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.
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