Effect of Education on Alzheimer’s Disease-Related Neuroimaging Biomarkers in Healthy Controls,and Participants with Mild Cognitive Impairment and Alzheimer’s Disease: A Cross-Sectional Study

Abstract

Background:

Cognitive reserve is the acquired capacity reflecting a functional brain adaptability/flexibility in the context of aging. Educational attainment is thought to be among the most important factors that contribute to cognitive reserve.

Objective:

The aim of this study is to investigate the relationships among duration of education and Alzheimer’s disease (AD) related neuroimaging biomarkers such as amyloid-β deposition, glucose metabolism, and brain volumes in each stage of AD.

Methods:

We reanalyzed a part of the datasets of the Alzheimer’s Disease Neuroimaging Initiative. Participants were between 55 and 90 years of age and diagnosed as one of the following: healthy controls (HC), mild cognitive impairment (MCI), or AD. Multiple regression analyses were conducted to examine the relationships among duration of education and amyloid-β deposition (n = 825), brain metabolism (n = 1,304), and brain volumes (n = 1,606) among three groups using data for ¹⁸F-Florbetapir (AV-45) imaging, fludeoxyglucose (FDG) positron emission tomography, and T1-weighted magnetic resonance imaging.

Results:

Duration of education had no correlations with amyloid-β deposition or brain metabolism in any groups. However, duration of education was positively associated with the total brain volume only in participants with MCI.

Conclusions:

Our findings suggest that education may exert a protective effect on total brain volume in the MCI stage but not in HC or AD. Thus, education may play an important role in preventing the onset of dementia through brain reserve in MCI.

Keywords

Alzheimer’s disease Alzheimer’s Disease Neuroimaging Initiative (ADNI)brain reserve brain volume cognitive reserve education mild cognitive impairment

INTRODUCTION

Alzheimer’s disease (AD) is a progressive neurodegenerative disease characterized by cognitive impairment as core symptoms in the elderly. AD represents a substantial burden on patients, families, and societies [1]. Although some pharmacological treatments are clinically available for alleviating partially the symptoms of AD, no disease-modifying or preventative treatments have been developed. Thus, there is an urgent need to develop new effective therapeutic methods to overcome the medical and socioeconomic issues associated with AD. To this end, elaborate research is warranted to understand the pathology of AD. Mounting evidence has supported the amyloid cascade hypothesis, which states that excessive accumulation of a peptide called amyloid-β may be the key contributor associated with the onset of AD [2]. According to this hypothesis, the pathology of AD gradually progresses in the order of accumulation of amyloid-β, reduced cerebral blood flow and hypometabolism of glucose, brain atrophy, and then cognitive decline in this population. However, it is also known that there is an inter-individual difference in the relationship between the pathological change in the brain and the onset of cognitive symptoms.

Brain reserve is a hypothetical concept that explains this individual heterogeneity: the more the reserve, the more change required to cause symptoms. Stern conceptualized cognitive reserve as functional aspect of such reserve reflecting individual life experience, as opposed to quantitative aspect of the reserve such as the number of neurons the brain can lose while maintaining its function [3, 4]. Education has been regarded as one of the most important contributors to cognitive reserve. Murray et al. revealed that educational attainment has a positive effect on late-life cognitive function in elderly people without dementia [5]. McDowell et al. found that high levels of educational attainment protect against progress of dementia [6]. Education is considered to have a protective effect against both cognitive decline and atrophy associated with aging and dementia possibly through neuroplasticity. For instance, a previous meta-analysis noted that brain size is positively correlated with intellectual levels especially in cognitively normal elderly adults [7]. In contrast, in participants with AD, a review article summarized that there is a negative correlation between cognitive reserve and brain size [8]. However, there are few reports that examine the relationship between cognitive reserve and AD-related brain imaging biomarkers. Further, it still remains unclear how education levels modify the pathology of AD. Furthermore, since mild cognitive impairment (MCI) is considered as an intermediary stage between normal aging and AD, it is important to explore this critical clinical stage to understand the underlying pathology of AD. However, few studies have examined relationships between education levels and brain imaging biomarkers related to AD pathology among participants with MCI.

Thus, the aim of this article was to investigate the relationships between education levels and brain imaging biomarkers such as amyloid-β, glucose metabolism, and brain volumes in healthy controls, participants with MCI and AD in order to explore the potential mechanism of cognitive reserve.

METHODS

Data used in the preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (http://adni.loni.usc.edu). The ADNI was launched in 2003 as a public-private partnership, led by Principal Investigator Michael W. Weiner, MD. The primary goal of ADNI has been to test whether serial magnetic resonance imaging (MRI), positron emission tomography (PET), other biological markers, and clinical and neuropsychological assessment can be combined to measure the progression of MCI and early AD. For up-to-date information, see http://www.adni-info.org.

Participants

Participants aged from 55 to 90 (inclusive) years old were enrolled in this study. Participants diagnosed with MCI or AD, as well as healthy controls (HC) were included in this study. The inclusion criteria are as follows: for HC, Mini-Mental State Examination (MMSE) scores between 24–30, a clinical dementia rating (CDR) of 0, non-depressed, non MCI, and non-demented; for participants with MCI, MMSE scores between 24–30, and objective memory loss measured by education adjusted scores on Wechsler Memory Scale Logical Memory II, a CDR of 0.5, absence of significant levels of impairment in other cognitive domains, essentially preserved activities of daily living, and an absence of dementia; for participants with AD, MMSE scores between 20–26, CDR of 0.5 or 1.0, and meets NINCDS/ADRDA criteria for probable AD [9]. General data of the participants (age, sex, apolipoprotein E (ApoE) type, past history of depression, and MMSE score) were extracted from ADNI-1, ADNI Grand Opportunity (ADNI-GO), and ADNI-2. The neuroimaging data was also obtained from the ADNI-1, ADNI-GO, and ADNI-2 databases (https://ida.loni.usc.edu) for our study. Past history of depression was collected from recent medical history details log in the ADNI database.

Amyloid-β PET analysis

Data for ¹⁸F-Florbetapir (AV-45) imaging from ADNI-2 and ADNI-GO as of 17 Oct 2016 were analyzed in this study. The acquisition protocol and image preprocessing of these data are publicly available on the ADNI website (http://adni.loni.usc.edu/). The dataset demonstrates mean AV-45 uptake in cortical grey matter weighted florbetapir of the regions of interest (ROIs) for all participants. The ROIs included the bilateral frontal, anterior/posterior cingulate, lateral parietal, and lateral temporal cortices which ADNI group defined. ROI-based AV-45 standardized uptake value ratios (SUVR) were calculated as follows: SUVR = (AV-45 uptake in cortical grey matter weighted florbetapir mean within ROI)/(AV-45 uptake florbetapir mean of whole cerebellum). The details of processing method of the data were described in “UC Berkeley - AV45 Analysis Methods (PDF)” (https://ida.loni.usc.edu/pages/access/studyData.jsp).

Fludeoxyglucose (FDG) PET analysis

Datasets for FDG-PET imaging from ADNI-1 and ADNI-GO as of 30 July 2015 were analyzed. The acquisition protocol and image preprocessing of these data were the same as those for AV-45 imaging. The dataset shows mean FDG-PET glucose metabolism normalized to pons within the bilateral angular, postcingulum, and temporal cortices which are defined by ADNI group. The details are described in “UC Berkeley –FDG Methods (PDF)” as well.

Voxel-based morphometry

Datasets for T1 imaging derived from ADNI-1 and ADNI-GO were analyzed and cortical reconstruction and volumetric segmentation were performed with the FreeSurfer image analysis suite. The details of the analysis method were described in “UCSF FreeSurfer Methods” (https://ida.loni.usc.edu).

Statistical analyses

Table 1

Comparison of demographic profile in any ROIs among the participants with HC, MCI, and AD

	Amyloid-β-PET			FDG-PET			Voxel-Based Morphometry
	HC (n = 303)	MCI (n = 426)	AD (n = 96)	HC (n = 426)	MCI (n = 649)	AD (n = 229)	HC (n = 443)	MCI (n = 661)	AD (n = 502)
	Mean (SD)	Mean (SD)	Mean (SD)	Mean (SD)	Mean (SD)	Mean (SD)	Mean (SD)	Mean (SD)	Mean (SD)
Age	75.0 (6.80)	74.5 (6.60)	73.4 (7.18)	74.0 (5.87)	72.7 (7.44)	74.9 (7.89)	74.0 (5.82)	72.9 (7.72)	74.4 (7.58)
Education	16.1 (2.83)	15.8 (2.82)	16.3 (2.86)	16.4 (2.73)	16.0 (2.75)	15.3 (2.97)	16.4 (2.69)	15.9 (2.85)	15.3 (2.94)
MMSE	27.0 (3.55)	26.5 (3.67)	26.8 (3.00)	29.0 (1.17)	27.7 (1.80)	23.2 (2.32)	29.0 (1.18)	27.9 (1.86)	22.8 (3.03)
	Number (%)	Number (%)	Number (%)	Number (%)	Number (%)	Number (%)	Number (%)	Number (%)	Number (%)
Female	101 (33.3)	175 (41.1)	29 (30.2)	221 (51.9)	264 (40.7)	95 (41.5)	233 (52.6)	283 (42.8)	217 (43.2)
0 ApoE	182 (60.1)	243 (57.0)	49 (51.0)	304 (71.4)	326 (50.2)	77 (33.6)	320 (72.2)	372 (56.3)	166 (33.1)
1 ApoE	91 (30.0)	137 (32.2)	33 (34.4)	111 (26.2)	254 (38.5)	109 (47.6)	112 (25.3)	234 (35.4)	235 (46.8)
2 ApoE	30 (9.1)	46 (10.8)	14 (14.6)	10 (2.3)	68 (10.5)	42 (18.3)	10 (2.3)	52 (7.9)	94 (18.7)
Depression	44 (14.5)	71 (16.7)	13 (13.5)	93 (21.8)	207 (31.9)	67 (29.3)	91 (20.5)	192 (29.0)	157 (31.2)

ApoE, apolipoprotein E4; 0 ApoE4, number of 0 ApoE4 allele holders; 1 ApoE4, number of 1 ApoE4 allele holders; 2 ApoE4, number of 2 ApoE4 alleles holders; Depression, number of participants with any lifetime history of major depression.

All statistical analyses were performed by IBM SPSS 23.0-J software package (IBM, Japan). Continuous and categorical variables were described as the mean±standard deviation (SD) and number (%), respectively. First, stepwise multiple regression analyses were performed to examine whether duration of education, age, sex, ApoE type, past history of depression, and MMSE score can predict SUVR (i.e., index of amyloid-β deposition) of the bilateral frontal, anterior/posterior cingulate, lateral parietal, and lateral temporal cortices among the participants with HC, MCI, and AD. Next, stepwise multiple regression analyses were conducted to examine whether aforementioned independent variables can predict glucose metabolism as measured by FDG-PET among those three groups in the bilateral angular, postcingulum, temporal cortices, separately. Finally, we performed stepwise multiple regression analyses to examine whether aforementioned independent variables can predict total brain volume and hippocampal volume among these three groups, except that for the analysis of the hippocampal volume, total brain volume was added as an independent variable in the statistical model

All statistical tests were performed with a two-tailed test and Bonferroni correction was applied for each multiple regression analysis to control the multiple comparisons. Thus, the significance levels of alpha of the model was set such as 0.0014 (=0.05/(3 groups×3 outcomes×4 ROIs)), 0.0011 (=0.05/(3 groups×3 outcomes×5 ROIs)), and 0.0028 (=0.05/(3 groups×3 outcomes×2 ROIs)) for the analyses of AV45, glucose metabolism, and brain volume, respectively.

RESULTS

Demographic profile

Demographic and biometrics profiles of participants in this study is shown in Table 1.

Cortical amyloid-β deposition

No significant associations were found between the variables of interest and AV-45 SUVR in any ROIs in the participants with HC, MCI, and AD (Table 2).

Brain metabolism

In HC, there were associations between age and the metabolism in all of the ROIs. In addition, ApoE type and age were also associated with the metabolism in the left angular cortex. In participants with MCI, age, sex, ApoE type, and MMSE score were related to the metabolism in the bilateral angular gyrus while age, ApoE type, and MMSE score were associated with the metabolism in the bilateral postcingulum and bilateral temporal gyri. In participants with AD, age and MMSE score were associated with the metabolism in all of the ROIs (Table 3).

Table 2

Multiple regression analysis between cortical amyloid-β deposition of each ROI and independent variables (duration of education, age, sex, ApoE type, past history of depression, and MMSE score) among HC, MCI, and AD

Variables	Frontal		cingulate		parietal		temporal
	standardized beta	p	standardized beta	p	standardized beta	p	standardized beta	p
HC
p value of the model		0.009		0.020		0.003		0.003
Education	0.013	0.822	0.018	0.758	0.010	0.859	0.005	0.935
Age	–0.020	0.726	–0.014	0.811	–0.003	0.955	–0.005	0.935
Sex	–0.025	0.662	–0.024	0.674	–0.017	0.766	–0.023	0.692
ApoE4	0.077	0.209	0.075	0.219	0.087	0.156	0.063	0.306
Depression	0.084	0.143	0.092	0.106	0.094	0.098	0.097	0.88
MMSE	–0.150	0.009^*	–0.133	0.020^*	0.002	0.003^*	–0.169	0.003^*
MCI
p value of the model		0.012				0.003		0.009
Education	–0.079	0.103			–0.051	0.297	–0.060	0.214
Age	0.033	0.503			0.034	0.489	–0.005	0.924
Sex	0.011	0.812			0.011	0.813	0.018	0.705
ApoE4	0.136	0.008^*			0.157	0.002^*	0.142	0.006^*
Depression	0.063	0.191			0.054	0.258	0.059	0.220
MMSE	0.112	0.029^*			0.131	0.010^*	0.112	0.029^*
AD
p value of the model
Education
Age
Sex
ApoE4
Depression
MMSE

Independent variables were included if they were statistically significant at p < 0.05, Models were included if they were statistically significant at p < 0.0014, Blank = the situation that all of independent variables were excluded from regression model. Bold characters = statistically significant findings, ^*and bold style = statistically significant findings.

Table 3

Multiple regression analysis between brain metabolism of each ROI and independent variables (duration of education, age, sex, ApoE type, past history of depression, and MMSE score) among HC, MCI, and AD

Variables	left angular		right angular		bilateral postcingulum		left temporal		right temporal
	standardized beta	p	standardized beta	p	standardized beta	p	standardized beta	p	standardized beta	p
HC
p value of the model		<0.001^*		<0.001^*		<0.001^*		0.016		0.026
Education	0.027	0.568	0.032	0.501	0.002	0.970	0.073	0.132	0.043	0.373
Age	–0.197	<0.001^*	–0.198	<0.001^*	–0.270	<0.001^*	–0.117	0.016^*	–0.108	0.026^*
Sex	0.078	0.105	0.037	0.444	–0.024	0.611	0.069	0.155	0.014	0.771
ApoE	–0.096	0.046^*	–0.085	0.076	–0.013	0.789	–0.054	0.269	–0.080	0.100
Depression	–0.021	0.658	–0.033	0.495	–0.043	0.365	0.020	0.685	0.017	0.725
MMSE	0.004	0.932	0.003	0.945	0.083	0.077	0.023	0.634	0.015	0.765
MCI
p value of the model		<0.001^*		<0.001^*		<0.001^*		<0.001^*		<0.001^*
Education	–0.017	0.639	0.031	0.406	–0.21	0.570	–0.056	0.129	0.008	0.827
Age	–0.168	<0.001^*	–0.157	<0.001^*	–0.235	<0.001^*	–0.139	<0.001^*	–0.149	<0.001^*
Sex	0.118	0.001^*	0.085	0.023^*	0.008	0.834	0.059	0.110	0.055	0.148
ApoE	–0.194	<0.001^*	–0.216	<0.001^*	–0.173	<0.001^*	–0.209	<0.001^*	–0.188	<0.001^*
Depression	–0.026	0.481	–0.003	0.942	–0.038	0.307	–0.024	0.516	–0.014	0.701
MMSE	0.242	<0.001^*	0.174	<0.001^*	0.193	<0.001^*	0.237	<0.001^*	0.178	<0.001^*
AD
p value of the model		<0.001^*		<0.001^*		0.001^*		<0.001^*		<0.001^*
Education	–0.046	0.466	–0.017	0.785	–0.002	0.980	–0.085	0.171	0.033	0.614
Age	0.321	<0.001^*	0.335	<0.001^*	0.186	0.005^*	0.302	<0.001^*	0.265	<0.001^*
Sex	0.107	0.087	0.068	0.272	0.121	0.065	0.049	0.437	0.003	0.959
ApoE	0.057	0.373	0.009	0.887	–0.057	0.380	0.020	0.757	–0.071	0.274
Depression	0.017	0.781	0.070	0.256	0.055	0.401	0.024	0.693	0.060	0.349
MMSE	0.208	0.001^*	0.220	<0.001^*	–0.155	0.018^*	0.256	<0.001^*	0.136	0.033^*

Independent variables were included if they were statistically significant at p < 0.05, Models were included if they were statistically significant at p < 0.0011, Blank = the situation that all of independent variables were excluded from regression model. Bold characters = statistically significant findings, ^*and bold style = statistically significant findings.

Table 4

Multiple regression analysis between brain volume and independent variables (duration of education, age, sex, ApoE type, past history of depression, and MMSE score) among HC, MCI, and AD

Variables	Total brain volume		Hippocampus
	standardized beta	p	standardized beta	p
HC
p value of the model		<0.001^*		<0.001^*
Education	0.072	0.074	–0.047	0.221
Age	–0.336	<0.001^*	–0.225	<0.001^*
Sex	–0.510	<0.001^*	0.051	0.252
ApoE4	–0.014	0.714	–0.051	0.168
Depression	–0.036	0.368	–0.037	0.319
MMSE	0.039	0.324	–0.010	0.779
Total brain volume			0.577	<0.001^*
MCI
p value of the model		<0.001^*		<0.001^*
Education	0.090	0.004^*	–0.061	0.061
Age	–0.404	<0.001^*	–0.314	<0.001^*
Sex	–0.458	<0.001^*	–0.043	0.241
ApoE4	–0.060	0.055	–0.118	<0.001^*
Depression	0.000	0.993	–0.038	0.237
MMSE	0.061	0.056	0.112	0.001^*
Total brain volume			0.434	<0.001^*
AD
p value of the model		<0.001^*		<0.001^*
Education	–0.009	0.809	0.006	0.875
Age	–0.250	<0.001^*	–0.260	<0.001^*
Sex	–0.536	<0.001^*	0.025	0.554
ApoE4	0.011	0.748	–0.145	<0.001^*
Depression	0.036	0.296	–0.036	0.297
MMSE	0.104	0.003^*	0.183	<0.001^*
Total brain volume			0.500	<0.001^*

Independent variables were included if they were statistically significant at p < 0.05, Models were included if they were statistically significant at p < 0.0028, Blank = the situation that all of independent variables were excluded from regression model. Bold characters = statistically significant findings, ^*and bold style = statistically significant findings.

Brain volume

In HC, there were associations between total brain volume and age as well as sex. Furthermore, age and total brain volume were associated with hippocampal volume in HC. In participants with MCI, duration of education, sex, and age were associated with the total brain volume. There were relationships between hippocampal volume and age, ApoE type, MMSE score, and total brain volume in MCI. In participants with AD, sex, age, MMSE score were related to total brain volume. In addition, age, ApoE type, MMSE, and the total brain volume were correlated with hippocampal volume in AD (Table 4).

DISCUSSION

In this study, we utilized ADNI data to investigate the relationships among duration of education and AD-related biomarkers including cortical amyloid deposition, glucose metabolism, and brain volume in the participants with HC, MCI, and AD. We found that duration of education was associated with the total brain volume in participants with MCI whereas it was not related to total brain volume in the participants with HC or AD. However, duration of education was not correlated with amyloid-β deposition or brain metabolisms in any ROIs for all the groups.

Brain volume

Our finding showed that duration of education was related to total brain volume only in participants with MCI but not in the those with HC or AD, which suggested that education attainment may have preventive effect on brain atrophy appearing after amyloid-β accumulation. With regard to our finding on MCI, we speculated that effect of cognitive reserve by education may differ depending on brain regions. Indeed, we found that volume of the hippocampus, the region responsible for the short-term memory, was negatively associated with education level with a trend-toward significance (p = 0.061), which was consistent with the finding that the more the reserve, the more change required to cause symptoms. In contrast, total brain volume may reflect the brain reserve, which may be more influenced by education year. Moreover, one meta-analysis (n = 8,036) noted that healthy elderly adults with the longer duration of education presented with the larger brain size [7]. Consistent with this, we found trend-toward significant association between education attainment and total brain volume in HC (p = 0.074) while this significance did not survive after a statistical correction for multiple comparisons. Contrary to our finding, on the other hand, previous studies have demonstrated that cortical thickness was significantly thinner in participants with AD with higher levels of education than those with lower levels of education [11]. Taken together, our finding indicated the influence of education on the brain volume, which was represented by a brain reserve, and it was also in line with evidence that education could play an important role for preventing the onset of dementia. However, it was unlikely that the compensatory effect of education could overcome the pathology of AD, especially volumetric atrophy in AD. This finding may support a previous report that paradoxically, brain atrophy progresses rather rapid in the individuals with higher levels of education than those without at the AD stage [10].

In summary, our finding may account for brain reserve by education in participants with MCI, however in participants with AD this compensation mechanism may no longer be effective once the pathology of AD progresses. In addition, our study also shows that the neuroprotective effect of education may exert on total brain volume without any specific effect on hippocampal volume at least in participants with MCI. However, there is little evidence showing the relationship between cognitive reserve and hippocampal volume in each stage of AD. Therefore, further research is clearly warranted to focus on AD-related brain regions.

Cortical amyloid-β deposition

No relationships were found between duration of education and the amyloid-β deposition among any group. One previous meta-analysis noted that highly educated participants had a higher prevalence of amyloid pathology than those with less formal education regardless of cognitive function, age, and APOE ɛ4 carrier status [12]. To the contrary, one postmortem study reported that education itself was not associated with amyloid-β deposition [13]. The discrepancies in the findings between the two studies may be due to differences in research approach as well as target population groups. Specifically, the disparity between our finding and previous studies may be at least partly due to the differences of number of the participants and population background. The ADNI may include individuals with relatively high social status, which would cause potential population bias (https://data.oecd.org/eduatt/population-with-tertiary-education.htm). The present result suggests that education has no significant effects to prevent amyloid-β accumulation in any of the ROIs analyzed at AD stage.

Brain metabolism

We did not find relationship between years of education and brain metabolism in any group. Previous studies have indicated that more highly educated individuals showed greater brain metabolism in the cognitively normal elderly adults [14, 15], while another study showed a negative relationship between duration of education and brain metabolism in participants with AD [16]. These results suggest that duration of education may have positive impact on brain metabolism in cognitively normal elderly adults whereas it may have negative influence on brain metabolism in participants with AD. The reason for our null findings on the relationships between education levels and brain metabolism may be attributable to differences in the population background as mentioned above. Thus, we speculate that education may have no significant effects on brain metabolism for any stages of AD but it may have strong influence on the total brain volume in the later process of the amyloid cascade.

Limitations

There are several limitations to our study. First, our findings might be, at least in part, attributable to the sampling bias of the ADNI study per se, since the participants of the ADNI were supposed to be those who had access to the research institutes or major psychiatric hospitals in the United States, and further had some interest to participate in this study. Such kind of bias would occur especially in social status. Indeed, the rates of the individuals who have completed tertiary education in the ADNI database is 64.9%, 65.5%, and 63.6% for AV45, glucose metabolism, and brain volume respectively, whereas the rate in the real world is only 41.9% among people whose age is from 55 to 65 years in the United States (https://data.oecd.org/eduatt/population-with-tertiary-education.htm). This fact indicates that the participants included in this study may be the individuals of social eminence, which may be one of the confounding factors. The individuals with higher social status may engage in healthy behaviors to compensate for the shortness of education years by themselves even if such individuals’ years of education are relatively short [17 –19]. Indeed, social status affect the lifestyle such as sleeping, eating, and exercise habit that have strong influence on the pathology of AD [20 –22]. For example, sleep deprivation suppresses the glymphatic distribution of amyloid-β into the brain and its elimination [20]. Physical exercise and the Mediterranean-type diet adherence were independently associated with a lower risk of developing AD [21]. Thus, these healthy behaviors may mask the relative shortness of education years in those individuals. Therefore, ADNI data may have potential sample bias that makes it difficult to interpret the results of this analysis in terms of the real effect of education. Second, we analyzed amyloid-β deposition, brain metabolism, and brain volume; however, connectivity, tau deposition, or CSF tau among the groups were not available. Thus, further research is needed to explore the relationships among these AD-related biological measures and duration of education. Third, with respect to the analysis of brain volume, we only investigated whole brain and the hippocampus, where AD pathology is likely to proceed. Fourth, we have explored only duration of education as cognitive reserve but other intellectual activity. Fourth, in the ADNI cohort, the inclusion criteria for participants with AD included the MMSE scores ranging from 20 to 26 and CDR ranging from 0.5 to 1, which suggests that our sample included only mild AD. Therefore, further research is needed to examine the relationship among education levels and AD-related neuroimaging biomarkers in participants with AD with various severity. Fifth, although it is known that the progression of AD starts in the posterior cingulated [23], we lumped anterior and posterior cingulate together since the original ADNI database did not provide the data separately. Sixth, a recent study noted that depressive symptoms in late life but not midlife were associated with increased risk for dementia, suggesting that depressive symptoms may be a prodromal feature of dementia or that late-life depression and dementia may share common causes [24]. However, we defined past history of depression based on the participants’ statement on it from the ADNI dataset, which did not allow us to clarify the onset of depression in the participants. Lastly, we analyzed only the cross-sectional datasets of the ADNI, which did not allow us to examine the direct causality between education attainment and AD-related biomarkers based on brain imaging.

Conclusions

In our analysis of the ADNI data, we found that longer duration of education has a significant impact not only on cognitive reserve but also on brain reserve specifically in participants with MCI. However, it still remains elusive how education prospectively plays a role in MCI/AD pathologies as cognitive reserve or brain reserve. Thus, there is a need for further research to investigate longitudinal changes of the pathology of AD with multimodal approach using biological measures to elucidate the pathophysiology underlying AD, taking into consideration behavioral and environmental factors such as exercise, social engagement, smoking as well as comorbidities such as hearing loss, depression, diabetes, and obesity [20–22 , 25].

Footnotes

ACKNOWLEDGMENTS

Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.

Authors’ disclosures available online ().

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