) | |
| 12 | −0.86 | (−0.99, −0.72) | |
| 13 | −0.75 | (−0.85, −0.65) | |
| CAG repeat length×age interaction | |||
| CAG ≤ 42; age ≤ 45 | −0.22 | (−0.72, −0.27) | |
| CAG ≤42; age 45–50 | −0.55 | (−0.83, −0.26) | |
| CAG ≤ 42; age >50 | −0.75 | (−0.85, −0.65) | |
| CAG 43-44; age ≤ 45 | −0.61 | (−0.90, −0.32) | |
| CAG 43-44; age 45–50 | −0.71 | (−0.93, −0.49) | |
| CAG 43-44; age > 50 | −0.90 | (−1.05, −0.75) | |
| CAG ≥ 45; age ≤ 45 | −1.04 | (−1.17, −0.91) | |
| CAG ≥ 45; age 45–50 | −1.07 | (−1.35, −0.80) | |
| CAG ≥ 45; age > 50 | −0.97 | (−1.49, −0.45) | 0.62 |
| Total motor score×sex interaction | 0.05 | ||
| Total motor score ≤ 18; women | −0.57 | (−0.74, −0.40) | |
| Total motor score 19–26; women | −0.69 | (−0.85, −0.53) | |
| Total motor score 27–36; women | −1.05 | (−1.21, −0.89) | |
| Total motor score > 36; women | −1.33 | (−1.49, −1.17) | |
| Total motor score ≤ 18; men | −0.54 | (−0.68, −0.39) | |
| Total motor score 19–26; men | −0.77 | (−0.92, −0.61) | |
| Total motor score 27–36; men | −0.80 | (−0.98, −0.61) | |
| Total motor score > 36; men | −0.99 | (−1.17, −0.81) | |
| Age×gender interaction | 0.60 | ||
| Age < 43; women | −1.04 | (−1.21, −0.87) | |
| Age < 43; men | −0.78 | (−0.97, −0.60) | |
| Age 43–51; women | −0.82 | (−0.98, −0.65) | |
| Age 43–51; men | −0.78 | (−0.96, −0.60) | |
| Age 52–58; women | −0.81 | (−0.98, −0.64) | |
| Age 52–58; men | −0.59 | (−0.76, −0.42) | |
| Age > 58; women | −0.99 | (−1.16, −0.81) | |
| Age > 58; men | −0.86 | (−1.02, −0.70) |
Fig. 1.
Relationship between CAG repeat length, age and rate of decline (slope) in Total Functional Capacity score.
Table 3 shows the estimated mean rates of TFC change by categorical baseline variables. A significant difference was seen between men and women, with women progressing faster (absolute difference 0.16, 95% CI 0.04 to 0.28) across all age ranges (Table 3). Education level, employment status, active depression or anxiety, antidepressant/anxiolytic use, and history of suicidal ideation were not associated with rate of TFC change. Those who were never married progressed faster (−0.97) than the married group (−0.85) and the separated/widowed/divorced group (−0.72) on the TFC; however, there was no evidence of an interaction between marital status and employment status in terms of functional decline. There was a suggestion that a history of alcohol abuse (−1.50 vs. −0.83, p = 0.07) and bipolar disorder (−1.68 vs. −0.83, p = 0.01) were strongly associated with functional decline, but the analyses of these variables was limited by the very small number of those with these issues (n = 4 for history of alcohol abuse; n = 6 for bipolar disorder).
Table 3.
Estimates of rate of change (slope) in Total Functional Capacity score by categorical baseline variables
| Characteristic | Slope | 95% CI | p |
|---|---|---|---|
| Sex | |||
| Female | −0.91 | (−1.00, −0.82) | |
| Male | −0.75 | (−0.84, −0.67) | |
| Difference | 0.16 | (0.04, 0.28) | 0.01 |
| Marital status | |||
| Never married | −0.97 | (−1.13, −0.81) | |
| Married | −0.85 | (−0.92, −0.77) | |
| Separated/widowed/divorced | −0.72 | (−0.84, −0.59) | |
| Difference | |||
| Never married vs. separated/widowed/divorced | −0.25 | (−0.05, −0.45) | 0.05 |
| Never married vs. married | −0.12 | (−0.30, 0.06) | |
| Separated/widowed/divorced vs. married | 0.13 | (−0.02, 0.28) | |
| Education level | |||
| ≤ High school | −0.86 | (−0.94, −0.78) | |
| > High school | −0.80 | (−0.89, −0.71) | |
| Difference | 0.06 | (−0.07, 0.18) | 0.35 |
| Currently in the labor force | |||
| No | −0.84 | (−0.91, −0.76) | |
| Yes | −0.83 | (−0.94, −0.72) | |
| Difference | 0.01 | (−0.13, 0.14) | 0.93 |
| History of alcohol abuse | |||
| No | −0.83 | (−0.89, −0.77) | |
| Yes | −1.50 | (−2.22, −0.77) | |
| Difference | −0.67 | (−1.39, 0.06) | 0.07 |
| History of tobacco abuse | |||
| No | −0.82 | (−0.89, −0.75) | |
| Yes | −0.89 | (−1.05, −0.74) | |
| Difference | −0.07 | (−0.24, 0.10) | 0.40 |
| Active psychiatric diagnosis | |||
| Depression | |||
| No | −0.80 | (−0.89, −0.71) | |
| Yes | −0.86 | (−0.95, −0.78) | |
| Difference | −0.06 | (−0.19, 0.06) | 0.31 |
| Anxiety | |||
| No | −0.83 | (−0.90, −0.76) | |
| Yes | −0.83 | (−0.96, −0.71) | |
| Difference | 0.001 | (−0.14, 0.14) | 0.99 |
| Bipolar disorder | |||
| No | −0.83 | (−0.89, −0.76) | |
| Yes | −1.68 | (−2.36, −1.00) | |
| Difference | −0.86 | (−1.54, −0.18) | 0.01 |
| Depression or anxiety or bipolar disorder | |||
| No | −0.79 | (−0.89, −0.70) | |
| Yes | −0.86 | (−0.94, −0.78) | |
| Difference | −0.07 | (−0.19, 0.06) | 0.29 |
| Medication use | |||
| Antidepressant use | |||
| No | −0.82 | (−0.91, −0.73) | |
| Yes | −0.84 | (−0.93, −0.76) | |
| Difference | −0.02 | (−0.15, 0.10) | 0.70 |
| Anxiolytic use | |||
| No | −0.85 | (−0.91, −0.78) | |
| Yes | −0.75 | (−0.92, −0.58) | |
| Difference | 0.10 | (−0.08, 0.28) | 0.29 |
| Antidepressant or anxiolytic use | |||
| No | −0.82 | (−0.91, −0.72) | |
| Yes | −0.84 | (−0.92, −0.76) | |
| Difference | 0.03 | (−0.15, 0.10) | 0.68 |
| History of suicidal ideation | |||
| No | −0.82 | (−0.89, −0.75) | |
| Yes | −0.93 | (−1.10, −0.76) | |
| Difference | −0.11 | (−0.30, 0.08) | 0.24 |
| Marital status×employment status interaction | |||
| Never married; not in the labor force | −1.01 | (−1.19, −0.82) | |
| Married; not in the labor force | −0.85 | (−0.94, −0.75) | |
| Separated/widowed/divorced; not in the labor force | −0.70 | (−0.85, −0.56) | 0.68 |
| Never married; currently in the labor force | −0.86 | (−1.17, −0.56) | |
| Married; currently in the labor force | −0.84 | (−0.98, −0.71) | |
| Separated/widowed/divorced; currently in the labor force | −0.77 | (−1.02, −0.51) |
DISCUSSION
In the present analysis, we examined baseline factors associated with the rate of TFC decline over the course of 2CARE, a randomized clinical trial with up to 5 years of participant follow-up. Demographic characteristics and clinical domains of the UHDRS (motor dysfunction, cognitive and behavioral disturbances) were considered as potential risk factors for more rapid functional decline, as well as lifestyle variables.
The sex difference observed, with women experiencing a more rapid functional decline than men, deserves attention. It is yet not clear what the biological basis for this asymmetry between sexes would be. The more rapid decline in women was present across all age ranges, including ages expected to be post-menopausal. Individual neuroprotective roles have been considered widely for testosterone, estrogen, and progesterone, although implications for the presence or absence of these hormones and functional decline in HD have limited description in the literature [19-21]. Testosterone and luteinizing hormone are lower in a small study of age-matched HD men compared to controls, with a moderate positive correlation with TFC scores (r = 0.678, p < 0.0001) [22]. Testosterone repletion in R6/1 and R6/2 mice did not have beneficial effects on phenotype, and it is not known if clinical features of HD improve after supplementation in humans [23, 24]. Progesterone is reported to promote behavioral improvement and antioxidant restoration in the 3-nitropropionic acid HD rat model, but no reports on progesterone and clinical features in humans with HD are available [25]. Nuzzo and colleagues have identified an estrogen-dependent protective role for neuroglobin (Ngb), a nuclear protein that localizes to the mitochondria in response to oxidative stress to lessen cytochrome c release and subsequent caspase-3 activation; this process is thought to involve wild-type huntingtin as a carrier protein critical for Ngb translocation and subsequent neuroprotection [26]. Estrogen-induced increases in Ngb-mediated neuroprotection are significantly attenuated by the silencing of huntingtin in neuroblastoma cell lines [27]. Estrogen upregulates huntingtin in a time- and dose-dependent manner in hippocampal and striatal tissue of male and female Wistar rats, with female rats exhibiting higher levels of striatal huntingtin than males [25]. These data suggest that the HD disease state, with its reduction in normal huntingtin levels, may confer added susceptibility to females by incapacitating an estrogen-driven, huntingtin-dependent protective mechanism. However, the contribution of estrogen to this hypothetical mechanism is unclear—longitudinal estrogen and progesterone levels in HD women are not described in the literature, nor is it known how these hormones correlate with clinical disease progression or functional decline. Other reports have not found an association between sex and TFC decline [2, 3]. Zielonka et al. conducted a cross-sectional analysis of 2191 REGISTRY subjects and found that motor impairment was more highly associated with functional status in women than in men [28]. This observation is consistent with our finding of a possible interaction between TMS and sex, where the relationship between baseline TMS and rate of functional decline was more pronounced in women than in men.
In considering the observed sex difference further, it is plausible that lower mean body weight is in some way responsible for this finding. Body weight was strongly inversely associated with functional decline, and may be confounding the observed association between sex and functional decline. Greater weight may promote better functional reserve in some way, perhaps mechanically (gait, falls), may represent a metabolic advantage independent of neurological features of the disease, may have some relation to differences in sex hormones between men and women, or may simply associate with less severe disease. The relationship between sex hormones and HD progression will require additional study to clarify roles in pathogenesis, if any, and potential therapeutic opportunities.
Among clinical UHDRS domains, worse baseline TMS and cognitive scores were strongly associated with faster functional decline. Baseline behavioral scores were not associated with faster functional decline. Depression and anxiety did not predict changes in function, differing from some reports in the literature [16]. While bipolar disorder was strongly associated with faster TFC decline, it was only observed in 6 participants. Behavioral difficulties may emerge early and remain relatively stable, be more responsive to treatment, be too episodic to meaningfully drive TFC change over time, or may be inherently less influential than motor or cognitive ability on TFC domains. Alternatively, the TFC may not have the capability of reflecting the day-to-day burden of psychiatric disease. Use of antidepressants and/or anxiolytics was not associated with TFC change either; given the prevalence of mood disturbances and anxiety in HD, it may be that pharmacological treatment of identified mood disturbances, however successful, does not influence functional decline and/or the TFC. Alternatively, it may be that medications used for anxiety or mood disturbances do help, promoting a pattern of functional decline similar to those who do not take mood medications (and presumably do not request or require them) that might otherwise have been worse. A history of suicidal ideation was not associated with TFC decline.
The relationships between CAG repeat length, age, and functional decline have been previously examined in the literature. Early studies noted a strong relationship between CAG repeat length and age at onset but no relationships between either CAG repeat length or age at onset and clinical decline [10, 29]. In other work, longer CAG repeats (at least 47) were associated with greater decline in the HD-ADL scale over a 2-year period [30]. Using 30-month data from the CARE-HD trial, Ravina and colleagues found that longer CAG repeat length correlated with worsening functional progression when adjusted for age; 10 additional CAG repeats was associated with a 37% increase in TFC worsening over 30 months [31]. Similarly, Rosenblatt and colleagues prospectively analyzed functional progression with the HD-ADL Scale over an average of 8 years and noted that adjusting for age substantially strengthened the association between CAG repeat length and functional progression [32]. Aziz and colleagues found that about two-thirds of variation in the rate of decline in TFC score was associated with CAG repeat length and residual age at onset, with CAG repeat length having the largest influence [33]. Taken together, there is accumulating evidence that some element of the aging process influences functional progression.
In this study, CAG repeat length was significantly associated with the rate of TFC decline. Moreover, we found an interaction between CAG repeat length and age such that the association between CAG repeat length and rate of functional decline was greater in younger subjects (those less than 45) compared to older subjects. Another way of viewing this is that the relationship between age and rate of TFC decline was most pronounced at lower CAG repeat lengths (<42 and 43–44), where increasing age was associated with a substantially worse annual TFC decline. For those with higher CAG repeat lengths (>45), age did not appear to be associated with the rate of TFC decline; functional decline was comparatively more severe and uniform in all age groups. It should be borne in mind that the subgroups of younger subjects with low CAG repeat lengths and older subjects with high CAG repeat lengths are quite small. Future work studying younger participants at a narrow band of lower CAG lengths, with identified cohorts of “faster” and “slower” functional progression over time, may help identify biomarkers of interest associated with variations in decline. It is of significant interest to identify molecules that may contribute to progression or delay clinical deterioration, particularly in early disease, as these may represent targets for novel disease-modifying therapeutics [34-37].
It is important to recognize that these findings were observed in a fairly homogeneous cohort of participants in a long-term, placebo-controlled clinical trial, so the generalizability of these results should be viewed with caution. Participants in 2CARE were somewhat homogeneous in terms of time since diagnosis, TFC score/early HD stage, and access to academic medical centers with comprehensive HD clinical care. In addition, people with HD who are inclined towards participating in clinical research may possess qualities that do not necessarily reflect the broader HD population—it may be that support mechanisms are more robust and health literacy higher in research participants, as a certain amount of psychosocial stability is likely required for participation in a long study such as 2CARE. Future studies should aim to clarify in more heterogenous populations (i.e., apart from participants in clinical trials) if the intriguing observation of faster functional decline in women can be replicated, as well as gender differences observed for the association between Total Motor Score and functional decline in HD. These data may have implications for clinical surveillance and future treatments.
Appendix
ACKNOWLEDGMENTS
The original 2CARE was study funded by NINDS (Grants NS052592 and NS052619).
The authors would like to acknowledge the following individuals, without whom 2CARE would not have been possible:
Participating sites (Investigators and coordinators)
Hereditary Neurological Disease Centre (Wichita, KS): Gregory Suter BA; The Centre for Movement Disorders (Toronto, ON): John Adams MD MSC, Theresa Moore, Jane Forsyth RN, Alanna Sheinberg BA; Indiana University School of Medicine (Indianapolis, IN): Joann Belden RN, Andrea Hurt, LPN, Katie Price, Marsha Hughes-Gay RN; Colorado Neurological Institute (Englewood, CO): Diane Erickson RN, Breanna Nickels; University of Tennessee Health Science Center (Memphis, TN): Misty M Thompson PhD; London Health Sciences Centre (London, ON): Mandar Jog MD, Christopher Hyson MD, Linda Cole RN, Julie Megens RN, Emilija Makaji MSC, Sara-Lynn Masse, RN, Kori Ladonna BA; Massachusetts General Hospital (Boston, MA):, Keith Malarick, BS, Louisa Mook BA, Susan Maya BA, Alex Bender BA, Jessica Meyer BA, Puja Turakhia, BS, MS, Katherine Harwood, BA, Erin Chung BA, Jennifer Lee BA, Rachel Goldstein, Lindsay Esposito, MPH; Struthers Parkinson’s Center (Minneapolis, MN): Kathryn Duderstadt RN, Sarah Lenarz CMA, Judy Hamerlinck RN, Patricia Ede RN, Catherine Wielinski MPH; University of Rochester (Rochester, NY): David Shprecher DO, Charlyne Hickey RN MS, Ashley Owens, Nancy Pearson RN MS, Nicholas Scoglio, Matthew Grana, BA, Carol Zimmerman RN; University of Kansas Medical Center (Kansas City, KS): Carolyn Gray RN CCRC; Rush University Medical Center (Chicago, IL): Jean A Jaglin RN, Kimberly Janko RN BSN, Lucia M Blasucci RN; University of Texas Southwestern Medical Center (Dallas, TX): Holly Lawrence, Barbara Estes RN CCRC, Brigid Hayward RN, Allison Johnson BS, Amit Gode MPH, Giselle Huet MS, Beverly Romero-Kersh PhD RN, William Thayer RN, Jennifer Hawkins MS, Julia KOCH, CRC; University of Michigan (Ann Arbor, MI): Kristine Wernette RN MS, Elizabeth Sullivan BGS CCRP, Jamie Guyot BS, Julie Konkle RN; Wake Forest University (Winston-Salem, NC): Christine O’Neill BS CCRC, Victoria Hunt RN, Jessica Bargoil, Michael Cartwright MD; University of Alberta (Edmonton, AB): Pamela King BSCN RN, Ingrid Scott RN; University of California Davis (Sacramento, CA): Amanda Martin, John Bautista, Nicole Mans, Terry Tempkin RNC MSN; Westmead Hospital (Westmead, NSW): Clement Loy MB BS FRACP PhD; Jane Griffith RN; Shanthi Graham BSc Hons; Linda Stewart RN; Emily Hayes RN; Donna Galea RN; Beatriz Belmar RN; David Gunn D Psych; Kylie Richardson PhD; Jillian McMillan M Clin Neuropsych; Baylor College of Medicine (Houston, TX): Joohi Jiminez-Shahed MD, Erica Surles, Christine Hunter RN, Sharon Halton LCSW, Alicia Palao MA, Ernesto Jimenez MEd; University of Miami Miller School of Medicine (Miami, FL): Nathalie Padron, Monica Quesada, Wendy Levy, Anita Blenke PA-C MS; University of South Florida (Tampa, FL): Kolleen Elliott RN, Lynn Oelke, London Butterfield; Duke University (Durham, NC): Peggy Perry-Trice CRC, Sarah Wyne MDIV, Lisa Gauger BA, Joanna Stoner; University of Calgary (Calgary, AB): Sarah Furtado MD PhD, Carol Pantella RN, Mary Lou Klimek RN BN MA, Lorelei Tainsh RN; Emory University School of Medicine (Atlanta, GA): Elaine Sperin LPN, Randi Jones PhD, Barbara Sommerfeld MSNRN; Albany Medical College (Albany, NY): Sharon Evans LPN, Mary Eglow RN, Katy Regan; University of Cincinnati (Cincinnati, OH): Andrew Duker MD, Maureen Gartner RN, Erin Neefus; Mayo Clinic Arizona (Scottsdale, AZ): Amy Duffy CRC, Marie Malikowski RN, Pamela Kristof PhD, Teri Radam, Marci Zomok; Butler Hospital (Providence, RI): Lisa Niles MS, Margaret Lannon RN MS, Steven Rainone NP, Rhonda Agramonte; Washington University (St. Louis, MO): Angie Wernle RN, Johanna Hartlein; Columbia University Medical Center (New York, NY): Ronda Clouse RN; Feinstein Institute for Medical Research (Manhasset, NY): Jean Ayan RN; University of Maryland School of Medicine (Baltimore, MD):, Melissa Armstrong MD, Christian Lachner MD, Bradley Robottom MD, Michelle Cines RN, Kelly Dustin RN MS CCRC, Maura Deeley, Constance Nickerson LPN, Samantha Gibson BA; University of Florida (Gainesville, FL): Hubert Fernandez MD, Erin Hastings Monari, PhD, Stacy Merritt MA, Heather Ferreri BS, Alison McMurray MAMC, Camille Swartz BA, Anne Smith-Bova, Kyle Rizer, Michael S Okun MD; Johns Hopkins University (Baltimore, MD): Adam Rosenblatt MD, Maryjane ONG, BS, Claire Welsh, Nadine Yoritomo RN, Gregory Churchill; University of Nevada School of Medicine (Reno, NV): Shamine Poynor, William Gryder, Kathy Tracey, Edwin Serna BS, Marjorie Hyderkhan, Christine Zades MA; University of British Columbia (Vancouver, BC): Joji Decolongon MSC CCRP, Jordana Hutchinson BSC, Kimberley Carter; University of Pittsburgh (Pittsburgh, PA): Robert Y Moore MD PhD, Timothy John Greenamyre MD PhD, Larry S Ivanco MSW, Nancy Lucarelli MA; Ohio State University (Columbus, OH): Allison Daley MS MPH, Jennifer Icenhour BA, Paige Pancake BA, Meredith Wessner MA, Nicole Vrettos BA; University of Pennsylvania (Philadelphia, PA): Stacy Horn DO, Matthew Stern MD, Lisa Altin BS, Iman Affan, Mary Lloyd RN PhD, Jeana La Brie BA BS; The Cooper University Health System (Camden, NJ): Cory Hackmyer BS, Christine Beswick, BA, CCRP; Andrew March, MA; Idaho Elks Rehabilitation Hospital (Boise, ID): Lisa Vogt-Feusi RN, Jane Harris, Sandra Albritton RN, Tracy Arthur RN; University of Iowa (Iowa City, IA): Leigh Beglinger PhD, Jacky Walker, William Adams BA, Jana Hanson MA, Nancy Hale BS RN, Jess Fiedorowicz MD; North York General Hospital 1 (Toronto, ON): Clare Gibbons MS, Lina Qi CCRA BSC, Maheleth Llinas; St. Luke‘s Hospital (Allentown, PA): Nancy Diaz MD, Elizabeth Christ LPN, Christen Kutz, PA-C, Eileen Taff, Tracy Greaser, Katie Anderson RN; North York General Hospital 2 (Toronto, ON): Clare Gibbons MS, Lina Qi CCRA BSC, Maheleth Llinas; Washington Regional Medical Center (Fayetteville, AR): Alan Diamond DO, Mary Craff RN CCRC, Evelyn Rudko RN; Boston University (Boston, MA): Denyse Turpin RN MPH, Melissa Diggin MS RN, Raymond C James RN BSN, Cathi-Ann Thomas RN MS; NJ Neuroscience Institute (Edison, NJ): Charles Porbeni MD, Albert Obiozo MD, Vahid Tohidi MD PhD, Nnamdi Uhegwu MD; University of California Irvine (Irvine, CA): Shari Niswonger RN, Katrina Samson, BS, Debbie Gonzalez; The University of Alabama at Birmingham (Birmingham, AL): Donna Pendley LPN, Rebecca McMurray RN MSN
Massachusetts General Hospital
Nazem Atassi MD, Haruhiko Banno MD, Janice O’Brien, Jillian Morse, Francine Murphy, Bryan Sweet
University of Rochester
Biostatistics: David Oakes PhD, Xueya Cai PhD, Christopher Beck PhD, Jan Bausch BA, Arthur Watts, BS, Shan Gao MS, Brian Griebner MBA
Clinical Trials Coordination Center: Carlinda Field, Liana Baker MPH, Amanda Van Laeken MS, Alice Rudolph PhD, Danielle Fisher MHS, Victoria Snively, Sherry Weston, Cari Rainville, Christine Weaver CCRP, Catherine Covert, Nichole McMullen, Tanya Henderson, Phounsavath Muneath, Victoria Ross, Michele Goldstein, Earl Westerlund, Patricia Lindsay, Gina Lau BS, Colleen McCallum MSW
Safety monitoring
Independent Medical Monitor: Carl Leventhal MD
Huntington study group
Shari Kinel JD, Elizabeth McCarthy.
Footnotes
CONFLICTS OF INTEREST
The authors have no conflicts of interest to report.
REFERENCES
- [1].McGarry A, Biglan K, Marshall F. Huntington’s disease. In: Rosenberg’s Molecular and Genetic Basis of Neurological and Psychiatric Disease. London: Elsevier; 2015. pp. 861–8. [Google Scholar]
- [2].Feigin A, Kieburtz K, Bordwell K, Como P, Steinberg K, Sotack J, et al. Functional decline in Huntington’s disease. Mov Disord. 1995;10(2):211–4. [DOI] [PubMed] [Google Scholar]
- [3].Marder M, Zhao H, Myers RH, Cudkowicz M, Kayson E, Kieburtz K, et al. Rate of functional decline in Huntington’s disease. Neurology. 2000;54(2):452–8. [DOI] [PubMed] [Google Scholar]
- [4].Dorsey ER, Beck CA, Darwin K, Nichols P, Brocht AF, Biglan KM, et al. Natural history of Huntington disease. JAMA Neurol. 2013;70(12):1520–30. [DOI] [PubMed] [Google Scholar]
- [5].McGarry A, McDermott M, Kieburtz K, de Blieck EA, Beal F, Marder K, et al. A randomized, double-blind, placebo-controlled trial of coenzyme Q10 in Huntington disease. Neurology. 2017;88(2):152–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [6].Frank S. Treatment of Huntington’s disease. Neurotherapeutics. 2014;11(1):153–60. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [7].Veenhuizen RB, Kootstra B, Vink W, Posthumus J, van Bekkum P, Zijlstra M, et al. Coordinated multidisciplinary care for ambulatory Huntington’s disease patients: Evaluation of 18 months of implementation. Orphanet J Rare Dis. 2011;6:77. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [8].Fritz NE, Rao AK, Kegelmeyer D, Kloos A, Busse M, Hartel L, et al. Physical therapy and exercise interventions in Huntington’s disease: A mixed methods systematic review. J Huntingtons Dis. 2017;6(3):217–35. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [9].Piira A, van Walsem MR, Mikalsen G, Nilsen KH, Knutsen S, Frich JC. Effects of a one year intensive multidisciplinary rehabilitation program for patients with Huntington’s disease: A prospective intervention study. PLoS Curr. 2013;5:ecurrents.hd.9504af71e0d1f87830c25c394be47027. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [10].Kieburtz K, MacDonald M, Shih C, Feigin A, Steinberg K, Bordwell K, et al. Trinucleotide repeat length and progression of illness in Huntington’s disease. J Med Genet. 1994;31(11):872–4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [11].The Unified Huntington’s Disease Rating Scale: Reliability and consistency. Huntington Study Group. Mov Disord. 1996;11:136–42. [DOI] [PubMed] [Google Scholar]
- [12].Shoulson I, Kurlan R, Rubin AJ, Goldblatt D, Behr J, Miller C, et al. Assessment of functional capacity in neurodegenerative movement disorders: Huntington’s disease as a prototype. In: Munsat T, editor. Quantification of Neurological Deficit. Boston: Butterworths; 1989. pp. 271–83. [Google Scholar]
- [13].The Huntington Study Group. A randomized, placebo-controlled trial of coenzyme Q10 and remacemide in Huntington’s Disease. Neurology. 2001;57:397–404. [DOI] [PubMed] [Google Scholar]
- [14].Landwehrmeyer GB, Dubois B, de Yebenes JG, Kremer B, Gaus W, Kraus PH, et al. Riluzole in Huntington’s disease: A 3-year, randomized controlled study. Ann Neurol. 2007;62(3):262–72. [DOI] [PubMed] [Google Scholar]
- [15].Hersch SM, Schifitto G, Oakes D, Bredlau AL, Meyers CM, Nahin R, et al. The CREST-E study of creatine for Huntington disease: A randomized controlled trial. Neurology. 2017;89(6):594–601. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [16].Beglinger LJ, O’Rourke JJ, Wang C, Langbehn DR, Duff K, Paulsen JS, et al. Earliest functional declines in Huntington disease. Psychiatry Res. 2010;178(2):414–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [17].McGarry A, McDermott M, Kieburtz K, Fung WLA, McCusker E, Peng J, et al. Risk factors for suicidality in Huntington’s disease: An analysis of the 2CARE Clinical Trial. Neurology. 2019;92(14):e1643–e1651. doi: 10.1212/WNL.0000000000007244 [DOI] [PMC free article] [PubMed] [Google Scholar]
- [18].Molenberghs G, Kenward MG. Missing data in clinical studies. Chichester, UK: John Wiley and Sons; 2007. [Google Scholar]
- [19].Bialek M, Zaremba P, Borowicz KK, Czuczwar SJ. Neuroprotective role of testosterone in the nervous system. Pol J Pharmacol. 2004;56:509–18. [PubMed] [Google Scholar]
- [20].Brann DW1, Dhandapani K, Wakade C, Mahesh VB, Khan MM. Neurotrophic and neuroprotective actions of estrogen: Basic mechanisms and clinical implications. Steroids. 2007;72(5):381–405. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [21].Singh M, Su C. Progesterone and neuroprotection. Horm Behav. 2013;63(2):284–90. doi: 10.1016/j.yhbeh.2012.06.003 [DOI] [PMC free article] [PubMed] [Google Scholar]
- [22].Markianos M, Panas M, Kalfakis N, Vassilopoulos D. Plasma testosterone in male patients with Huntington’s Disease; relations to severity of illness and dementia. Ann Neurol. 2005;57(4):520–5. [DOI] [PubMed] [Google Scholar]
- [23].Papalexi E, Persson A, Bjorkqvist M, Petersen A, Woodman B, Bates GP, et al. Reduction of GnRH and infertility in the R6 / 2 mouse model of Huntington’s disease. Eur J Neurosci. 2005;22:1541–6. [DOI] [PubMed] [Google Scholar]
- [24].Hannan AJ, Ransome MI. Deficits in spermatogenesis but not neurogenesis are alleviated by chronic testosterone therapy in R6 / 1 Huntington’s disease mice. J Neuroendocrinol. 2012;24:341–56. [DOI] [PubMed] [Google Scholar]
- [25].Kumar P, Kumar P, Deshmukh R, Sharma PL. P2–044: Neuroprotective role of progesterone against 3-nitropropionic acid-induced Huntington’s disease-like symptoms in rats. Alzheimers Dement. 2013;9(4 Suppl):P360. [Google Scholar]
- [26].Nuzzo MT, Marino M. Estrogen/Huntingtin: A novel pathway involved in neuroprotection. Neural Regen Res. 2016;11(3):402–3. doi: 10.4103/1673-5374.179045 [DOI] [PMC free article] [PubMed] [Google Scholar]
- [27].Nuzzo MT, Fiocchetti M, Servadio M, Trezza V, Ascenzi P, Marino M. 17β-Estradiol modulates huntingtin levels in rat tissues and in human neuroblastoma cell line. Neurosci Res. 2016;103:59–63. [DOI] [PubMed] [Google Scholar]
- [28].Zielonka D, Ren M, De Michele G, Roos RAC, Squitieri F, Bentivoglio AR, et al. The contribution of gender differences in motor, behavioral, and cognitive features to functional capacity independence and quality of life in patients with Huntington’s disease. Parkinsonism Relat Disord. 2018;49:42–7. [DOI] [PubMed] [Google Scholar]
- [29].Feigin A, Kieburtz K, Bordwell K, Como P, Steinberg K, Sotack J, et al. Functional decline in Huntington’s disease. Mov Disord. 1995;10(2):211–4. [DOI] [PubMed] [Google Scholar]
- [30].Brandt J, Bylsma FW, Gross R, Stine OC, Ranen N, Ross CA. Trinucleotide repeat length and clinical progression in Huntington’s disease. Neurology. 1996;46(2):527–31. [DOI] [PubMed] [Google Scholar]
- [31].Ravina B, Romer M, Constantinescu R, Biglan K, Brocht A, Kieburtz K, et al. The relationship between CAG repeat length and clinical progression in Huntington’s disease. Mov Disord. 2008;23(9):1223–7. [DOI] [PubMed] [Google Scholar]
- [32].Rosenblatt A, Kumar BV, Mo A, Welsh CS, Margolis RL, Ross CA. Age, CAG repeat length, and clinical progression in Huntington’s disease. Mov Disord. 2012;27(2):272–6. [DOI] [PubMed] [Google Scholar]
- [33].Aziz NA, van der Burg JMM, Tabrizi SJ, Landwehrmeyer GB. Overlap between age-at-onset and disease-progression determinants in Huntington disease. Neurology. 2018;90:e2099–e2106. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [34].Shin B, Jung R, Oh H, Owens GE, Lee H, Kwak S, et al. Novel DNA Aptamers that bind to mutant Huntingtin and modify its activity. Mol Ther Nucleic Acids. 2018;11:416–28. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [35].Lee JM, Chao MJ, Harold D, Abu Elneel K, Gillis T, Holmans P, et al. A modifier of Huntington’s disease onset at the MLH1 locus. Hum Mol Genet. 2017;26(19):3859–67. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [36].Genetic Modifiers of Huntington’s Disease (GeM-HD) Consortium. Identification of genetic factors that modify clinical onset of Huntington’s disease. Cell 2015;162:516–26. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [37].Moss DJH, Pardiñas AF, Langbehn D, Lo K, Leavitt BR, Roos R, et al. Identification of genetic variants associated with Huntington’s disease progression: Agenome-wide association study. Lancet Neurol. 2017;16(9):701–11. [DOI] [PubMed] [Google Scholar]