The relationship between physical activity, apolipoprotein E ε4 carriage and brain health

doi:10.21203/rs.2.22674/v1

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The relationship between physical activity, apolipoprotein E ε4 carriage and brain health

https://doi.org/10.21203/rs.2.22674/v1

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published 24 Apr, 2020

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BACKGROUND

Neuronal hyperexcitability and hypersynchrony have been described as key features of neurophysiological dysfunctions in the Alzheimer’s disease (AD) continuum. Conversely, physical activity (PA) has been associated with improved brain health and reduced AD risk. However, there is controversy regarding whether AD genetic risk (in terms of APOE ε4 carriage) modulates these relationships. The utilization of multiple outcome measures within one sample may strengthen our understanding of this complex phenomenon.

METHOD

The relationship between PA and functional connectivity (FC) was examined in a sample of 107 healthy older adults using magnetoencephalography. Additionally, we explored whether ε4 carriage modulates this association. The correlation between FC and brain structural integrity, cognition and mood was also investigated.

RESULTS

A relationship between higher PA and decreased FC (hyposynchrony) in the left temporal lobe was observed among all individuals (across the whole sample, in ε4 carriers and in ε4 non-carriers), but its effects manifest differently according to genetic risk. In ε4 carriers, we report an association between this region-specific FC profile and preserved brain structure (greater gray matter volumes and higher integrity of white matter tracts). In this group decreased FC also correlated with reduced anxiety levels. In ε4 non-carriers, this profile is associated with improved cognition (working and episodic memory).

CONCLUSIONS

PA could mitigate the increase in FC (hypersynchronization) that characterizes prodromal AD, being beneficial for all individuals, including ε4 carriers.

Cognitive Neuroscience

Physical Activity

APOE

MEG

synaptic function

Alzheimer’s disease

temporal lobe

Physical Activity (PA) has been persistently referred to as the 21^st century panacea. In both clinical and non-clinical populations, PA is related to improvements in sleep quality, mood, cognitive performance, and perceived quality of life (1). In addition, PA is associated with marked decreases in the risk of a broad spectrum of diseases, including diabetes mellitus, cancer, and dementia (1). In the specific case of Alzheimer’s disease (AD; the most common form of dementia), PA has been found to reduce incidence, AD-associated neuropathological burden, and cognitive decline (2–4).

The major genetic risk factor for sporadic AD, namely the apolipoprotein E (APOE) ε4 allele, is present in 60-80% of AD cases and is linked to a 3.2-fold increased AD risk in heterozygosis and up to 14.9-fold increased AD risk in homozygosis (5). While most studies claim that PA-protective effects mainly manifest among ε4 carriers (6,7), others identified this same effect exists only in non-carriers (8,9), while other studies report benefits regardless of APOE ε4 allele carriage (10,11). This inconsistent literature is likely due to the use of varying study designs and outcome measures (i.e. hippocampal volume, short-term memory or rate of AD conversion). To further understand the modulating effect of AD genetic risk on the relationship between PA and brain health, a more detailed investigation using varied outcome measures is warranted.

Based on the inconsistencies in previous literature, the current study will first investigate the relationship between PA and synaptic activity in cognitively normal older adults. Synaptic activity will be captured employing magnetoencephalography and analyzed under the framework of network synchronization. We hypothesize that PA will be associated with network reduced synchrony in both groups, in opposition to the state of synaptic hyperexcitability that characterizes preclinical AD [12], [13]. Then, once we have identified the functional connectivity (FC) patterns that are influenced by PA level, we will explore if there are any associations between these FC patterns and structural integrity (grey and white matter), cognition, and mood. Additionally, we will examine if APOE ε4 allele carriage modulates these relationships.

Participants

262 individuals participated in a study aimed to characterize the neurophysiological features of healthy aging. Participants were recruited from local hospitals and through several dissemination talks, and a team of expert neuropsychologists assessed that they met inclusion criteria. A detailed list of exclusion criteria can be found in (12). The procedure was performed following current guidelines and regulations, and the study was approved by the Hospital Universitario San Carlos Ethics Committee. Every participant signed an informed consent.

We included participants who had available data regarding our main variables of interest (n = 158; Mini Mental State Examination, MMSE, score, genetic information and validated magnetic resonance imaging, MRI, MEG and actigraphy data). We then excluded anyone with an MMSE score less than 26 (n = 5), aged less than 50 years (n = 8) and participants carrying less frequent APOE genotypes (ε2ε3, n = 11; ε2ε4, n = 1; ε4ε4, n = 7). Among the remaining 127 participants, there were 36 APOE ε4 carriers and 91 non-carriers. We carefully selected 33 APOE ε4 carriers and 74 non-carriers so that both subsamples would match in PA levels (TPA and MVPA), age, sex, educational level, MMSE and body mass index. There were two main reason to match the sample according to all these relevant variables instead of using them as covariates in subsequent analyses. First, including several covariates in a cluster-based permutation test could have introduced a methodological pitfall in the permutation procedure. Second, using covariates only controls for linear influences on the data, dismissing any other possible non-linear confound.

The final sample was composed of 107 healthy older adults, aged 50-82 years. A detailed list of the sample characteristics can be found in Table 1, including scores extracted from the neuropsychological tests: Geriatric Depression Scale (13), the anxiety subscale from the Goldberg Anxiety and Depression Inventory (14) and the Digit Span Forward, Digit Span Backward and Logical Memory II (delayed recall, units and gist) subscales from the Weschler Adult Intelligence Scale IV (WAIS-IV, (15)).

Physical Activity Measurement

For PA measurement we used the ActiGraph GT3X+ accelerometer (LLC, Pensacola,FL). Participants were requested to wear the accelerometers on their right hip for 7 complete days, taking them off only during water-based activities (16,17). For cleaning and processing the data, we used ActiLife software(6.13.3) (LLC, Pensacola, FL). The validation criteria required each individual to wear the accelerometer during at least 3 weekdays and 1 weekend day for a minimum of ten hours per day (17).We considered ≥60 min of continuous zeroes while allowing for up to 2 min of counts ≤100 counts as non-wear time (18). To classify the PA, we categorised sedentary time as <100 counts/min, light activity as 100–1951 counts/min, and moderate to vigorous physical activity (MVPA) as ≥1952 counts/min (19).

In this study, two different measures of PA were incorporated: Total Time In Freedson Bouts, which is a standardized measure of PA volumes (total PA, TPA), and daily average of MVPA. TPA was normalized by total wear time.

APOE Genotyping

As described in (12), we obtained genomic DNA from 10 ml blood samples in ethylenediaminetetraacetic acid (EDTA). Employing TaqMan assays on an Applied Biosystems 7500 Fast Real Time PCR machine (Applied Biosystems, Foster City, CA), single nucleotide polymorphisms (SNPs) rs7412 and rs429358 genotypes were determined. APOE genotype was established accordingly. In this study, only ε3ε3 and ε3ε4 individuals were considered.

MRI acquisition and Volumetric Analyses

To generate the T1-weighted MRI images from each participant, a General Electric 1.5 T system was employed. We applied a high-resolution antenna and a homogenization PURE filter (Fast Spoiled Gradient Echo sequence, TR/TE/TI = 11.2/4.2/450 ms; flip angle 12°; 1 mm slice thickness, 256x256 matrix and FOV 25 cm).

The resulting images were processed using Freesurfer software (version 5.1.0) and its specialized tool for automated cortical parcellation and subcortical segmentation (20). The measures that were included in further analyses were total grey matter, amygdala, precuneus and hippocampus (in mm³). The volumes of bilateral structures were collapsed in order to obtain a single measure for each region.

Diffusion Tensor Imaging

Data acquisition

The same General Electric 1.5 T magnetic resonance scanner was also used to collect diffusion weighted images (DWI). The acquisition parameters for DWI were: TE/TR 96.1/12,000 ms; NEX 3 for increasing the SNR; 2.4 mm slice thickness, 128 × 128 matrix and 30.7 cm FOV yielding an isotropic voxel of 2.4 mm; 1 image with no diffusion sensitization (i.e., T2-weighted b0 images) and 25 DWI (b = 900 s/mm2). Data were recorded with a single shot echo planar imaging sequence.

Preprocessing

DWI images were processed following the procedure previously published in (21). Probabilistic fiber tractography was run on the he automated tool AutoPtx (https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/AutoPtx ) as reported in (21). Two bilateral tracts were later used for correlation analyses: the uncinate and the parahippocampal fasciculi. In order to reduce the number of tests, the weighted arithmetic mean of left and right structures was used. Likewise, a measure of global fractional anisotropy (FA) was calculated averaging all 27 original tracts provided by the system.

Magnetoencephalography

Data acquisition and signal preprocessing

MEG data was recorded using a 306-channel whole-head MEG system (Vectorview, ElektaNeuromag, Finland), placed in a magnetically shielded room at Center for Biomedical Technology in Madrid, following the protocol described in (12).

Raw data were first submitted to the Maxfilter software to remove external noise (22). Fieldtrip software (23) was used to automatically scan MEG data for artifacts, which were visually confirmed by an MEG expert. Artifact-free data were segmented in 4 seconds epochs. Then MEG time series were filtered into delta (2-4Hz), theta (4-8Hz), alpha (8-12Hz) and beta (12-30 Hz). This procedure has been reported in detailed in (24).

Source reconstruction and connectivity analyses

We used a regular 1cm grid in the Montreal Neurological Institute (MNI) template. The resulting model comprised 2459 sources distributed across the brain, which were transformed to each subject’s space following the methodology detailed in (24).

We used phase locking value (PLV) to calculate functional connectivity. The Automated Anatomical Labeling atlas (AAL, (25)) was applied to segment the source template with 2459 nodes excluding the cerebellum, basal ganglia, thalamus, and olfactory cortices. The resulting 78 regions of interest included 1202 nodes. Symmetrical whole-brain matrices of 1202x1202 nodes were obtained by averaging PLV values across trials for each participant and frequency band. Each node’s strength was computed by averaging its corresponding FC with the whole grid. Such averaging resulted in a source-reconstructed FC matrix of 1202 nodes by 4 frequency bands by 107 participants.

Statistical Analyses

Functional Connectivity Strenght (FC-st) analyses

Network-based statistics (NBS) were carried out for each frequency band (26). Clusters consisted of several adjacent nodes that presented a significant partial correlation (age as covariate) between FC-st values and each PA variable (Spearman correlation, p< 0.01). To form a cluster, the correlation coefficients of all nodes were required to have the same sign. Only clusters including at least 1% of the grid (i.e. a minimum of 12 nodes) were considered. Spearman rho values were Fisher Z-transformed. Cluster-mass statistics were computed as the sum of all z values corresponding with all nodes within each cluster. To control for multiple comparisons, the whole process was repeated 5000 times, shuffling the correspondence between FC-st and each PA measure across all participants. At each repetition, the maximum surrogate cluster’s statistic was kept creating a maximal null permutation distribution. For each main cluster, cluster-mass statistics in the original and the randomized datasets were compared. In NBS, p-value represent the proportion of the permutation distribution with cluster-mass statistic values greater or equal than the cluster-mass statistic value of the original data. Only clusters which survived NBS (permutations p-value < 0.05) were considered in further analyses. For each main cluster, FC-st values were averaged across all nodes. These markers were used in subsequent correlation analyses with measures of specific AD signatures (the complete list is shown in Table 4). These were carried out taking the whole sample and following stratification of the cohort by APOE ε4 carriers and non-carriers. p-values were corrected using false discovery rate (FDR) to account for multiple testing. Statistical analyses were carried out using Matlab R2018b (Mathworks Inc).

Seed-based analyses

In order to examine whether the FC-st results were caused by global or local effects, we performed corresponding seed analysis, using the previous clusters as seeds. The FC values assessed were the average FC between each node of the grid and corresponding cluster’s nodes. Then, a set of partial correlation (age as covariate) between these FC values and each PA variable (Spearman correlation, p< 0.01) were computed. Only clusters that did not overlap with the original seed-cluster were reported in this study.

Physical activity is associated with decreased temporal lobe FC-st in ε4 carriers and non-carriers

FC-st is computed as the average FC between each specific source and the rest of the network. Here, we examined whether any brain regions, henceforth referred to as clusters, had FC-st values that significantly correlated with PA levels, using age as a covariate. Significant clusters comprised brain regions that behave as functional units.

Applying NBS methodology independently for each frequency band, three significant clusters emerged, located mainly on the left temporal lobe (see Table 2). Using TPA, we found two significant main (m) clusters, one in the theta band (mθTPA, Figure 1) and one in the delta band (mδTPA, Figure 2A). In the case of MVPA, only one cluster in the delta band showed a significant correlation with FC-st (mδMVPA). Since mδTPA and mδMVPA overlapped to a great degree, only mδTPA is depicted in Figure 2. In the three clusters, FC-st negatively correlated with PA; thus, higher levels of PA were associated with lower cluster FC-st. In addition, the correlation between PA and FC-st in both delta band clusters remained significant when looking at the APOE ε4 carrier and non-carrier groups separately. The cluster in the theta band was significant among APOE ε4 carriers; however, within the APOE ε4 non-carriers the relationship was not significant.

Within the current study, greater levels of PA are associated with lower left temporal functional connectivity in both APOE ε4 carriers and non-carriers. Our findings are relevant to AD risk, as prodromal AD is usually characterized, in low frequency bands, as a stage of temporal lobe hyperexcitability (24,27–30).

Decreased left temporal FC-st is mainly driven by reduced temporo-occipital and temporo-frontal FC

Decreased cluster FC-st indicates that the oscillatory activity (within a given frequency band) of the cluster regions is less synchronously paired with activity from all across the brain. However, in order to more specifically identify which connections drove such an effect, we performed a seed-based analysis. In this seed-based correlation analysis we identified the specific connections (secondary clusters) of each of the main clusters with the rest of the brain that were significantly modulated by PA. We found two significant secondary (s) clusters for each main cluster in the delta band (s1δTPA and s2δTPA for mδTPA, Figures 2B and 2C respectively, and s1δMVPA and s2δMVPA for mδMVPA). The detailed list of areas belonging to these clusters is shown in Table 3. This result was significant among the whole sample and both APOE ε4 carriage subgroups. No significant secondary clusters emerged for mθTPA.

Lower temporal lobe FC is differently associated with cognitive function and brain structure in APOE ε4 carriers and non-carriers.

Once we had described how greater levels of PA related to a distinctive FC-st profile, we aimed to characterize the relationship between this profile and parameters of brain health in order to better understand our results. Significant correlations were quite consistent across clusters. In the whole sample, most AD markers negatively correlated with FC-st, so that lower FC-st values were associated with healthier scores over different domains. Additionally, structural measures (total grey matter, hippocampus, precuneus and amygdala volumes, as well as parahippocampal fasciculus fractional anysotropy) were significantly negatively associated with FC-st among APOE ε4 carriers. Anxiety levels also significantly correlated with FC-st in this group, so that lower FC-st was associated with lower anxiety levels. In contrast, only a few significant correlations were found in APOE ε4 non-carriers, all of them related to cognition (working and episodic memory). The complete set of correlation results can be found in Table 4.

The purpose of the current study was to deepen our understanding of the role that APOE ε4 plays as a modulator in the relationship between PA and brain structure and function. The most relevant finding of the present work is that greater engagement in PA is related to lower left temporal FC, both in APOE ε4 carriers and non-carriers. Similar results were obtained with volumes of both total PA and PA at moderate to vigorous intensity. This FC profile was correlated with varying beneficial effects in AD-related features in both APOE ε4 carriers and non-carriers. However, these favorable associations differed according to AD genetic risk. More specifically, we found a relationship between region-specific decrease in FC-st and greater total GM volumes, greater integrity of the uncinate fasciculus, higher episodic and working memory scores and reduced anxiety levels across the whole sample. In the APOE ε4 non-carriers only, network profile correlated with enhanced episodic and working memory; cognitive skills known to be affected early in the course of AD. In contrast, in APOE ε4 carriers, left temporal hypoconnectivity was associated with more preserved brain structure, particularly in areas that are more vulnerable to AD pathology (hippocampus, precuneus, amygdala and uncinate tract). Figure 3 summarizes these results.

Our finding that PA is associated with reduced temporal lobe hypersynchrony in healthy older adults (even in individuals at greater risk for AD) is noteworthy, considering that AD has been traditionally described as a disconnection syndrome (31). However, recent evidence is building on the idea that preclinical AD is characterized by a dual neurophysiological profile. Through MEG, it has been discovered that in subjective memory decline and mild cognitive impairment, a state of hypersynchrony precedes the well-known phase of hypoconnectivity (24,27–30). A closer look at these individuals’ brain microstructure provides a plausible explanation for these chronological changes. At the very early silent stages of AD pathology, inhibitory neurons are lost, mainly in middle temporal regions (32,33). Such loss of inhibitory synapses leads to a state of brain hyperexcitability and hypersynchrony, which can be tracked through MEG (34). Sustained hyperactivation elicits neurotoxic effects and increased release of amyloid (which ultimately lead to neuronal damage (35,36)). As a result, extensive brain atrophy and generalized hypoconnectivity are evident features by the time AD clinical symptomatology arises (37). Also, it is interesting that it was the left temporal lobe which exhibited significant results, since decreased synchronization in AD, already at the dementia stage, seems to mainly affect the left hemisphere (38). On the other hand, both in normal aging and AD brain activity presents a marked “slowness”, this is, an increase in power in low frequency bands (delta and theta) (39). In fact, increases in delta connectivity had already been described as a pathological sign in other clinical conditions, as well as decreases upon cognitive recovery (40).

In an attempt to understand the meaning of our FC results, we also studied the association between the observed brain activity patterns and cognitive/emotional functioning, brain volumes and white matter integrity: within these analyses, differences between APOE ε4 carriers and non-carriers are evident. Previous literature offers mixed results with regards to whether APOE ε4 carriers or non-carriers gain the greatest benefit from PA engagement. This inconsistent literature may be a result of the utilization of varying PA measurements (questionnaires, fitness measures and PA interventions), different outcomes (AD risk, cognitive scores or brain activity/structure), conducted in samples of different characteristics (in terms of age, AD risk and cognitive status). In the current study, we examined the effect of objectively measured PA on a wide range of AD markers employing a sample of cognitively normal APOE ε4 carriers and non-carriers properly matched on an extensive list of potential confounders.

While measures of different brain volumes correlated with FC-st in the ε4 carrier group, these associations did not exist in ε4 non-carriers. The same pattern arose when looking at the integrity of the uncinate tract. It is important to highlight that these are all brain structures that are particularly vulnerable to AD pathology. Since this is a sample of cognitively normal participants, our results could be better understood if we consider that at-risk individuals already present greater variability in GM and WM state of preservation. Therefore, we could assume that there is more room for PA to counterbalance early neuropathological signs within this group. This is consistent with previous studies that demonstrate the effect of PA on brain pathology is most predominant among ε4 carriers (7,41,42).

Contrary to our aforementioned findings within APOE ε4 carriers, we observed a relationship between decreased FC and greater scores in specific measures of episodic and working memory, but only among APOE ε4 non-carriers. Most studies investigating the relationship between AD incidence and PA have concluded that only APOE ε4 non-carriers benefit from reduced AD risk at greater levels of PA (9,43,44), although there are some exceptions (45,46). Presently, AD diagnosis is based on clinical progression and cognitive status. Therefore, our finding that the profile of FC associated with PA only predicts cognitive functioning among APOE ε4 non-carriers is somewhat consistent with previous research.

Finally, across the whole sample and APOE ε4 carriers, we found a positive correlation between greater FC-st and higher anxiety. In previous studies anxiety has been identified as a marker of conversion from preclinical AD to AD (47,48). In addition, higher levels of anxiety are related to greater temporal lobe atrophy (49). Hence our results demonstrate that PA-associated reduced FC-st is also associated with lower anxiety levels and greater temporal lobe volumes.

PA has been widely studied as a protective ally against AD. Our study sheds light on the potential mechanisms through which PA could exert its action. According to the neurogenic reserve hypothesis, throughout evolution acute bouts of PA were linked to an increased likelihood of a potential cognitive challenge (50,51). As hunter-gatherers, going through long distances relied on improved spatial orientation, memory and executive functions. Locomotion would signal the brain such increase in cognitive demands. In response, the reserve of neuronal precursor cells would grow. In the presence of cognitive stimulation, new neurons would maturate, differentiate and migrate. Multiple sources of evidence support the postulate that PA promotes synaptogenesis and neurogenesis, mainly within the hippocampal network (52,53). Newborn granule cells do not produce hyperactivation but rather present sparse activity during learning (54). Such mechanisms could explain the decreased temporal FC profile that we detect in older adults who regularly engage in greater levels of PA. But most importantly, they could explain why PA is one the most relevant modifiable protective factor against AD. Indeed, Raicheln & Alexander hypothesize that 2 million years ago PA was able to counterbalance the detrimental effects of APOE ε4, when our ancestors carried two copies of this risk allele (55). However, although in this study we report that the association between PA and FC-st is associated to beneficial effects in both ε4 carriers and non-carriers, it remains possible that the underlying mechanisms differ based on AD genetic risk.

The results observed in the current study provide a more comprehensive picture of the relationship between PA, APOE and AD pathology Our findings help strengthen the understanding of the complex dynamics that underpin the varying outcomes observed in previous studies. Although the age range in this sample was fairly broad, mean age was still quite low to study the effects of advanced aging or prodromal AD. Therefore, it is possible that we missed out on certain effects that might only appear at later stages of life. Follow-up studies are required to determine how the FC profiles identified within this study are associated with pathological progression and cognitive change among, at-present, cognitively normal older adults. Also, future studies should include a group of APOE ε4 homozygotes, since our sample size did not allow us to incorporate that comparison. Additionally, it would be interesting to see how diverse PA parameters, such as intensity or frequency of practice, affect certain markers of the disease. In addition, it would be useful to include specific AD biomarkers to better characterize brain health in individuals at risk.

Altogether, our study offers novel insights into this field, clarifying some of the specific mechanisms that underlie the beneficial effect of PA in APOE ε4 carriers and non-carriers. It enables the integration of previous publications and leads the way to future findings. Previous literature offered apparently inconsistent results but our study suggests that the specific brain health outcomes considered could be differently affected by PA in ε4 carriers and non-carriers. Nevertheless, we were able to describe an association between PA and decreased temporal lobe hypersynchrony across the whole sample that highlights the need to design new policies that foster PA among older adults, including those more vulnerable to develop AD.

Ethics approval and consent to participate

The procedure was performed following current guidelines and regulations, and the study was approved by the Hospital Universitario San Carlos Ethics Committee. Every participant signed an informed consent.

Consent for publication

Not applicable.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Competing interests

None of the contributors to this study have any conflict of interest to declare.

Funding

This study was funded by the Spanish Ministry of Economy and Competitiveness under the Grant PSI2015-68793-C3-1-R [D601] and by the project B2017/BMD-3760 from NEUROCENTRO. Complimentary, it was supported by a predoctoral fellowship from La Caixa Foundation to J.F.L., a post-doctoral fellowship from the Spanish Ministry of Economy and Competitiveness to P.C. (FJCI-2015-26755), a grant from the Spanish Ministry of Science, Innovation and Universities to J.V.R. (FJCI-2017-33396) and a predoctoral grant by the Spanish Ministry of Economy (BES-2016-076869) to F.R.T.

Authors’ contributions

F.M., R.L.H. and A.M. outlined the research project; J.F.L., P.C. and D.L.S. designed the experiments; F.R.T., I.C.R.R. and J.F.L. coordinated the data collection; F.R.T. and J.F.L. processed the MEG recordings; R.B. developed the MEG processing pipeline; A.M.L.S., A.P.S. and E.C.S. contributed the actigraphy data; M.L.D.L. coordinated the neuropsychological assessment; A.B. performed the genotyping analyses; J.V.R. provided the DTI analysis methodology; P.C. performed the statistical analyses and prepared the figures; J.F.L. drafted the original manuscript and P.C., D.L.S., B.B., J.M.S., S.M.L. and F.M. revised the original draft and contributed relevant suggestions to the final manuscript. All authors revised and approved the final paper draft.

Acknowledgements

Not applicable.

AAL: Automated anatomical labeling atlas

AD: Alzheimer’s disease

APOE: Apolipoprotein E

DWI: Diffusion weighted images

EDTA: Ethylenediaminetetraacetic acid

FA: Fractional anisotropy

FC: Functional connectivity

FC-st: Functional connectivity strength

FDR: false discovery rate

GM: Grey matter

mθTPA: main cluster found in the theta band using TPA

mδTPA: main cluster found in the delta band using TPA

mδMVPA: main cluster found in the delta band using MVPA

MEG: Magnetoencephalography

MNI: Montreal Neurological Institute

MVPA: Moderate to vigorous physical activity

NBS: Network-based statistics

PA: Physical activity

PLV: Phase locking value

TPA: Total physical activity

s1δTPA and s2δTPA: secondary clusters associated with mδTPA

s1δMVPA and s2δMVPA: secondary clusters associated with mδMVPA

SNP: Single nucleotide polymorphism

WAIS-IV: Weschler Adult Intelligence Scale IV

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TABLE 1. Descriptive measures of the final sample

VARIABLE	WHOLE SAMPLE	CARRIERS	NON-CARRIERS	P
Sex (M; F)	32; 75	8; 25	24; 50	0.49
Age	60.48±8.10	59.36±7.46	60.97±8.36	0.39
Education	4.62±0.68	4.61±0.75	4.622±0.63	0.86
MMSE	29.26±0.84	29.33±0.89	29.23±0.82	0.42
BMI	25.03±3.61	25.01±3.82	25.04±3.55	0.90
TPA	0.0125±0.0117	0.0109±0.0107	0.0133±0.0122	0.48
MVPA	36.34±21.12	33.12±17.64	37.77±22.47	0.54
Forward Digits	5.95±1.22	5.88±1.22	5.99+1.23	0.75
Reverse Digits	4.50±1.36	4.67±1.65	4.43±1.22	0.55
Logical mem. Units	18.79±11.13	20.97±11.87	17.86±10.76	0.25
Logical mem. Gist	15.74±10.87	13.69±8.14	16.63±11.80	0.52
Anxiety	1.71±0.84	1.86±2.37	1.65±2.05	0.84
Depression	1.24±1.62	1.27±1.44	1.23±1.70	0.70
Total GM	545939±51206	546825±66891	545538±42833	0.80
Hippocampus	7549±834	7583±954	7533±780	0.81
Amygdala	2730±470	2721±573	2734±420	0.51
Precuneus	16337±2002	16403±2491	16306.83±1757	0.87
Global FA	0.4355±0.017362	0.4395±0.01645	0.4336±0.0176	0.11
Uncinate	0.4378±0.0238	0.4387±0.0232	0.4374±0,0242	0.87
Parahippocampus	0.4355±0.0173	0.4177±0.0315	0.4145±0.0289	0.71

TABLE 1 provides mean values ± standard deviation for all matching variables as well as variables used for correlation analyses. These include: sex (where M stands for male and F for female), age (in years), education (in terms of educational level on a 0 -illiterate- to 5 -postsecondary education- scale) , Mini Mental State Examination (MMSE), Body Mass Index (BMI), total physical activity (TPA, normalized by actigraphy wear time), daily average of moderate to vigorous physical activity (MVPA, in minutes), working memory (Forward and Reverse Digit Span, -Forward and Reverse Digits-), episodic memory (Logical Memory II -delayed recall-, for Units and Gist -Logical mem. Units and Gist), anxiety (Goldberg’s test), depression (Geriatric Depression Scale), total grey matter volume (GM, in mm3), hippocampus, amygdala, and precuneus volumes (left plus right, in mm3), global fractional anisotropy (FA), uncinate and parahippocampal fasciculus fractional anisotropy (left and right weighted arithmetic mean). Results are displayed for the whole sample and also for each subsample of interest (APOE ε4 carriers and non-carriers). p-values for Mann Whitney and Fisher (sex) tests are also shown. No significant between-group differences arose across all comparisons.

TABLE 2. Main clusters presenting decreased FC-st at higher PA levels.

CLUSTER

mθTPA

mδTPA

mδMVPA

AREAS

Left Amygdala

Left Hippocampus

Left Inferior Temporal gyrus

Left Inferior Frontal gyrus, Orbital

Left Insula

Left Middle Temporal gyrus

Left Postcentral gyrus

Left Superior Temporal gyrus

Left Temporal pole, Middle Temporal gyrus

Left Temporal pole, Superior Temporal gyrus

Left Amygdala

Left Fusiform gyrus

Left Inferior Frontal gyrus, Orbital

Left Inferior Frontal gyrus, Opercular

Left Inferior Temporal gyrus

Left Insula

Left Middle temporal gyrus

Left Parahippocampus

Left Postcentral gyrus

Left Rolandic operculum

Left Superior Temporal gyrus

Left Temporal pole, Middle Temporal gyrus

Left Temporal pole, Superior Temporal gyrus

Left Fusiform gyrus

Left Inferior Frontal gyrus, Orbital

Left Inferior Temporal gyrus

Left Insula

Left Middle temporal gyrus

Left Temporal pole, Middle temporal gyrus

Left Temporal pole, Superior Temporal gyrus

Left Superior Temporal gyrus

TABLE 2. Total physical activity (TPA) and daily average of moderate to vigorous physical activity (MVPA) correlated with functional connectivity strength (FC-st) in three main clusters. This table shows the list of areas upon each significant main cluster fall (in alphabetical order).

TABLE 3. Seed-based Analyses

SEED

mδTPA

mδMVPA

CLUSTER

Anterior (s1δTPA)

Posterior (s2δTPA)

Anterior (s1δMVPA)

Posterior (s2δMVPA)

AREAS

Left Cingulate gyrus, Anterior part

Left Middle Frontal gyrus

Left Paracentral lobule

Left Precentral gyrus

Left Superior Frontal gyrus

Left Supplementary Motor area

Left Superior Frontal gyrus, Medial

Left Calcarine fissure and surrounding cortex

Left Lingual gyrus

Right Calcarine fissure and surrounding cortex

Right Cuneus

Right Lingual gyrus

Right Middle Occipital lobe

Right Middle Temporal gyrus

Left Middle Frontal gyrus

Left Precentral gyrus

Left Superior Frontal gyrus

Left Supplementary Motor area

Left Calcarine fissure and surrounding cortex

Left Cuneus

Left Lingual gyrus

Right Calcarine fissure and surrounding cortex

Right Lingual gyrus

TABLE 3. Each main cluster (mθTPA, mδTPA and mδMVPA) whose functional connectivity strength (FC-st) was significantly correlated with PA was used as a seed in a seed-based analyses. Clusters in the delta band (mδTPA and mδMVPA) presented lower FC to two extra clusters each (s1δTPA, (s2δTPA, s1δMVPA and s2δMVPA). This table presents the regions that are comprised in each additional cluster.

TABLE 4. Correlation analyses

	WHOLE SAMPLE						CARRIERS						NON-CARRIERS
	mθTPA		mδTPA		mδMVPA		mθTPA		mδTPA		mδMVPA		mθTPA		mδTPA		mδMVPA
	rho	p	rho	p	rho	p	rho	p	rho	P	rho	p	rho	p	rho	p	rho	p
Forward Digits	-0.20	0.03	-0.18	0.061	-0.20	0.04	-0.30	0.09	-0.08	0.66	-0.04	0.84	-0.17	0.14	-0.23	0.05	-0.28	0.01*
Reverse Digits	-0.23	0.02*	-0.21	0.03*	-0.22	0.03*	-0.29	0.11	-0.23	0.20	-0.22	0.23	-0.23	0.05	-0.22	0.06	-0.22	0.06
Logical mem. Units	-0.00	0.96	-0.00	0.99	-0.03	0.78	-0.11	0.54	-0.16	0.40	-0.12	0.51	0.05	0.65	0.09	0.47	0.06	0.63
Logical mem. Gist	-0.18	0.07	-0.27	0.01*	-0.23	0.02*	-0.09	0.62	-0.13	0.51	-0.09	0.66	-0.20	0.10	-0.34	0.01*	-0.30	0.01*
Anxiety	0.29	<0.01*	0.22	0.03	0.2	0.05	0.46	0.01*	0.28	0.15	0.29	0.13	0.20	0.09	0.19	0.13	0.16	0.18
Depression	0.08	0.423	0.08	0.43	0.079	0.44	-0.04	0.84	-0.06	0.72	-0.11	0.54	0.14	0.25	0.17	0.17	0.20	0.10
Total GM	-0.27	0.01*	-0.29	<0.01*	-0.29	<0.01*	-0.39	0.03	-0.44	0.01*	-0.43	0.02*	-0.23	0.05	-0.23	0.05	-0.23	0.05
Amygdala	-0.28	<0.01*	-0.27	<0.01*	-0.27	<0.01*	-0.33	0.07	-0.41	0.02*	-0.39	0.03*	-0.25	0.03	-0.21	0.08	-0.21	0.07
Hippocampus	-0.26	0.01*	-0.27	<0.01*	-0.28	<0.01*	-0.50	<0.01*	-0.47	0.01*	-0.42	0.02*	-0.16	0.18	-0.17	0.15	-0.20	0.08
Precuneus	-0.14	0.15	-0.20	0.04	-0.21	0.03*	-0.15	0.41	-0.42	0.02*	-0.42	0.02*	-0.11	0.35	-0.10	0.42	-0.10	0.41
Global FA	-0.03	0.72	-0.08	0.43	-0.04	0.69	-0.18	0.33	-0.16	0.38	-0.11	0.56	-0.00	0.97	-0.04	0.73	-0.00	0.98
Uncinate	-0.24	0.02*	-0.18	0.07	-0.14	0.16	-0.61	<0.01*	-0.44	0.01*	-0.39	0.03*	-0.07	0.58	-0.07	0.59	-0.01	0.91
Parahippocampus	-0.00	0.96	-0.07	0.49	-0.03	0.76	-0.17	0.37	-0.30	0.10	-0.22	0.23	0.05	0.68	0.03	0.77	0.07	0.55

Results for Spearman correlation analyses between mean FC-st of each main cluster (mθTPA, mδTPA and mδMVPA) and a series of AD markers (rho and p-values) are shown. The list of variables includes: working memory (Forward and Reverse Digit Span, -Forward and Reverse Digits-), episodic memory (Logical Memory II -delayed recall-, for Units and Gist -Logical mem. Units and Gist), anxiety (Goldberg’s test), depression (Geriatric Depression Scale), total grey matter volume (GM, in mm3), hippocampus, amygdala, and precuneus volumes (left plus right, in mm3), global fractional anisotropy (FA), uncinate and parahippocampal fasciculus fractional anisotropy (left and right weighted arithmetic mean). Outcomes that were significant for α<0.05 and FDR q=0.1are bolded and marked with an asterisk (*).

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The relationship between physical activity, apolipoprotein E ε4 carriage and brain health

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