Exp Biol Med (Maywood). 2003 (Jul); 228 (7): 800–810 ~ FULL TEXT
Lemon JA, Boreham DR, Rollo CD.
Departments of Biology and Medical Physics and Applied Radiation Sciences Unit,
Hamilton Ontario, Canada L8S 4K1.
We previously found that transgenic mice overexpressing growth hormone (TGM) have elevated and progressively increasing free radical processes in brain that strongly correlates with reduced survivorship. Young mature TGM, however, displayed vastly enhanced learning of an eight-choice cued maze and qualitatively different learning curves than normal controls. Here we document the age-related patterns in learning ability of TGM and normal mice. Learning appeared inferior in both genotypes of very young mice but TGM were confirmed to be superior to normal mice upon maturity. Older TGM, however, showed rapid age-related loss of their exceptional learning, whereas normal mice at 1 year of age showed little change. The cognitive decline of TGM was abolished by a complex "anti-aging" dietary supplement formulated to promote membrane and mitochondrial integrity, increase insulin sensitivity, reduce reactive oxygen and nitrogen species, and ameliorate inflammation. Results are discussed in the context of reactive oxygen and nitrogen species, long-term potentiation, learning, aging and neuropathology, based on known impacts of the growth hormone axis on the brain, and characteristics of TGM.
From the FULL TEXT Article:
Age-related cognitive deterioration varies widely
among individuals, suggesting that intervention is
possible [1–4]. Promising approaches include antioxidants
and anti-inflammatories , dietary restriction [6, 7], growth factor augmentation , and stem cell manipulations. [8, 9] Transgenic animals with alterations of the
growth hormone axis are promising models of brain structure,
function, and age-related changes relevant to all of
Hypothalamic somatostatin (SRIF) and growth hormone-
releasing hormone(GHRH) regulate pituitary synthesis
and secretion of growth hormone (GH), which in turn
stimulates insulin-like growth factor-I (IGF-I) expression in
many tissues and secretion from the liver. [10, 11] Interventions
to offset the reliable declines in GH and IGF-I that
occur during mammalian aging ameliorate some symptoms
of senescence.  Both are antiapoptotic (i.e., neuroprotective) [2, 13, 14] and IGF-I regulates neurogenesis and
regeneration, glial cell proliferation, myelination, neurite
outgrowth, neuronal excitability, synaptic plasticity,
and other growth factors [2, 15, 16]. Whether activity of the
GH axis offsets or exacerbates aging, however, remains
Remarkably, 50% of young mature transgenic growth
hormone mice (TGM) learned an eight-choice cued maze
before a single normal control.  All TGM learned the
task within 24 trials, whereas 30% of normal mice did not.
TGM displayed an exponentially declining error rate
whereas that of normal mice was linear.  We and our
colleagues showed TGM to be hypoactive , with greatly
increased sleep, altered EEG patterns [20, 21], and altered
feeding behavior.  Others showed that TGM have inferior
performance on aversive learning tasks , altered
preferences for nicotine and ethanol , increased activity
in open field tests, and higher sensitivity to amphetamine. [35, 36]
The free radical theory of aging postulates that endogenously
generated reactive oxygen and nitrogen species
(RONS) cause accumulating damage to cellular lipids, proteins,
and nucleic acids. Mitochondrial damage may engage
an escalating cycle of increasing free radical generation and
further damage. [27–30] Damage may also induce inflammation
and further generation of RONS by immunocytes
and microglia.  TGM have highly elevated and progressively
increasing levels of lipid peroxidation and superoxide
radical, particularly in the brain, where levels of superoxide
radical and lipid peroxidation strongly correlate with longevity.  A meta-analysis of the rodent literature confirmed
that intraspecific longevity significantly declines
with higher growth rates and larger mature body sizes in
both rats and mice. 
TGM express pathological changes in several organs
consistent with elevated RONS (e.g., liver, kidney, heart)
and that resemble senescent features of old rodents (e.g.,
Refs. [32, 33]). The reduced longevity of TGM was consistent
with their larger mass according to our meta-analysis. 
Thus, TGM at the very least represent a model of elevated
free radical processes and pathologies resembling accelerated
aging. Models of extended longevity are considered
preferable for studying aging, but those expressing alterations
in free radical generation, antioxidant defenses, and/
or other processes related to aging also contribute valuable
understanding (e.g., Refs. [34–36]). Delayed cognitive aging
occurs in dietary restricted rodents and dwarf mice characterized
by small body size and reduced (but sometimes extended)
GH axis activity. [6, 37, 38] TGM appear dichotomously
opposite to dietary restricted and dwarf rodents [17, 18], highlighting the need to know how their exceptional
cognitive abilities change with age. Here we report that
TGM show rapid loss of cognitive abilities with age.
Growth factors, including GH, IGF-1, and insulin, activate
cellular transduction pathways that generate and require
RONS as mediators of signal transduction. Conversely,
RONS (e.g., nitric oxide [NO], superoxide radical
[SOR], and hydrogen peroxide [H2O2]) mimic growth factor
receptor ligands in activating the mitogen-activated protein
kinase (MAPK/ERK) and phosphatidylinositol-3 kinase
(PI3K) pathways.  Across broad phylogenies genetic
alterations affecting aging mainly involve the PI3K pathway.  PI3K critically regulates glucose transport, antioxidant
expression and apoptosis, and shows severe resistance to
insulin signaling in TGM. 
RONS and activation of MAPK/ERK (and perhaps
PI3K) in neurons are essential for induction of long-term
potentiation (LTP), long-term memory, and neuronal survival
and development. RONS modulate neurotransmitter
release, retrograde signaling to post-synaptic neurons during
LTP and non-synaptic inter-cellular signaling between
neurons and other brain cells [2, 41–43]. Alternatively,
RONS are implicated in cognitive aging and neuropathologies
(e.g., Alzheimer’s, Parkinson’s, and Huntington’s diseases,
Down’s syndrome, ALS, diabetic neuropathies, and
ischemia reperfusion injuries; e.g., Refs. [5, 27, 44, 45]. Such
linkages suggest that both the early enhancement of cognition
and its subsequent rapid decline in TGM may involve
With notable exceptions (e.g., Refs. [27–29, 46–50]),
supplements intended to ameliorate oxidative stress, inflammation,
or associated manifestations of aging or neuropathology
have yielded inconsistent or poor results. Furthermore,
cocktails popularly self-administered by the public
have had scant scientific scrutiny. [5, 51] Most studies have
tested one or at most, a few materials in combination. These
materials commonly have synergistic or recycling interactions,
however, that cannot be addressed by single-factor
experiments (e.g., Refs. [27, 48, 51, 52]).
Developing a complex supplement by adding and testing
one material at a time could require inordinate time
given the potential clinical value of an effective intervention.
Consequently, we designed a complex anti-aging
supplement (AASUP) based on known efficacy of ingredients
to reduce RONS and inflammation, promote membrane
and mitochondrial integrity and increase insulin sensitivity
(all features strongly associated with aging and age-related
pathologies). The ASSUP contains 31 ingredients, including
several recently highlighted as highly effective in combination.  A large cohort of mice has received the AASUP
daily for nearly three years. The AASUP not only abolished
the age-related cognitive decline seen in TGM, but older
treated mice learned a cued spatial maze in even fewer trials
than younger TGM.
Materials and Methods
Our TGM mice have metallothionein promoters
fused to rat GH structural genes.  The rat GH
genes are incorporated into one chromosome, chronically
elevating plasma GH levels more than 100 fold. The original
stock was C57BL/6J male × SJL female hybrids, donated
by Dr. R. Brinster. The GH transgenes are inherited in
Mendelian proportions so breeding normal females to heterozygously
transgenic males yields equal numbers of normal
and transgenic mice with otherwise similar genetic
backgrounds. TGM were distinguishable by their larger size
by 28 days of age. All mice were checked for cataracts and
visual responsiveness to the experimenter. All mice were
able to fully traverse the maze, although some untreated
older TGM were slower than other mice.
General Housing Protocols.
Four mice were
maintained per cage (27 × 12 × 15.5 cm) bedded with woodchip.
A stainless steel hopper provided food ad libitum
(LabDiet® 5001, PMI Feeds) and supported a water bottle.
The housing room maintained a 12:12 h light:dark photoperiod
at 22 ± 2°C. All protocols adhered to the Canada
Council guidelines on animal care.
The apparatus was a cued variant of the
8-arm radial maze commonly employed in rodent learning
research , but lacked arms. A circular arena was constructed
from a 25-cm high barrier of flexible plastic (60 cm
in diameter). This was surrounded by eight inverted plastic
flower pots (15 cm in diameter) placed equidistantly around
the outside of the barrier. A 5 cm × 5 cm door cut through
the rim of each pot and aligned with similar holes in the
barrier allowed mice access to pots from the central arena.
The apparatus was set upon a plastic sheet covered with
A number from 1 to 8 was affixed on the top of each
inverted pot, and the entire apparatus was videotaped using
an overhead camera. Discriminatory cues consisting of
white circles or black squares (5 cm wide) were mounted
above the doorways of even- or odd-numbered pots, respectively.
The doorways were also outlined in black or white to
correspond with the color of the associated shapes to augment
Cues were respectively paired with Petri dishes baited
with a small dab of peanut butter that was either available or
not. Accessible Petri dishes had a large hole cut through
their lid, whereas the inaccessible dishes had numerous
small holes pierced in the lid that allowed olfaction but
prevented access to the food. The pairing of available food
with either white or black discriminatory cues was randomly
varied among the mice. Although most young mice
learned the maze within 24 trials , 39 trials was chosen
as a cut-off point for the present study because old mice
often required more than 30 trials to learn. For each trial
fresh food and Petri dishes were used, the paper floor was
replaced, and the entire apparatus was washed and swabbed
with 95% ethanol.
Thirty-two female normal mice (age range: 73 days to
892 days), 32 female TGM (age range: 48 to 452 days), and
24 supplemented female TGM (age range: 122 to 424 days)
were tested. Mice were isolated 3 days before testing and
fed small amounts of peanut butter to offset potential novelty
responses to this food. Mice were fasted 1 h before
testing to ensure motivation to eat. A perfect run in the maze
consisted of the mouse entering each pot with available food
only once while not entering any pots with inaccessible
food. This is a difficult task for mice, so for the present
study the task was considered to have been learned when, in
three successive trials, the mouse made no more that a total
of two errors. Two blocks of trials were run per day. Each
block consisted of three trials presented sequentially with
10-min intervals between each trial. Five hours separated
each block. All trials were conducted in the photophase.
Behavior was monitored and
scored from videotapes. If the rear paws crossed the doorway
threshold, a mouse was considered to have entered or
exited a pot. Types of errors were: 1) wrong entry: entering
a pot with inaccessible food; 2) wrong re-entry: re-entering
a pot with screened food; and 3) correct re-entry: re-entering
a pot with previously available food. All types of errors
were summed to yield total errors.
Mature mass was obtained from animals
in the breeding colonies that had complete lifetime
records. Male mature mass was considered the maximum
achieved since older animals often lost weight. For females,
mass during inter-pregnancy periods was used. Mature mass
was analyzed using analysis of variance (ANOVA) and a
Newman Keuls test.
The AASUP was designed to
simultaneously ameliorate several processes implicated in
aging (see Introduction). Criteria for materials were that
they were documented as effective for one or more of the
targeted features, that they could be taken orally, and that
humans could safely ingest them. The size of the literature
review precludes discussion here.
Dosages for the mice were reformulated based on
amounts commonly prescribed to humans. Firstly, values
were adjusted for the smaller body size of the mice. Next,
the dosages were increased by a factor of ten based on the
higher gram-specific metabolic rate (and consequently
faster utilization and turnover) of mice compared to humans
(54). The AASUP was prepared in liquid form, soaked onto
a small piece of bagel and allowed to dry (dry weight of
supplement = 140.3 mg per mouse based on a 35 g mouse).
All treated mice received this dose midway through the
photoperiod. The bagel bits were avidly eaten, ensuring
mice obtained full and equivalent doses. Details of the ingredients
are provided in Table I. This formulation was
maintained for the duration of the study.
Learning was assessed using two indicators; the number
of trials required to learn the task (Figure 1), and the total
number of errors committed during learning (Figure 2). In
mice < 270 days of age (log 5.6, Fig. 1), untreated TGM
(UTGM) made fewer errors and required fewer trials to
learn than normals (Fig. 2). As early as 150 days of age,
however, the learning ability of UTGM began to deteriorate
(log 5.2, Figs. 1 and 2). This deterioration progressively
increased until approx. 330 days, when most UTGM were
unable to learn the task (log 5.9, Figs. 1 and 2). The lowest
values for the number of trials to learn and total errors
committed occurred when UTGM were approx. 100 days
old. These scores were significantly lower than similarly
aged normal mice and either group of untreated older mice
(log 4.8, Figures 1 and 2).
Age-related patterns for trials to learn were best fitted
by second-order polynomial regression. The overall pattern
of age-related learning (i.e., parabolic) strongly suggests
that mice younger than 85 days of age did not learn as well
as youthful adults. Indeed, the only mice that failed to learn
the task were either quite young, or considerably older (log
3.9, Fig. 1). There were too few mice younger than 85 days
of age, however, to specifically test this statistically. For
UTGM, the regression of number of trials to learn against
age was highly significant (P < 0.000079, r2 = 0.48, n = 32). No age-specific pattern of learning was statistically
resolved for normal mice (P > 0.13, r2 = 0.14, n = 32).
TGM treated with the dietary supplement (AASUP TGM)
had qualitatively and quantitatively different age-related
patterns of learning than UTGM. Age-related patterns for
trials to learn for AASUP TGM were best described by a
linear regression (Fig. 1, P < 0.00058, r2 = 0.42, n =24).
Whereas old UTGM showed rapid deterioration in learning,
AASUP TGM learned in progressively fewer trials.
Data for total errors were more variable than for trials
to learn (compare Figs. 1 and 2); however, a second-order
polynomial regression against age was again highly significant
for UTGM (P < 0.0016, r2 = 0.36, n = 32). As with
trials to learn, normal untreated mice showed no significant
age-related pattern (P > 0.28, r2 = 0.087, n = 32). AASUP
TGM showed linearly declining error rates with age (P <
0.0043, r2 = 0.31, n = 24). For both trials to learn and
errors committed, the age-related trends were in opposite
directions for the two groups; a dramatic loss of cognitive
function with increasing age in the UTGM and a progressive
improvement in learning ability with age in AASUP
TGM (Figs. 1 and 2).
To further test the effect of age on learning, untreated
mice were assigned to two age classes (young < 270 days,
n = 20 UTGM, 16 normals; and older > 270 days, n = 12
UTGM, 16 normals). To assess the effect of age and the
AASUP on learning, and because of the unavailability of
very young AASUP mice, AASUP TGM and UTGM were
assigned to two age classes (younger > 120 days < 260 days,
n = 10 UTGM, 11 AASUP TGM and older > 260 days,
n = 13 UTGM, 13 AASUP TGM), based on the trends
evident in the figures and the need to balance sample sizes
and match age ranges. The focus of interest was the effects
of the AASUP on older TGM and because of the substantial
amount of time this cognition test takes (up to 7 days per
mouse) treated normal mice and very young AASUP TGM
have not yet been tested. The data did not allow for a finer
designation of age classes without loss of statistical power.
For untreated mice, both age and genotype of mouse
were significantly different (ANOVA) for either trials to
learn (Mouse type: F = 20.46, df = 60, P < 0.00029; Age:
F = 5.72, df = 60, P < 0.020), or total errors (Mouse type:
F = 9.85, df = 60, P < 0.0026; Age: F = 10.27, df = 60,
P < 0.0022). There was also a significant interaction between
age and genotype for trials to learn (i.e., learning
changed with age differently between untreated normals and
UTGM; P < 0.029). ANOVA for total errors yielded similar
results except no significant interaction between age and
genotype was resolved (P > 0.36). Newman–Keuls testing
detected that young UTGM learned significantly better than
older UTGM and either age class of normal mice (P < 0.05).
Older UTGMs were not significantly different than normal
mice, and no significant impact of age was detected in untreated
Planned comparisons were performed with t tests. The
average trials to learn for young UTGM was 17.71 ± 6.52
(mean ± SD), about 43% less than older UTGM or either
age class of normal mice (32.92 ± 10.17, 28.13 ± 9.10, and
33.25 ± 10.04, respectively). Both age classes of AASUP
TGM expressed significantly better performance than normal
mice and older UTGM. Young AASUP TGM averaged
17.2 ± 6.3 (mean ± SD), n = 10, trials to learn, a difference
that was not statistically resolved (P > 0.92, df = 18) from
young UTGM. The regression analysis, however, suggests a
trend for reduced performance in young AASUP TGM (Fig.
1). The average number of trials to learn for older AASUP
TGM was 11.8 ± 3.8, n = 14, an improvement of about
260% (P < 0.000013, df = 26) over older UTGM. Remarkably,
the performance of the older AASUP TGM was also
about 30% better than either treated (P < 0.016, df = 22),
or untreated younger TGM (P < 0.035, df = 22). The least
number of trials to learn for each class of mice was: young
UTGM (8 trials); older UTGM (15 trials); young untreated
normals (15 trials); and older untreated normals (15 trials);
young AASUP TGH (10 trials) and older AASUP TGM
The mean total errors for young UTGM was 77.85 ±
46.61 trials (mean ± SD), whereas untreated young normals
and older UTGM had higher and very similar scores (139.50
± 60.30 and 138.50 ± 58.67, respectively; i.e., young
UTGM made about 44% fewer errors during learning).
Older untreated normal mice committed the greatest mean
total of errors at 172.69 ± 70.24.
As for trials to learn, the number of errors committed
by younger AASUP TGM (96.8 ± 59.7 errors) was not
statistically resolved from young UTGM (P > 0.12, df = 18), despite an evident trend. Older AASUP TGM (50.7±
6.2 errors) learned significantly better than younger AASUP
TGM (P < 0.013, df = 22), but older AASUP TGM did not
differ from younger UTGM (P > 0.35, df = 22).
For total errors committed, older AASUP TGM had the
best learning performance with a mean of 50.4 ± 21.4 errors,
a substantial (> 240%) improvement over older UTGM
(P < 0.00019, df = 26). The least number of total errors
committed for each class of mice was: older untreated normals
(76 errors); older UTGM (49 errors); young untreated
normals (47 errors); young UTGM (32 errors); young
AASUP TGM (30 errors) and older AASUP TGM (19 errors).
Because many older untreated mice did not learn the
task prior to the cut-off point (39 trials), their error rates and
trials to learn are undoubtedly considerably underestimated
(Figs. 1 and 2). Thus, a conservative estimate suggests that
the learning of young adult TGM (treated or not) and older
AASUP TGM was at least 200–250% better than untreated
normal animals, or older UTGM.
Because many cellular signaling pathways require
RONS, we considered that the AASUP might well have
negative consequences on associated functions. Since mitogenesis
is one such process, we analyzed the mice for possible
growth impacts. Table II illustrates that there was virtually
no impact of the AASUP on growth of either TGM or
normal mice of either sex. TGM were larger than normal
mice as expected. A surprising sexual dichotomy in body
size expressed in TGM was also unaffected by the AASUP.
Two AASUP normal mice >540 days of age, learned in
17 and 18 trials while making 109 and 75 errors respectively
(Figs. 1 and 2). The averages for these two mice (17.5 trials,
92.0 total errors) were much lower than those of agematched
untreated normal mice (average: 34.8 trials, 200.3
total errors, n = 8) or even young untreated normals (27.8
trials, 138.9 total errors, n = 18), suggesting that larger
samples of very old mice may resolve positive effects of the
AASUP on learning in normal animals.
Results confirm the enhanced learning of this task by
younger mature UTGM.  A second-order polynomial
regression best described age-related learning, identifying
both rapid early declines in performance (Figs. 1 and 2) and
inferior acquisition by younger UTGM (30–85 days of age).
The AASUP effectively offset age-related cognitive deterioration
(Figs. 1 and 2). The performance of AASUP TGM
actually improved, older mice learning in fewer trials than
younger treated or untreated TGM. This is remarkable considering
that young UTGM express approximately 2-fold
better performance than normal mice of any age. Several
mechanisms likely explain these results, given the known
properties of the GH axis and TGM. Earlier reviews discuss
more general aspects. [18, 55]
IGF-I is elevated 3-fold in TGM. 
IGFs regulate brain development, with IGF-I transgenic
mice achieving brains 50% larger than normal. Alterations
include the hippocampus, a structure highlighted in spatial
memory.  Mice deficient in IGF hormones have reduced
brain size and hypomyelination.  IGF-I, receptors for
GH, IGF-I, and IGF-II, and IGF binding protein (IGFBP)-4
are strongly expressed in the hippocampus [2, 59] and may
enhance memory. 
IGF-I induces neurogenesis from progenitor cells in the
dentate granule cell layer of the hippocampus , and
adult neurogenesis contributes to formation of trace memories.  IGF-I gene disruption reduces the granule cell
layer of the dentate gyrus , whereas IGF-I overexpression
increases cell numbers by 29–61%.  IGF-I mRNA
increases after brain injury, suggesting a regenerative or
protective function.  The only known genetic marker
associated with exceptional learning in children is IGF-II. 
Elevation of plasma IGF-I only occurs 2 weeks postnatally
in TGM. Despite this elevation in IGF-I, the TGM
brain was reported to be no larger than controls and decreased
relative to body size as the mice grew.  It remains
that IGF-I increases neuronal complement and myelination
and extends the post-natal period of brain maturation.
The increased brain size of IGF-I transgenic mice
occurs after birth.  The immaturity of the hippocampus
at birth may explain the poorer performance of very young
mice (Figs. 1 and 2). The anatomy and micro-structure of
the TGM brain warrants detailed examination, particularly
the hippocampus. [57, 60]
GH and IGF-I are expressed in brain and
also enter via regulated uptake mechanisms and cerebral
spinal fluid.  Elevated GH and IGF-I could increase
youthful neuronal complement and offset losses in old age
by inhibiting apoptosis. [2, 57] Furthermore, chronic GH
overexpression in TGM does not decline with age ,
unlike normal mammals. Alternatively, increasing GHassociated
RONS  might override anti-apoptotic thresholds,
exacerbating apoptosis, necrosis and neuronal excitotoxicity.
Lymphocytes from UTGM are more sensitive to
radiation-induced apoptosis than controls, implying that
elevating free radicals may accentuate apoptosis if basal
levels are already high. Lymphocytes from AASUP TGM
and AASUP normal mice are resistant to radiation-induced
apoptosis (unpublished). Similar results may extend to
Many GH axis elements (e.g.,
GH, IGF-I, IGF-II, SRIF, IGFBPs), and their receptors are
widely expressed in brain, often co-localized or associated
with particular neurotransmitter systems. At least seven IGFBPs
regulate IGF-I receptor activity and IGF-I availability. [2, 55] The GH axis may influence memory by modulating
cholinergic transmission, particularly in the septal-
hippocampal tract. [18, 63] Fragments of IGF-I or GH bind
to glutamate (NMDA) or opioid receptors, respectively [63, 64], and SRIF can bind GABA receptors.  IGF-I modulates
L-type Ca2+ channels and impacts Na+ channels crucial
to depolarization.  IGF-I alters glutamate receptor
expression, and increases neuronal sensitivity to glutamate
(including excitotoxicity; Ref. 2). IGF-I can elevate GABA
release in the cerebellum.  In rats, antibodies against
IGF-I impair acquisition of aversive learning and reduce
locomotion in the open field.  Gene arrays may reveal
complex neurotransmitter alterations in TGM.
Despite controversy, sleep is strongly associated with
memory consolidation.  The GH axis regulates sleep
and EEG patterns. [20, 21] Protein synthesis required for
long-term memory  is elevated in sleep.  TGM sleep 3.4 h longer than normal [19, 21], which could upregulate systems relevant to long-term memory. The GH axis appears
to regulate large-scale coordination of brain functioning
(e.g., brain waves and sleep-wake cycles), with associated
neurotransmitter impacts in diverse brain regions [15, 18, 55]. Future studies must consider temporal cycles.
Dopamine is strongly connected to the GH axis ,
and is reduced in several brain regions of TGM in association
with elevated serotonin in others.  TGM were more
active in an open field test, indicative of reduced emotionality. [25, 26] Environmental enrichment improved spatial
learning in rats and was associated with increased SRIF and
SRIF mRNA in the cortex. More rapid habituation to novelty
was also observed.  Given linkages of dopamine
and serotonin to emotionality, it is significant that GH has
antidepressant effects, and that antidepressants evoke GH
secretion.  Normal mice moved more quickly in our
arena and some errors may have been due to their greater
Transduction Pathways and Oxidative Stress.
LTP and learning require activation of MAPK/
ERK (and perhaps PI3K) by neurotransmitters [67, 72–75].
Receptors for IGF-1, GH, and insulin occur on neurons in
regions associated with cognition and memory [15, 18, 55],
and also activate MAPK-ERK and PI3K. Activation of
MAPK/ERK by neurotransmitters may require transactivation
by growth factor receptors.  Activation of these
pathways generates and requires RONS. Sources include
NAD(P)H oxidase systems, arachidonic acid metabolites,
cyclo-oxygenase systems, nitric oxide synthase, and mitochondria.
NAD(P)H oxidase generates superoxide radical
(and secondarily H2O2), that are particularly important in
early MAPK-ERK and PI3K signaling [42, 74, 77, 78].
Neuronal NAD(P)H oxidase is also involved in apoptosis [79, 80], whereas mice with dysfunctional NAD(P)H oxidase
are resistant to free radical injury. 
Antioxidants can inhibit MAPK/ERK and LTP [82, 83]. Transgenic mice over-expressing cytoplasmic or extracellular
superoxide dismutase have defective LTP and
memory [36, 84]. Thus, antioxidants could negatively impact
cognition, even though they might ameliorate agerelated
deterioration. Learning of younger AASUP TGM
was not statistically different than younger UTGM, which is
reassuring, but a trend is apparent that might be resolved
with larger samples (Figs. 1 and 2). Younger AASUP TGM
also had higher scores and more variability in both parameters
tested, particularly for total errors.
If learning requires an optimal level of RONS, then the
improving performance of AASUP TGM with age could
reflect increases in RONS sufficient to offset damping by
the AASUP. Regardless, younger AASUP TGM still expressed
superior maze learning than normal untreated mice,
suggesting that cognitive enhancement without associated
damage is possible.
RONS enhance phosphorylation of tyrosine and threonine
kinases in transduction networks (required for MAPK/
ERK activation), while reversing or inhibiting the action of
antagonistic phosphatases [83, 85]. IGF-I acts similarly to
RONS.  Chronic overexpression of GH/IGF-I could bias
redox status and thus potentiate protein kinase phosphorylation
and enhance signal transduction relevant to neurotransmission
and synaptic LTP. Additionally, induction of
MAPK-ERK by growth factors and/or neurotransmitters
could promote oxidative stress or contribute to senescent
phenotypes by differential activation of redox-sensitive
signaling cascades, transcription factors, and gene
Moderate increases in RONS can frequently elicit super-
normal functioning whereas higher levels may inhibit
signal transduction and damage nucleic acids, proteins and
lipids. Severe damage induces apoptosis or necrosis. This
extends to the MAPK/ERK pathway that can be either neuroprotective
or apoptotic. [83, 87] RONS generally express
an inverted “U”-shaped dose–response curve for physiological
impacts that may underlie the age-related changes
in learning expressed by UTGM (Figs. 1 and 2).
Maintaining GH axis function attenuated age-related
memory deficits (but not sensorimotor deterioration) in normally
aging rats.  In our model, upregulation of the GH
axis is associated with remarkably enhanced task performance
but subsequent premature and rapid cognitive declines
associated with increasing RONS stress [17, 31]. Possibly,
RONS damage accumulates above a threshold that
exceeds effective defense, repair or replacement, and TGM
express such features early in life.
Better task performance of young UTGM over normal
controls  may reflect RONS enhancement of signal
transduction in neurons. As RONS increase with age they
may then become inhibitory, induce damage or initiate apoptosis.
Chronic or frequent upregulation of RONS processes
could accumulate damage, even if short-term impacts
are positive or enhancing. GH and IGF-I suppress antioxidant
enzymes in cell cultures, and generally, GH axis activity
negatively correlates with antioxidant activity. 
Inflammatory processes are intimately
linked to cellular damage associated with RONS.
Inflammation involves RONS generation by immunocytes
via NAD(P)H oxidase systems similar to those found in
neurons. Oxidative bursts by immunocytes are upregulated
by GH.  Aging may involve low-level inflammatory
processes  that can be ameliorated by NSAIDs and antioxidants
included in the AASUP (Table I; Ref. 5).
Brain Energy Supply.
Energy supply strongly
modulates cognition, as evidenced by enhancement of
learning and memory by glucose.  Energetic constraints
are implicated in age-related cognitive dysfunctions and
neurodegeneration [91, 92] The GH axis, particularly IGF-I,
is critical in the development and maintenance of brain vasculature
crucial to glucose supply and respiratory exchanges  and GH, IGF-I and SRIF all regulate energy balance.
GH favors mobilization of fatty acids and reduced adiposity.  TGM express strong insulin resistance, hyperinsulinemia,
altered PI3K signaling, but normoglycemia. [10, 40] Such alterations likely reflect the role of the GH
axis in sleep-associated metabolism, when glucose is sequestered
for the brain while peripheral tissues develop insulin
resistance and preferentially metabolize fatty acids.
The reduced physical activity, increased sleep , and altered
brain EEG patterns  of TGM likely reflect alterations
in global energy allocation. Impacts of insulin on
learning and LTP highlight alterations in glucose metabolism,
and reduced insulin sensitivity is associated with several
neuropathologies.  Significantly, sucrose supplements
completely restored normal activity and sleep patterns
in TGM , suggesting alterations in brain
vascularization and/or metabolism.
Dietary restricted rodents,
dwarfs and other models of downregulated GH axis function
express high insulin sensitivity and extended longevity.  Alternatively, TGM, obesity, type 2 diabetes, acromegaly
and in many cases, aging, display insulin resistance
and hyperinsulinemia. [10, 92] Insulin may play a major
role in aging and several neuropathologies and chromium
picolinate (Table I) increases insulin sensitivity. [92, 95]
Antioxidants also effectively reduce insulin resistance (see
Ref. ). Impairment of glucose metabolism or mitochondrial
substrate supply can increase production of free radicals
(e.g., Ref. ), which could accelerate cellular damage
and aging. Insulin resistance can also increase glycation,
another mechanism implicated in general aging.  If the
AASUP reduces insulin resistance, we expect associated
reductions in plasma insulin.
Cellular and Mitochondrial Membranes.
membranes are crucially involved in transport of cellular
nutrients, maintenance of ion channels and functioning of
receptors for hormones, cytokines, and neurotransmitters.
Aging is associated with increasing levels of lipid peroxidation,
changes in fatty acid composition and reductions in
membrane fluidity. Such alterations have major impacts on
all of the above functions , and RONS strongly contribute
to age-related membrane alterations. Neuronal membranes
are additionally involved in electrical signaling and
are particularly rich in unsaturated fatty acids. Antioxidants,
anti-inflammatory and fatty acid components of the supplement
(Table I) were included to prevent LP, maintain membrane
fluidity and supply unsaturated fatty acids.
Mitochondrial functioning is crucial in aging, and interventions
can maintain mitochondrial integrity. [27–29]
Mitochondrial membranes are particularly important as they
support respiration and generation of ATP. The main lipid
constituent of the inner mitochondrial membrane, cardiolipin,
is composed largely of unsaturated fatty acids and
shows age-related alterations that impact mitochondrial
functioning and free radical generation. [95, 98] Unsaturated
fatty acids are more susceptible to LP that can actually
exacerbate further generation of RONS. We reasoned that
antioxidants would offset this possibility. Others recently
showed that indeed, L-carnitine increases generation of free
radicals, but this is prevented by α-lipoic acid (Table I;
Our AASUP may lower RONS processes to
levels that maintain enhanced cognition while still preventing
damage or apoptosis associated with neuronal excitotoxicity.
These results suggest great promise for clinical
applications, but there are potential risks. Youthful cognition,
growth or reproduction could be inhibited, particularly
in normal mammals. Inhibition of immunological RONS
bursting could compromise abilities to kill pathogens or
destroy aberrant cells. Mice with such deficiencies are prone
to infections.  It is promising that growth of TGM and
normal mice is not altered by the AASUP (Table II), suggesting
that associated RONS generation is sufficiently
maintained in mitogenic pathways. It is also noteworthy that
the AASUP also had no effect on a sexual size dichotomy
expressed only in TGM.
Suppression of RONS can also inhibit apoptosis, possibly
increasing the risk of tumors. Generally, any intervention
that extends longevity by offsetting a limiting pathology
will allow expression of those next most limiting at
later ages. Our supplement extends longevity of TGM, but
their liver pathology is not strongly ameliorated (unpublished
results). Consequently, we cannot yet exclude the
possibility of toxicity. Notably the learning ability of
UTGM closely paralleled survivorship (see Ref. 31).
AASUP TGM, however, show no decline in learning ability,
even when approaching death. We may, however, be
achieving differential organ–specific results.
Our AASUP was formulated several years ago, and
because we were interested in lifetime consequences, was
maintained despite reformulations based on further scientific
progress. Most other studies have treated old animals
for much shorter periods (weeks to a few months). Reformulations
do not include DHEA. PBN (α-phenyl-N-t-butyl
nitrone), known to offset cognitive aging in rodents ,
was not included originally because of uncertainty regarding
safety. A breakdown product of PBN, N-t-butyl hydroxylamine
may be more potent and non-carcinogenic. 
TGM provide a valuable model of enhanced
RONS processes with features resembling aging.
There are very few models of both early cognitive enhancement
and accelerated age-related declines. Moreover, the
ability to modify these processes via a dietary supplement
provides a model that promises insights into both normal
cognition and its age-related deterioration.
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