Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2016 Jan 18.
Published in final edited form as: J Int Neuropsychol Soc. 2015 Nov;21(10):841–850. doi: 10.1017/S1355617715000843

Cardiorespiratory fitness attenuates the influence of amyloid on cognition

Stephanie A Schultz 1,2,3, Elizabeth A Boots 1,2,3, Rodrigo P Almeida 1,2,3,4, Jennifer M Oh 1,2,3, Jean Einerson 5, Claudia E Korcarz 5, Dorothy F Edwards 2,3,6, Rebecca L Koscik 3, Maritza N Dowling 7, Catherine L Gallagher 1,2,8, Barbara B Bendlin 1,2,3, Bradley T Christian 2,9, Henrik Zetterberg 10,11, Kaj Blennow 10, Cynthia M Carlsson 1,2, Sanjay Asthana 1,2, Bruce P Hermann 2,3,8, Mark A Sager 2,3, Sterling C Johnson 1,2,3, James H Stein 5, Ozioma C Okonkwo 1,2,3
PMCID: PMC4716656  NIHMSID: NIHMS740912  PMID: 26581795

Abstract

Objective

To examine cross-sectionally whether higher cardiorespiratory fitness (CRF) might favorably modify amyloid-β (Aβ)-related decrements in cognition in a cohort of late-middle-aged adults at risk for Alzheimer's disease (AD).

Methods

Sixty-nine enrollees in the Wisconsin Registry for Alzheimer's Prevention participated in this study. They completed a comprehensive neuropsychological exam, underwent 11C Pittsburgh Compound B (PiB)-PET imaging, and performed a graded treadmill exercise test to volitional exhaustion. Peak oxygen consumption (VO2peak) during the exercise test was used as the index of CRF. Forty-five participants also underwent lumbar puncture for collection of cerebrospinal fluid (CSF) samples, from which Aβ42 was immunoassayed. Covariate-adjusted regression analyses were used to test whether the association between Aβ and cognition was modified by CRF.

Results

There were significant VO2peak*PiB-PET interactions for Immediate Memory (p= .041) and Verbal Learning & Memory (p= .025). There were also significant VO2peak*CSF Aβ42 interactions for Immediate Memory (p<.001) and Verbal Learning & Memory (p <.001). Specifically, in the context of high Aβ burden—i.e., increased PiB-PET binding or reduced CSF Aβ42—individuals with higher CRF exhibited significantly better cognition compared with individuals with lower CRF.

Conclusion

In a late-middle-aged, at-risk cohort, higher CRF is associated with a diminution of Aβ-related effects on cognition. These findings suggest that exercise might play an important role in the prevention of AD.

Keywords: Alzheimer's disease, Physical fitness, Amyloid, Cerebrospinal fluid, Cognition, Neuroimaging

INTRODUCTION

Alzheimer's disease (AD) is the most common cause of dementia and is neuropathologically marked by extracellular amyloid-β (Aβ) deposits (Hyman et al., 2012; Thal, Rub, Orantes, & Braak, 2002). Furthermore, alterations in in vivo measurements of Aβ, such as cerebrospinal fluid (CSF) Aβ42, in cognitively normal (CN) individuals indicates a preclinical stage of AD (Jack et al., 2013; Jack et al., 2010; Sperling et al., 2011). This preclinical stage portends increased risk for prospective cognitive decline and eventual development of AD dementia in initially CN adults (Fagan et al., 2007; Villemagne et al., 2011). Several studies have shown that low CSF Aβ42 levels in CN individuals predict incident cognitive impairment (Gustafson, Skoog, Rosengren, Zetterberg, & Blennow, 2007; Roe et al., 2013; Skoog et al., 2003). Similarly, Resnick and colleagues (2010) found that Aβ deposition, as assessed with 11C Pittsburgh Compound B (PiB)-positron emission tomography (PET), was associated with steeper trajectories of cognitive decline in the years preceding and concurrent to PiB-PET scans in CN older adults. Specifically, these findings were observed in cognitive domains of immediate free recall and executive function. Furthermore, in a study of CN individuals, Morris and colleagues (2009) found an association between the level of Aβ deposition, measured by PiB-PET, and progression to AD dementia.

A growing body of literature suggests that a physically active lifestyle, which conduces to greater cardiorespiratory fitness (CRF), may ameliorate AD-related pathology (Boots et al., 2014; Erickson et al., 2011) and boost cognitive function (Barnes, Yaffe, Satariano, & Tager, 2003; Boots et al., 2014; Lautenschlager et al., 2008; Pizzie et al., 2014; Zhu et al., 2014). Several of these studies (Brown et al., 2013; Head et al., 2012; Liang et al., 2010) have found that individuals who were physically active had significantly lower Aβ deposition compared to inactive individuals. More recently, our group (Okonkwo et al., 2014) assessed whether a physically active lifestyle might favorably alter the adverse influence of age on key biomarkers of AD. By utilizing a comprehensive neuropsychological evaluation and an array of neuroimaging techniques, we found that those who were physically active exhibited an attenuation in age-related changes in Aβ burden, glucose metabolism, hippocampal volume, and Immediate Memory and Visuospatial Ability cognitive test scores (Okonkwo et al., 2014).

Although physical activity has been shown to be associated with both Aβ accumulation and cognition independently, it is yet to be determined whether the deleterious effects of Aβ burden on cognition are modified by CRF. Accordingly, in this study, we investigated whether CRF attenuates Aβ-related alterations in cognition in a cohort of CN late-middle-aged adults.

MATERIALS AND METHODS

Participants

Sixty-nine CN late-middle-aged adults from the Wisconsin Registry for Alzheimer's Prevention (WRAP) cohort participated in this study. WRAP is a longitudinal registry composed of more than 1500 late-middle-aged adults who were between the ages of 40 and 65 at study entry (Sager, Hermann, & La Rue, 2005). The 69 subjects included in the analyses underwent PiB-PET imaging, performed a physician-supervised graded exercise test (GXT), and completed a comprehensive neuropsychological evaluation. A subset (n = 45) also underwent lumbar puncture for CSF collection. The University of Wisconsin Institutional Review board approved all study procedures and each subject provided signed informed consent before participation.

Graded exercise testing

GXT was performed using a modified Balke protocol (Balke & Ware, 1959). Comfortable brisk walking speeds were determined prior to testing as a safety precaution and to ensure a valid test. For participants who were capable of walking at 3.5 miles per hour (mph) comfortably, this speed was used throughout the test. For participants who found this walking speed uncomfortable, a slower speed was chosen. Of the 69 participants included in our analyses, 47 walked at a speed slower than 3.5 mph (29 of these 47 walked at a speed between 3.0 mph and 3.5 mph whereas 18 walked at a speed less than 3.0 mph). The grade of the treadmill was increased by 2.5% every two minutes until the participant reached volitional exhaustion or the examiner stopped the test due to safety concerns. Continuous measurements of oxygen uptake (VO2), carbon dioxide production, minute ventilation, heart rate, and work rate were obtained using a metabolic cart and two-way non-rebreathing valve (TrueOne® 2400 metabolic cart, Parvomedics, Sandy, UT). The system was calibrated prior to each test using standard gases with known concentrations and with a calibrated three-liter syringe. Peak effort was determined based on the American College of Sports Medicine (ACSM) criteria (ACSM, 2014) which require meeting at least two of the following: (1) respiratory exchange ratio ≥ 1.1, (2) change in VO2 < 200 ml with an increase in work, (3) rating of perceived exertion of 17 or greater, and (4) achieving at least 90% of age predicted maximal heart rate. All 69 individuals included in this report met peak effort criteria. Peak oxygen consumption (VO2peak, mL/kg/min) during exercise was used as the index of CRF.

PiB-PET protocol

Details on the acquisition and post-processing of the PiB-PET examinations have been previously described (Johnson et al., 2014; Okonkwo et al., 2014). Briefly, 3-dimensional PiB-PET data were acquired on a Siemens EXACT HR+ scanner (Siemens AG, Erlangen, Germany). Imaging consisted of a 6-minute transmission scan and a 70-minute dynamic scan upon bolus injection. Post-processing was based on an in-house automated pipeline (Floberg et al., 2012). We derived distribution volume ratio (DVR) maps from the PiB images using the Logan method, with a cerebellar gray matter reference (Price et al., 2005).

An anatomical atlas (Tzourio-Mazoyer et al., 2002) was used to extract quantitative DVR data from eight bilateral regions of interest (ROIs) that are sensitive to Aβ accumulation (Rosario et al., 2011). These ROIs were the precuneus, posterior cingulate, orbitofrontal cortex, anterior cingulate, angular gyrus, supramarginal gyrus, middle temporal gyrus, and superior temporal gyrus. The DVR data from the ROIs were combined to form a composite measure of global Aβ load. The time interval between the PET scan and the GXT was 3.10 ± .47 years. GXT was subsequent to the PiB-PET scan for all 69 participants.

CSF assessment

In a subset of individuals (n = 45), lumbar puncture for collection of CSF samples was performed the morning after a 12-hour fast, with a Sprotte 24- or 25-gauge spinal needle at L3/4 or L4/5 using gentle extraction into polypropylene syringes. Each sample consisted of 22 mL of CSF, which was then combined, carefully mixed, and centrifuged at 2000g for 10 minutes. Supernatants were frozen in 0.5 mL aliquots in polypropylene tubes and stored at −80°C. The samples were immunoassayed for Aβ42 using INNOTEST enzyme-linked immunosorbent assays (Fujirebio, Gent, Belgium) by board-certified laboratory technicians who were blind to clinical data and used protocols accredited by the Swedish Board for Accreditation and Conformity Assessment as previously described (Palmqvist et al., 2014). Participants completed the lumbar puncture procedure at the same study visit as the PiB-PET scan.

Neuropsychological assessment

The participants underwent an extensive battery of neuropsychological tests (Sager et al., 2005), which spanned conventional cognitive domains of memory, attention, executive function, language, and visuospatial ability. A previous factor analytic study (Koscik et al., 2014) of these tests within the larger WRAP cohort found that they map onto six cognitive factors. These cognitive factors and their constituent psychometric tests included Immediate Memory: Rey Auditory Verbal Learning Test (RAVLT) learning trials 1 and 2 (Schmidt, 1996); Verbal Learning & Memory: RAVLT learning trials 3- 5 and Delayed Recall (Schmidt, 1996); Working Memory: Digit Span and Letter-Number Sequencing subtests of the Wechsler Adult Intelligence Scale, 3rd edition (Wechsler, 1997); Speed & Flexibility: Stroop Color-Word Test, Interference Trial (Trenerry, Crosson, DeBoe, & Leber, 1989) and Trail-Making Test A and B (Reitan, 1958); Visuospatial Ability: Block Design and Matrix Reasoning subtests from the Wechsler Abbreviated Scale of Intelligence (WASI)(Wechsler, 1999) and Judgment of Line Orientation Test (Benton, 1994); and Verbal Ability: Reading subtest of Wide-Range Achievement Test, 3rd edition (Wilkinson, 1993), Vocabulary and Similarities subtest from the WASI (Wechsler, 1999), and the Boston Naming Test (Kaplan, 1983). The Mini Mental State Examination (MMSE) was also administered as a measure of global cognitive function.

For this study, we focused on the cognitive factors related to episodic memory and executive function, due to the known association of these cognitive domains with physical activity and aerobic fitness (Boots et al., 2014; Zhu et al., 2014). The selected factors were Immediate Memory, Verbal Learning & Memory, Working Memory, and Speed & Flexibility. Test scores on the psychometric measures that comprise each cognitive factor were first standardized (N (0, 1)) using means and standard deviations from the entire WRAP sample and then averaged to yield the factor score. The time interval between cognitive testing and the GXT was 1.08 ± .74 years, with GXT being subsequent to the neuropsychological assessment for 62/69 participants. The time interval between cognitive testing and the PET scan/CSF sampling was 2.04 ± .81 years, with neuropsychological assessment being subsequent to the PET scan for 67/69 participants.

Statistical analyses

To investigate whether increased CRF modifies the influence of Aβ burden on cognition, we fitted linear regression models—one for each cognitive domain—that included terms for age, sex, education, body mass index (BMI), beta-blocker usage, time interval between PET scan and GXT, time interval between cognitive testing and GXT, VO2peak, Aβ burden, and a VO2peak*Aβ burden interaction. This set of analyses was repeated using the two different Aβ burden variables: PiB-PET DVR measure and CSF Aβ42 level. The VO2peak*Aβ burden interaction term was the effect of primary interest in all models. Where significant, it would indicate a differential effect of Aβ burden on cognitive performance as a function of CRF. For all analyses conducted, evaluations of assumptions for ordinary least squares were carried out via graphical analyses as described by Tabachnick and Fidell (2007). All analyses were conducted using IBM SPSS, version 21.0. Only findings with p ≤ .05 (two-tailed) were considered to be significant.

RESULTS

Background characteristics

Table 1 details background characteristics of study participants. The average age at the time of the GXT was 63.54 ± 5.93 years and 68.1% were women. The average years of education was 16.46 ± 2.12, 47.8% were apolipoprotein E (APOE) ε4 allele carriers, and 72.5% had a parental family history of AD. The average VO2peak was 25.95 ± 5.50 mL/kg/min.

Table 1.

Characteristics of study participants*

Variable Value
Female, % 68.1
Age at GXT visit, mean (SD) [range], y 63.54 (5.93) [49.53-74.23]
Education, mean (SD) [range], y 16.46 (2.12) [12-22]
APOE4 positive, % 47.8
Family history positive, % 72.5
Caucasian, % 94.2
MMSE Score, mean (SD) [range] 29.38 (1.16) [24-30]
VO2peak, mean (SD) [range], ml/kg/min 25.95 (5.50) [11.64-38.43]
Taking a beta-blocker at GXT visit, % 5.8
Systolic blood pressure at GXT visit, mean (SD) [range], mmHg 123.68 (13.89) [94-160]
Diastolic blood pressure at GXT visit, mean (SD) [range], mmHg 71.32 (10.19) [44-90]
BMI at GXT, , mean (SD) [range], kg/m2 27.70 (5.40) [17.65-48.03]
Interval between PET scan and GXT, mean (SD) [range], y 3.10 (.47) [2.23-4.10]
Interval between cognitive testing and GXT, mean (SD) [range], y 1.08 (.74) [0-3.79]
Open in a new tab

APOE4=the varepsilon 4 allele of the apolipoprotein E gene; MMSE=Mini-Mental State Examination; VO2peak=peak oxygen consumption during graded exercise test (GXT); BMI=body mass index; PET=positron emission tomography.

CRF and Aβ-related alterations in cognition

There was a significant VO2peak*PiB-PET DVR interaction for Immediate Memory (p= .041) and for Verbal Learning & Memory (p= .025) (Table 2). To display this graphically we followed standard procedure for generating plots for interactions between two continuous variables, which entails solving the regression equation at specific “anchor points” for each of the continuous variables (Tabachnick & Fidell, 2007). In our case, we solved the equation for ±1 standard deviation away from the mean for both VO2peak and PiB-PET DVR, representing Low vs. High VO2peak and Low vs. High Aβ burden respectively. These solutions revealed that among individuals who had Low Aβ burden (and thus were at reduced risk for AD), the Low and High VO2peak groups did not differ statistically from each other: p= .499 for Immediate Memory and p= .441 for Verbal Learning and Memory. In contrast, among those who had High Aβ burden (and thus at increased risk for AD), the High VO2peak group performed better on these cognitive domains than the Low VO2peak group: p= .058 for Immediate Memory and p= .039 for Verbal Learning and Memory (Figure 1).

Table 2.

CRF favorably alters Aβ-related changes in cognition detected by PiB-PET

Cognitive domain VO2peak*PiB-PET DVR§ VO2peak (Low Aβ burden)† VO2peak (High Aβ burden)‡
β (SE) p Partial η2 β (SE) p β (SE) p
Immediate Memory .523 (.25) .041 .07 −.251 (.37) .499 .901 (.47) .058
Verbal Learning & Memory .526 (.23) .025 .08 −.261 (.34) .441 .897 (.42) .039
Speed & Flexibility .214 (.19) .274 .02 - - - -
Working Memory −.047 (.29) .875 <.01 - - - -
Open in a new tab

CRF=cardiorespiratory fitness; Aβ=β-amyloid; PiB=11C Pittsburgh Compound B; PET=positron emission tomography; DVR=distribution volume ratio; VO2peak=peak oxygen consumption during graded exercise test (GXT); β=regression estimate; SE=standard error; η2=eta squared.

§

The regression estimates and associated p values are for the VO2peak*PiB-PET DVR interaction term in each cognitive measure's model. This term assessed whether VO2peak modifies the effect of amyloid burden on the examined cognitive domain.

†

The regression estimates and associated p values are for the simple main effect for the influence of VO2peak on cognition within the Low Aβ burden group.

‡

The regression estimates and associated p values are for the simple main effect for the influence of VO2peak on cognition within the High Aβ burden group.

Variables included in the model were age, sex, education, body mass index, beta-blocker usage, time interval between PET scan and GXT, time interval between cognitive testing and GXT, VO2peak, PiB-PET DVR, and a VO2peak*PiB-PET DVR interaction; with the VO2peak*PiB-PET DVR interaction term being the effect of primary interest.

Figure 1. CRF modifies Aβ-related alterations in cognition detected by PiB-PET.

Figure 1

Open in a new tab

CRF=cardiorespiratory fitness; Aβ=β-amyloid; PiB=11C Pittsburgh Compound B; PET=positron emission tomography; a.u.=arbitrary units; DVR=distribution volume ratio; VO2peak=peak oxygen consumption during graded exercise test (GXT). Panels display adjusted means and standard errors from analyses that modeled Immediate Memory (A) and Verbal Learning & Memory (B) as a function of age, sex, education, body mass index, beta-blocker usage, time interval between the PET scan and GXT, time interval between cognitive testing and GXT, VO2peak, PiB-PET DVR, and a VO2peak*PiB-PET DVR. The VO2peak*PiB-PET DVR interaction term was the effect of primary interest in all models.

Although VO2peak and PiB-PET DVR were included in the analyses as continuous variables, for the purposes of graphing the study findings we chose two anchor points (i.e., ±1 standard deviation away from the mean) to represent Low vs. High VO2peak, and Low vs. High Aβ burden.

There were also significant VO2peak*CSF Aβ42 interactions for the domains of Immediate Memory (p< .001) and Verbal Learning & Memory (p< .001) (Table 3). To graph these findings, we solved the regression equation at ±1 standard deviations for VO2peak as described above. For CSF Aβ42, we created a dichotomy (i.e., Low vs. High CSF Aβ42) using the established cut-point for normality (i.e., ≥ 550 ng/L) (Hansson et al., 2006). These solutions revealed that among individuals with High Aβ42 (i.e., at reduced risk for AD), the Low and High VO2peak groups did not differ statistically from each other: p= .200 for Immediate Memory and p= .188 for Verbal Learning & Memory. Whereas among those with Low CSF Aβ42 (i.e., at increased risk for AD), the High VO2peak group performed significantly better on these cognitive domains than the Low VO2peak group: p= .002 for Immediate Memory and p= .014 for Verbal Learning & Memory (Figure 2).

Table 3.

CRF favorably alters Aβ-related changes in cognition detected by CSF Aβ42

Cognitive domain VO2peak*CSF Aβ42§ VO2peak (High Aβ42)† VO2peak (Low Aβ42)‡
β (SE) p Partial η2 β (SE) p β (SE) p
Immediate Memory −.128 (.03) <.001 .38 −.498 (.38) .200 1.580 (.48) .002
Verbal Learning & Memory −.102 (.03) <.001 .31 −.482 (.36) .188 1.169 (.45) .014
Speed & Flexibility −.047 (.03) .072 .09 - - - -
Working Memory −.029 (.04) .435 .02 - - - -
Open in a new tab

CRF=cardiorespiratory fitness; Aβ=β-amyloid; CSF=cerebrospinal fluid; VO2peak=peak oxygen consumption during graded exercise test (GXT); β=regression estimate; SE=standard error; η2=eta squared.

§

The regression estimates and associated p values are for the VO2peak*CSF Aβ42 interaction term in each cognitive measure's model. This term assessed whether VO2peak modifies the effect of amyloid burden on the examined cognitive domain.

†

The regression estimates and associated p values are for the simple main effect for the influence of VO2peak on cognition within the High Aβ42 group.

‡

The regression estimates and associated p values are for the simple main effect for the influence of VO2peak on cognition within the Low Aβ42 group.

Variables included in the model were age, sex, education, body mass index, beta-blocker usage, time interval between the CSF sampling and GXT, time interval between cognitive testing and GXT, VO2peak, CSF Aβ42, and a VO2peak*CSF Aβ42 interaction; with the VO2peak*CSF Aβ42 interaction term being the effect of primary interest.

Figure 2. CRF modifies Aβ-related alterations in cognition detected by CSF Aβ42.

Figure 2

Open in a new tab

CRF= cardiorespiratory fitness; Aβ=β-amyloid; CSF=cerebrospinal fluid; a.u.=arbitrary units; VO2peak = peak oxygen consumption during graded exercise test (GXT).

Panels display adjusted means and standard errors from analyses that modeled Immediate Memory (A) and Verbal Learning & Memory (B) as a function of age, sex, education, body mass index, beta-blocker usage, time interval between the CSF sampling and GXT, time interval between cognitive testing and GXT, VO2peak, CSF Aβ42, and a VO2peak*CSF Aβ42 interaction. The VO2peak*CSF Aβ42 interaction term was the effect of primary interest in all models.

Although VO2peak and CSF Aβ42 were included in the analyses as continuous variables, for the purposes of graphing the study findings we chose two anchor points (i.e., ±1 standard deviation away from the mean) to represent Low vs. High VO2peak, and dichotomized CSF Aβ42 (i.e., Low vs. High CSF Aβ42) using the established cut-point for normality (i.e.; ≥ 550 ng/L).

For both the PiB-PET and CSF Aβ42 analyses, we removed the VO2peak*Aβ term from those models wherein it was not significant (i.e., Speed & Flexibility and Working Memory) and refit the model to assess the main effect of VO2peak and Aβ on those cognitive domains. The only significant finding was an association between VO2peak and Speed & Flexibility (p= .033), wherein more fit individuals had better scores compared with less fit individuals. We also failed to find a significant association between VO2peak and Aβ burden—whether measured by PiB-PET or CSF Aβ42—in our sample (p's > .789).

Secondary analyses

Number of parameters in our regression models

Our original models included beta-blocker usage and BMI because beta-blocker usage was negatively correlated with all cognitive outcomes and BMI was similarly negatively associated with VO2peak. However, because these additional parameters may have resulted in model overfit, we re-analyzed our data after excluding them. These revised models still showed significant VO2peak*PiB-PET DVR interactions for Immediate Memory (p= .048) and Verbal Learning & Memory (p= .024), and significant VO2peak*CSF Aβ42 interactions for Immediate Memory (p <.001) and Verbal Learning & Memory (p= .001), suggesting that our initial findings were not driven by the number of regressors in our models.

Criteria for peak effort

Compared to younger individuals, older adults are usually more reluctant to give maximal effort during a GXT, which may attenuate their recorded VO2peak (ACSM, 2012). Thus, we employed the ACSM criteria for ascertaining peak effort in order to minimize the likelihood of age-dependent effects in our observed VO2peak. This was of particular concern since both Aβ burden and cognitive function track closely with age. Even so, to verify that our results were not solely a function of the peak criteria implemented, we refit our models after implementing alternative criteria used in other studies of older adults, i.e., (1) a respiratory exchange ratio ≥ 1.0 and (2) at least 85% of age predicted maximal heart rate (Billinger, Vidoni, Honea, & Burns, 2011). Our results remained essentially the same with the exception that there was no longer a significant VO2peak*Aβ interaction for the domain of Verbal Learning & Memory.

DISCUSSION

In this cross-sectional study, we found that CRF modifies the effect of Aβ burden on cognition. This effect was observed across the two indices of Aβ accumulation that were examined (i.e., PiB-PET DVR and CSF Aβ42 levels). Specifically, among persons with minimal Aβ burden, performance on measures of Immediate Memory and Verbal Learning & Memory did not vary significantly as a function of CRF. In contrast, among persons with higher Aβ burden, increased CRF was associated with better scores on these domains. To our knowledge, this is the first study to show that CRF might modify the well-established influence that Aβ burden has on cognition.

We noted with great interest that the essence of our findings is in line with prior studies done in AD mouse models (Ke, Huang, Liang, & Hsieh-Li, 2011; Parachikova, Nichol, & Cotman, 2008). One such study (Wang et al., 2013) revealed that voluntary exercise mitigates the deleterious effects of a neurotoxic Aβ form (Aβ25-35) on memory. When injected intracerebroventricularly (ICV), Aβ25-35 induces cognitive impairment in adult mice. However, voluntary wheel running for 12 days was shown to diminish Aβ25-35-generated memory deficits. Similarly, findings from Kim and colleagues (2014) suggest that treadmill exercise alleviates Aβ25-35-induced short-term memory impairment in adult rats. They observed that animals receiving bilateral ICV injection of Aβ25-35 performed significantly worse on a short-term memory test compared to the sham group. Importantly, when a group of the Aβ25-35-injected rats also underwent treadmill exercise, they failed to exhibit this Aβ25-35-associated memory deficit. Our study nicely translates these preclinical findings to humans by showing that, in late-middle-aged adults, increased CRF might diminish Aβ-related cognitive changes.

Further, we add to the growing literature suggesting that CRF in adults is beneficial for cognitive function. One study (Barnes et al., 2003) found preserved cognitive function spanning domains of attention/executive function, verbal memory, and verbal fluency, over a 6-year period in participants who were determined to have high CRF levels at baseline. Additionally, a large community-based study (Zhu et al., 2014), found that higher CRF, measured by maximal duration during a GXT, was associated with better performance on cognitive measures of episodic memory, cognitive flexibility, and working memory. Similarly, Boots and colleagues (2014) demonstrated an association between a CRF estimate and cognitive function in middle-aged adults enrolled in the WRAP cohort. Specifically, they reported that individuals with higher levels of estimated CRF performed better on domains of Verbal Learning & Memory, Speed & Flexibility, and Visuospatial Ability compared to those with lower CRF levels. Overall, our study extends these earlier investigations by showing that there is an interaction between Aβ load and aerobic fitness on cognitive performance, such that increased aerobic fitness may be particularly beneficial in the context of elevated Aβ accumulation.

Our findings were consistent across two Aβ detection platforms (i.e., PiB-PET binding and CSF Aβ42 levels), strengthening their validity. It has been well-established that low CSF Aβ42 highly correlates with increased PiB-PET signal (Degerman Gunnarsson et al., 2010; Fagan et al., 2006; Jagust et al., 2009; Palmqvist et al., 2014; Tolboom et al., 2009). Our findings are consistent with this notion by showing that CRF similarly modifies PiB- and Aβ42-associated changes in cognition. Even so, it is noteworthy that the interactions between CRF and Aβ42 seemed qualitatively stronger than those between CRF and PiB-PET signal. These observations are supported by results from recent work by Landau and colleagues (2013). They found that individuals classified as having abnormal CSF Aβ42 levels but normal PiB-PET Aβ load had more cognitive impairment compared to their peers with normal Aβ42 levels and abnormal PiB-PET Aβ load (Landau et al., 2013). Another study also showed that discordance between these Aβ biomarkers, specifically low CSF Aβ42 but normal amyloid PET binding, is primarily found in the pre-dementia stages of AD, suggesting that CSF Aβ42 may be an earlier indicator of disturbances in Aβ metabolism than amyloid PET (Mattsson et al., 2015). In combination with our study, this suggests that, in some populations, CSF Aβ42 may be more sensitive than PiB-PET for predicting cognitive performance.

It bears mentioning that although we only observed significant VO2peak*Aβ interactions for Immediate Memory and Verbal Learning & Memory, we did detect a significant main effect of VO2peak on Speed & Flexibility performance, wherein individuals with greater VO2peak also had higher scores on this cognitive domain. This facilitation of Speed & Flexibility (which indexes cognitive skills commonly called executive function) by VO2peak is a consistent finding in the literature including two recent papers from our group (Boots et al., 2014; Okonkwo et al., 2014). In the Okonkwo et. al. (2014) study, we found that whereas physical activity modified the influence of age on cognitive domains of Immediate Memory and Visuospatial Ability, it had a direct (i.e., main) effect on Speed & Flexibility. Taken together, those findings and our present observations suggest that physical activity/fitness might facilitate some cognitive functions (e.g., executive function) by impacting them directly while preserving others (e.g., episodic memory) by attenuating the influence that specific risk factors, such as age and Aβ burden, exert on them. We also note that whereas some studies have reported a direct association between physical activity and Aβ [ e.g., Brown et al. (2013); Liang et al., (2010)] the present study and others [e.g., Gidicsin et al. (2015); Landau et al. (2013); Vemuri et al. (2012)] have failed to uncover such effects. Future studies might yet elucidate the variables (e.g., age of cohort) that account for this heterogeneity in study findings.

Curiously, the plots generated from our VO2peak*Aβ models appeared to suggest that, among individuals with low Aβ burden, higher CRF was associated with somewhat lower cognitive test scores compared with lower CRF (note though this lower performance was not significantly so, and was always around −0.5 SD of the mean). Such seemingly counterintuitive observations have been made in prior reports. For example, a study by Bugg and Head (2011) found that in younger age, individuals who were less active had larger medial temporal lobe volumes compared to those deemed to be more active. In a recent paper (Okonkwo et al., 2014) we also found that, among younger middle-aged adults, inactive persons had lower Aβ burden and higher scores on some cognitive domains compared with active individuals, whereas the reverse was true among older middle-aged adults. There is clearly a need for additional studies in order to further understand the complex interplay between physical activity/CRF and AD risk factors.

This study was not without limitations, the most significant being the cross-sectional design. Although we have used statistical approaches to estimate the potential for CRF to ameliorate Aβ-related decrements in cognition, a prospective design will be critical in determining whether fitness exerts a causal influence on cognition in people at risk of AD. Additionally, there was, on average, a three-year time interval between the PET/CSF exams and the GXT. Although we attempted to accommodate this asynchrony by including the PET-GXT time interval in all of our models, we cannot exclude the possibility of reverse causality, whereby increased Aβ burden adversely affects CRF levels. Future studies would benefit from collecting Aβ and GXT measurements in closer temporal proximity. Lastly, the demographic make-up of the study sample is not representative of the U.S. population. Therefore, the generalizability of our findings might be somewhat limited.

Overall, this study provides cross-sectional evidence that the deleterious effect of Aβ burden on cognition might be modified by higher CRF among late-middle-aged CN individuals at risk for AD. These findings add to the existing literature suggesting that a physically-fit lifestyle may delay or prevent the onset of symptomatic AD.

ACKNOWLEDGEMENTS

This work was supported by the National Institute on Aging grants K23 AG045957 (OCO), R01 AG031790 (CMC), R01 AG021155 (SCJ), R01 AG027161 (SCJ), and P50 AG033514 (SA); and by a Clinical and Translational Science Award (UL1RR025011) to the University of Wisconsin, Madison. Portions of this research were supported by the Wisconsin Alumni Research Foundation, the Helen Bader Foundation, Northwestern Mutual Foundation, Extendicare Foundation, and from the Veterans Administration including facilities and resources at the Geriatric Research Education and Clinical Center of the William S. Middleton Memorial Veterans Hospital, Madison, WI. We acknowledge the researchers and staff of the Clinical Neurochemistry Laboratory, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at University of Gothenburg, Sweden, where the CSF assays took place; and Dustin Wooten, PhD, Ansel Hillmer, MSc, and Andrew Higgins with PET data production and processing; and Caitlin A. Cleary, BSc, Sandra Harding, MS, Jennifer Bond, BA, Janet Rowley, BA, Amy Hawley, BS, and the WRAP psychometrists with study data collection. In addition, we would like to acknowledge the support of researchers and staff at the Waisman Center, University of Wisconsin–Madison, where the brain scans took place. Finally, we thank study participants in the Wisconsin Registry for Alzheimer's Prevention without whom this work would not be possible. The funders had no role with respect to design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and the decision to submit the manuscript for publication.

Footnotes

All authors report no conflicts of interest with respect to the data presented in this manuscript.

REFERENCES

  1. ACSM . American College of Sports Medicine. ACSM's resource manual for guidelines for exercise testing and prescription. 7th ed. Lippincott Williams and Wilkins; Philadelphia, PA: 2012. [Google Scholar]
  2. ACSM . ACSM's Guidelines for Exercise Testing and Prescription. 9th ed. Wolters Kluwer/ Lippincott Williams and Wilkins; Philadelphia, PA: 2014. [Google Scholar]
  3. Balke B, Ware RW. An experimental study of physical fitness of Air Force personnel. U S Armed Forces Med J. 1959;10(6):675–688. [PubMed] [Google Scholar]
  4. Barnes DE, Yaffe K, Satariano WA, Tager IB. A longitudinal study of cardiorespiratory fitness and cognitive function in healthy older adults. J Am Geriatr Soc. 2003;51(4):459–465. doi: 10.1046/j.1532-5415.2003.51153.x. [DOI] [PubMed] [Google Scholar]
  5. Benton AL. Neuropsychological assessment. Annu Rev Psychol. 1994;45:1–23. doi: 10.1146/annurev.ps.45.020194.000245. [DOI] [PubMed] [Google Scholar]
  6. Billinger SA, Vidoni ED, Honea RA, Burns JM. Cardiorespiratory response to exercise testing in individuals with Alzheimer's disease. Arch Phys Med Rehabil. 2011;92(12):2000–2005. doi: 10.1016/j.apmr.2011.07.194. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Boots EA, Schultz SA, Oh JM, Larson J, Edwards D, Cook D, Okonkwo OC. Cardiorespiratory fitness is associated with brain structure, cognition, and mood in a middle-aged cohort at risk for Alzheimer's disease. Brain Imaging Behav. 2014 doi: 10.1007/s11682-014-9325-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Brown BM, Peiffer JJ, Taddei K, Lui JK, Laws SM, Gupta VB, Martins RN. Physical activity and amyloid-beta plasma and brain levels: results from the Australian Imaging, Biomarkers and Lifestyle Study of Ageing. Mol Psychiatry. 2013;18(8):875–881. doi: 10.1038/mp.2012.107. [DOI] [PubMed] [Google Scholar]
  9. Bugg JM, Head D. Exercise moderates age-related atrophy of the medial temporal lobe. Neurobiol Aging. 2011;32(3):506–514. doi: 10.1016/j.neurobiolaging.2009.03.008. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Degerman Gunnarsson M, Lindau M, Wall A, Blennow K, Darreh-Shori T, Basu S, Kilander L. Pittsburgh compound-B and Alzheimer's disease biomarkers in CSF, plasma and urine: An exploratory study. Dement Geriatr Cogn Disord. 2010;29(3):204–212. doi: 10.1159/000281832. [DOI] [PubMed] [Google Scholar]
  11. Erickson KI, Voss MW, Prakash RS, Basak C, Szabo A, Chaddock L, Kramer AF. Exercise training increases size of hippocampus and improves memory. Proc Natl Acad Sci U S A. 2011;108(7):3017–3022. doi: 10.1073/pnas.1015950108. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Fagan AM, Mintun MA, Mach RH, Lee SY, Dence CS, Shah AR, Holtzman DM. Inverse relation between in vivo amyloid imaging load and cerebrospinal fluid Abeta42 in humans. Ann Neurol. 2006;59(3):512–519. doi: 10.1002/ana.20730. [DOI] [PubMed] [Google Scholar]
  13. Fagan AM, Roe CM, Xiong C, Mintun MA, Morris JC, Holtzman DM. Cerebrospinal fluid tau/beta-amyloid(42) ratio as a prediction of cognitive decline in nondemented older adults. Arch Neurol. 2007;64(3):343–349. doi: 10.1001/archneur.64.3.noc60123. [DOI] [PubMed] [Google Scholar]
  14. Floberg JM, Mistretta CA, Weichert JP, Hall LT, Holden JE, Christian BT. Improved kinetic analysis of dynamic PET data with optimized HYPR-LR. Med Phys. 2012;39(6):3319–3331. doi: 10.1118/1.4718669. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Gidicsin CM, Maye JE, Locascio JJ, Pepin LC, Philiossaint M, Becker JA, Johnson KA. Cognitive activity relates to cognitive performance but not to Alzheimer disease biomarkers. Neurology. 2015 doi: 10.1212/WNL.0000000000001704. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Gustafson DR, Skoog I, Rosengren L, Zetterberg H, Blennow K. Cerebrospinal fluid beta-amyloid 1-42 concentration may predict cognitive decline in older women. J Neurol Neurosurg Psychiatry. 2007;78(5):461–464. doi: 10.1136/jnnp.2006.100529. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Hansson O, Zetterberg H, Buchhave P, Londos E, Blennow K, Minthon L. Association between CSF biomarkers and incipient Alzheimer's disease in patients with mild cognitive impairment: a follow-up study. Lancet Neurol. 2006;5(3):228–234. doi: 10.1016/S1474-4422(06)70355-6. [DOI] [PubMed] [Google Scholar]
  18. Head D, Bugg JM, Goate AM, Fagan AM, Mintun MA, Benzinger T, Morris JC. Exercise Engagement as a Moderator of the Effects of APOE Genotype on Amyloid Deposition. Arch Neurol. 2012;69(5):636–643. doi: 10.1001/archneurol.2011.845. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Hyman BT, Phelps CH, Beach TG, Bigio EH, Cairns NJ, Carrillo MC, Montine TJ. National Institute on Aging-Alzheimer's Association guidelines for the neuropathologic assessment of Alzheimer's disease. Alzheimers Dement. 2012;8(1):1–13. doi: 10.1016/j.jalz.2011.10.007. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Jack CR, Jr., Knopman DS, Jagust WJ, Petersen RC, Weiner MW, Aisen PS, Trojanowski JQ. Tracking pathophysiological processes in Alzheimer's disease: an updated hypothetical model of dynamic biomarkers. Lancet Neurol. 2013;12(2):207–216. doi: 10.1016/S1474-4422(12)70291-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Jack CR, Jr., Knopman DS, Jagust WJ, Shaw LM, Aisen PS, Weiner MW, Trojanowski JQ. Hypothetical model of dynamic biomarkers of the Alzheimer's pathological cascade. Lancet Neurol. 2010;9(1):119–128. doi: 10.1016/S1474-4422(09)70299-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Jagust WJ, Landau SM, Shaw LM, Trojanowski JQ, Koeppe RA, Reiman EM, Alzheimer's Disease Neuroimaging, I Relationships between biomarkers in aging and dementia. Neurology. 2009;73(15):1193–1199. doi: 10.1212/WNL.0b013e3181bc010c. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Johnson SC, Christian BT, Okonkwo OC, Oh JM, Harding S, Xu G, Sager MA. Amyloid burden and neural function in people at risk for Alzheimer's Disease. Neurobiol Aging. 2014;35(3):576–584. doi: 10.1016/j.neurobiolaging.2013.09.028. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Kaplan E, Goodglass H, Weintraub S. Boston naming test. Lea & Febiger; Philadelphia: 1983. [Google Scholar]
  25. Ke HC, Huang HJ, Liang KC, Hsieh-Li HM. Selective improvement of cognitive function in adult and aged APP/PS1 transgenic mice by continuous non-shock treadmill exercise. Brain Res. 2011;1403:1–11. doi: 10.1016/j.brainres.2011.05.056. [DOI] [PubMed] [Google Scholar]
  26. Kim BK, Shin MS, Kim CJ, Baek SB, Ko YC, Kim YP. Treadmill exercise improves short-term memory by enhancing neurogenesis in amyloid beta-induced Alzheimer disease rats. J Exerc Rehabil. 2014;10(1):2–8. doi: 10.12965/jer.140086. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Koscik RL, La Rue A, Jonaitis EM, Okonkwo OC, Johnson SC, Bendlin BB, Sager MA. Emergence of mild cognitive impairment in late middle-aged adults in the wisconsin registry for Alzheimer's prevention. Dement Geriatr Cogn Disord. 2014;38(1-2):16–30. doi: 10.1159/000355682. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Landau SM, Lu M, Joshi AD, Pontecorvo M, Mintun MA, Trojanowski JQ, Alzheimer's Disease Neuroimaging, I Comparing positron emission tomography imaging and cerebrospinal fluid measurements of beta-amyloid. Ann Neurol. 2013;74(6):826–836. doi: 10.1002/ana.23908. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Lautenschlager NT, Cox KL, Flicker L, Foster JK, van Bockxmeer FM, Xiao J, Almeida OP. Effect of physical activity on cognitive function in older adults at risk for Alzheimer disease: a randomized trial. JAMA. 2008;300(9):1027–1037. doi: 10.1001/jama.300.9.1027. [DOI] [PubMed] [Google Scholar]
  30. Liang KY, Mintun MA, Fagan AM, Goate AM, Bugg JM, Holtzman DM, Head D. Exercise and Alzheimer's disease biomarkers in cognitively normal older adults. Ann Neurol. 2010;68(3):311–318. doi: 10.1002/ana.22096. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Mattsson N, Insel PS, Donohue M, Landau S, Jagust WJ, Shaw LM, Alzheimer's Disease Neuroimaging, I Independent information from cerebrospinal fluid amyloid-beta and florbetapir imaging in Alzheimer's disease. Brain. 2015;138(Pt 3):772–783. doi: 10.1093/brain/awu367. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Morris JC, Roe CM, Grant EA, Head D, Storandt M, Goate AM, Mintun MA. Pittsburgh compound B imaging and prediction of progression from cognitive normality to symptomatic Alzheimer disease. Arch Neurol. 2009;66(12):1469–1475. doi: 10.1001/archneurol.2009.269. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Okonkwo OC, Schultz SA, Oh JM, Larson J, Edwards D, Cook D, Sager MA. Physical activity attenuates age-related biomarker alterations in preclinical AD. Neurology. 2014;83(19):1753–1760. doi: 10.1212/WNL.0000000000000964. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Palmqvist S, Zetterberg H, Blennow K, Vestberg S, Andreasson U, Brooks DJ, Hansson O. Accuracy of brain amyloid detection in clinical practice using cerebrospinal fluid beta-amyloid 42: a cross-validation study against amyloid positron emission tomography. JAMA Neurol. 2014;71(10):1282–1289. doi: 10.1001/jamaneurol.2014.1358. [DOI] [PubMed] [Google Scholar]
  35. Parachikova A, Nichol KE, Cotman CW. Short-term exercise in aged Tg2576 mice alters neuroinflammation and improves cognition. Neurobiol Dis. 2008;30(1):121–129. doi: 10.1016/j.nbd.2007.12.008. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Pizzie R, Hindman H, Roe CM, Head D, Grant E, Morris JC, Hassenstab JJ. Physical activity and cognitive trajectories in cognitively normal adults: the adult children study. Alzheimer Dis Assoc Disord. 2014;28(1):50–57. doi: 10.1097/WAD.0b013e31829628d4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Price JC, Klunk WE, Lopresti BJ, Lu X, Hoge JA, Ziolko SK, Mathis CA. Kinetic modeling of amyloid binding in humans using PET imaging and Pittsburgh Compound-B. J Cereb Blood Flow Metab. 2005;25(11):1528–1547. doi: 10.1038/sj.jcbfm.9600146. [DOI] [PubMed] [Google Scholar]
  38. Reitan RM. Validity of the trail making test as an indicator of organic brain damage. Perceptual and Motor Skills. 1958;8:271–276. [Google Scholar]
  39. Resnick SM, Sojkova J, Zhou Y, An Y, Ye W, Holt DP, Wong DF. Longitudinal cognitive decline is associated with fibrillar amyloid-beta measured by [11C]PiB. Neurology. 2010;74(10):807–815. doi: 10.1212/WNL.0b013e3181d3e3e9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Roe CM, Fagan AM, Grant EA, Hassenstab J, Moulder KL, Maue Dreyfus D, Morris JC. Amyloid imaging and CSF biomarkers in predicting cognitive impairment up to 7.5 years later. Neurology. 2013;80(19):1784–1791. doi: 10.1212/WNL.0b013e3182918ca6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Rosario BL, Weissfeld LA, Laymon CM, Mathis CA, Klunk WE, Berginc MD, Price JC. Inter-rater reliability of manual and automated region-of-interest delineation for PiB PET. Neuroimage. 2011;55(3):933–941. doi: 10.1016/j.neuroimage.2010.12.070. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Sager MA, Hermann B, La Rue A. Middle-aged children of persons with Alzheimer's disease: APOE genotypes and cognitive function in the Wisconsin Registry for Alzheimer's Prevention. J Geriatr Psychiatry Neurol. 2005;18(4):245–249. doi: 10.1177/0891988705281882. [DOI] [PubMed] [Google Scholar]
  43. Schmidt M. Rey Auditory Verbal Learning Test: A Handbook. Western Psychological Services; Torrance, CA: 1996. [Google Scholar]
  44. Skoog I, Davidsson P, Aevarsson O, Vanderstichele H, Vanmechelen E, Blennow K. Cerebrospinal fluid beta-amyloid 42 is reduced before the onset of sporadic dementia: a population-based study in 85-year-olds. Dement Geriatr Cogn Disord. 2003;15(3):169–176. doi: 10.1159/000068478. doi: 68478. [DOI] [PubMed] [Google Scholar]
  45. Sperling RA, Aisen PS, Beckett LA, Bennett DA, Craft S, Fagan AM, Phelps CH. Toward defining the preclinical stages of Alzheimer's disease: recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease. Alzheimers Dement. 2011;7(3):280–292. doi: 10.1016/j.jalz.2011.03.003. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Tabachnick BG, Fidell LS. Using multivariate statistics. 5th ed. Pearson/Allyn & Bacon; Boston: 2007. [Google Scholar]
  47. Thal DR, Rub U, Orantes M, Braak H. Phases of A beta-deposition in the human brain and its relevance for the development of AD. Neurology. 2002;58(12):1791–1800. doi: 10.1212/wnl.58.12.1791. [DOI] [PubMed] [Google Scholar]
  48. Tolboom N, van der Flier WM, Yaqub M, Boellaard R, Verwey NA, Blankenstein MA, van Berckel BN. Relationship of cerebrospinal fluid markers to 11C-PiB and 18F-FDDNP binding. J Nucl Med. 2009;50(9):1464–1470. doi: 10.2967/jnumed.109.064360. [DOI] [PubMed] [Google Scholar]
  49. Trenerry M, Crosson B, DeBoe J, Leber L. Stroop Neuropsychological Screening Test. Psychological Assessment Resources, Inc; Odessa, FL: 1989. [Google Scholar]
  50. Tzourio-Mazoyer N, Landeau B, Papathanassiou D, Crivello F, Etard O, Delcroix N, Joliot M. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage. 2002;15(1):273–289. doi: 10.1006/nimg.2001.0978. [DOI] [PubMed] [Google Scholar]
  51. Vemuri P, Lesnick TG, Przybelski SA, Knopman DS, Roberts RO, Lowe VJ, Jack CR., Jr. Effect of lifestyle activities on Alzheimer disease biomarkers and cognition. Ann Neurol. 2012;72(5):730–738. doi: 10.1002/ana.23665. [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Villemagne VL, Pike KE, Chetelat G, Ellis KA, Mulligan RS, Bourgeat P, Rowe CC. Longitudinal assessment of Abeta and cognition in aging and Alzheimer disease. Ann Neurol. 2011;69(1):181–192. doi: 10.1002/ana.22248. [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Wang Q, Xu Z, Tang J, Sun J, Gao J, Wu T, Xiao M. Voluntary exercise counteracts Abeta25-35-induced memory impairment in mice. Behav Brain Res. 2013;256:618–625. doi: 10.1016/j.bbr.2013.09.024. [DOI] [PubMed] [Google Scholar]
  54. Wechsler D. WAIS-III: Wechsler Adult Intelligence Scale. 3rd edition The Psychological Corporation; San Antonio, TX: 1997. [Google Scholar]
  55. Wechsler D. Wechsler abbreviated scale of intelligence. The Psychological Corporation; San Antonio: 1999. [Google Scholar]
  56. Wilkinson G. Wide range achievement test administration manual. 3 ed. Wide Range Incorporated; Wilmington: 1993. [Google Scholar]
  57. Zhu N, Jacobs DR, Jr., Schreiner PJ, Yaffe K, Bryan N, Launer LJ, Sternfeld B. Cardiorespiratory fitness and cognitive function in middle age: the CARDIA study. Neurology. 2014;82(15):1339–1346. doi: 10.1212/WNL.0000000000000310. [DOI] [PMC free article] [PubMed] [Google Scholar]

RESOURCES