8OHdG as a marker for Huntington disease progression
Jeffrey D. Longa, Wayne R. Matsonb, Andrew R. Juhlc, Blair R. Leavittd, Jane S. Paulsena,⁎
and the PREDICT-HD Investigators and Coordinators of the Huntington Study Group1
aThe University of Iowa Carver College of Medicine, Department of Psychiatry, Iowa City, IA, USA
bEdith Nourse Rogers Memorial VA Hospital, Bedford, MA, USA
cDepartment of Psychological and Quantitative Foundations, University of Iowa, Iowa City, IA, USA
dUniversity of British Columbia, Department of Medical Genetics, Vancouver, BC, Canada
a b s t r a c ta r t i c l ei n f o
Received 29 August 2011
Revised 20 January 2012
Accepted 20 February 2012
Available online 5 March 2012
Leukocyte 8-hydroxydeoxyguanosine (8OHdG) is an indicator of oxidative stress, impaired metabolism, and
mitochondrial dysfunction, features that have been implicated in Huntington disease (HD). Increased levels
of 8OHdG have been reported in the caudate, parietal cortex, and peripherally in the serum and leukocytes, in
patients diagnosed with HD. However, little is known about levels in prodromal patients and changes that
might occur as the disease progresses. To address these issues, 8OHdG was tracked over time for a subset
of participants enrolled in the PREDICT-HD study. Participants were stratified into four groups based on prox-
imity to HD diagnosis at study entry: Controls (gene-negative individuals), Low (low probability of near-
future diagnosis), Medium, and High. Blood samples were analyzed using Liquid Chromatography Electro-
chemical Array, and for comparison purposes, a separate cross-sectional sample was analyzed using liquid
chromatography coupled with multiple-reaction-monitoring mass spectrometry. Longitudinal data analysis
showed that initial status (at study entry) and annual rate of change varied as a function of proximity
group, adjusting for sex, education, age at study entry, and site effects. Overall levels were lowest for the Con-
trol group and highest for the High group, and the rate of increase varied in a similar manner. The finding that
8OHdG concentrations increased as a function of proximity to projected disease diagnosis and duration indi-
cates support for the continued assessment of 8OHdG as a robust clinical HD biomarker.
© 2012 Elsevier Inc. All rights reserved.
Huntington disease (HD) is an autosomal dominant neurodegen-
erative disorder of the brain caused by the trinucleotide cytosine–
adenine–guanine (CAG) expansion in the gene of the protein hun-
tingtin. There is currently no cure and the few treatments available
are for symptom modification. Clinical diagnosis or phenoconversion
is often based on the presence of motor symptoms consistent with the
disease phenotype and most commonly occur during the fourth decade
of life (Langbehn et al., 2004). In addition to motor deterioration,
declines in cognition and psychiatric deficits are common (Duff et al.,
2007; Paulsen et al., 2008; Stout et al., 2011; Tabrizi et al., 2009).
Identification of the gene responsible for HD (The Huntington's
Disease Collaborative Research Group, 1993) has made it possible to
identify prodromal research participants who are gene-positive, but
diagnosis. Research emphasis has since shifted to the development of
clinical trials for possible disease-modifying treatments in prodromal
persons (Paulsen et al., 2010b), prior to the disease consequences of
tigations with prodromal HD participants allow researchers to gain
valuable insight into pathogenesis through phenoconversion and be-
yond, theunderstandingof whichcouldpotentiallyleadtointervention
measures preventing disease manifestation (Ross and Tabrizi, 2011).
Despite ample advances in the characterization of clinical and imaging
changes in prodromal HD, few studies have identified possible bio-
markers that might be useful in successfully monitoring therapeutic in-
tervention efficacy. Aggressive research in the identification and
validation of possible biomarkers for HD is critical to efforts to devise
treatments for this relentless brain disease.
One biomarker of interest is leukocyte 8-hydroxydeoxyguanosine
(8OHdG), which is an indicator of oxidative stress, impaired metabolism,
and mitochondrial dysfunction. Such features have been implicated in
Neurobiology of Disease 46 (2012) 625–634
Abbreviations: 8OHdG, 8-hydroxydeoxyguanosine; HD, Huntington disease; CAG,
cytosine–adenine–guanine; ROS, radical oxygen species; UHDRS, Unified Huntington's
Disease Rating Scale; DCL, diagnostic confidence level; CAP, CAG length/age product;
LCECA, Liquid Chromatography Electrochemical Array; MRM, multiple-reaction-moni-
toring; PPD, pharmaceutical product development; LMER, linear mixed effects regres-
sion; LRT, likelihood ratio test; AIC, Akaike's information criterion; LR, linear regression.
⁎ Corresponding author at: The University of Iowa Carver College of Medicine, 1-305
Medical Education Building, Iowa City, IA 52242, USA. Fax: +1 319 353 3003.
E-mail addresses: email@example.com (J.D. Long), firstname.lastname@example.org
(W.R. Matson), email@example.com (A.R. Juhl), firstname.lastname@example.org
(B.R. Leavitt), email@example.com (J.S. Paulsen).
1See the Acknowledgments section for a complete list of PREDICT-HD Investigators
Available online on ScienceDirect (www.sciencedirect.com).
0969-9961/$ – see front matter © 2012 Elsevier Inc. All rights reserved.
Contents lists available at SciVerse ScienceDirect
Neurobiology of Disease
journal homepage: www.elsevier.com/locate/ynbdi
the neuronal dysfunction associated with HD (Kikuchi et al., 2002).
8OHdG is a compound formed through hydroxylation of the guanine
base by radical oxygen species (ROS). Following oxidation, damaged
DNA is repaired by cellular mechanisms, and the hydroxylated guanine
is excreted via bodily fluids. Consequently, available levels of 8OHdG in
the blood and urine correlate with the degree of internal DNA damage
and have been used as markers for such purposes in disease modeling
(Valavanidis et al., 2009).
Disruptions to mitochondrial function and metabolism and an in-
crease in oxidative stress through the production of free radical mol-
ecules have been implicated in HD (Browne and Beal, 2006; Klepac
et al., 2007; Sorolla et al., 2008). Increased levels of 8OHdG have
been reported in the caudate and parietal cortex (Browne et al.,
1997; Polidori et al., 1999), as well as peripherally in the serum
(Hersch et al., 2006) and leukocytes (Chen et al., 2007) of patients di-
agnosed with HD. Additionally, the R6/2 mouse model, which ex-
presses exon 1 of the human HD gene and an expanded CAG repeat,
shows increased levels of 8OHdG in urine, plasma, and striatal micro-
dialysates (Bogdanov et al., 2001). These studies provide support for
the utility of 8OHdG measurement in HD and strengthen the rationale
for therapeutic strategies that either potentiate antioxidant defenses
or avoid oxidative stress generation to delay disease progression.
The relationship between prodromal HD individuals and 8OHdG
levels, however, has remained unexplored until now.
The PREDICT-HD study is a longitudinal observational study aimed
agnosed individuals who have undergone genetic testing for the HD
gene mutation (Paulsen et al., 2006). The study has previously reported
the prodromal existence of mild cognitive impairment (Duff et al.,
2010), behavioral symptoms (Beglinger et al., 2008), psychiatric symp-
toms (Duff et al., 2007), motor deficits (Biglan et al., 2009), and neuro-
structural abnormalities (Nopoulos et al., 2011; Paulsen et al., 2010a).
biochemical changes occurringin HD, specifically thoserelated to mito-
plasma samples taken from prodromal HD participants who vary in
their disease progression at study entry. The primary goal is to evaluate
if 8OHdG is sensitive to change over time and whether such change is a
among individuals varying in disease progression are of interest. These
goals are addressed using two methods of blood specimen analysis.
Three separate samples of participants already enrolled in
PREDICT-HD were considered for the analysis. Table 1 shows descrip-
tive information of the three samples for a number of demographic
variables. Each sample had two types of participants, those who test-
ed positive for the expanded CAG repeat length (cases), and those
who tested negative (controls). All participants had undergone ge-
netic testing for the CAG repeat expansion prior to the study. Inclu-
sion criterion for PREDICT-HD and this study was that participants
had to be “prodromal” at the first visit, meaning expansion-positive
participants did not meet the traditional definition of HD diagnosis
as determined by question 17 of the Unified Huntington's Disease
Rating Scale (UHDRS). Question 17, also known as “diagnostic confi-
dence level” (DCL), is a 5-point scale on which investigators rate
their level of confidence that an individual fulfills the criteria for a
clinical motor diagnosis based on the presence of an otherwise unex-
plained movement disorder. A score of zero indicates that no motor
abnormalities are present, whereas a score of 4 corresponds to a con-
fidence level≥99% that an individual is displaying unequivocal signs
of HD. Participants with DCL=4 at the first visit were excluded
from the analysis. Additional details regarding recruitment proce-
dures in PREDICT-HD are provided by Stout et al. (2011).
The first sample was longitudinal (L) consisting of NL=77 partic-
ipants with up to three repeated measures over visits 1–3. The longi-
tudinal participants had their blood specimens processed according
to the first method to be discussed below (LCECA analysis). The longi-
tudinal participants were sampled from 13 sites.
The second sample was cross-sectional (C) consisting of NC=80
participants with observations mainly collected over visits 3–6. The
cross-sectional participants had their blood specimens processed
according to the second method presented below (LC–MS/MS analy-
sis). The cross-sectional participants were sampled from 5 sites.
The third sample was cross-sectional and consisted of eighteen
participants who overlapped (O) the two previous samples. That is,
NO=18 participants were common among the previous two samples,
but only had cross-sectional observations. The overlapping partici-
pants had their blood specimens processed by both methods dis-
cussed below and were sampled from 4 sites.
The participants were sampled so as to represent various levels of
disease progression at time of entry into PREDICT-HD. All expansion-
positive participants were classified into groups according to their
predicted proximity to HD diagnosis at study entry based on the
CAG length/age product (CAP) variable (Zhang et al., 2011). CAP
groups are Low, Medium, and High, with the labels reflecting levels
of cumulative toxicity of mutant huntingtin at study entry. The Con-
trol group consists of the expansion-negative individuals who will
never develop HD (all individuals had CAGb31). Table 2 shows the
frequency (% of total) of CAP group membership by type of sample.
As seen in Table 2, the High group was the largest for all the samples.
Validity evidence of the CAP groups for the larger PREDICT-HD data
set is provided by Zhanget al. (2011), whoshow group differences ona
large number of clinical and behavioral phenotypes. For present pur-
poses, limited phenotypic information is provided for the longitudinal
sample, as this was the sample for the primary analysis (see below).
Means and SDs are provided for key phenotypic variables at study
entry by CAP group in Table 3, along with the appropriate omnibus
tions, testing any type of difference among the groups). The variables
Descriptive statistics for the three samples used in the analysis.
Sample VariableMinMedMean MaxSD
(NL=77, 13 sites)
(NC=80, 5 sites)
(NO=18, 4 sites)
Note. Duration=current age−age at entry; sex = proportion of males.
CAP group frequency (% of total) by sample.
J.D. Long et al. / Neurobiology of Disease 46 (2012) 625–634
are striatal volume to total volume×100 (Striatum), UHDRS Total
Motor Score (TMS; possible range 0–100 with higher scores reflecting
greater symptoms), UHDRS Functional Assessment (FAS; range 0–25
with lower scores reflecting worse functioning), Symbol Digit Modali-
pairment), Beck Depression Inventory (BDI; range 0–63 with higher
scores indicating greater depression), and the control variables years
of education, age at study entry, and proportion of males (Sex). The
last row shows the proportion who received a DCL motor diagnosis
(i.e., phenoconverted) at some point over the observed duration for
the sample (larger values reflect greater disease progression).
Table 3 illustrates the baseline progression differences among the
progression increased, though FAS group differences were not statisti-
cally reliable (F(3,74)=1.48, p>0.05). TMS, diagnosis proportion,
and BDI means increased as disease progression increased, though
BDI group differences were not statistically reliable (F(3,74)=1.86,
p>0.05). Additional analyses not presented suggested that differences
among the Control and Low groups were attributable to differences in
et al. (2011).
Blood specimen collection protocol
Un-fasted blood specimenswere obtained from the cubitalvein into
EDTA-coated tubes, inverted multiple times, and then centrifuged at
4000 rpm at 4 °C for 10 min. Resultant plasma was divided into
1.5–2 mL aliquots, then frozen and stored at −80 °C until analysis.
For the longitudinal participants (NL=77), blood specimens were
analyzed using a long gradient Liquid Chromatography Electrochem-
ical Array (LCECA) (Bogdanov et al., 1999; Matson et al., 1984, 1987).
The LCECA survey method isolates ca. 1500–2000 responses from
plasma components to levels of ca. 200 pg/mL. A mixed standard of
60 components of the tyrosine, tryptophan, and purine pathways, sul-
fur amino acids, and markers of oxidative stress, were used to deter-
mine the expression levels of previously reported known compounds.
At the initiation of the study, a pool of all samples was created
from sub-aliquots of each sample and was used for both quality as-
sessment and normalization of the analytical data. Samples were ran-
domized for analysis sequence. Samples were run in the sequence
order: mixed standard, pool, eight samples, mixed standard, pool,
eight samples, etc. All individual samples were time-normalized to a
pool in the middle of the study. The data were then exported as dig-
itized values (digital maps) capturing all information from the analyt-
ical platform. The digital maps were analyzed in raw form using
principle component analysis to determine the extent of any analyti-
cal drift over the study, and subsequently normalized to the average
pool value for the entire study against the pools bracketing each sam-
ple sequence of eight. The digital maps were then trimmed to contain
only values below the noise threshold of the instrument.
For the cross-sectional participants (NC=80), 8OHdG was
assessed using reverse phase C-18 liquid chromatography coupled
to a multiple-reaction-monitoring (MRM) mass spectrometer (LC–
MS/MS). This assay was developed and performed by Pharmaceutical
Product Development (PPD) and used a stable-heavy-isotope-labeled
form of 8OHdG.
The 80 human plasma specimens were received from Coriell Insti-
tute in dry ice. The average volume of a specimen was 1.2 mL. A pool
produced from human plasma QC samples was obtained from the
Stanford University Blood Bank and maintained in a −80 °C freezer
before processing. For each sample, 500 μL plasma was mixed with
500 μL water, and 5 μL
150 μL 1 M ammonium acetate (NH4Ac, pH 5.25) was added to the so-
lution and the solution was vigorously vortexed for 10 min on a shak-
er. The solution was centrifuged at 15,800×g at RT for 2 min, because
precipitates were observed in many specimens, which can cause a va-
riety of processing problems. The supernatant was loaded onto Sep-
pak tC18 solid-phase-extraction cartridge (200 mg, Waters, MA)
that was preconditioned with 1 mL methanol and 1 mL water. The
cartridges were then washed with 3 mL water before the fraction
containing 8OHdG was eluted with 1 mL 40% methanol.
The eluent was dried under centrifugation and vacuum and then
reconstituted with 100 μL buffer A (10 mM NH4Ac in water, pH 4.75
adjusted by formic acid). Reconstituted solution was filtered with a
pre-cleaned 10KD MWCO centrifugal device (wash: 200 μL for
15 min; sample: 100 μL for 12 min, both centrifuged at 15,800×g)
and stored in a −80 °C freezer for LC–MS/MS analysis.
Online HPLC–MS/MS (MRM) measurements were carried out chro-
matographically on an Agilent capillary 1100 binary pumping system
(Agilent Technologies, Santa Clara, CA). Reverse phase-HPLC separa-
tions were achieved on a Zorbax® SB C18 capillary HPLC column
(150.0.5 mm, particle size: 5 μm, pore size: 8 Å, Agilent Technologies,
CA). LC flow rate was 15 μL/min, using 10 mM NH4Ac in water adjusted
to pH 4.6 with formic acid (buffer A) and 0.1% formic acid in MeCN
(buffer B). Gradient elution was performed as follows: 0–2 min, 0–0%
buffer B; 2–10 min, 0–50% buffer B; 10–12 min, 50–50% buffer B;
12–13 min, 50–0% buffer B; 13–20 min, 0–0% buffer B. MS/MS analysis
was performed in positive ion mode, using a Q-Trap 4000 triple–qua-
druple ion trap (ABI-Sciex, Foster City, CA). MRM acquisition mode
15N5-8OHdG was added to the mixture.
Mean (SD) of key variables for the longitudinal sample by disease progression group at the first visit.
VariableControl LowMedium HighTest
Note. Striatum=ratio of striatal volume to total brain volume×100; TMS = Total Motor Score; FAS = Functional Assessment; SDMT = Symbol Digit Modalities Test; BDI = Beck
Depression Inventory; Sex = proportion of males; Diagnosis = proportion who eventually phenoconverted.
J.D. Long et al. / Neurobiology of Disease 46 (2012) 625–634
was used; m/z 168 and 173 (the most abundant product ions resulting
from the precursor ions [M+H]+at m/z 284 and 289, light and heavy)
were utilized as quantifier ions with a dwell time 200 ms/cycle per ion.
The overall goal of the analysis was to examine CAP group differ-
ences in 8OHdG controlling for the covariates of years of education,
age at entry, and sex. For the longitudinal participants the CAP
group differences were in terms of initial status (intercept) and
mean annual rate of change (linear slope). With the cross-sectional
participants, the CAP group differences consisted of overall mean dif-
ferences controlling for the covariates. The secondary goal of the anal-
ysis was to compare the 8OHdG processing methods for the NO=18
Linear mixed effects regression (LMER) was used for the longitudi-
nal analysis to account for dependency due to repeated measures and
to accommodate missing data (Verbeke and Molenberghs, 2000). The
timemetric fortheanalysis wasduration,definedastheageatobserva-
tion minus the age at study entry. Thus, initial status reflected the level
at duration=0. Site variation was a consideration, as the longitudinal
participants were sampled from a relatively large number of sites (see
Table 1). The LMER model had duration nested within participants,
and participants nested within sites. Random intercepts and random
slope terms were specified for duration, along with a random intercept
for site. Preliminary statistical and graphical analyses not presented
suggested that linear growth curves were adequate.
The first part of the longitudinal analysis consisted of fitting three
models: (1) a model with no CAP group differences, i.e., the same ini-
tial status and annual rate of increase for all groups; (2) a model with
initial status differences among the CAP groups, but equal rates of in-
crease; and (3) a model with initial status and rate of change differ-
ences among the CAP groups. All models included the covariates of
years of education, age at study entry, and sex. Details of the models
are provided in the Appendix A.
Parameters for the LMER analysis were estimated using maximum
likelihood methods. The models were compared with the likelihood
ratio test (LRT) and Akaike's information criterion (AIC) (Akaike,
1973, 1974) corrected for small sample bias (AICc) (Hurvich and
Tsai, 1989). Two scalings of the AICc were also computed, dAICc and
wtAICc. dAICc is the AICc for a model minus the smallest AICc of the
set. dAICc=0 for the best fitting model and dAICc>0 for the remain-
ing models. Though no statistical testing is performed with dAICc, a
rule of thumb is that dAICc>3 represents a “very strong” difference
between models (Anderson, 2008). wtAICc is the weight of evidence,
which is a probability scaling of the AICc, indicating the probability
that a model is the most plausible given the set of models and the
data (Burnham and Anderson, 2002; Long, 2012). The weight of evi-
dence has the range 0≤wtAICc≤1, with larger values indicating bet-
ter fit to the data and higher plausibility.
The second part of the longitudinal analysis focused on specific ef-
fects of the best fitting model. The Control group was the reference
group for comparison with all the other groups.
The analysis of the cross-sectional sample was similar to that of
the longitudinal data in the respect that three models were com-
pared. Though the data were cross-sectional, there was variability in
duration due to different individuals having different values (rather
individuals having repeated duration observations). Consequently, it
was possible to examine the relationship between 8OHdG and dura-
tion controlling for the covariates and test the cross-sectional analogs
of the three models in the longitudinal analysis.
Preliminary LMER analysis for the cross-sectional data (not pre-
sented) indicated zero fitted model variance for sites. Therefore, site
variability was not modeled and linear regression (LR) was used for
the cross-sectional analysis. The three models were compared using
the F-test, AICc, dAICc, and wtAICc.
The size of the overlapping sample was small (NO=18). There-
fore, no statistical analysis was performed other than to fit LR models
of 8OHdG by duration for descriptive purposes. Graphs of the ob-
served data with the fitted curves are presented below.
The results of the longitudinal data analysis indicate statistically
reliable differences among the CAP groups in terms of initial levels
of 8OHdG and differences in the mean rate of 8OHdG increase over
Fig. 1. 8OHdG levels over time (duration) for individuals (dashed lines) and groups (solid lines). The individual curves are observed 8OHdG levels and the group curves are 8OHdG
levels based on linear fits.
J.D. Long et al. / Neurobiology of Disease 46 (2012) 625–634
time. The nature of the effects is illustrated in Fig. 1, which is paneled
by CAP group. The dashed lines depict observed levels of 8OHdG
tracked over time (duration) for individuals, and the solid lines depict
linear fits of the mean 8OHdG level over time for the groups. It is the
starting point of the solid lines (i.e., the intercepts) and their slopes
(i.e., rates of change in 8OHdG per year) that are the focus of the
LMER analysis described above.
As Fig. 1 shows, the slopes of the solid lines varied as a function of
CAP group, with the Control group having a slight decrease in 8OHdG
level per year, and the High group having the fastest increase in
8OHdG per year. The graph also shows intercept differences in levels
of 8OHdG, with the Control group having the lowest mean level at du-
ration=0, and the other groups having higher initial levels.
The results of the global model comparison are shown in Table 4.
As seen in the table, Model 3 had substantially better fit than the
other two models (wtAICc=0.92). Recall that Model 3 had unequal
mean intercepts and unequal mean slopes among the CAP groups,
similar to the solid lines in Fig. 1.
Because Model 3 was the best fitting, additional details are pre-
sented. Table 5 shows the estimates of the intercepts (upper portion)
and the slopes (lower portion) for each CAP group, adjusting for the
covariates. The inferential statistics (SE, z, p) reference the difference
between the Control group and the remaining CAP groups (Low–
Medium–High), thus they are omitted for the Control group rows in
Table 5. The Control group slope estimate (^β0C¼ 1:05) had an associ-
ated z=0.40 indicating the slight increase in 8OHdG level after con-
trolling for the covariates was not statistically reliable. The slight
increase of the model-based estimate in Table 5 is in conflict with
the slight decrease shown in Fig. 1 due to the inclusion of the covari-
ates (Fig. 1 is not adjusted for the covariates). The covariates had rel-
atively small effects compared to CAP group differences, with age at
entry having the strongest initial status effect (z=−1.49; younger
participants had a weak tendency to start at higher levels), and sex
having the strongest slope effect (z=1.04; males had a small tenden-
cy to increase at a faster rate).
Focusing first on the CAP group slopes, Table 5 shows that the
8OHdG level increased at a faster rate as the CAP group increased
(from Control,^β1C¼ 1:05, to High,^β1H¼ 3:99). The z-ratio column
versus Control (z=0.35).
trol group had the smallest estimated initial 8OHdGlevel (^β0C¼ 19:61)
and the High group had the largest estimated level (^β0H¼ 24:20). In
terms of effect size, the largest difference was between the Control ver-
sus High group (z=3.18), followed by Medium versus Control
(z=1.81), and Low versus Control (z=1.42).
The results indicate no statistically reliable differences among the
CAP groups for the cross-sectional sample. The individual observed
points and the fitted linear curves are shown in Fig. 2, with the panels
representing CAP groups. The reader is reminded that the data in
Fig. 2 are cross-sectional as the variability in duration is due to differ-
ent individuals rather than repeated measures within individuals.
Results of the longitudinal sample model comparison.
Note. K = number of parameters.
Estimated mean intercept and mean slope for the 8OHdG regression lines represented
by Model 3.
EffectCAP group Estimate
aSE, z, and p refer to the difference between each group and the Control group. Re-
sults in this table are adjusted for years of education, age at study entry, and sex.
Fig. 2. Cross-sectional sample observed 8OHdG values (filled circles) and linear fitted curves (solid lines) by CAP group.
J.D. Long et al. / Neurobiology of Disease 46 (2012) 625–634
The results of the LR analysis are shown in Table 6. The table
shows that the model with no CAP group differences was the best fit-
ting model (Model 1; wtAICc=0.93). Parameter estimates for Model
1 not presented showed there was a strong effect for sex (z=3.67),
with males having a substantially higher mean 8OHdG level than
As previously mentioned, there were NO=18 participants whose
blood specimens were processed using both methods. The observed
points and fitted linear curves are shown in Fig. 3.
Each vertical cross-section of duration has two points, an open cir-
cled and a filled circle, indicating the two types of processing for the
same individual. As can be seen in Fig. 3, the fitted curves are similar
fortheHighand Mediumgroups,butnotfortheLow andControlgroups.
The aim of this study was to examine 8OHdG expression levels as a
CAP group (Low, Medium, High), which is based on a proxy variable of
time to diagnosis (Zhang et al., 2011) and showed initial differences
on several key phenotypic variables for the participants in the primary
analysis (see Table 3). Three samples of participants were considered,
a longitudinal sample with LCECA bloodanalysis, a cross-sectionalsam-
ple with LC–MS/MS analysis, and an overlapping sample that used both
methods. The results of the longitudinal sample show that plasma con-
centrations of 8OHdG varied as a function of CAP group controlling for
numerous covariates. Specifically, there were intercept differences (at
duration=0) among the groups, with the mutation-negative partici-
pants (Control group) having the lowest level, and the participants
with the most advanced disease progression (High group) having the
highest level. The rate of annual increase in 8OHdG level also varied as
a function of disease progression. The mutation-negative participants
had no appreciable change over time, similarly for the Low CAP group.
The Medium group had a slight increase over time, and the High group
had the fastest increase over time (see Fig. 1).
The longitudinal sample results are consistent with previous ob-
servations of irregular mitochondrial functioning and increases in
DNA damage caused by oxidative stress. Although previous investiga-
tors were able to show a similar increase in 8OHdG associated with
severity of disease symptoms in diagnosed patients (Chen et al.,
2007; Hersch et al., 2006), this is the first study to demonstrate ele-
vated oxidative DNA damage stress with prodromal individuals.
8OHdG concentrations increased as a function of proximity to pro-
jected disease diagnosis, indicating support for the continued assess-
ment of 8OHdG as a robust clinical HD biomarker.
This study showed a substantial difference between mutation-
negative individuals and mutation-positive individuals who were rel-
atively far in their disease progression. However, this comparison
does not establish 8OHdG as a biomarker specific to HD. 8OHdG has
been implicated in various forms of cancer (Marnett, 2000), and in
different types of neurodegenerative diseases (Sayre et al., 2001). Ad-
ditional research, possibly with comparison groups representing mul-
tiple diseases, is needed to investigate the extent to which 8OHdG is a
reliable biomarker for HD.
Due to the cross-sectional nature of the LC–MS/MS data, it was not
sitive to changes over time. Though the8OHdG by duration relationship
was examined by CAP group (see Fig. 2), this provided only indirect in-
in overall level (average level) or in 8OHdG growth rate (see Table 6).
The discrepancy between the results of the longitudinal LCECA
analysis and the cross-sectional LC–MS/MS analysis needs to be inter-
preted in light of participant characteristics and differences in the
blood processing. Regarding the first point, the cross-sectional LC–
MC/MS sample consisted of individuals who had been in the study
Results of the cross-sectional sample model comparison.
Note. K = number of parameters.
Fig. 3. Observed 8OHdG for two methods of processing by duration and CAP group. Open circles and gray lines indicate LCECA processing; filled circles and solid lines indicate LC–
J.D. Long et al. / Neurobiology of Disease 46 (2012) 625–634
longer on average than the longitudinal LCECA sample and were
recruited from fewer sites (see Table 1).
Regarding blood processing, there was an increased level of han-
dling, freeze/thawing and transport of specimens to the PPD laborato-
ry for the LC–MS/MS analysis, and all the specimens were processed
after the LCECA analysis and nothing was excluded from analysis.
The lab used for the LCECA analysis had a larger sample size due to
the repeated measures, a more rigorous exclusion criteria, and faster
access to samples (less freeze/thaw). The absolute 8OHdG values of
the LC–MS/MS analysis were lower than the LCECA analysis and
may represent a decreased sensitivity of this assay. The LCECA analy-
sis produced 8OHdG results similar to those previously reported in
the literature (Hersch et al., 2006), and the lab used for the LCECA is
more familiar with analytical procedures specific to this compound
(Bogdanov et al., 1999).
The 18 participants who had their blood specimens analyzed by
both methods potentially illustrate the differences in the assay sensi-
tivity. For the Low, Medium, and High groups, the LCECA method pro-
duced 8OHdG values larger than the LC–MS/MS method for each
individual (see Fig. 3). This was not the case for every individual in
the Control group, however. In terms of 8OHdG change over time,
the regression lines were very similar for both methods in the High
group, which had the largest number of participants (seven). On the
other hand, the regression lines for the Control group (six) were dis-
similar (see Fig. 3).
One limitation of the current study, as with any research involving
prodromal participants, is the classification of groups in regard to
proximity to diagnosis. Exclusion criteria for the PREDICT-HD study
disqualify individuals clinically diagnosed with HD from enrolling in
the study. Therefore, the initial selection of participants for the longi-
tudinal sample was performed following participants' third annual
assessment. Subsequently, this retrospective grouping may have in-
flated the 8OHdG increase seen in participants approaching predicted
The development of robust biomarkers for HD is of the utmost im-
portance. Due the genetic nature of the disease and the ability to
identify participants before manifestation of clinical symptoms,
there is a unique window for intervention, whereby the course of
the disease could theoretically be arrested. Traditional clinical mea-
sures, such as the TMS and FAS are often too insensitive to detect sub-
tle changes years prior to phenoconversion. As such, the development
of suitable markers of progression, such as 8OHdG, is integral to the
formation of earlier therapeutic interventions, as well as assessments
of their efficacy.
The findings for the longitudinal sample show that 8OHdG, a bio-
chemical compound easily isolated from multiple bodily fluids, in-
creases with HD disease progression and is sensitive to individual
change over time. Though a larger sample is preferred, our results
suggest that plasma 8OHdG levels could be employed clinically as a
tool to help measure disease progression in gene positive individuals
prior to the appearance of overt motor dysfunction. It is unlikely that
a single chemical compound would be employed for such purposes,
but the current findings could contribute to the development of a
combination of robust measures with other biochemical biomarker
candidates, as well as complement multiple other marker modalities,
such as neuroimaging measures and cognitive and motor assessments
to aid in the development and evaluation of interventional HD
Role of the funding sources
The funding sources had no involvement in the study design; in
the collection, analysis, or interpretation of the data; in the writing
of the report; nor in the decision to submit the article for publication.
The authors report no conflicts of interest.
This work was supported by the National Institutes of Health, the
National Institute of Neurological Disorders and Stroke [NS40068 to
J.S.P.] and the CHDI Foundation, Inc. We thank the PREDICT-HD
sites, the study participants, and the National Research Roster for
Huntington Disease Patients and Families.
PREDICT-HD Investigators, Coordinators, Motor Raters, Cognitive
Active: September 2009–August 2010
Thomas Wassink, MD, Stephen Cross, BA, Nicholas Doucette, BA,
Mycah Kimble, BA, Patricia Ryan, MSW, LISW, MA, Jessica Wood,
MD, PhD, Eric A. Epping, MD, PhD, and Leigh J. Beglinger, PhD (Uni-
versity of Iowa, Iowa City, Iowa, USA);
Edmond Chiu, MD, Olga Yastrubetskaya, PhD, Joy Preston, Anita
Goh, D.Psych, Chathushka Fonseka, and Liz Ronsisvalle (St. Vincent's
Hospital, The University of Melbourne, Kew, Victoria, Australia);
Phyllis Chua, MD, and Angela Komiti, BS, MA (The University of
Melbourne, Royal Melbourne Hospital, Melbourne, Australia);
Lynn Raymond, MD, PhD, Rachelle Dar Santos, BSc, and Joji
Decolongon, MSC, CCRP (University of British Columbia, Vancouver,
British Columbia, Canada);
Adam Rosenblatt, MD, Christopher A. Ross, MD, PhD, Barnett
Shpritz, BS, MA, OD, and Claire Welsh (Johns Hopkins University, Bal-
timore, Maryland, USA);
William M. Mallonee, MD, Greg Suter, BA, and Judy Addison (He-
reditary Neurological Disease Centre, Wichita, Kansas, USA);
Ali Samii, MD, and Alma Macaraeg, BS (University of Washington
and VA Puget Sound Health Care System, Seattle, Washington, USA);
Randi Jones, PhD, Cathy Wood-Siverio, MS, Stewart A. Factor, DO,
and Claudia Testa, MD, PhD (Emory University School of Medicine, At-
lanta, Georgia, USA);
Roger A. Barker, BA, MBBS, MRCP, SarahMason, BSC, Anna Goodman,
PhD, Rachel Swain, BA, and Anna DiPietro (Cambridge Centre for Brain
Repair, Cambridge, UK);
Elizabeth McCusker, MD, Jane Griffith, RN, Clement Loy, MD, David
Bernhard G. Landwehrmeyer, MD, Michael Orth MD, PhD, Sigurd
Süβmuth, MD, RN, Katrin Barth, RN, and Sonja Trautmann, RN (Uni-
versity of Ulm, Ulm, Germany);
Kimberly Quaid, PhD, Melissa Wesson, MS, and Joanne Wojcieszek,
MD (Indiana University School of Medicine, Indianapolis, IN);
Mark Guttman, MD, Alanna Sheinberg, BA, and Irita Karmalkar,
BSc (Centre for Addiction and Mental Health, University of Toronto,
Markham, Ontario, Canada);
Susan Perlman, MD and Arik Johnson, PsyD (University of Califor-
nia, Los Angeles Medical Center, Los Angeles, California, USA);
Michael D. Geschwind, MD, PhD, Jon Gooblar, BA, and Gail Kang,
MD (University of California San Francisco, California, USA);
Tom Warner, MD, PhD, Maggie Burrows, RN, BA, Marianne Novak,
MD, Thomasin Andrews, MD, BSC, MRCP, Elisabeth Rosser, MBBS,
FRCP, and Sarah Tabrizi, MD, PhD (National Hospital for Neurology
and Neurosurgery, London, UK);
Anne Rosser, MD, PhD, MRCP, Kathy Price, RN, and Sarah Hunt, BSc
(Cardiff University, Cardiff, Wales, UK);
Frederick Marshall, MD, Amy Chesire, LCSW-R, MSG, Mary
Wodarski, BA, and Charlyne Hickey, RN, MS (University of Rochester,
Rochester, New York, USA);
Oksana Suchowersky, MD, FRCPC, Sarah Furtado, MD, PhD, FRCPC,
and Mary Lou Klimek, RN, BN, MA (University of Calgary, Calgary, Al-
Peter Panegyres, MB, BS, PhD, Elizabeth Vuletich, BSC, Steve
Andrew, and Rachel Zombor, MPSYC (Neurosciences Unit, Graylands,
Selby-Lemnos & Special Care Health Services, Perth, Australia);
J.D. Long et al. / Neurobiology of Disease 46 (2012) 625–634
Joel Perlmutter, MD, Stacey Barton, MSW, LCSW, and Amy Schmidt
(Washington University, St. Louis, Missouri, USA);
Zosia Miedzybrodzka, MD, PhD, Sheila A. Simpson, MD, Daniela
Rae, RN, and Mariella D'Alessandro, PhD (Clinical Genetics Centre, Ab-
erdeen, Scotland, UK);
David Craufurd, MD, Ruth Fullam, BSC, and Elizabeth Howard, MD
(University of Manchester, Manchester, UK);
Pietro Mazzoni, MD, PhD, Karen Marder, MD, MPH, and Paula
Wasserman, MA (Columbia University Medical Center, New York,
New York, USA);
Rajeev Kumar, MD and Diane Erickson, RN (Colorado Neurological
Institute, Englewood, Colorado, USA);
Vicki Wheelock, MD, Terry Tempkin, RNC, MSN, Nicole Mans, BA,
MS, and Kathleen Baynes, PhD (University of California Davis, Sacra-
mento, California, USA);
Joseph Jankovic, MD, Christine Hunter, RN, CCRC, and William
Ondo, MD (Baylor College of Medicine, Houston, Texas, USA);
Justo Garcia de Yebenes, MD, Monica Bascunana Garde, Marta
Fatas, BA, and Asuncion Martinez-Descales (Hospital Ramón y Cajal,
Wayne Martin, MD, Pamela King, BScN, RN, and Satwinder Sran,
BSC (University of Alberta, Edmonton, Alberta, Canada);
Anwar Ahmed, PhD, Stephen Rao, PhD, Christine Reece, BS, Janice
Zimbelman, PhD, PT, Alexandra Bea, BA, Emily Newman, BA, and Alex
Bura, BA (Cleveland Clinic Foundation, Cleveland, Ohio, USA).
Jane Paulsen, PhD, Principal Investigator, Eric A. Epping, MD, PhD,
Hans Johnson, PhD, Megan M. Smith, PhD, Janet Williams, PhD, RN,
FAAN, Leigh Beglinger, PhD, James Mills, MS (University of Iowa Hos-
pitals and Clinics, Iowa City, IA); Elizabeth Aylward, PhD (Seattle Chil-
dren's Research Institute, WA); Kevin Biglan, MD (University of
Rochester, Rochester, NY); Blair Leavitt, MD (University of British Co-
lumbia, Vancouver, BC, Canada); Marcy MacDonald, PhD (Massachu-
setts General Hospital); Martha Nance, MD (Hennepin County
Medical Center, Minneapolis, MN); and Cheryl Erwin, JD, PhD (Uni-
versity of Texas Medical School at Houston).
Bio Markers: Blair R. Leavitt, MDCM, FRCPC (Chair) and Michael
Hayden, PhD (University of British Columbia); Stefano DiDonato,
MD (Neurological Institute “C. Besta,” Italy); Ken Evans, PhD (Ontario
Cancer Biomarker Network); Wayne R. Matson, PhD (VA Medical
Center, Bedford, MA); Asa Peterson, MD, PhD (Lund University, Swe-
den), Sarah Tabrizi, MD, PhD (National Hospital for Neurology and
Neurology and Neurosurgery, London); Beth Borowsky, PhD (CHDI);
Andrew Juhl, BS, James Mills, MS, and David Weir, BSc (University
of British Columbia).
Brain: Jean Paul Vonsattell, PhD (Chair), and Carol Moskowitz,
ANP, MS (Columbia University Medical Center); Anne Leserman,
MSW, LISW, Lynn Schaul, BA, and Stacie Vik, BA (University of Iowa).
Cognitive: Deborah Harrington, PhD (Chair), Gabriel Castillo, BS,
Jessica Morison, BS, and Jason Reed, BS (University of California, San
Diego), Michael Diaz, PhD, Ian Dobbins, PhD, Tamara Hershey, PhD,
Erin Foster, OTD, and Deborah Moore, BA (Washington University Cog-
nitive Science Battery Development); Holly Westervelt, PhD (Chair,
Quality Control and Training, Alpert Medical School of Brown Universi-
ty), Jennifer Davis, PhD, and Geoff Tremont, PhD, MS (Scientific Consul-
tants, Alpert Medical School of Brown University); Megan M. Smith,
PhD (Chair, Administration), David J. Moser, PhD, Leigh J. Beglinger,
Gehl, PhD (VA Medical Center, Iowa City, IA); Kirsty Matheson (Univer-
sity of Aberdeen); Karen Siedlecki, PhD (Fordham University); Marleen
Van Walsem (EHDN); Susan Bonner, BA, Greg Elias, BA, and Melanie
Faust, BS (Rhode Island Hospital); Beth Borowski, PhD (CHDI); Noelle
Carlozzi (University of Michigan); Kevin Duff, PhD (University of
Utah); Nellie Georgiou-Karistianis (St. Vincent's Hospital, The Universi-
ty of Melbourne, Australia); Julie Stout, PhD (Monash University, Mel-
bourne, Australia); Herwig Lange (Air-Rahazentrum); and Kate Papp
(University of Connecticut).
Functional: Janet Williams, PhD (Chair), Leigh J. Beglinger, PhD, Anne
Leserman, MSW, LISW, Eunyoe Ro, MA, Lee Anna Clark, Nancy Downing,
Rebecca Ready, PhD (University of Massachusetts); Anthony Vaccarino,
PhD (Ontario Cancer Biomarker Network); Sarah Farias, PhD (University
of California, Davis); Noelle Carlozzi, PhD (University of Michigan); and
Carissa Gehl, PhD (VA Medical Center, Iowa City, IA).
Genetics: Marcy MacDonald, PhD (Co-Chair), Jim Gusella, PhD, and
Rick Myers, PhD (Massachusetts General Hospital); Michael Hayden,
PhD (University of British Columbia); Tom Wassink, MD (Co-Chair) Eric
A. Epping, MD, PhD, Andrew Juhl, BA, James Mills, MS, and Kai Wang,
PhD (University of Iowa); Zosia Miedzybrodzka, MD, PhD (University of
Aberdeen); and Christopher Ross, MD, PhD (Johns Hopkins University).
Imaging: Administrative: Ron Pierson, PhD (Chair), Kathy Jones, BS,
Jacquie Marietta, BS, William McDowell, AA, Greg Harris, BS, Eun
Young Kim, MS, Hans Johnson,PhD, and Thomas Wassink, MD (Univer-
sity of Iowa); John Ashburner, PhD (Functional Imaging Lab, London);
Steve Potkin, MD (University of California, Irvine); and Arthur Toga,
PhD (University of California, Los Angeles). Striatal: Elizabeth Aylward,
PhD (Chair, Seattle Children's Research Institute). Surface Analysis: Eric
Axelson, BSE (University of Iowa). Shape Analysis: Christopher A. Ross
(Chair), MD, PhD, Michael Miller, PhD, and Sarah Reading, MD (Johns
Hopkins University); Mirza Faisal Beg, PhD (Simon Fraser University).
DTI: Vincent A. Magnotta, PhD (Chair, University of Iowa); Karl Helmer,
PhD (Massachusetts General Hospital); Kelvin Lim, MD (University of
Ulm, Germany); Mark Lowe, PhD (Cleveland Clinic); Sasumu Mori,
PhD (Johns Hopkins University); Allen Song, PhD (Duke University);
and Jessica Turner, PhD (University of California, Irvine). fMRI: Steve
Rao, PhD (Chair), Erik Beall, PhD, Katherine Koenig, PhD, Michael
Phillips, MD, Christine Reece, BS, and Jan Zimbelman, PhD, PT
(Cleveland Clinic); and April Bryant (University of Iowa).
(Columbia University), and Jody Corey-Bloom, MD, PhD (University of
California, San Diego) all Co-Chairs; Michael Geschwind, MD, PhD (Uni-
versity of California, San Francisco); Ralf Reilmann, MD and Zerka Unds
(Muenster, Germany); and Andrew Juhl, BS (University of Iowa).
Psychiatric: Eric A. Epping, MD, PhD (Chair), Nancy Downing, RN,
MSN, Jess Fiedorowicz, MD, Robert Robinson, MD, Megan M. Smith,
PhD, Leigh Beglinger, PhD, James Mills, MS, Kristine Rees, BA, Adam
Ruggle, Stacie Vik, BA, Janet Williams, PhD, Dawei Liu, PhD, David
Moser, PhD, and Kelly Rowe (University of Iowa); Karen Anderson, MD
(University of Maryland); David Craufurd, MD (University of Manches-
ter); Mark Groves, MD (Columbia University); Anthony Vaccarino, PhD
MD (Queen Elizabeth Psychiatric Hospital); Eric van Duijn, MD (Leiden
University Medical Center, Netherlands); Irina Antonijevic, MD, PhD,
and Joseph Giuliano (CHDI); Phyllis Chua (The University of Melbourne,
School of Medicine).
Statistics: James Mills,MS,DaweiLiu,PhD, JeffreyLong, PhD,Wenjing
Lu, Spencer Lourens, and Ying Zhang, PhD (University of Iowa).
Recruitment/Retention: Martha Nance, MD (Chair, University of
Minnesota); Anne Leserman, MSW, LISW, Nicholas Doucette, BA,
Mycah Kimble, BA, Patricia Ryan, MSW, LISW, MA, Kelli Thumma,
BA, Elijah Waterman, BA, and Jeremy Hinkel, BA (University of Iowa).
Ethics: Cheryl Erwin, JD, PhD, (Chair, McGovern Center for Health,
Humanities and the Human Spirit); Eric A. Epping, MD, PhD Janet
J.D. Long et al. / Neurobiology of Disease 46 (2012) 625–634
Williams, PhD, Nicholas Doucette, BA, Anne Leserman, MSW, LISW,
James Mills, MS, Lynn Schaul, BA, and Stacie Vik, BA (University of
Iowa); Martha Nance, MD (University of Minnesota); and Lisa
Hughes, MEd (University of Texas Medical School at Houston).
IT/Management: Hans Johnson, PhD (Chair), R.J. Connell, BS, Karen
Pease, BS, Ben Rogers, BA, BSCS, Jim Smith, AS, Shuhua Wu, MCS,
Roland Zschiegner, Erin Carney, Bill McKirgan, Mark Scully, and
Ryan Wyse (University of Iowa); Jeremy Bockholt (AMBIGroup).
Administrative: Chris Werling-Witkoske (Chair), Karla Anderson,
BS, Jennifer Rapp, BA, Ann Dudler, Jamy Schumacher, Sean Thompson,
BA, Leann Davis, Machelle Henneberry, Greg Ennis, MA, and Stacie
Vik, BA (University of Iowa).
Financial: Steve Blanchard, MSHA, Kelsey Montross, BA, and Phil
Danzer (University of Iowa).
This Appendix provides details of the statistical models considered
in the analysis. As mentioned, the LMER analysis considered three
models. The most complex model is considered here, as the simpler
models omit terms included in the full model.
The time metric for the LMER models was duration (dur), defined as
the current age minus the age at study entry. Suppose that agehijis the
current age for the ith participant (i=1, …, 77) in the hth site(h=1, …,
ariates of age at entry (agehi1), years of education (educhi), and gender
(genhi). Define dummy variables for the CAP groups as capLhi=1 if in
the Low group and zero otherwise, capMhi=1 if in the Medium group
and zero otherwise, and capHhiif in the High group and zero otherwise.
If yhijis the 8OHdG value for the ith participant in the hth site at the jth
time point, then Model 3 (see Table 4 and text) is:
yhij¼ β0þ β1durhij
Þ þ β7capHhi
ð Þ þ β3educhi
Þ þ β8durhij? agehi1
þ β10durhij? genhi
þ β12durhij? capMhi
þ ahþ b0hiþ b1hidurhij
ð Þ þ β4genhi
ð Þ þ β5capLhi
as the slope effects are indexed by β11, β12, and β13. The intercept for
the Control group is β0C=β0, for the Low group it is β0L=β0+β5, etc.
The slope for the Control group is β1C=β1, for the Low group it is
to be normally distributed with a mean of zero and variance σ2a; b0hi
isthe random intercept, and b1hiis the random slope. The random in-
tercept and slope are assumed to have zero-means with variance–
covariance matrix G, a joint-normal distribution, and orthogonal to
ah. Finally, εhijis random error assumed to have zero-mean, a normal
distribution, constant variance σ2ε, and orthogonal to the random ef-
fects. In the LMER analysis, Model 2 omits all the duration interaction
terms for the CAP groups, and Model 1 omits all the CAP group terms.
The LR models for the cross-sectional data omit the random effects.
At the request of CHDI, the authors agree to the corrigendum
The data generated at PPD/Caprion using the LC-MS/MS-based
assay and directed and founded by CHDI Foundation that are reported
here were included without the permission of CHDI. Further, CHDI
researchers were not consulted about the presentation, analysis, or
interpretation of those or any other data included in this article or
any conclusions expressed here. Both of these omissions were inad-
vertent and are regretted.
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