Gender and Iron Genes May Modify Associations Between
Brain Iron and Memory in Healthy Aging
George Bartzokis*,1,2,3, Po H Lu4, Kathleen Tingus4, Douglas G Peters1,3, Chetan P Amar1,3,
Todd A Tishler1,3, J Paul Finn5, Pablo Villablanca5, Lori L Altshuler1, Jim Mintz6, Elizabeth Neely7and
James R Connor7
1Department of Psychiatry and Biobehavioral Sciences, The David Geffen School of Medicine at UCLA, Los Angeles, CA, USA;2Laboratory of
Neuroimaging, Department of Neurology, Division of Brain Mapping, UCLA, Los Angeles, CA, USA;3Department of Psychiatry, Greater Los
Angeles VA Healthcare System, Los Angeles, CA, USA;4Department of Neurology, The David Geffen School of Medicine at UCLA, Los Angeles,
CA, USA;5Department of Radiology, The David Geffen School of Medicine at UCLA, Los Angeles, CA, USA;6Department of Epidemiology and
Biostatistics, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA;7Department of Neurosurgery, Penn State Hershey
Medical Center, Hershey, PA, USA
Brain iron increases with age and is abnormally elevated early in the disease process in several neurodegenerative disorders that impact
memory including Alzheimer’s disease (AD). Higher brain iron levels are associated with male gender and presence of highly prevalent
allelic variants in genes encoding for iron metabolism proteins (hemochromatosis H63D (HFE H63D) and transferrin C2 (TfC2)). In this
study, we examined whether in healthy older individuals memory performance is associated with increased brain iron, and whether
gender and gene variant carrier (IRON+) vs noncarrier (IRON?) status (for HFE H63D/TfC2) modify the associations. Tissue iron
deposited in ferritin molecules can be measured in vivo with magnetic resonance imaging utilizing the field-dependent relaxation rate
increase (FDRI) method. FDRI was assessed in hippocampus, basal ganglia, and white matter, and IRON+ vs IRON? status was
determined in a cohort of 63 healthy older individuals. Three cognitive domains were assessed: verbal memory (delayed recall), working
memory/attention, and processing speed. Independent of gene status, worse verbal-memory performance was associated with higher
hippocampal iron in men (r¼?0.50, p¼0.003) but not in women. Independent of gender, worse verbal working memory performance
was associated with higher basal ganglia iron in IRON? group (r¼?0.49, p¼0.005) but not in the IRON+ group. Between-group
interactions (p¼0.006) were noted for both of these associations. No significant associations with white matter or processing speed
were observed. The results suggest that in specific subgroups of healthy older individuals, higher accumulations of iron in vulnerable gray
matter regions may adversely impact memory functions and could represent a risk factor for accelerated cognitive decline. Combining
genetic and MRI biomarkers may provide opportunities to design primary prevention clinical trials that target high-risk groups.
Neuropsychopharmacology (2011) 36, 1375–1384; doi:10.1038/npp.2011.22; published online 9 March 2011
Keywords: memory; iron; gene; sex; dementia; treatment
Iron is essential for cell function, however, elevated tissue
iron can promote tissue-oxidative damage to which the
brain is especially vulnerable (Halliwell and Gutteridge,
1985; Kell, 2009; Zecca et al, 2004). Abnormally high brain
iron levels are observed in age-related degenerative diseases
such as Alzheimer’s disease (AD), dementia with Lewy
bodies (DLB), and Parkinson’s disease (PD) (Bartzokis et al,
2007a; Kell, 2009). Brain iron levels increase with age
(Bartzokis et al, 2007c; Hallgren and Sourander, 1958) and
recent studies reveal elevated levels even in preclinical
stages of AD (Lavados et al, 2008; Smith et al, 2010),
suggesting an accelerated trajectory of brain iron accumula-
tion may be occurring during the transition from healthy
aging into preclinical stages and eventually dementia
characterized by progressive memory deficits that begin
developing years before the diagnosis can be made (Amieva
et al, 2008; den Heijer et al, 2006). The hippocampus (Hip)
is a key region in memory function that is severely affected
in aging and dementing disorders such as AD (Braak and
Received 30 September 2010; revised 21 January 2011; accepted 22
*Correspondence: Dr G Bartzokis, Department of Psychiatry and
Biobehavioral Sciences, The David Geffen School of Medicine at
UCLA, 300 UCLA Medical Plaza, Suite 2200, Los Angeles, CA 90095-
6968, USA, Tel: +1 310 206 3207, Fax: +1 310 268 3266,
Neuropsychopharmacology (2011) 36, 1375–1384
& 2011 American College of Neuropsychopharmacology.All rights reserved 0893-133X/11 $32.00
Braak, 1991; Squire and Zola-Morgan, 1991). Hippocampal
iron levels increase with age in healthy individuals
(Bartzokis et al, 2007a) and postmortem studies have
shown that hippocampal iron is increased in AD beyond
levels of non-demented controls (Bouras et al, 1997; Deibel
et al, 1996; Pankhurst et al, 2008; Smith et al, 1997).
Magnetic resonance imaging (MRI) can be used to
indirectly assess relationships between cognition and brain
iron in living individuals. Several methods of varying
sensitivity and specificity have been published (reviewed in
Haacke et al, 2005; Pfefferbaum et al, 2009). MRI can
measure brain iron levels through the effect of iron on
transverse relaxation rates (R2) (Bartzokis et al, 1993;
Bartzokis et al, 1994; Vymazal et al., 1996a; Yao et al, 2009).
The bulk of brain iron is stored in ferritin molecules (Floyd
and Carney, 1993; Morris et al, 1992) and an in vivo MRI
method called field-dependent relaxation rate (R2) increase
(FDRI) can measure their iron content (Bartzokis et al,
1993; Bartzokis et al, 1994). Briefly, FDRI is the difference in
measures of brain R2 obtained with two different field-
strength MRI instruments. The FDRI is specifically asso-
ciated with the total iron contained in ferritin molecules
(Bartzokis et al, 1993; Vymazal et al, 1996a) and has been
shown to be independent of the amount of iron loading
(number of iron atoms per molecule of ferritin) (Vymazal
et al, 1996b) and to increase linearly with field-strength
(Bartzokis et al, 1993; Gossuin et al, 2004; Vymazal et al,
1996a; Vymazal et al, 1995a; Vymazal et al, 1996b; Yao et al,
2009). In vivo FDRI data has been validated by demonstrat-
ing very high correlations with published postmortem data
on adult human brain iron distribution as well as
replicating the striking age-related increases in iron levels
in basal ganglia regions documented in postmortem studies
(Bartzokis et al, 2007c; Hallgren and Sourander, 1958;
Klintworth, 1973). Thus, FDRI measures the iron contained
in ferric oxyhydroxide particles that form the mineral core
of ferritin molecules. In human tissue, ferritin and its
breakdown product (hemosiderin) are the only known
physiologic sources of such particles (Bartzokis et al, 1993;
Bulte et al, 1997; Vymazal et al, 1996a; Vymazal et al,
1995b). The FDRI measure will therefore be referred herein
as ferritin iron (Bartzokis et al, 1999; Bartzokis et al, 2000).
Recently, Ding et al (2009) used a phase-shift imaging
MRI technique that, amongst other things, is affected by
tissue iron, and reported that increased iron in the Hip of
subjects with AD may be related to worse cognitive
performance and duration of illness. Whether age-related
increases in hippocampal iron levels in healthy individuals
may represent a trajectory of increasing risk of cognitive
decline into AD (Bartzokis, 2009) and are associated with
decreased memory performance have yet to be assessed.
Herein we present the first study that examines associations
between ferritin iron levels in the Hip as well as basal
ganglia and white matter regions on memory and proces-
sing speed of healthy older individuals.
We, recently, observed higher ferritin iron in men than
women (Bartzokis et al, 2007c) and suggested that the
increased levels may contribute to the risk of developing
neurodegenerative diseases at earlier ages in men (Bartzokis
et al, 2007a; Bartzokis et al, 2004; Raber et al, 2004). We also
observed that gene variants involved in iron metabolism
(hemochromatosis H63D (HFE H63D) and transferrin C2
(TfC2) variants) are associated with higher brain iron levels
in healthy older men (Bartzokis et al, 2010). These two gene
variants are highly prevalent (affecting approximately 50%
of the population), and some studies have shown an
association of these variants with higher risk of developing
AD (Connor and Lee, 2006; Lehmann et al, 2010; Sampietro
et al, 2001; Moalem et al, 2000). We therefore examined
memory function in the context of gender and the presence
(IRON+) or absence (IRON?) of these iron gene variants.
We hypothesized that even in healthy individuals, age-
related increases in ferritin iron levels in the Hip, which is
damaged early and severely in dementia-causing diseases
such as AD and DLB (Braak et al, 1996; Kotzbauer et al,
2001), will negatively impact memory function (Bartzokis
et al, 2007a; Bartzokis et al, 2007c; Bartzokis et al, 2004).
Based on data that men may develop neurodegenerative
diseases at younger ages (Barker et al, 2002; Friedman, 1994;
Miech et al, 2002; Pantelatos and Fornadi, 1993; Raber et al,
2004) (reviewed in Bartzokis et al, 2004), we also
hypothesized that healthy older men may be at increased
risk for such iron-associated declines in memory function
compared to women (Bartzokis et al, 2007c; Bartzokis et al,
2004). In exploratory analyses we also examined the effects
of gender and iron gene variants (HFE H63D and TfC2) in
basal ganglia and white matter regions on working memory/
attention and processing speed functions.
SUBJECTS AND METHODS
The subjects, imaging, and genetic methods were described
in detail in prior publications (Bartzokis et al, 2010;
Bartzokis et al, 2007c) and will only be summarized here.
Normal older adult volunteers were recruited from the
community and hospital staff for a study of healthy aging
(Bartzokis et al, 2007c). Potential subjects were excluded
if they had a history of neurological disorder or a
family history of AD or other neurodegenerative disorder,
psychiatric illness (including drug or alcohol abuse), or
head injury resulting in loss of consciousness for more than
10min. The subjects were physically very healthy and were
excluded if they were obese, or if they had a current or prior
serious illness, or a medical history of diabetes, cardiovas-
cular disease, or difficult-to-control hypertension. They
were independently functioning and had no evidence of
neurocognitive impairment or gross neurological abnorm-
alities on clinical interview and brief neurological examina-
tion with the study PI. The final population of 63
individuals contained 33 men and 30 postmenopausal
women ranging in age from 55 to 76 years (mean¼67.0
years, SD¼6.1). The sample averaged 15.8 years of
education (SD¼2.5; range¼10–23 years) and their re-
ported racial composition was 45 (71%) Caucasian, 13
(21%) Asian, and 5 (8%) African-American. All subjects
received written and oral information about the study and
signed written informed consents approved by the local
Institutional Review Board prior to study participation.
The participants were first assessed and evaluated with
MRI, clinical assessment, and neurocognitive measures. The
Brain iron, memory, gender, and genes
G Bartzokis et al
subjects were later genotyped for the presence of TfC2 or
HFE H63D and C282Y genes. There were no carriers of the
rare HFE C282Y variant in this sample so the only genes
present were TfC2 and HFE H63D (Bartzokis et al, 2010). Of
the total sample, 32 subjects had one or both genes (IRON+
group; 18 males, 14 females) while 31 had neither gene
(IRON? group; 15 males, 16 females).
The neurocognitive measures were collected within one
month of the MRI scan.
Memory related. The California Verbal Learning Test
(CVLT; Delis et al, 1987) is a measure of rote verbal
learning and memory in which a list of 16 words is
presented over 5 trials and recalled after an interference list
and again after a 20-min delay. The total number of words
recalled after 20min serves as a measure of delayed memory
for unstructured verbal material.
Verbal working memory/attention related. Auditory Con-
sonant Trigrams (ACT; Peterson and Peterson, 1959) is a
sensitive measure of working memory, requiring the subject
to sustain information in short-term memory while
performing other cognitive operations. Basic and complex
attention span was assessed using the Digit Span subtest
from the WAIS III (Wechsler, 1997).
Processing speed related. Trailmaking TestFpart A and B
(Reitan and Wolfson, 1985) assesses information processing
Part A requires subjects to rapidly connect consecutively
numbered circles, and part B requires subjects to consecu-
tively connect circles containing numbers and letters by
alternating between the two sequences (e.g., 1-A-2-B). Time
to complete the task serves as the variable of interest. Digit
Symbol subtest from the WAIS-R (Wechsler, 1981) involves
rapid copying of symbols and integrates several cognitive
processes including psychomotor speed, visual scanning,
and simple constructional ability. The score reflects number
of symbols copied within 90seconds.
and mental flexibility.
The methods have been previously described in detail
(Bartzokis et al, 1993; Bartzokis et al, 1994; Bartzokis et al,
2007c). The participants were scanned using two MRI
instruments (1.5 and 0.5T) within 1h of each other using
the same imaging protocol. Coronal and sagittal pilot scans
were first obtained to specify the location and spatial
orientation of the head and the position of the axial
sequence acquired interleaved contiguous slices using a
Carr–Purcell–Meiboom–Gill dual spin–echo sequence (time
to repetition (TR)¼2500ms; time to echo (TE)¼20 and
90ms; 2 excitations; 3mm slice thickness; 192 gradient
steps; and 25cm field-of-view). The coronal and sagittal
pilot scans were used to determine the alignment and
accuracy of head repositioning in the second MRI instru-
ment (Bartzokis et al, 1993; Bartzokis et al, 1994).
Data for each region of interest (ROI; depicted in Figure 1)
were obtained from contiguous pairs of slices. The slice
containing the anterior commissure and the slice immedi-
ately superior to it were used to obtain the putamen (P) and
globus pallidus (G) transverse relaxation time (T2) data. The
third and fourth slices above the anterior commissure were
used to obtain the T2data for caudate nucleus (C) and the
second and third slices superior to the orbitofrontal gray
matter were used to obtain the frontal lobe white matter
(Fwm) data. For the genu of the corpus callosum (Gwm),
the two slices on which the angle formed by the left and the
right sides of the genu appeared the most linear were chosen
in order to obtain a sample that would be consistently in the
middle of the structure, which contains primarily fibers
connecting the prefrontal cortices. For the splenium of the
corpus callosum (Swm), the second and third lowest slices
on which the fibers of the splenium connected in the
midline were chosen in order to sample primarily the lower
half of the splenium that contains predominantly primary
sensory (visual) fibers. For thalamus (T), the second and
third highest slices on which thalamus appears are chosen.
The lateral border of the ROI is drawn along the white
matter of the internal capsule using the TE¼20 image. The
medial and inferior portions bordering CSF are defined
using the TE¼90 image. For Hip, the two contiguous slices
(appears light gray) and white matter (appears dark gray). The TE90 has optimal contrast between brain (appears gray) and CSF (appears white). Both TE20
and TE90 images are used to draw each ROI as this combination of images maximizes contrast needed for accurate ROI definition. As an example, the use
of both contrasts is depicted in the thalamus ROI that borders CSF medially and white matter laterally and posteriorly (a and b). Data for each ROI are
obtained from contiguous pairs of slices. Only one hemisphere ROI is depicted on the figures although ROIs are measured bilaterally for all regions except
for midline corpus callosum regions (Gwm and Swm). Please see text (‘MRI Protocol’) for further details.
Regions of interest (ROIs). ROI definition is depicted on axial MRI TE20 (a) and TE90 (b–d). The TE20 has optimal contrast between gray
Brain iron, memory, gender, and genes
G Bartzokis et al
that contained the largest areas of these structures were
used in the data analysis. The Hip measure was obtained
from the anterior third of the structure and was limited by
drawing a horizontal line at the level of the cerebral
peduncle to exclude any tissue posterior to that line
(Bartzokis et al, 2010; Bartzokis et al, 2007c).
Transverse relaxation times (T2) were calculated for each
voxel by an automated algorithm from the two signal
intensities (TE¼20 and 90) of the dual spin–echo sequence
to produce gray-scale encoded T2 maps of the brain
(Bartzokis et al, 1994). The T2 measures were extracted
using an Apple Macintosh-configured image analysis work-
station. T2data for each of the ROIs were obtained from
contiguous pairs of slices. The R2was calculated as the
reciprocal of T2?1000ms/s. The average R2 of the two
slices from both hemispheres were the final measures used
in the subsequent analyses. The FDRI measure was
calculated as the difference in R2(high-field R2?low-field
R2). Test–retest reliability for FDRI measures was very high
with intraclass correlation coefficients ranging from 0.88 to
0.99 (po0.0023) (Bartzokis et al, 1993; Bartzokis et al,
Parametric approaches were used to assess the impact of the
iron gene variants and gender on the association between
brain iron and cognition. We first performed multiple
regression analysis that crosses cognitive scores with
measures of brain iron. To supplement the regression
analyses, Pearson’s correlations were conducted to examine
the relationship between brain iron measures and cognitive
performance in relevant subgroups (eg, stratified by gender
and gene grouping). Differences between correlations were
then tested by the non-parametric approach of normal
curve test on Fisher’s z-transformed values. As in this
restricted age range the FDRI measure was not significantly
associated with age in any of the regions, age was not
included in the analyses.
Hippocampal Iron and Episodic Memory
A specific hypothesis regarding hippocampal iron and
episodic memory performance was tested by performing a
multiple regression analysis that crosses the CVLT delayed
recall score with the FDRI measure of iron in the Hip. The
memory measure was the dependent variable, and the
independent variables were gender, iron genes (presence or
absence of genes), and the Hip FDRI measure. The
independent variable factors were fully crossed and
our interest focused on the two-factor interactions of
gene?brain iron measure and the three-factor gene?brain
Basal Ganglia and White Matter Iron and Non-Memory
We then assessed whether the iron genes impact the
association between iron levels in other brain regions and
non-memory aspects of cognitive functioning. For the
purpose of data reduction, principal components analysis
was performed separately for the cognitive tasks and the
measures of brain iron. In each case, the analysis suggested
retention of two components for rotation, and the inter-
pretation was based on eigenvalues 41 and a clear break in
the scree curve. Substantive interpretations of the compo-
nents were based on highest loadings for each variable. In
the cognitive variables, the components were interpreted as
being related to processing speed (Trailmaking TestFparts
A and B, Digit Symbol) and auditory working memory/
attention (ACT, Digit Span). In the FDRI measures of brain
iron, the components represented measures of the basal
ganglia (caudate, globus pallidus, and putamen) and white
matter (frontal, splenium of the corpus callosum, genu of
the corpus callosum, and thalamus). Component scores
were computer generated by regression-based scoring,
producing standardized scores with mean¼0 and standard
Using the same statistical methodologies detailed above,
four multiple regression analyses were generated by cross-
ing the two cognitive factors (working memory/attention
and processing speed) with the two measures of brain iron
(basal ganglia and white matter). The two cognitive
measures were the dependent variables and the independent
variables were gender, iron genes (presence (IRON+) or
absence (IRON?) of genes), and either the basal ganglia or
white matter component score. The independent variable
factors were fully crossed, and our interest focused on the
two-factor interactions of gene?brain iron measure and
the three-factor interactions of gene?brain iron?gender.
All p-values were assessed as significant at an a level of
0.05. The p-values for the multiple regression and Pearson’s
correlation analyses testing the associations between basal
ganglia and white matter iron with nonmemory cognitive
performance were assessed as significant at an a level of
0.0125 to reflect Bonferroni correction for the four
The iron absorption genes did not have a significant effect
on the relationship between Hip iron and CVLT. The
analysis of Hip iron and CVLT performance yielded a
significant interaction of gender by iron level (F¼10.56,
df¼1,55, p¼0.002). Pearson’s correlation analyses indi-
cated a significant negative association between the Hip
iron and CVLT in men (N¼33, r¼?0.50, df¼31,
between these two measures in women (N¼30, r¼0.19,
df¼28, p¼0.31). The difference between these correlation
coefficients was significant and similar to the result
from the more complex regression model above (Fisher’s
z-test: t¼2.74, p¼0.006). The results are depicted in
Figure 2 and were not substantially altered after controlling
for age in the analyses.
The regression models based on iron and cognitive
components yielded a significant effect of the iron genes on
the association between basal ganglia iron level and the
working memory/attention score (interaction F¼8.15,
df¼1,55, p¼0.006). As gender was not a significant factor
anda nonsignificantpositive association
Brain iron, memory, gender, and genes
G Bartzokis et al
in this model, it was not examined separately. We computed
the Pearson’s correlation between basal ganglia iron and
working memory/attention and found a significant negative
relationship in the IRON? group (N¼31, r¼?0.49,
df¼29, p¼0.005); thus for those without either of the iron
genes (H63D and TfC2), increased basal ganglia iron was
significantly associated with worse performance in working
memory/attention. A positive but nonsignificant association
was observed in the IRON+ group (N¼32, r¼0.19,
df¼30, p¼0.30). The difference in correlation coefficients
between the IRON+ and IRON? groups were statistically
significant and mirrored the regression result (Fisher’s
z-test: t¼2.75, p¼0.006). The results are depicted in
Figure 3 and were not substantially altered by controlling
for age in the analyses. The results from the multiple
regression and Pearson’s correlation analyses remain
significant after Bonferroni correction (?4 tests) for
multiple comparisons (corrected: p¼0.024 and p¼0.020,
respectively). None of the analyses involving white matter
iron approached significance.
When the above regression analyses were confined to
only Caucasian subjects (N¼45), all the findings remained
This is the first demonstration that in healthy older men,
declarative memory function may be adversely affected by
increased Hip ferritin iron. A male-specific association is in
line with our previously observed higher brain iron levels in
men compared to women across the lifespan (Bartzokis
et al, 2007c) and increased iron levels in male carriers of
highly prevalent allelic variants of genes (HFE H63D and/or
TfC2) that encode proteins involved in iron metabolism
(Bartzokis et al, 2010). These observations are consistent
with the proposition that a portion of the suggested earlier
age at onset in men of neurodegenerative diseases such as
AD, PD, and DLB (Barker et al, 2002; Raber et al, 2004) may
be accounted for, at least in part, by their higher brain
ferritin iron (Bartzokis et al, 2004) (reviewed in Bartzokis
et al, 2007c). In women, the iron–memory association was
significantly different from the one observed in men. The
men and women. Men: r¼?0.50, p¼0.003. Women: r¼0.19, p¼0.31.
Memory function: assessed using The California Verbal Learning Test
(CVLT) (Delis et al, 1987), which is a measure of rote verbal learning and
memory (number of words recalled after a 20-min delay). FDRI, field-
dependent transverse relaxation rate (R2) increase, an MRI measure of
ferritin iron (the iron content of ferritin molecules).
Memory function vs hippocampal ferritin iron in healthy older
healthy older men and women with and without iron genes. IRON?: wild
type gene carriers (r¼?0.49, p¼0.005). IRON+: carriers of either
transferrin C2 (TfC2) and/or hemochromatosis H63D (HFE H63D) gene
variants (r¼0.19, p¼0.30). Working memory function: assessed using a
composite score of Auditory Consonant Trigrams (ACT; Peterson and
Peterson, 1959) and Digit Span, which require subjects to either manipulate
information or simultaneously perform another cognitive operation while
sustaining the original information in short-term memory. Basal ganglia:
composite score of caudate, putamen, and globus pallidus. FDRI, field-
dependent transverse relaxation rate (R2) increase, an MRI measure of
ferritin iron (the iron content of ferritin molecules).
Working memory function vs basal ganglia ferritin iron in
Brain iron, memory, gender, and genes
G Bartzokis et al
association could be substantially modified in women
because of the gender-specific pattern of iron requirements
such as those produced by menstruation and/or metabolism
(see below) (Coppus et al, 2009; Whitfield et al, 2003)
(reviewed in Bartzokis et al, 2007c).
Animal data suggest that during early postnatal develop-
ment, increases in Hip ferritin iron are necessary for normal
cognitive function and memory (Carlson et al, 2009;
Georgieff, 2008; Schmidt et al, 2007; Shoham and Youdim,
2002; Siddappa et al, 2004) and that early deficits can have
long-lasting detrimental effects on cognition even when
corrected through supplementation (Schmidt et al, 2007).
Differentiating oligodendrocytes have very high iron
requirements and may have a critical role in brain iron
uptake (reviewed in Bartzokis et al, 2007a; Bartzokis et al,
2007b). This may be especially important in the hippo-
campus where 50% of oligodendrocytes are juxtaposed
directly on blood vessels and may thus be in a position to
acquire iron directly from the vasculature (Vinet et al,
2010). On the other hand, iron overload may also cause
cognitive deficits (Maaroufi et al, 2009) and increased
neonatal iron intake as well as overexpression of ferritin
may be associated with increased risk of neurodegenerative
disease in late life (Carlson et al, 2008; Kaur et al, 2006a;
Kaur et al, 2006b). It is thus becoming evident that
regulating the processes underlying brain iron accumula-
tion trajectories has important consequences throughout
the lifespan. Furthermore, many neurodegenerative diseases
are associated with dysregulated iron metabolism (Bartzo-
kis, 2009; Kell, 2009; Roth et al, 2010; Zecca et al, 2004).
Such dysregulations are often manifested as increases in
tissue iron that go beyond those observed in healthy
individuals (reviewed in Bartzokis et al, 2007c; Kell, 2009;
Zecca et al, 2004). One mechanism leading to such iron
increases could result from abnormal intracellular and
extracellular protein deposits (proteinopathies) triggering
inappropriate iron accumulation in multiple disorders
(Singh et al, 2009; Smith et al, 1997) (reviewed in Bartzokis,
2009; Bartzokis et al, 2007a).
Iron regulation in neurons seems to be strongly
dependent on the ferroxidase activity of amyloid precursor
protein (APP) that is responsible for removing excess iron
(Duce et al, 2010) and may thus mitigate intracellular
iron-catalyzed oxidative damage (Halliwell and Gutteridge,
1988). The iron-management function of APP is also
supported by the observation that APP transcription is
controlled, at least in part, by the same iron-sensing
mechanism that controls transcription of two canonical
iron metabolism proteins: ferritin and transferrin (Cahill
et al, 2008). Neurons may be especially susceptible to excess
iron because of the disproportionately long processes
(axons) connecting neuron body to synapses, and APP is
well suited for its iron management function because of its
presence throughout the neuron. The other well-known
function of APP is to adhere the ‘cargo’ (eg, vesicles) to the
‘motors’ powering the fast axonal transport (FAT) that
moves supplies from the neuron body to axons and
synapses, and back. The two-way FAT traffic thus
automatically provides the ferroxidase function of APP
throughout, and especially near mitochondria that amass all
along the axon at the nodes of Ranvier as well as at distant
synapses. Removal of iron liberated from any source, such
as damaged iron-rich mitochondria being transported back
to the neuronal body for degradation, would be crucial
throughout the FAT transport system (reviewed in Bartzo-
The Hip region has been shown to have elevated iron
levels in AD (Deibel et al, 1996; Good et al, 1992; Smith et al,
1997; Thompson et al, 1988) (Bartzokis et al, unpublished
data), and is affected early and severely in age-related
proteinopathies such as AD and DLB (Braak et al, 1996;
Kotzbauer et al, 2001) that cause the vast majority (over
70%) of dementia (Barker et al, 2002; Fratiglioni et al, 2000;
Lobo et al, 2000). The observation that increased hippo-
campal iron is associated with poorer memory function
even in healthy older individuals supports the suggestion
that the ‘normal’ trajectory of age-related increases in brain
ferritin iron may represent an underlying risk factor for
age-related degenerative brain diseases (Chen et al, 2009)
(reviewed in Bartzokis, 2009). Age-related accumulations of
brain iron in structures such as basal ganglia and Hip of
healthy individuals (Bartzokis et al, 2007c; Hallgren and
Sourander, 1958) may be conceptualized as ‘normal’
trajectories toward brain iron overload of vulnerable
regions (Bartzokis, 2009) that may be modified by gender
and genetic differences (Bartzokis et al, 2010; Bartzokis
et al, 2007c). These differences may modify trajectories of
iron accumulation from normal age-related memory
declines (Figures 2 and 3) into preclinical stages of
degeneration and eventually dementias (Bartzokis, 2009;
Lavados et al, 2008; Smith et al, 1997; Smith et al, 2010).
As reviewed above, however, both iron deficiency and
brain iron excess can have deleterious consequences on
cognition. In old age, adequate iron levels are essential for
the continual process of myelin repair/replacement, a key
process in maintaining cognitive functions (reviewed in
Bartzokis, 2009). Thus the opposite association we observed
in men compared to that in women in the Hip–memory
correlations are not entirely unexpected given known
gender differences in iron status. Women begin their
postmenopausal years in a state of relative peripheral iron
deficiency compared with men and their iron levels increase
for the first 15–20 years after menopause without fully
‘catching up’ to those in men (Whitfield et al, 2003). As the
peripheral iron levels can influence brain iron accumulation
(Bartzokis et al, 2007c; House et al, 2010; Li et al, 2010),
these peripheral effects could manifest in the brain. Thus, as
the two genders approach older ages, it is possible that the
already higher levels of iron observed in men push them
into a toxic range earlier as they enter older ages, whereas
women, who start with considerably lower iron levels, may
initially experience a cognitive benefit from increasing
postmenopausal iron levels.
The basal ganglia accumulate markedly more iron with
age than most other brain structures (Bartzokis et al, 2007c;
Hallgren and Sourander, 1958) and may thus require
additional protection from iron-associated toxicity. Further
analyses revealed an unexpected modifying effect of
prevalent iron metabolism gene variants on the association
between basal ganglia ferritin iron and working memory
function (Figure 3). This observation is consistent with the
direct involvement of the basal ganglia in working memory
networks (Battig et al, 1960; Chorover and Gross, 1963)
(reviewed in Constantinidis and Procyk, 2004; Simpson
Brain iron, memory, gender, and genes
G Bartzokis et al
et al, 2010). Striatofrontal networks interconnect specific
areas of the prefrontal cortex to subregions of the basal
ganglia, forming loops that are intricately involved in higher
cognitive functions including working memory (Alexander
et al, 1986; Battig et al, 1960; Chorover and Gross, 1963;
Landau et al, 2009; Lewis et al, 2004). In iron gene
noncarriers (IRON? group), higher brain iron in the basal
ganglia was again significantly associated with worse verbal
working memory. A different (opposite, albeit nonstatisti-
cally significant) relationship was seen in the gene carrier
(IRON+) group. These genes have been associated with
brain iron uptake as well as complex interactions between
basal ganglia ferritin iron levels, metabolic processes, and
cognitive functions (Burdo et al, 2004; Lee et al, 2007;
Li et al, 2010; Ma et al, 2008; Mitchell et al, 2009). The
presence of these genes seems to mitigate the detrimental
effect of basal ganglia iron accumulation on working
memory function observed in the IRON? group and this
may help explain, at least in part, the very high prevalence
of these genes in the population.
Animal models support the suggestion that iron overload
is associated with memory decline (Maaroufi et al, 2009),
and treatment with iron chelators have been reported to
result in improved memory even in healthy aged rodents
(de Lima et al, 2007). Human studies have also suggested
that chelation treatment may be helpful in degenerative
brain diseases (reviewed in Kell, 2009). The mechanism of
iron toxicity is likely related to promotion of damaging
free-radical reactions and associated inflammation (Smith
et al, 1997) (reviewed in Kell, 2009). It is thus not surprising
that invertebrate data (Drosophila) suggest that age-related
iron accumulation is proportional to the rate of aging
(Massie et al, 1985) and inhibition of iron absorption
prolongs lifespan (Massie et al, 1993). Similarly, human
gender differences in longevity have been proposed to relate
to reproduction-related iron losses in women (Sullivan,
1989) and life-extending effects of calorie restriction have
been associated with reduced dietary iron uptake and
lowered iron deposits in tissue (Cook and Yu, 1998;
Kastman et al, 2010; Valle et al, 2008; Xu et al, 2008).
Several limitations of the current study also need to be
considered. First, strict criteria for inclusion of healthy
older individuals and small sample size may underestimate
the strength of correlations between tissue iron and
cognition by restricting sample variance. Second, inter-
pretation of ‘changes’ from cross-sectional data must be
made with caution (Kraemer et al, 2000), and prospective
studies and larger samples are needed to further define
age-related trajectories in each gender and genetic sub-
group. Third, the absence of a sample of younger adults
limits the ability to detect quadratic or other nonlinear
associations. Fourth, peripheral iron measures and detailed
information on blood loss during life, and other environ-
mental influences such as iron supplementation that may
affect brain iron were not available. Fifth, although we
observed a significant effect of HFE and TfC2 genes on
brain iron levels (Bartzokis et al, 2010) and the basal
ganglia–working memory association, these genes likely
account for a minority of the variance, much of the genetic
influence on iron levels remains unknown (Njajou et al,
2006; Whitfield et al, 2000), and future studies may identify
additional genetic influences on brain iron and its impact
on cognitive performance. Finally, although reproducible
(Bartzokis et al, 1994; Bartzokis et al, 2000) and very highly
correlated with postmortem iron levels (Bartzokis et al,
2007c), the FDRI measure specifically quantifies ferritin
iron load that may be only indirectly related to the amount
of free iron or other transition metals that may be more
directly associated with toxicity (Lavados et al, 2008;
Rajendran et al, 2009).
The significantly different associations we observed in
subgroups suggest that toxic consequences on cognitive
function of age-related brain iron increases may differ
substantially by gender and genotypes, and that brain iron
deposition is a complex multifactorial endophenotype.
Treatment efforts to reduce the deleterious effects of brain
iron accumulations in old age (Adlard et al, 2008; Cahill
et al, 2008; Chen-Roetling et al, 2009; Hider et al, 2008;
Mandel et al, 2008; Zecca et al, 2008) (for review see Kell,
2009) should take into consideration that large subgroups of
the population may substantially differ in brain iron
regulation mechanisms and thus differ in their response
to such interventions.
The advent of in vivo neuroimaging methods that can
assess tissue ferritin iron deposits on a regional basis with
high specificity provides the means to prospectively
examine the impact of age-related changes in iron on
trajectories of cognitive decline into neurodegenerative
diseases. These methods could be used to measure iron
accumulations as well as target emerging therapeutic
interventions (Adlard et al, 2008; Cahill et al, 2008; Chen-
Roetling et al, 2009; Hider et al, 2008; Mandel et al, 2008;
Zecca et al, 2008) (for review see Kell, 2009) to high-risk
groups identified by MRI, genetic, and clinical biomarkers,
years before clinical manifestations of disease. Early
intervention in higher-risk subgroups may make it possible
to increase effectiveness of treatments, decrease the need for
more-aggressive approaches at later stages, and may
identify heretofore unexplored opportunities for primary
prevention for the exponentially increasing burden of
age-related neurodegenerative diseases (Bartzokis, 2009;
Bartzokis et al, 2007c).
This work was supported in part by NIH grants (AG027342
and MH0266029), the Department of Veterans Affairs, the
RCS Alzheimer’s Foundation, and the George M Leader
Family. This work was presented in part at the 451st Annual
Winter Conference on Brain Research, Snowbird, Utah,
January 2008, and the Alzheimer’s Association International
Conference on Alzheimer’s Disease (ICAD), Vienna, Aus-
tria, July 2009.
The authors declare no conflict of interest.
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