Vascular Cognitive Impairment and Dementia

Tables 2 and 3 summarize two sets of diagnostic criteria for vascular dementia, the California (Chui et al. 1992) and NINDS-AIREN (Roman et al. 1993) criteria. These criteria are similar, in that they require evidence of strokes, both clinically and by imaging studies (and not just white mat­ter changes on MRI), and also evidence of cognitive impairment. Both make clear that definite vascular dementia can be diagnosed only with neuropathology, usually an autopsy study, so that the most a clinician can diagnose is “probable” or “presumed” vascular dementia. Both sets of criteria include either supporting and contravening factors, in the case of the California criteria (e.g., aphasia without an infarct on MRI in the language area would favor Alzheimer’s disease) or “typical features” in the NINDS-AIREN criteria; both listings contain clinically useful items. There are some important differences. The California criteria utilize only ischemic strokes, whereas the NINDS-AIREN criteria allow both infarc­tions and hemorrhages. The California criteria also include more explicit rules for imaging evidence of strokes, and they require progressive cogni­tive dysfunction, whereas the NINDS-AIREN criteria specify only that the dementia and cerebrovascular disease must be “reasonably related,” usu­ally meaning onset of cognitive problems within 3 months of a stroke.

Table 1

Hackinski Ischemic Score

Evidence of associated atherosclerosis

Source: Hachinski et al. 1975

Note: Patients with a total score of > 7 are considered to have multi-infarct dementia; those scoring < 4 have primary degenerative dementia.

The following factors support the diagnosis of IVD: history of TIA’s, hypertension, or other risk factors for cerebrovascular disease; early gait disorder; extensive deep white matter disease; focal abnormalities on PET or SPECT functional brain imaging. Against ischemic vascular dementia were: absence of focal neurological signs other than cognitive abnormali­ties; and presence of aphasia, apraxia, or agnosia without appropriate lesions on CT or MRI scans.

Source: Chui et al. 1992.

The diagnosis of vascular dementia, by these criteria, also must include a decline in memory and at least two other domains of intellectual ability, with resultant impairment of activities of daily living. Single strokes are permitted, if the other criteria apply. The NINDS-AIREN criteria also emphasize typical clinical features of impairment of multiple cognitive domains, usual presence of focal neurological signs, gait abnormalities, mood changes, psychomotor slowing, and extrapyramidal signs. Against vascular dementia were early onset and progres­sive worsening of a deficit in memory or other cognitive functions, in the absence of focal lesions on CT or MRI scans; absence of focal neurological signs, other than cognitive ones; and absence of infarcts on brain imaging studies.

Source: Roman et al. 1993.

The diagnosis of vascular dementia, by these criteria, also must include a decline in memory and at least two other domains of intellectual abil­ity, with resultant impairment of activities of daily living. Single strokes are permitted if the other criteria apply. The NINDS-AIREN criteria also emphasize typical clinical features of impairment of multiple cognitive domains, usual presence of focal neurological signs, gait abnormalities, mood changes, psychomotor slowing, and extrapyramidal signs. Against vascular dementia were early onset and progressive worsening of a deficit in memory or other cognitive functions, in the absence of focal lesions on CT or MRI scans; absence of focal neurological signs, other than cognitive ones; and absence of infarcts on brain imaging studies.

Cognitive Impairment in Parkinson’s Disease

Parkinson’s disease (PD) is a neurodegenerative movement disorder that is classically characterized by the motor symptoms of bradykinesia, muscle rigidity, resting tremor, and, in later stages, postural instability. It affects between 4.1 and 4.6 million people worldwide and is poised to double in prevalence in the next 20 years (Dorsey et al. 2007). This anticipated expansion is due to greater worldwide life expectancy, increased survival of affected individuals, and increasing diagnosis of previously unrecog­nized cases. Untreated, most patients become severely disabled or die 10 to 14 years after disease onset (Poewe and Wenning 1996). While medical and surgical therapies have substantially improved motor disability, there is an increasing recognition that nonmotor symptoms contribute greatly to disability and quality of life (Hely et al. 2005). These nonmotor manifesta­tions include sensory, psychiatric, autonomic, and cognitive disturbances. This  post will focus on cognitive impairment (CI) in PD.

Although James Parkinson originally reported that intellectual func­tion is preserved in the “shaking palsy” (Parkinson 1817), CI is now rec­ognized as a common and very problematic complication of PD (Emre et al. 2007). CI encompasses a spectrum of cognitive disturbance ranging from CI with no dementia (CIND) to frank PD-related dementia (PDD). CIND has been reported in 19% of newly diagnosed, untreated patients with PD, a twofold increase over similarly aged adults (Aarsland, Bron- nick, et al. 2009). In a prospective study of newly diagnosed PD patients, 57% developed CIND by 3-5 years and a further 10% were diagnosed with dementia (Williams-Gray et al. 2007). PD patients with CIND have increased risk of developing dementia (Janvin et al. 2006), analogous to the increased risk of Alzheimer’s dementia in non-PD patients with mild cognitive impairment (Petersen et al. 2001). Dementia is more common in patients with PD compared to age-matched controls, with studies report­ing a five- to sixfold increased risk (Aarsland et al. 2001; Hobson and Meara 2004). The point prevalence of PDD among PD patients in community based studies has been estimated to be between 25% and 30% (Aarsland et al. 2003; Buter et al. 2008; Riedel et al. 2008) . Prospective longitudi­nal studies of CI in PD have consistently demonstrated that PD patients are at high risk for dementia. Large, often community-based, studies in Norway, Germany, Australia, and the United Kingdom have reported high cumulative prevalence and incidence of PDD (Table 1 ).

Thus, development of dementia is to be expected in the vast major­ity of patients with PD, even more frequent than some motor complica­tions, such as dyskinesias (Muller et al. 2007). Of particular concern, the presence of dementia in PD is associated with nursing home placement (Aarsland et al. 2000), greater disability (Weintraub et al. 2004), greater caregiver distress (Aarsland et al. 1999), and mortality (Buter et al. 2008). Thus, it is important for the clinician to recognize CI as an important com­plication of PD, to treat it, and for the research community to assist in the development of better treatments.

The DSM-IV diagnostic criteria for dementia require the presence of impairment in multiple cognitive domains and that these cognitive impair­ments are associated with significant impairment in social or occupational functioning (American Psychiatric Association 2000) . These impairments must represent a decline from previous levels of performance or function­ing. We will use these criteria for our discussion of dementia in PD. In addi­tion, per current criteria (discussed further below [Emre et al. 2007]), PDD will refer specifically to patients who fulfilled criteria for motor PD one year prior to the development of dementia. In many PD patients with CI, the CI it is not sufficiently severe to compromise daily function. These patients will be classified as “cognitive impairment with no dementia” (CIND). CIND is also referred to in the PD literature as mild CI (MCI), but this term may cause confusion with the MCI observed in prodromal Alzheimer’s disease patients and thus for the sake of this review we will use the term CIND. PD patients with normal cognition are referred to as PD, nondemented (PDND).

PDD has an insidious onset and progression but the time to onset and rate of progression vary significantly between patients (Aarsland et al. 2004; Williams-Gray et al. 2007). CIND is present in some patients at time of PD diagnosis (Aarsland, Bronnick, et al. 2009). CIND may be a prodrome to frank dementia in some patients, although it is not currently clear whether all patients with CIND will progress to PDD (Janvin et al. 2006). In a lon­gitudinal study, the annual decline of the Mini-Mental State Examination (MMSE) score was 2.3 points in PDD patients compared to less than 1 point in PDND patients and healthy control subjects (Aarsland et al. 2004).

Cognitive Features

PDD is associated with a variety of cognitive deficits including the domains of executive function, attention, visuospatial function, and memory.

Generally the profile for cognitive domain impairment is different in PDD than observed in other dementias such as AD, although these differences are less obvious in severe dementia due to the eventual impairments in almost all cognitive domains. In PDD and PD-CIND, executive dysfunc­tion and attention are perhaps the most consistently reported deficits, while memory and language dysfunction are much less prominent in PDD than in AD (Watson and Leverenz 2010). For example, in a large study of 488 PDD patients and 488 AD patients, the neuropsychological profile accurately predicted 75% of the diagnoses. While both groups demonstrated memory impairment, the AD group performed significantly worse on memory mea­sures. The tests with the greatest discrimination between the two groups were a measure of orientation (dependent on memory), in which the AD group performed most poorly, and a measure of attention, in which the PDD group showed the greatest deficits (Bronnick et al. 2007). Executive function, high-level cognitive skills involved in regulating behavior and monitoring other cognitive processes, is also reported to be frequently impaired cogni­tive domain among PD patients with CI (Caviness et al. 2007). Patients with PD also perform more poorly on visuospatial tasks. Memory impairment, although less severe than in AD, is a frequent feature of PDD. However, in PDD and CIND, memory recall is reportedly more impaired than memory recognition, unlike AD in which both are impaired (Emre et al. 2007; Watson and Leverenz 2010).

There is some heterogeneity of the neuropsychological profile in PDD and subtypes of dysexecutive, amnestic, and mixed CI have been pro­posed (Lewis et al. 2005). Subtypes have also been reported in PD-CIND including amnestic, single-domain nonamnestic, and mild multidomain impairment (Janvin et al. 2006). The pathophysiologic significance of this variability in neuropsychological profile is not clear, but certainly raises the possibility of differing underlying pathologies (Leverenz et al. 2009).

Behavioral Features

PDD is associated with several behavioral changes including depres­sion, apathy, hallucinations, and delusions. These neurobehavioral symp­toms predominate in later disease, are difficult to treat, and are associated with reduced quality of life (Aarsland et al. 1999, 2007; Hely et al. 2005; Schrag, Jahanshahi, and Quinn 2000). Often overlooked in PD, depressive symptoms can be confused with motor findings (psychomotor retardation mistaken for bradykinesia and masked facies) and somatic complaints (fatigue and sleep disturbance are common to both disorders). In addi­tion, demented patients may have increased difficulty articulating their mood-related symptoms. Depressed PD patients demonstrate diminished global cognitive performance and specifically have shown impairment in the cognitive domains of naming and verbal memory (Fernandez et al. 2009). Executive functions have also been shown to be impaired in depressed nondemented PD patients (Santangelo et al. 2009). It is unclear whether the primary cause of poor performance was depression, intrinsic CIND, or a third factor (such as common neuropathology) causing both CI and depression. It is unknown whether these mood changes are the result of coping with a chronic, debilitating illness or whether they may be inherent changes due to underlying PD neuropathology. PDD and PD- CIND patients should be screened for depression and, when appropriate, treated both pharmacologically and behaviorally.

Apathy is also very common in PD and, while frequently associated with depression, it can occur independently (Oguru et al. 2009). In a four- year population based, longitudinal study, greater than 60% of PD patients reported apathy (Pedersen et al. 2009), as measured by the Neuropsychi­atric Inventory (Cummings et al. 1994). Dementia and depression were both found to be risk factors for apathy (Pedersen et al. 2009). Apathy has also been linked to frontal and visuo-constructional deficits ( Santan- gelo et al. 2009), suggesting a cognitive underpinning of this symptom. It is clinically important to differentiate apathy from depression as the pharmacologic and behavioral management can differ. For example, anti­depressants have been shown to improve depression in PD, while acetyl­cholinesterase inhibitors may improve apathy (Devos et al. 2008; Figiel and Sadowsky 2008).

Psychotic symptoms are common in PD; in particular, visual halluci­nations are reported in up to 74% of 20-year PD survivors (Hely et al. 2008)   .  In contrast, less than 10% of autopsy-confirmed AD patients, with­out coexistent Lewy bodies, suffer from hallucinations, indicating an asso­ciation between hallucinations and Lewy body pathology (Tsuang et al. 2009)   .  Visual hallucinations are much more common than auditory, olfac­tory, or tactile hallucinations, although all forms may occur (Diederich et al. 2009). Visual hallucinations in PD patients are often well formed, taking the shape of people, animals, or objects. They can be adverse effects of dopaminergic treatment, particularly dopamine agonists (Goetz et al. 2001)   , but also result from the underlying disease (Fenelon, Goetz, and Karenberg 2006, . Delusions appear to be less common than hallucina­tions in PD and, when present, often co-occur with hallucinations and CI (Marsh et al. 2004; Kulisevsky et al. 2008). Delusions are often of a para­noid or jealous nature and are associated with increased caregiver burden (Marsh et al. 2004).

PD patients can suffer from a wide variety of sleep disturbances; among these, rapid eye movement sleep behavior disorder (RBD) may be particularly associated PD and other disorders associated with Lewy body pathology (Boeve et al. 2003). RBD is characterized by loss of normal muscle atonia during REM sleep resulting in complex, sometimes violent, movements. Several studies have suggested an association between RBD and PD-CIND with the majority of PD-CIND patients also suffering from RBD (Gagnon et al. 2009; Vendette et al. 2007); notably, another group has not confirmed this association (Yoritaka et al. 2009).

Risk Factors

In addition to CIND and RBD (discussed above), several other risk fac­tors have been identified for development of PDD. Older age has been consistently associated with increased risk for dementia in PD (Aarsland et al. 2001; Riedel et al. 2008; Hely et al. 2008; Williams-Gray et al. 2007). This association with age may result from an increased susceptibility to dementia, an increased risk of co-morbid dementia from other causes such as AD or cerebrovascular disease, or likely both. Greater disease sever­ity and longer duration of disease, factors correlated with each other and with age, are also linked to development of dementia (Aarsland et al. 2001; Riedel et al. 2008). Lower education attainment has been reported among PDD patients (Riedel et al. 2008). Other clinical features associated with the development of PDD are a nontremor-predominant motor phenotype, poor pentagon copying, and impaired semantic fluency (Williams-Gray et al. 2007 ).

Diagnostic Criteria

In the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV), PDD is categorized as dementia due to a general medical condition and defines dementia in terms of functional impairment due to CI (American Psychiatric Association 2000). More specific criteria were proposed by the Movement Dis­orders Society (MDS) Task Force in 2007 (Emre et al. 2007) (see Table 2).

The core features include the development of dementia in the con­text of already established motor PD, as determined by the UK Parkin­son’s Disease Society Brain Bank criteria (Gibb and Lees 1988) . The CI must be a decline from premorbid performance and affect two or more of the following four cognitive domains: attention, executive function, visuo-constructive ability, and memory. CI must be sufficiently severe

Table 2

Features of Parkinson’s Disease Dementia

Core diagnostic features Diagnosis of Parkinson’s diseaseDementia of slow onset and progression developing greater than one year after onset of Parkinson’s disease and characterized byImpairment in at least one cognitive domainA decline from pre-morbid level of functionCognitive deficits sufficiently severe to impair daily functioningAssociated clinical features Cognitive FeaturesAttention impairedExecutive functions impairedVisuo-spatial processing impairedMemory impairedLanguage function largely intactBehavioral features 1 . ApathyPersonality changeMood changeHallucinationsDelusionsSomnolenceFeatures that make PDD diagnosis uncertain Co-morbid abnormalities that can contribute to cognitive dysfunction but are not sufficiently severe to cause dementiaUncertain time-course between onset of motor symptoms and dementiaFeatures suggesting alternative cause of cognitive impairment Cognitive symptoms arising in context of systemic disease, intoxication, or mood disorder.Clinical and imaging features suggestive of vascular dementia Source: Table adapted from Emre et al. 2007

to impair daily function (in social, occupational, or self-care domains) independent of functional impairments from motor or autonomic symp­toms. Supportive criteria include the presence of typical behavioral fea­tures (depression, apathy, anxiety, delusions, hallucinations, or excessive daytime somnolence) and absence of features suggesting alternative diagnoses. Criteria for probable and possible PDD were delineated. This assessment is based on history and clinical and cognitive examinations.

The MDS criteria specify that dementia must arise in the context of an established PD diagnosis (Hughes et al. 1993). This condition serves

to distinguish PDD from dementia with Lewy bodies (DLB) and other dementias (including Alzheimer’s dementia, AD). DLB is diagnosed when dementia either precedes or occurs within one year of onset of PD motor symptoms and when other core symptoms such as fluctuations and visual hallucinations are present (Emre et al. 2007; McKeith et al. 2005).

The MDS has subsequently proposed a two-tiered system for PDD diagnosis (Dubois et al. 2007). Level 1 is designed for clinicians without expertise in neuropsychological assessment who require simple, practi­cal diagnostic criteria and brief neuropsychological measures. A proposed algorithm for this level includes (1) diagnosis of PD by Queen, s Square Brain Bank criteria (Hughes et al. 1993); (2) PD onset at least one year prior to onset of dementia; (3) decline of global cognitive efficiency as evi­denced by MMSE (Folstein, Folstein, and McHugh 1975) score < 26; (4) CI severe enough to impair daily life (e.g., based on caregiver questionnaire or questioning patient about medication regimen); (5) impairment in two of the four cognitive domains most impacted in PDD: attention as assessed by months reversed (Shum et al. 1990) or serial sevens (Folstein, Folstein, and McHugh 1975), executive function as measured by phonological ver­bal fluency (Benton and Hamsher 1989), visuo-constructive function such as clock drawing (Sunderland et al. 1989) or MMSE pentagons (Folstein, Folstein, and McHugh 1975), or memory as demonstrated in three-word recall (Folstein, Folstein, and McHugh 1975) (see Table 2). There should be an absence of major depression, delirium, or other confounding cause of dementia. Level 2 involves extensive neuropsychological and behav­ioral testing and is appropriate when more detailed neuropsychological information is required because of diagnostic uncertainty in Level 1 or for research purposes.

Ancillary Diagnostic Evaluations

PDD must be distinguished from other forms of dementia because treat­ment and prognosis differ according to etiology. The American Academy of Neurology (AAN) recommends structural brain imaging, with either noncontrast computerized tomography (CT) or magnetic resonance imag­ing (MRI), to exclude structural lesions in demented patients (Knopman et al. 2001) . Neuroimaging studies have found structural lesions in 5% of demented patients without clinical history or signs suggesting those lesions (Chui and Zhang 1997). In particular, subdural hematoma, normal pressure hydrocephalus, and brain tumor should be detected and treated appropriately. Other findings, such as vascular lesions or lesions sugges­tive of a Parkinson-plus disorder, may alter the diagnosis and course of
medical treatment. MRI studies in PDD have shown whole brain and regional cortical atrophy, but these changes overlap with those seen in other dementias and, thus, offer only limited contribution in PDD diagno­sis (see Emre et al. 2007 for review)

The AAN also recommends screening demented patients for depres­sion, vitamin B12 deficiency, hypothyroidism, and, if clinically suspected, syphilis. While these disorders can cause or contribute to a dementia-like syndrome (Knopman et al. 2001), more often they are co-morbidities of an underlying dementing disease such as AD and PD. Appropriate treatment of these can improve cognitive function even in the context of another primary etiology of dementia such as PD.

The pathological processes that underlie CI in PD are likely primar­ily related to the pathology linked to PD: Lewy bodies and Lewy-related neurites (Lewy Related Pathology, LRP). LRP is best detected utilizing immunohistochemistry for alpha-synuclein, the predominant protein component of this pathologic change of PD (Spillantini et al. 1997; Sch­neider et al. 2002). Pathologic studies using this technique to detect LRP in well-characterized PD patients who fulfilled criteria for PDD, that is, motor parkinsonism preceding dementia by one year or more, have found a high frequency of LRP in brainstem, limbic, and neocortical regions of the brain (Aarsland et al. 2005; Apaydin et al. 2002; Braak et al. 2005; Galvin, Pollack, and Morris 2006; Hurtig et al. 2000). All of these studies have found that the presence or severity of LRP in limbic and neocortical regions is associated with a clinical history of dementia in PD.

The contribution of other pathological processes such as Alzheimer’ s disease and vascular disease to dementia in PD are unclear. Some stud­ies have suggested that co-existent Alzheimer’s disease occurs in less than 10% of PDD patients at autopsy (Aarsland et al. 2005; Apaydin et al. 2002; Braak et al. 2005), while others have observed co-existent pathologic AD in 30 to 40% of PDD patients (Galvin, Pollack, and Morris 2006; Hurtig et al. 2000). Given the limited number of autopsy cases in each of these studies, fewer than 200 in total, it is not surprising that there is disagree­ment between these studies. One alternate method to examine this issue is to evaluate biomarkers linked to Alzheimer’ s disease pathology, specifi­cally cerebrospinal fluid (CSF) levels of Ap (senile plaques) and tau (neu­rofibrillary tangles). In AD multiple studies have consistently found that CSF levels of Ap are lower than normal and tau levels are elevated (Blen- now et al. 2010). At this point, it appears that CI in PD is associated with
lowered CSF Ap levels, similar to AD, but not elevated tau (Montine et al.

2010)    . These findings would appear to be most consistent with the neu­ropathological studies that have observed elevated Ap deposition in the brain of PDD patients, without significant coexistent tau deposition (i.e., neurofibrillary tangle) (Apaydin et al. 2002). Thus, the preponderance of clinical biomarker and neuropathologic data suggest that the majority of PD cases with CI do not have all the pathologic features of AD, but some AD-associated pathologic changes may play a role in the pathophysiol­ogy of CI in PD. Clinically, one can hypothesize that the subset of PD with predominant declarative short-term memory impairments might also have coexistent LRP and Alzheimer’s disease.

The contribution of vascular disease to CI in PD is even less well stud­ied. To the best of our knowledge there have been no systematic autopsy studies examining this issue. It is clear from autopsy studies in community- based autopsy samples that vascular disease is an important contributor to dementia in the general population (Sonnen et al. 2007; White et al. 2002). Given these findings it is likely that there are a subset of PD patients with CI who have both LRP and vascular pathology contributing to their deficits.

While the consensus appears to be that LRP drives the CI observed in PD, further research is necessary to further elucidate other potential contributors to CI in PD. This becomes particularly relevant as disease- specific treatments for Alzheimer’s disease and other neurodegenerative disorders become available.

While PDD and AD appear to have distinct neuropathology, both share a common cholinergic deficit. In AD, the deficit presumably arises from neurofibrillary tangle pathologic change in the cholinergic basal fore­brain, while in PDD the deficit is likely due to LRP in those same neural structures (Tiraboschi et al. 2000). Interestingly, Frederick Lewy’s original description of Lewy bodies was in the same cholinergic basal forebrain neurons, not in the dopaminergic substantia nigra. Given this, acetylcho­linesterase inhibitors (AChEi), originally designed to address the cholin­ergic deficiency in AD, can be rationally used in PDD.

To date only one large-scale placebo-controlled study has examined the use of AChEi in PDD (see Table 3). A 24-week study randomly assigned 541 patients with mild or moderate PDD to placebo or 3mg to 12mg daily oral doses of Rivastigmine (Emre et al. 2004). The primary outcome mea­sures were the cognitive subscale of the Alzheimer’s Disease Assessment Scale (ADAS-cog) (Rosen, Mohs, and Davis 1984) and Alzheimer) s Dis­ease Cooperative Study-Clinician’s Global Impression of Change (ADCS- CGIC) (Schneider et al. 1997). Secondary outcomes included other cognitive measures (MMSE, clock drawing, verbal fluency test, and a measure of attention), a behavior measure (neuropsychiatric inventory, NPI [Cum­mings et al. 1994]), and a metric for activities of daily living (ADL). In the 410 patients who completed the study, there was statistically significant improvement in all of the primary and secondary measures in patients treated with rivastigmine. There was clinically meaningful ADCS-CGIC improvement in 5% of patients on rivastigmine and worsening in 10% of patients on placebo, both favoring use of rivastigmine in PDD and similar to the benefit of this class of medications in AD. In an extension study, 273 patients (from both placebo and active drug groups) completed an addi­tional 24 weeks of open-label rivastigmine (Poewe et al. 2006). The group as a whole showed improvement in the ADAS-cog and subjects initially treated with placebo showed gains similar to the drug treatment group in the original study. Rivastigmine has also been shown to improve cognitive and behavior symptoms in DLB (McKeith et al. 2000).

Smaller studies using the other available AChEi medications done- pezil and galantamine have not consistently found positive cognitive or behavioral effects (Aarsland et al. 2002; Leroi et al. 2004; Ravina et al. 2005; Grace, Amick, and Friedman 2009). It is not clear if this indicates an actual difference in effectiveness between the different AChEi agents, or whether these studies were just too small to adequately detect the modest effects observed in the larger rivastigmine trial.

AChEi are associated with a number of adverse effects. In the study by Emre and colleagues (2004), patients treated with rivastigmine suffered nau­sea, vomiting, and tremor with greater frequency than those treated with placebo. Not surprisingly, there was also greater attrition in the rivastig- mine treated group. Another study using the same database reported that tremor exacerbation occurred only transiently during dose titration (Oertel et al. 2008). Transdermal rivastigmine has fewer gastrointestinal side effects (Winblad et al. 2007) and would appear to be a reasonable alternative to oral treatment of PDD, but the clinical effectiveness and side effect profile in PDD has not yet been specifically studied.

Memantine is an N-methyl D-aspartate receptor antagonist that is used to limit the potential negative effects of glutamate overactivity in neuro- degenerative disease. It has shown symptomatic benefit as mono-therapy in moderate to severe AD (McShane, Areosa Sastre, and Minakaran 2006) and also can be combined with an AChEi for a positive clinical effect in AD (Tariot et al. 2004). In a small (40 PDD cases) placebo-controlled
study combining PDD and DLB patients, memantine was associated with improved global clinical ratings, but other measures of cognition, behavior, and activities of daily living failed to demonstrate a benefit of treatment (Aarsland, Ballard et al. 2009). In another small study (N=25), memantine showed no benefit compared to placebo except that the memantine-treated group had greater deterioration after withdrawal of the drug (Leroi et al. 2009)   . In these studies, memantine was generally well tolerated with mild adverse events that were similar between treatment and placebo groups (Aarsland, Ballard et al. 2009; Leroi et al. 2009). These small studies are encouraging, but larger-scale studies are needed to better determine effi­cacy and tolerability.

Measuring and Interpreting the Efficacy of Nutraceutical Interventions for Age-Related Cognitive Decline

The world’s population is aging rapidly, with the proportion of the population over 60 growing at a rate of around 2% per annum in the developed world (United Nations, 2009). In the most developed regions, 264 million people (21% of the population) were estimated to be 60 years and older in 2009, with this figure projected to increase to around 416 million (33% of the population) by the year 2050 (United Nations, 2009). A major societal health issue for an aging population is not only the greater incidence of neurodegenerative disorders such as Alzheimer’s disease but also the impact of normal age-related cognitive decline. Up to 50% of adults aged 64 and over have reported difficulties with their memory (Reid and MacLullich, 2006). In response to the reality of an aging population, there has been increased research focus in recent years on the development of effective interventions that may ameliorate the declines in cognitive ability.

Age-related deficits in cognitive abilities have been consistently reported across a range of cognitive domains including processing speed, attention, episodic memory, spatial ability, and executive function (Craik, 1994; Hultsch et al., 2002; Park et al., 1996, 2002; Rabbitt and Lowe, 2000; Salthouse, 1996; Schaie, 1996; Verhaeghen and Cerella, 2002; Zelinski and Burnight, 1997). While an overall decline in processing speed may explain some of the age-related variance in cognitive ability (Salthouse, 1996), processing speed alone cannot explain why a range of neuropsychological measures still remain significantly related to age once processing speed is taken into account (Pipingas et al., 2010). Further, there is growing evidence documenting a more rapid decline for certain cognitive functions in comparison to others, a find-ing which suggests that factors in addition to processing speed are also involved in cognitive decline (Buckner, 2004).

Hedden and Gabrieli (2004) differentiate three categories of cognitive decline in normal aging: (1) lifelong declines, including processing speed, working memory, and encoding of information into episodic memory; (2) late-life declines, including well-practiced tasks and those that require previous knowledge such as vocabulary and semantic knowledge; and (3) life-long stability, including autobiographical memory, emotional processing, and implicit memory. Another common distinction is often made between crystallized abilities (e.g., vocabulary and general knowledge), which remain stable until later life versus fluid abilities (e.g., attention, executive function, and memory) that decline from middle adulthood until late old age (Gunstad et al., 2006).

The reason for disproportionate declines across cognitive domains is that certain brain structures are more heavily affected by these processes than others during aging (Buckner, 2004; Grieve et al., 2005; Hedden and Gabrieli, 2004). Cortical volume decreases in the frontostriatal system are most strongly correlated with age-related cognitive decline (Bugg et al., 2006; Hedden and Gabrieli, 2004; Kramer et al., 2006; Schretlen et al., 2000; West, 1996). It has been estimated that decreases in Prefrontal Cortex (PFC) volume occur at a rate of around 5% per decade after the age of 20, in contrast to relatively small volumetric declines in the hippocampus and medial temporal lobe structures, which occur at a rate of around 2%–3% per decade (Hedden and Gabrieli, 2004; Kramer et al., 2006). In the absence of neurodegenerative diseases such as Alzheimer’s (AD), the medial temporal lobes are relatively spared by the aging process (Albert, 1997).

Age-related reductions in the brain’s gray matter are due to a number of factors including neuron apoptosis, neuron shrinkage, and lowered numbers of synapses; whereas reductions in white matter may be attributed in part to large age-related decreases in the length of myelinated axons (Fjell and Walhovd, 2010). During aging the brain suffers accumulative damage due to a number of cellular processes includ-ing reactive oxygen species formation (Halliwell, 1992), chronic inflammation (Sarkar and Fisher, 2006), redox metal accumulation (Connor et al., 1995), and homocysteine accumulation (Kruman et al., 2000). In addition to direct cellular damage, the brain is also indirectly impaired by insults to the cardiovascular system (Pase et al., 2010).

While the unfortunate decline in cognitive ability is ubiquitous, it is also evident that a great deal of variability exists in both the rate and the extent of cognitive decline experienced by individuals as they age (Shammi et al., 1998; Wilson et al., 2002). While some of the variance may be explained by genetic factors (e.g., Hariri et al., 2003; Price and Sisodia, 1998), there is also a great deal of research highlighting the importance of diet and lifestyle during aging. Chronic nutraceutical interventions hold great promise in ameliorating age-related cognitive decline because they simultaneously target multiple cellular mechanisms of cognitive decline. Many natural sub-stances already identified through in vivo as well as clinical studies have been found to have potent anti-oxidant and anti-inflammatory properties as well as being of benefit to the cardiovascular system (Ghosh and Scheepens, 2009; Head, 2009; Kidd, 1999). In order to be able to accurately assess and interpret the clinical efficacy of these natu-ral substances, it is recommended in the following review that highly accurate and specific cognitive tests are needed, together with the creation of normative databases that may be used to interpret clinical data in terms of years of cognitive function recovered.

Traditional ways of measuring cognitive decline in the elderly have involved clinical neuropsychological tests, tests that were often designed for the diagnosis of dementia. Commonly used dementia assessment scales include the mini mental state exam (MMSE; Folstein et al., 1975), the clinical dementia rating scale (Morris, 1997) and the cognitive subtest of the Alzheimer’s disease assessment scale (ADAS-cog; Mohs et al., 1983). Such scales involve structured interviews to determine the presence of dementia, and if present then the severity of dementia symptoms. While these scales may be useful in the diagnosis of dementia, they lack the sensitivity to be able to assess cognitive decline in the normal population, and hence a strong ceiling effect would be expected. Another limitation of these clinical assessment scales is that they primarily assess global cognitive function, as opposed to specific cognitive domains that may be disproportionately affected by the aging process. Further, these tests often rely on pen and paper recording, which lacks the measurement precision associated with modern computerized testing.

For these reasons it is recommended that computerized tests that target specific cognitive abilities and have a high degree of sensitivity to fluctuations in cognitive function be used for testing the efficacy of interventions for age-related cognitive decline, rather than the more traditional dementia assessment scales. The cognitive drug research (CDR; Wesnes et al., 1999) neuropsychological assessment battery has previously been found to be a particularly sensitive measure for the detection of changes to cognitive function associated with chronic nutraceutical and dietary interventions (Ryan et al., 2008; Stough et al., 2008; Wesnes et al., 2000). The computerized mental performance assessment system (COMPASS; Scholey et al., 2010), which was developed at Northumbria University, United Kingdom, has also been found to be a sensitive measure in nutraceutical intervention trials, as has the Cambridge neuropsychological test automated battery (CANTAB; Cambridge Cognition, Cambridge, United Kingdom). Using large normative samples, the CANTAB has been found to be sensitive enough to detect declines in cognitive ability associated with normal aging as well as mild cognitive impairment preceding dementia (Égerházi et al., 2007; Robbins et al., 1994).

More recently our laboratory has developed a neuropsychological assessment battery designed specifically for the assessment of age-related cognitive decline, the Swinburne University computerized cognitive aging battery (SUCCAB; Pipingas et al., 2008, 2010). The SUCCAB is a computerized test battery consisting of nine tasks designed to capture the range of cognitive functions that decline with age: immediate/ delayed word recall, simple reaction time, choice reaction time, immediate/delayed recognition, visual vigilance, n-back working memory, Stroop color-word, spatial working memory, and contextual memory (Pipingas et al., 2010). In preliminary studies from our laboratory, the tasks contained in the SUCCAB have been found to be highly sensitive to age-related cognitive decline (Pipingas et al., 2008).

When reporting the results of timed cognitive tasks times, it is informative to not only state the results of parametric statistical tests and p-values but also report the average millisecond improvement that is observed in the treatment group. Further, if the mean difference in reaction times has also been found to change in the placebo group, then the mean change in the treatment group above and beyond the change observed in the placebo group is the most informative metric.

An example of reported millisecond improvements in SUCCAB tasks that were found to be associated with a chronic intervention is provided by Pipingas et al. (2008). In a randomized, placebo-controlled trial, Pipingas et al. (2008) investigated the cognitive effects associated with 5 weeks supplementation with the Pinus radiata bark extract Enzogenol® in 42 males aged 50–65 years. Significant differences between the treat-ment and placebo groups were found for SUCCAB spatial working memory (SWM) and immediate recognition memory. The average reduction in reaction time for the SWM task in the Enzogenol group was found to be 65 ms, while reaction times for the control group were unchanged. Similarly, for SUCCAB immediate recognition memory, the average reduction in reaction time for the Enzogenol group was found to be 60 ms, while the reaction time in the control group increased by 7 ms (Pipingas et al., 2008).

The same approach can also be used for computerized cognitive tests that are scored according to accuracy (percent correct). An example of improvements to SWM accu-racy associated with a nutraceutical intervention is provided by Stough et al. (2008). In a 90 day randomized placebo-controlled trial, Stough et al. (2008) investigated the cognitive effects of Bacopa monniera in 62 participants aged 18–60 years. Significant differences between the treatment and placebo groups were found for change in CDR SWM accuracy over the 3 month period. The average improvement in accuracy for the SWM task in the Bacopa monniera group was found to be 5.44%, while the aver-age improvement in the control group was found to be 2.3%. If we make the assumption that the average improvement in accuracy for the control group is a measure of improvement due to practice effects, then we can see that there is still a 3.14% improvement observed in the treatment group above and beyond this value.

When baseline normative data are collected in regard to the results of computerized cognitive tests across a wide age range, the regression coefficient of age can be compared with the treatment effect when a nutraceutical intervention is applied.

This approach was recently used in our laboratory, whereby the SUCCAB cognitive battery was administered to 120 participants between the ages of 21 and 86 years (Pipingas et al., 2010). Significant correlations between both accuracy and reaction time measures were found across a wide range of cognitive domains including word recall, recognition memory, SWM, contextual memory, simple and choice reaction time, visual vigilance, n-back working memory, and Stroop reaction times (Pipingas et al., 2010). Regression analysis was used to predict reaction time and accuracy measures as a function of age for each cognitive domain from the SUCCAB. SWM ability was found to display the greatest degree of age-related decline amongst all cognitive measures, followed by contextual memory and immediate recognition tasks.

Here it can be seen that there is a steady linear increase in reaction time and a decrease in task accuracy from the age of 20 years onward. While further normative data for the SUCCAB is required in order to predict this relationship more accurately, this preliminary study nevertheless illustrates the strong relationship between age and cognitive decline.

An illustrative example is the previously mentioned intervention study by Pipingas et al. (2008) using P. radiata bark extract in elderly adults. Average SWM reaction time for the treatment group decreased from 1018 ms at baseline to 953 ms post-treatment. By using the regression equation for SWM reaction time as a function of age established in the SUCCAB normative study (Pipingas et al., 2010), the 65 ms improvement in reaction time can be interpreted as a SWM cognitive age-recovery of approximately 6.5 years.

Another example of this approach was a study by Durga et al. (2007), which investigated the effects of 3 year folic acid supplementation on cognition. By com-paring the regression coefficient of age with the treatment effect, the authors were able to calculate that 3 year supplementation conferred an individual the performance of someone 4.7 years younger for memory, 1.7 years younger for sensorimotor speed, 2.1 years younger for information processing speed, and 1.5 years younger for global cognitive function (Durga et al., 2007). Such interpretations of the data place cognitive change in a more meaningful context, changes that can be directly interpreted in terms of the amelioration of cognitive decline.

Natural Substances as Treatments for Age-Related Cognitive Declines

Cognitive function has long been known to decline with normal ageing, and recent findings indicate that this decline starts in early adulthood. While these declines are recognised, there is currently no regulatory acceptance to encourage the pharmaceutical industry to develop medicines to treat these normal age-related deterioration; and the industry is therefore currently focused on Alzheimer’s and other dementias, as well as prodromes for Alzheimer’s disease including Mild Cognitive Impairment. Recent surveys have shown that students, various professional groups, and the military are using ‘smart drugs’ like modafinil off-label to promote cognitive function, and such use is producing much controversy, due in part to the possible safety risks associated with such use.

However, a growing body of data is accumulating showing that naturally occurring substances can enhance cognitive function, even in young volunteers. This provides an alternative strategy for individuals who wish to optimise their mental performance and to attempt to correct age-related declines, i.e. by consuming naturally occurring substances which are more widely available. Accepting that naturally occurring substances can have the same range of health risks as prescription medicines, this paper considers research findings that could provide a rationale for self-medication of cognitive function with natural substances.

Bostrom and Sandberg (2009) define cognition enhancement as “the amplification or extension of core capacities of the mind through improvement or augmentation of internal or external information processing systems.” Aspects of cognitive function that are targets for enhancement include attention, vigilance, information processing, memory, planning, reasoning, decision making, and motor control. Bostrom and Sandberg argue that an intervention aimed at correcting a specific pathology or defect of a cognitive subsystem may be characterized as therapeutic, while enhance-ment is an intervention that improves a subsystem in some way other than repairing something that is broken or remedying a specific dysfunction. This distinction is interesting, and accurately characterizes the various compounds which are being developed and studied in this rapidly growing field.

Substances to enhance cognitive function are currently receiving a large amount of public interest and ethical debate (Cakic, 2009), due to the recognition of their widespread use by students, the military, and many professional groups (Sahakian and Morein-Zamir, 2007). In 2008, the journal Nature reported the results of an online poll, in which 20% of the 1400 respondents admitted that they had used “neuro-enhancers” to stimulate their focus, concentration, or memory (Maher, 2008). Although 96% of respondents felt that individuals with neuropsychiatric disorders who have severe memory and concentration problems should receive such substances, 80% of respondents felt that anyone who wanted such substances should be allowed access, and 69% said they would take one provided the side-effects were low. The high level of interest can be illustrated by articles in The Times Online (Bannerman, 2010) entitled “Bring smart drugs out of the closet, experts urge Government,” and in Time Magazine entitled “Popping Smart Pills: The Case for Cognitive Enhancement” (Szalavitz, 2009).

Cognitive function concerns mental abilities which enable us to conduct the activities of daily living. Some aspects of cognitive function are relatively stable and unaffected by, for example, aging, fatigue, drugs, or trauma; while other aspects such as attention and memory are variable by nature and highly susceptible to change. Tests of cognitive function assess how well various cognitive skills are operating in an individual at any particular time. Such evaluations require individuals to perform tasks which involve one or more cognitive domains. Thus if a researcher wished to assess memory, the test would involve the memorization of information and the outcome measure would reflect how well such information could be retrieved. Equally, to assess the ability to sustain attention, the test could involve monitoring a source of information in order to detect predefined target stimuli over a period of time, and the outcome measures would reflect the speed and accuracy of the detections. It is important to note that the only way to measure cognitive function directly is by assessing the quality of performance on cognitive tests or behavioral tasks. It is of interest to assess how the individual feels about his or her levels of cognitive function, but this is sim-ply supportive evidence for the objective assessment of task performance. Similarly, various measures of brain activity (for example, electroencephalography and fMRI scanning) do not measure the quality of cognitive function directly, but rather provide us with independent but nonetheless hugely valuable information about the activation of certain brain areas as well as the interconnecting pathways between various areas which are crucial for successful completion of various cognitive operations.

It is important that the researcher in this field identifies the appropriate domain of cognitive function to investigate. While “cognition enhancement” is an acceptable generic term, as is “health promoting,” both science and regulators require more specific targets, which respect the independence of different domains when considering specific claims. For example, why in medicine would a drug which helped pulmonary function be expected to help the liver? This illustrates the limitation of global scores of cognition for nutritional claims, and should guide researchers to seek assessments of specific target domains of function. There are a number of core cognitive domains which can be evaluated, including attention, information processing, reason-ing, memory, motor control, problem solving, and executive function. Taking memory as an example, there are four major types: episodic or declarative memory, working memory, semantic memory, and procedural memory (see Budson and Price, 2005). As Budson and Price illustrate, relatively few conditions are associated with impairments to semantic memory and procedural memory, while working and episodic memory are impaired in a wide variety of neurological, psychiatric, surgical, and medical conditions. This creates a rationale for directing testing toward working and episodic memory as a more fruitful potential area to evaluate in novel conditions, and most test systems recognize this approach. Further, tests specific to particular domains are, when available, ideal, as this helps to facilitate the substantiation of any claims made on the basis of the research findings. The most specific tests are attentional tests, as well-designed tests of attention do not require aspects of memory or reasoning for task performance, and thus changes in performance can be relatively clearly attributable to effects on attentional processes. As attention is important for the performance of any task, when seeking to evaluate other domains, it is useful to also assess attention additionally in order that the relative contribution to any effects of changes to attention can be established. Most well-established test batteries include assessments of atten-tion, working and episodic memory, motor control, and aspects of executive function.

The automation of cognitive tests brings numerous advantages (e.g., Wesnes et al., 1999); the most relevant to the area of cognition enhancement is improving the signal-to-noise ratio. Noise, i.e., unwanted variability, is decreased by the standardization such testing can bring to test administration and the reduction of errors in scoring. However, the signal can also be increased due to the extra precision in assessment which millisecond resolution of response times can bring. Furthermore, aspects of cognitive function can be assessed, which cannot be measured using traditional pencil and paper measures. Major tests of attention such as simple and choice reaction time have always been automated, as have intensive vigilance tests like the continuous performance test and digit vigilance tasks. Further, computerized tests of verbal and object recognition permit, besides the assessment of the accuracy of recognition, the time actually taken to successfully retrieve the information from memory. This important aspect of memory has been overlooked by traditional tests which cannot make this assessment, but this aspect of memory declines markedly and independently of accuracy with normal aging, and is severely compromised in many debilitating diseases such as dementia (e.g., Simpson et al., 1991; Nicholl et al., 1994; Wesnes et al., 2002). Further in MCI, such slowed speed of retrieval of information is an early characteristic of the disease (Nicholl et al., 1995), which also can respond to pharmacological treatment (Newhouse et al., 2012). Automation also provides the same benefits for tests of the ability to retain information in working memory, as the role of working memory is to facilitate the performance of ongoing tasks; and clearly it is not just the ability to correctly retain the information that is important but also the time taken to decide correctly retrieve this information, something which cannot be assessed with traditional tests such as digit span. A further important benefit of assessing speed is that it permits “speed-accuracy trade-offs” to be identified, which helps to avoid misinterpretations of study findings.

Our understanding of cognition enhancement is at an early stage, and there are few, if any, established criteria. For a compound to be established as an enhancer of one or more aspects of cognitive function, the following criteria have been recently pro-posed (Wesnes, 2010).

Improvements must be identified by well recognized and extensively vali-dated tests of cognitive function.Improvements should be to one or more major domains of cognitive function.Improvements must be seen on core measures of task performance, and any suggestions of speed-accuracy trade-offs should be interpreted with caution.Improvements in one cognitive domain should not occur at the cost to another.Improvements should not be followed by rebound declines.Improvements should be of magnitudes which are behaviorally and clinically relevant.Improvements should not be subject to tachyphylaxis over the period for which the treatment is intended to be used.Self-ratings are of interest, and may be used as supportive evidence, but are not sufficient in the absence of objective test results.

COGNITIVE FUNCTION AND NORMAL AGING

There is much debate about the declines in the quality of mental functioning which accompany aging. A traditional approach has been to compare young adults (e.g., 18–25 years) to the elderly (e.g., 65–80 years), and much research has shown that a variety of aspects of cognitive functioning are poorer in the elderly. One consistent criticism of this approach is that the elderly group grew up in a different era, which may have limited their subsequent abilities (for example, due to socioeconomic fac-tors such as more limited educational abilities and poorer nutrition), and thus the differences may not simply have been due to aging. A research group based at the University of Virginia, the United States, led by Timothy Salthouse, has comprehensively investigated this area over the past few decades. The outcomes of this research program have been recently summarized (Salthouse, 2010). The approach of Salthouse and colleagues has been to assess thousands of healthy individuals across the age range on a variety of traditional neuropsychological tests and to evaluate the pattern of change by decade from early adulthood until the 1980s. The consistent finding has been for linear declines to be present in a range of measures of attention, information processing, reasoning, and various aspects of memory from the twenties onward. Using a variety of analytic techniques, the groups have established that despite com-mon assumptions to the contrary, age-related declines in measures of cognitive functioning are relatively large, begin in early adulthood, are evident in several different types of cognitive abilities, and are not always accompanied by increases in between-person variability. This pattern has also been identified over the same age range using computerized tests of cognitive function, showing linear declines in 5 year cohorts to the speed and accuracy of various aspects of attention, working and episodic memory (Wesnes and Ward, 2000; Wesnes, 2003, 2006).

As can be seen, the declines are linear across the age range, starting in the late 1920s, which is entirely consistent with the work of Salthouse. Further, a decline of one standard deviation can be seen by early middle age, and by at least another by the 1960s. An important aspect of the latter findings is that the individuals tested had participated in clinical trials as healthy volunteers, and had thus undergone extensive medical screening. These individuals were thus free of major medical or psychiatric conditions, and such declines actually represent a best case for normal aging. The same tests have been administered to patients with a variety of conditions including hypertension, heart disease, fibromyalgia, ADHD, epilepsy, narcolepsy, chronic fatigue syndrome, schizophrenia, and multiple sclerosis. When each of these populations is compared to age-matched healthy controls, cognitive deficits of one or more standard deviations are seen on, for example, the ability to focus attention (Wesnes, 2006). This body of research therefore indicates that major aspects of cognitive function decline with normal aging, and that a variety of mental and physical illnesses will further exacerbate this deterioration.

In recognition of cognitive declines in normal aging, the U.S. National Institute of Mental Health (NIMH) set up a working group in 1986 to agree criteria for the condition of age-associated memory impairment (AAMI) (Crook et al., 1986). The aims of the criteria were to identify those elderly individuals (50 years and older) who were aware of memory loss that had occurred gradually, who scored at least one standard deviation below the normal score for that of the young on a widely recognized test of memory (e.g., the Benton visual retention test; the logical memory subtest of the Wechsler memory scale, etc.), who showed evidence of adequate intellectual functioning (using the vocabulary subtest of the Wechsler adult intelligence scale), and who showed no evidence of dementia (as assessed by a mini-mental status examination score of 24 or above). The exclusion criteria were designed to exclude those whose poor performance was not due to normal aging, for example, being secondary to disease or actually being dementia. A number of clinical trials subsequently evaluated the effect of various pharmacological and herbal treatments for AAMI, with some limited success (for review, see Wesnes and Ward, 2000). The Fourth Edition of the Diagnostic and Statistical Manual of Mental Disorders of the American Psychiatric Association (DSM-IV) identified age-associated cognitive decline (AACD) as a condition which may be a focus of clinical attention (diagnostic code 780.9). The advantage of AACD is that it extended the range of impairments from simply memory to cognitive functioning in general, thus encompassing attention, information processing, and a range of other aspects now known to deteriorate with aging. The definition was for a “decline in cognitive functioning consequent to the aging process that is within normal limits given the person’s age. Individuals with this condition may report problems remembering names or appointments or may experience difficulty in solving complex problems. This category should be considered only after it has been determined that the cognitive impairment is not attributable to a specific mental disorder or neurological complaint”.

However, regulatory bodies have not accepted AAMI, AACD, or other similar conditions as legitimate conditions for drug registration, and much of the focus of drug development in the past decade has moved to the condition of MCI (Petersen and Morris, 2005). However, the criteria for an individual to be classified for this

Natural Substances as Treatments for Age-Related Cognitive Declines

condition is to be 1.5 standard deviations poorer than age-matched controls on a recognized test of memory, which limits the condition to less than 10% of the population, which thus has no relevance for the majority of the population who are experiencing age-related cognitive decline. Further, despite some very large clinical trials of potential treatments for MCI, only occasional findings of enhancements have been identified in the condition (e.g., Newhouse et al., 2012). Part of the problem was that the endpoint of many trials was the rate of conversion to Alzheimer’s disease, which required long-term trials with large samples of patients, and the fact that in many trials the expected rate of conversion did not occur in the placebo-treated groups.

Amphetamine was the first synthetic compound shown to improve human cognitive function (e.g., Mackworth, 1965), and both caffeine and nicotine are widely recognized to improve aspects of cognitive function such as attention, even in non-habitual users (e.g., Wesnes and Warburton, 1984; Haskell et al., 2005). There is a commonly held opinion that normal individuals, especially the young, are operating at optimum levels and cannot be enhanced. However, there exist hundreds of studies that demonstrate acute improvements to attention and memory in healthy student populations with a wide variety of substances such as oxygen (Moss et al., 1998), chewing gum (Wilkinson et al., 2000), nicotine (Wesnes and Warburton, 1984), caffeine (Haskell et al., 2005), gingko biloba (Kennedy et al., 2000), amphetamine, and methamphetamine (Silber et al., 2006).

The reticence of regulatory bodies to accept age-related cognitive decline as a suitable condition for treatment is obviously at odds with current general medical practice which seeks to treat a huge variety of other age-related conditions, ranging from failing hearing and eyesight to hip replacement. In the absence of regulatory acceptance or any consistent pressure from advocate groups, individuals the world over are left alone to seek to attempt to preserve their cognitive abilities as they age through a variety of techniques including physical and mental exercise (e.g., brain training), as well as by taking “smart drugs.”

An alternative approach available to individuals who wish to minimize age-related declines in mental efficiency is to seek various natural and nutritional sub-stances which can be obtained “over the counter.” Certainly, there has been a large research effort over recent decades to evaluate the effects of natural therapies upon cognitive functioning. While many naturally occurring plant extracts are commonly misconstrued to be “safe,” the use in Eastern cultures over millennia of substances such as ginkgo biloba and ginseng has identified the general absence of side effects of such products. Ginkgo biloba, e.g., has been the subject of enduring worldwide research interest for the past four decades, and a large and generally consistent body of research identifying positive effects on cognitive function has been identified by various research groups in healthy young and elderly volunteers (e.g., D’Angelo et al., 1986; Brautigam et al., 1998; Kennedy et al., 2000) and mildly cognitively impaired elderly patients (e.g., Wesnes et al., 1987; Rai et al., 1991; Kleijnen and Knipschild, 1992). While large well-controlled trials have shown the ability of ginkgo to treat the cognitive deficits in patients with Alzheimer’s disease and other dementias (e.g., LeBars et al., 1997; Napryeyenko and Borzenko, 2007), a very large trial has shown that the compound is not able to prevent the development of dementia (DeKoskey et al., 2008); though it has to be acknowledged that none of the registered treatments for the disease have been demonstrated to do this either. A follow-up publication on the DeKoskey study, however, showed that gingko did not prevent the rates of cognitive decline over a median 6 year period in individuals aged 72–96 years (Snitz et al., 2009). On balance, while ginkgo clearly does not prevent cognitive decline in elderly individuals, or prevent the onset of dementia, it does appear to have beneficial cognitive effects on younger populations, and also patients with dementia.

Other research programmes have evaluated the effects of a combination of standardized extracts of Ginkgo biloba and Panax ginseng, showing improvements to working and episodic memory with acute doses in volunteers (Kennedy et al., 2001, 2002), patients with neurasthenia (Wesnes et al., 1997), and middle-aged volunteers (Wesnes et al., 2000). In each of these four studies, statistically reliable improvements were seen to the ability to successfully hold and retrieve information in short-term (working) and long-term (episodic) memory. There were no improvements to attention, or to the speed with which the information could be retrieved from memory. In the Wesnes et al. (2000) study, 256 healthy volunteers with a mean age of 56 years (range 38–66) were tested in a 14 week randomized placebo-controlled double-blind study, and over the period of the study, an overall improvement in the ability to store and retrieve information in memory of 7.5% was identified. A subsequent analysis of these data showed that the magnitude of the improvement was sufficient to counteract the decline that would have occurred in the population compared to a younger population of 18–25 years. This is evidence that age-related cognitive declines can be reversed by natural substances, which can be purchased over the counter in pharmacies, and offers individuals the chance to self-medicate with relatively safe substances to maintain cognitive function into late middle age.

In addition to the above research, a wide range of other natural substances have been found to improve cognitive function in the young and elderly, including caffeine (e.g., Smit and Rogers, 2000; Haskell et al., 2005; Smith et al., 2005), pyro-glutamic acid (Grioli et al., 1990), phosphatidylserine (Crook et al., 1991), guanfacine (McEntee et al., 1991), huperzine (Wang, 1994; Zangara et al., 2003), ginseng + vitamins (Neri et al., 1995; Wesnes et al., 2003), Panax ginseng (Kennedy et al., 2001b; 2007; Sunram-Lea et al., 2004), acetyl-L-carnitine (Salvioli and Neri M, 1994; Thal et al., 1995), Bacopa monniera (Maher et al., 2002; Stough et al., 2008), sage (Tildesley et al., 2003; 2005; Scholey et al., 2008), Melissa officinalis (Kennedy et al., 2002; 2003), alpha lipoic acid (Hager et al., 2001), guarana (Kennedy et al., 2004), essential oils and aromas (Moss et al., 2003, 2008), pycnogenol (Ryan et al., 2008), and thiamine (Haskell et al., 2008). Benefits have also been identified with breakfast cereals (Wesnes et al., 2003; Ingwersen et al., 2007), energy drinks (e.g., Scholey and Kennedy, 2004), and chewing gum (Wilkinson et al., 2002).

The level of evidence required in this field should not differ from any other field of clinical research. Therefore, randomized, double-blind, placebo-controlled trials must be employed, and cognitive test systems utilized, which are fit-for-purpose for the requirement of detecting enhancements to various aspects of cognitive function. Only properly characterized substances should be tested, and standardized extracts

are clearly essential to allow replication in different laboratories. Safety is of crucial concern; only substances which have an established safety profile should be evaluated, and safety should be carefully monitored in any clinical trial in this field. One large well-conducted study has just been accepted for publication, which satisfies these various requirements. The trial evaluated the effects of docosahexaenoic acid (DHA) on cognitive function in 485 elderly people who fulfilled the DSM-IV criteria described earlier for AACD (Yurko-Mauroa et al., 2010). Six months of supplementation was found to produce statistically reliable improvements to memory. Though the effect size of the improvement was small (0.19), as with the Wesnes et al. (2000) trial, the computerized cognitive assessment system used in the study had a normative data-base; and using this database the authors were able to identify that the effect reflected a 7 year reduction in normal aging (3.4 years when compared to placebo), which may well be attractive to the population studied (mean age 70 years). An important aspect of this study was the careful monitoring of safety, the adverse events not being different between the placebo and active treated groups. Besides being conducted to the rigorous standards required in this field and carefully monitoring safety, an important aspect of the study for future research was the presentation of effect sizes as well as an assessment of the potential “cognitive age-reducing” effect of treatment.

Metabolic Agents and Cognitive Function

There are a number of agents which are believed to impact on metabolic functions which may ultimately impact on neuronal cell survival and cognitive function. Aging is characterized by a progressive deterioration in physiological functions and metabolic processes. Regimes that buffer intracellular energy levels may impede the progression of the neurodegenerative process. This post focuses on some of the metabolic agents that may prove to be effective in combating neurodegeneration and lead to better cognitive aging through the life span. The metabolic agents specifically focused on in this post are glucose and oxygen, pyruvate, creatine, and L-carnitine. Each of these agents is directly responsible for generating adenosine triphosphate (ATP), the molecular unit of currency of intracellular energy transfer. Their roles as cognitive agents are explored.

During normal aging neuronal cell injury and death are accelerated and lead to region-specific brain shrinkage. In brain regions particularly important for the formation of memories and decision making, for example, the hippocampus and the prefrontal white matter, shrinkage increases with age (Raz et al. 2005). Reduction in the total number of viable cells may lead to an accelerated decline in brain functioning. A likely cause of reduced neuronal cell number is impaired energy metabolism. Impeded energy metabolism may trigger pro-apoptotic signaling (programmed cell death), oxidative damage, and excitotoxicity and impede mitochondrial DNA repair (Klein and Ferrante 2007). These processes can interact and potentiate one another, which in turn results in a continuation of energy depletion. Reduced energy levels threaten cellular homeostasis and integrity. The brain is the most metabolically active organ in the body and as such is particularly vulnerable to disruption of energy resources. In addition, because of the high levels of oxygen metabolism in brain tissue, mitochondria are highly susceptible to oxidative stress (Chinnery et al. 2006). Therefore interventions that improve mitochondrial function by sustaining ATP levels may have direct and indirect importance for improving neuronal dysfunction and loss.

Regimes that buffer intracellular energy levels may significantly impede the progression of neurodegenerative diseases and disorders. Metabolic function may be improved in a variety of ways, either by improving the availability of substrates necessary for energy production or by improving the transport and effectiveness of cells involved in the metabolic process, for example, by improving mitochondrial transport and respiration. Many of these agents have multifaceted mechanisms of action and may lead to numerous cascades of biological events. It is beyond the scope of this post to review all of the possible nutritional contributors to optimal metabolic function; therefore, this post will focus on the agents which have a central action of improving availability of energy. These agents are listed next as “energy enhancers.”

The principal source of energy for brain function is derived from the oxidative breakdown of glucose. The human brain is an extremely metabolically active organ accounting for approximately 30% of the total basal energy expenditure. The brain remains metabolically active at all times, including sleep, and is thus entirely dependent on continuous and uninterrupted supply of energy in the form of the substrates glucose and oxygen. Compared to other organs in the body, the brain is particularly vulnerable to small and transient changes in its energy supply. Interrupted delivery leads within seconds to unconsciousness and within minutes may cause irreparable brain damage. Thus, the concentration of glucose in the blood plasma is tightly regulated to stay within the normal range of 60–90 mg/100 mL for humans. When blood glucose drops below 40 mg/100 mL (hypoglycemic condition) in humans, it can cause discomfort, confusion, coma, convulsions, or even death (Lehninger et al. 2005). Beyond infancy, and under normal conditions, the brain’s energy requirements are met almost exclusively by the oxidative breakdown of glucose. During times of hypoglycemia other tissues will cease to utilize glucose all together in order to increase glucose availability to the brain (Thomson 1967). Compared with other organs the brain possesses paradoxically limited stores of glycogen, which without replenishment are exhausted in up to 10 min. There is, however, no storage capacity for oxygen; thus, disruption leads to instantaneous effects. Associated measurements of oxygen and glucose levels in blood sampled upon entering and leaving the brain in humans show that almost all the oxygen utilized by the brain can be accounted for by the oxidative metabolism of glucose (McIlwain 1959). Since the brain is clearly susceptible to small changes in energy supply, metabolic activity is limited by glucose and oxygen resources.

A few early studies demonstrated the effects of glucose on cognition around the 1950s. For example, administration of 10 g of glucose to school children every 45 min throughout a morning demonstrated improved mathematical ability and generally improved concentration (Hafermann 1955). However, a more widespread interest in glucose did not occur until the 1980s when the glucose effect was reevaluated by psychopharmacologists examining possible mechanisms of action for neuroendocrine facilitation of memory. Since then there have been increasing reports that cognitive functioning is influenced by the increased availability of glucose provision. Many reports have illustrated the robust association between changes in blood glucose levels and cognition in animals (Gold 1986; Wenk 1989; White 1991), the elderly (Gonder-Frederick et al. 1987; Craft et al. 1992, 1994), and the young (Benton and Sargent 1992; Benton and Owens 1993; Sünram-Lea et al. 2001, 2002a,b, 2004; Riby et al. 2008; Scholey et al. 2009). Thus, the cognition-enhancing action of glucose is well established. In terms of dosing the most optimal glucose dose for cognitive enhancement generally appears to follow the classic Yerkes–Dodson inverted-U dose–response profile (Sunram-Lea et al. 2011). For young adults 25 g seems to most reliably facilitate cognitive performance; however, there is some contention regarding whether the dose–response profile may be dependent upon the cognitive domain being assessed. In rats bimodal response variability was observed when different tasks were used which represented the action of glucose on two different brain substrates: the caudate nucleus and the hippocampus (Packard and White 1990). In humans the inverted-U dose–response profile has been specifically observed for tasks of verbal declarative memory, where other tasks (specifically spatial and numeric working memory) demonstrated slightly different response profiles (cubic and quartic respectively) (Sunram-Lea et al. 2011).

The clearest enhancement effects of increased glucose supply have been observed for declarative memory tasks in the form of word and paragraph recall; for a review see Hoyland et al. (2008). These findings have led to the notion that glucose facilitation may be particularly pronounced in tasks which pertain to the hippocampal formation (Sünram-Lea et al. 2001). Furthermore several studies have shown that an important mediating factor for cognitive enhancement by increased energy resources is level of task demands. That is, tasks which are more demanding appear to be more sensitive to the effect of glucose (Kennedy and Scholey 2000; Scholey et al. 2001; Sünram-Lea et al. 2002a). It has also been demonstrated that tasks which are more demanding lead to a significantly accelerated reduction in blood glucose levels compared with a semantically matched task (Scholey et al. 2001). However recent research has shown that at high dosages (60 g) implicit memory which is not regarded as either demanding nor hippocampally mediated may also be enhanced by glucose (Owen et al. 2010), adding further support to the notion that different domains of memory may follow different glucose dose–response profiles.

It is widely acknowledged that oxygen restriction and ischemic deprivation exert marked effects on cognitive function (Volpe and Hirst 1983). Furthermore restriction of oxygen supply due to altitude results in cognitive impairment on a number of cognitive parameters with these effects being instantaneously reversed by the administration of oxygen (Crowley et al. 1992). Evidence suggests that even small fluctuations in cerebral oxygen delivery within normal physiological limits may impact on cognitive performance (Walker and Sandman 1979). While cognitive deficits from oxygen restriction due to altitude (Crowley et al. 1992), carbon monoxide poisoning (Weaver et al. 2002), and isovolemic anemia (Weiskopf et al. 2002) can all be reversed by oxygen administration, impairment effects may be permanent if treatment is not administered in time. Similarly cognitive degeneration due to age is not reversed by oxygen treatment when administered either normobaric or hypobaric oxygen treatment (Raskin et al. 1978). There is very limited research of oxygen administration on cognition in normal healthy individuals. Early research examining the effects of hyperbaric oxygen supplementation demonstrated improved cognitive function (short-term memory and visual organization) in elderly outpatients com-pared to baseline performance. However this study failed to compare with a control group (Edwards and Hart 1974).

In normal healthy humans research has demonstrated that oxygen administra-tion can improve cognitive functioning compared to air-breathing control conditions. Research has shown that oxygen administration leads to improved long-term memory and reaction times compared to a control group of normal air-breathing (Moss and Scholey 1996; Moss et al. 1998; Scholey et al. 1998). Furthermore, similar to glucose facilitation, oxygen administration appears to facilitate cognition most effectively for tasks with a higher cognitive load (Moss et al. 1998; Scholey et al. 1998). In addition to this finding a further study also examined heart rate during cognitive testing with oxygen versus air-breathing controls. Compared to baseline, heart rate was significantly elevated during cognitive testing tasks in both the air and oxygen groups. In the oxygen group, significant correlations were found between changes in oxygen saturation and cognitive performance. In the air group, greater changes in heart rate were associated with improved cognitive performance (Scholey et al. 1999). These findings suggest that during times of cognitive demand avail-ability of metabolic resources impact on cognitive functioning.

A more recent study has further demonstrated the importance of metabolic resources during cognitive demand by manipulating level of cognitive demand during oxygen administration. In this study oxygen administration of 40% versus 21% was examined during completion of an addition task with three levels of difficulty. It was observed that 40% oxygen improved accuracy scores across the task compared to the 21% oxygen dose, with the difference in accuracy rate increasing between the two dosages as the task difficulty level increased (Chung et al. 2008). While cognitive demand is clearly a moderating factor for cognitive enhancement by oxygen, enhancement has been observed on several cognitive domains; for example, oxygen supplementation has been shown to improve everyday memory tasks such as memory for shopping lists and putting names to faces when participants received 100% oxygen compared with air-breathing controls (Winder and Borrill 1998). The dose–response for oxygen administration on performance appears to follow the Yerkes–Dodson inverted-U shape in a similar fashion to glucose facilitation with shorter doses of 30 s to 3 min appearing to be most beneficial while continuous oxygen breathing for longer than 10 min leading to decline in performance (Moss et al. 1998). The window for cognitive improvement through oxygen administration therefore appears to be quite brief, with research demonstrating that administration of oxygen increases blood oxygen levels for only 4–5 min (Moss et al. 1998).

Neuronal cell death resulting from hypoglycemia and hypoxia is the result of a series of events triggered by reduced energy availability, and the normalization of blood glucose and oxygen levels does not necessarily block or reverse this cell death process once it has begun. During times of low availability of glucose and oxygen the brain utilizes other, less efficient energy sources that can be produced aerobically. Pyruvate is the end product of glycolysis, which is converted into acetyl coenzyme A that enters the Krebs cycle when there is sufficient oxygen available. When the oxygen is insufficient, pyruvate is broken down anaerobically, creating lactate in humans and animals. Lactate has recently been considered as a central neuroprotective agent (Gladden 2004). The blood-brain barrier normally transports pyruvate at a rate much slower than glucose, but prior work suggests that significant pyruvate entry to the brain can be achieved by elevating plasma pyruvate concentrations (Lee et al. 2001).

During pathological insult or general aging, the main upstream event most responsible for neuronal cell death is excitotoxicity from glutamate receptor activity (Wieloch 1985). Recent research has shown that cells that would otherwise go on to die after the cascade of excitotoxic activity could be rescued by providing pyruvate (Ying et al. 2002).

However, there is remarkably little research evaluating the effects of pyruvate on cognitive function. One recent study assessed the effect of pyruvate administration in rats with hypoglycemia-induced brain injury. Insulin was used to induce hypoglycemia then hypoglycemia was terminated with either glucose alone or with glucose plus pyruvate. They found that in the four brain regions studied (CA1, subiculum, dentate gyrus of the hippocampus, and piriform cortex) the addition of pyruvate reduced neuron death by 70%–90%. Neuron survival was also observed when pyruvate delivery was delayed for up to 3 h. The improved neuron survival was accompanied by a sustained improvement in cognitive function as assessed by the Morris water maze (Suh et al. 2005).

Furthermore recent animal research has demonstrated the potential usefulness of ethyl pyruvate as a stroke therapy. Yu et al. (2005) found that ethyl pyruvate affords the strong protection of delayed cerebral ischemic injury with significant reduction in infarct volume accompanied by the suppression of the clinical manifestations associated with cerebral ischemia, including motor impairment and neurological deficits.

There are, as yet, no studies evaluating the effects of pyruvate administration on cognitive function in humans; however, pyruvate may be a good candidate for further research in those with energetic depletion and neurodegenerative diseases. Impaired energy metabolism is an early, predominant feature in Alzheimer’s disease and it is believed that impaired cerebral oxidative glucose metabolism is responsible, at least in part, for cognitive impairment in AD. Research has demonstrated that in both animals and humans increased cerebrospinal pyruvate is a biomarker for AD (Parnetti et al. 1995; Pugliese et al. 2005). Since pyruvate appears to be quite safe, aside from mild side effects, such as occasional stomach upset and diarrhea, pyruvate therapy might represent an excellent candidate for therapy in disease states accompanied by energy depletion.

Creatine (Cr) is a naturally occurring substance found in vertebrates and is essential for maintaining energy homeostasis. Cr participates in metabolic reactions within cells and eventually is catabolized in the muscles creating creatinine, which is then excreted by the kidney in urine. In the average-sized adult (70 kg) Cr store is approximately 120 g, with the daily turnover of Cr to creatinine being estimated to be about 1.6% of the body’s total Cr (Balsom et al. 1995). The daily requirement of Cr either through diet or endogenous synthesis is suggested to be approximately 2 g/day (Walker 1979).

Since Cr is concentrated in muscle tissue dietary sources of Cr are fish and red meat, with a much lower concentration found in some plants (Balsom et al. 1995). Unsurprisingly Cr levels of vegetarian or vegan individuals are much lower than omnivores. In a typical omnivorous diet between 0.25 and 1 g of Cr per day is obtained. It appears that Cr derived from the diet, after passing through the intestinal lumen, enters the bloodstream intact (Conway and Clark 1996).

Cr is stored in the high-energy form of phosphocreatine (PCr). PCr acts as a high-energy reserve in a coupled reaction in which energy derived from donating a phosphate group is used to regenerate the compound ATP. PCr plays a particularly

important role in tissues that have high, fluctuating energy demands such as muscle and brain. During times of brain activity, brain phospocreatine decrease rapidly in order to maintain constant ATP levels (Sappey-Marinier et al. 1992; Rango et al. 1997). Cr supplementation has pronounced effects on the body including increased muscle mass and improvements in physical performance on exercise tasks (Kreider 2003). Furthermore Cr supplementation can increase brain Cr. Studies using nuclear magnetic resonance spectroscopy have demonstrated that Cr and PCr can be increased in the brains of healthy adults by Cr supplementation (Dechent et al. 1999; Lyoo et al. 2003).

The majority of previous research examining the effects of Cr has focused on muscle mass, body mass index, and physical performance; however, more recently attention has been directed toward Cr’s effects on the brain and the metabolic changes therein.

Animal research has shown that Cr is particularly important for normal brain development and function. In its absence deleterious effects on cognition and brain development are observed, in abundance evidence for neuroprotection has been observed. For example, deletion of cytosolic brain-type creatine kinase in mice has been shown to result in slower learning of a spatial task and diminished open-field habituation as well as increased intra- and infra-pyramidal hippocampal mossy fiber area suggesting that the creatine–creatine kinase network is involved in brain plasticity in addition to metabolism (Jost et al. 2002).

Animal research has demonstrated that Cr affords significant neuroprotection against ischemic and oxidative insults (Holtzman et al. 1998; Wilken et al. 1998; Balestrino et al. 1999). One experiment investigated the possible effect of Cr dietary supplementation on brain tissue damage after experimental traumatic brain injury. Results demonstrated that chronic administration of Cr ameliorated the extent of cortical damage by as much as 36% in mice and 50% in rats. The authors suggested that protection is mediated by Cr-induced maintenance of mitochondrial bioenergetics as they observed that mitochondrial membrane potential was significantly increased, intra-mitochondrial levels of reactive oxygen species and calcium were significantly decreased, and ATP levels were maintained. Induction of mitochondrial permeability transition was significantly inhibited in animals fed Cr. The authors further suggested that Cr may be a good candidate as a neuroprotective agent against acute and delayed neurodegenerative processes (Sullivan et al. 2000).

In rodents where neurodegenerative symptoms are induced, Cr attenuated these deficits, for example, rats administered 3-nitropropionic acid (3NP) displayed neuropathological and behavioral abnormalities that are analogous to those observed in Huntington’s disease (HD). Rats fed diets containing 1% Cr over an 8 week period showed attenuation of 3NP-induced striatal lesions, striatal atrophy, ventricular enlargement, cognitive deficits, and motor abnormalities on a balance beam task compared to non-Cr supplemented rats. These findings indicate that Cr provides significant protection against neuropathological insult specifically associated with 3NP-induced behavioral and neuropathological abnormalities (Shear et al. 2000).

Clearly Cr plays a fundamental role in brain protection and development in the ani-mal model. Specifically deleterious effects were observed on cognition and brain development when Cr is absent (and/or PCr and creatine kinases), and the neuroprotective attributes of Cr in supplemented animals. These data provide a strong rationale for examination of Cr supplementation on the brain and cognition in the human model.

Despite the obvious impact Cr has on brain development and metabolic actions in the brain, there are relatively few studies assessing the effects of Cr on cognitive performance in humans. One study assessed the effect of 20 g Cr supplementation over 7 days in sleep-deprived individuals, following 24 h sleep deprivation. Individuals who received Cr supplementation demonstrated significantly reduced decrement in performance on a number of mood, cognitive, and physical performance parameters including random movement generation, choice reaction time, balance, and mood state (McMorris et al. 2006). In a further study following 36 h sleep deprivation, Cr-supplemented individuals also demonstrated improved performance on a random number generation task (McMorris et al. 2007b). These studies appear to demonstrate benefits of Cr supplementation in young individuals who are temporarily cognitively impaired through sleep deprivation. However, these studies were considerably underpowered having no higher than 10 participants per group. Cr supplementation has also been demonstrated to improve cognition in individuals who are not cognitively impaired. One study assessed the effects of 8 g Cr per day for 5 days in healthy individuals and demonstrated reduced mental fatigue when subjects repeatedly perform a simple mathematical calculation. After Cr supplementation, task-evoked increase of cerebral oxygenated hemoglobin in the brains of subjects and reduced cerebral oxygenated hemoglobin (measured by near-infrared spectroscopy) was significantly reduced, which is compatible with increased oxygen utilization in the brain (Watanabe et al. 2002). Again, however, this study appeared to be rather underpowered with only 12 participants per group. Nonetheless, it appears that Cr supplementation may impact on cognitive function even over a relatively short period of time as these studies assessed the effects of acute supplementation over periods of 5–7 days. A more recent study assessed the impact of a new form of creatine, creatine ethyl ester, over a 2 week period (5 g/day dose compared to dextrose control group) in healthy 18–24 year old participants. The overall findings demonstrated consistent improvements for reaction time across a range of measures as well as improved accuracy on some and also improved IQ scores. The most mod-est improvements appeared to be on tasks that were less demanding, indicating that creatine supplementation may be particularly useful when performing particularly demanding or complex cognitive tasks (Ling et al. 2009).

In chronic administration conditions, one study examining Cr supplementation in young healthy adults failed to observe any effect of Cr on cognitive performance (Rawson et al. 2008). In this study 0.03 g/kg was administered daily for 6 weeks and a battery of neurocognitive tests was administered to asses cognitive processing and psychomotor performance including simple reaction time, code substitution, code substitution delayed, logical reasoning symbolic, mathematical processing, running memory, and Sternberg memory recall. No effect of Cr was observed on any of these outcome measures.

However, research examining young adults who only produce Cr endogenously (vegetarian sample), Cr supplementation was shown to improve cognitive performance following chronic administration (6 week period) (Rae et al. 2003). In this work, 5 g Cr supplementation (Cr monohydrate) was administered per day for 6 weeks

to 45 young vegetarian adults in a counterbalanced cross-over design. They observed that Cr supplementation had a significant positive effect on both working memory (backward digit span) and intelligence (Raven’s Advanced Progressive Matrices).

The pattern emerging from the present literature examining Cr and cognitive function appears to demonstrate that cognition is ameliorated specifically during times of metabolic impairment or depletion, either through low creatine availability (vegan and vegetarian samples) or by inducement (sleep deprivation or high cognitive demand). Furthermore, since there is some evidence that creatine supplementation improves cognitive function in young, non-vegetarian, healthy individuals over shorter periods of administration (5 days to 2 weeks) but not longer periods (6 weeks) it may be the case that creatine supplementation might merely have been redressing nutritional imbalances.

Since elderly populations are generally metabolically impaired and often nutritionally deficient, it seems likely that elderly and degenerative populations would most benefit from creatine interventions over time. To our knowledge only one study has assessed the impact of Cr supplementation in an elderly human population. McMorris et al. (2007a) administered 20 g of Cr per day for 7 days which resulted in improved performance of random number generation, forward and backward number and spatial recall, and long-term memory tasks but no effect on backward recall performance (McMorris et al. 2007b). In terms of neurodegeneration, there has been no research examining the effects of creatine supplementation in dementia sufferers; however, there appears to be some differences in creatine levels in those with genetic risk of developing dementia (apolipoprotein E4 carriers). Laakso et al. (2003) demonstrated that compared with the noncarriers, the levels of creatine were significantly lower in the E4 carriers. This finding may suggest increased metabolic demands in the brain of the E4 carriers. They also observed that the levels of creatine also correlated significantly with age and performance on the Mini-Mental State Examination test in the E4 carriers, but not in the noncarriers (Laakso et al. 2003). Creatine supplementation in this sample seems like a logical next step for creatine and cognitive function research.

Despite the obvious potential benefits of Cr supplementation, there is considerable lack of research examining the cognitively enhancing capabilities of Cr and a number of questions remain to be answered. Firstly there has been no research examining whether an acute administration of one single dose of Cr can affect cognitive performance. Secondly the only study to examine the effects of Cr on cognition in the elderly was only over a period of 7 days. Further to this there has been no examination of the usefulness of creatine in dementia research where there appears to be some evidence that creatine may be of particular therapeutic value. Since the evidence seems to suggest that Cr acts to buffer intracellular energy levels and potentially impede the progression of neurodegenerative processes a more systematic evaluation of Cr mapping cognitive performance over a more substantial timeframe is required.

CARNITINE/ACETYL-L-CARNITINE

In animals and humans, carnitine is biosynthesized primarily in the liver and kid-neys from the amino acids lysine or methionine (Steiber et al. 2004) with Vitamin C (ascorbic acid) being essential to the synthesis of carnitine. In food, the highest concentrations of carnitine are found in red meat and dairy products. Other natural sources of carnitine include nuts and seeds, legumes or pulses, vegetables, and cereals. Carnitine is a quaternary ammonium compound that, in living cells, is required for the transport of fatty acids from the cytosol into the mitochondria during the breakdown of lipids (or fats) for the generation of metabolic energy. Carnitine exists in two stereoisomers: its biologically active form is L-carnitine, while its enantiomer, D-carnitine, is biologically inactive (Liedtke et al. 1982). Carnitine transports long-chain acyl groups from fatty acids into the mitochondrial matrix, so that they can be broken down through 0-oxidation to acetate to obtain usable energy via the citric acid cycle. Under normal nutritional conditions and in healthy persons, L-carnitine availability is not a limiting step in 0-oxidation; however, L-carnitine is required for mitochondrial long-chain fatty acid oxidation (Simon 2005), which is a main source of energy during exercise (Wasserman and Whipp 1975). Furthermore increase in L-carnitine content might increase the rate of fatty acid oxidation, permitting a reduction of glucose utilization, preserving muscle glycogen content, and ensuring maximal rates of oxidative ATP production. In one study L-carnitine improved glucose disposal among 15 patients with type II diabetes and 20 healthy volunteers (Mingrone et al. 1999). Glucose storage increased between both groups and glucose oxidation increased in the diabetic group. Furthermore glucose uptake increased by approximately 8% for both diabetic and non-diabetic groups.

In neuronal cells, the L-carnitine shuttle mediates translocation of the acetyl moiety from mitochondria into the cytosol and contributes to the synthesis of acetylcholine and of acetylcarnitine (Imperato et al. 1989; Nalecz and Nalecz). The neurobiological effects of acetyl carnitine include modulation of brain energy and phospholipids metabolism, cellular macromolecules (such as neurotrophic factors and neurohormones), synaptic morphology, and synaptic transmission of multiple neurotransmitters (see review [Furlong 1996]).

The majority of research assessing the effects of L-carnitine or acetyl-L-carnitine (acetylated derivative of L-carnitine with improved bioavailability) has focused on its benefits to elderly and demented populations. It has been established that acetyl L-carnitine transverses the blood brain-barrier efficiently. With CSF concentrations increasing sufficiently via both intravenous and oral rout in patients with severe dementia (Parnetti et al. 1992). In terms of efficacy, a meta-analysis examining the effects of acetyl-L-carnitine in mild cognitive impairment and mild (early) Alzheimer’s disease was conducted (Montgomery et al. 2003). Studies included in the analysis were at least 3 months in duration, with a dosage of 1.5–3.0 g/day. The results showed beneficial effects on both clinical scales and psychometric tests with improvements being observed at the first assessment (3 months) and increasing over time.

In a more recent study, the effects of 2 g of L-carnitine per day for 6 weeks were assessed in centenarians aged between 100 and 106 (Malaguarnera et al. 2007). Those treated with L-carnitine demonstrated significant physiological improvements in fat mass, muscle mass, plasma total carnitine, and plasma long- and short-chain acetylcarnitine. They also showed significantly improved mental fatigue and cognitive function assessed by the Mini-Mental State Examination (MMSE).

There are, as yet, no studies examining the effect of L-carnitine or acetyl-L-carnitine on cognitive function in young human populations. Since the action of L-carnitine avail-ability is not a limiting step in 0-oxidation, any beneficial effects are most likely to be observed in populations with depleted energy resources or under physically fatigued conditions. Therefore, the utility of L-carnitine/acetyl-L-carnitine may be more pronounced in age and degenerative disease.