18 F-AV45 PET and MRI Reveal the Influencing Factors of Alzheimer’s Disease Biomarkers in Subjective Cognitive Decline Population

Abstract

Background:

Subjective cognitive decline (SCD) is a self-perceived decline in cognitive ability, which exhibits no objective impairment but increased risk of conversion to mild cognitive impairment and Alzheimer’s disease (AD).

Objective:

To investigate how influencing factors (risk gene, age, sex, and education) affect amyloid-β (Aβ) deposition and gray matter (GM) atrophy in SCD population.

Methods:

281 SCD subjects were included in this study, who underwent clinical evaluation, cognitive ability assessment, apolipoprotein E (APOE) genotyping, ¹⁸F-Florbetapir positron emission computed tomography, and magnetic resonance imaging screening. Two-sample t tests and analysis of variance were performed based on voxel-wise outcome.

Results:

In 281 SCD subjects with an average age of 63.86, 194 subjects (69.04%) were females, and 56 subjects carried APOE ɛ4 genes. Statistical results revealed APOE ɛ4 gene, age, and sex influenced Aβ deposition in different brain regions; moreover, only the interaction exhibited between age and APOE ɛ4 genes. The GM atrophy of hippocampal, amygdala, precentral, and occipital lobes occurred in the group age over 60. The GM volume of the hippocampal, frontal, and occipital lobe in females was less than males. Education had an effect only on cognitive function.

Conclusion:

In SCD, APOE ɛ4 gene, age, and sex significantly influenced Aβ deposition and APOE ɛ4 gene can interact with age in impacting Aβ deposition. Both age and sex can affect GM atrophy. The results suggested that female SCD with APOE ɛ4 genes and aged more than 60 years old might exhibit advanced AD biomarkers.

Keywords

Alzheimer’s disease amyloid-β atrophy deposition gray matter subjective cognitive decline

INTRODUCTION

Alzheimer’s disease (AD) is the most common type of dementia, characterized by the decline of cognitive ability and independence of routine activities [1]. In recent years, subjective cognitive decline (SCD) is proposed as a potential preclinical stage with ∼10 years before the diagnosis of dementia [2, 3], in which subjects exhibit a self-perception of cognitive decline in absence of objective indicators to prove the cognitive impairment [4, 5]. Previous longitudinal studies have found that SCD population have a higher likelihood of converting to mild cognitive impairment (MCI) and AD, with a 40–62% risk of progressing either to MCI or dementia within 3 years and a 19% incidence of progressing to dementia after 6 years [6 –8]. The strong association between SCD and cognitive impairment was suspected. AD patients may exhibit amyloid-β (Aβ) deposition as early as a few years to two decades before clinical dementia period [9 –14], which means that there is an opportunity to get the early-stage feature of AD. At the same time, Aβ deposition in late AD stage is irreversible [15 –17], which also encouraged us to study the early phase features and influencing factors of AD-related biomarkers in preclinical or prodromal stages of AD.

It has been shown that several potential factors are relevant to the progression from SCD to MCI or AD or cognitive appearance in SCD population. The apolipoprotein E (APOE) ɛ4 allele is one of the most common genetic risk factor for AD [18, 19], since one longitudinal study [20] found that the conversion proportion for MCI in 5 years were the highest in homozygous APOE ɛ4 group of SCD in women. In terms of age, the previous relevant work [21] concluded that participants with SCD in the subgroup of 65–75 years old showed a higher risk of developing objective cognitive disorders. With regards to sex, some population-based studies [22, 23] found that females report more subjective memory complaints or SCD than males. At the same time, Heser et al. [24] found that female SCD was more strongly associated with dementia than male SCD. Similarly, long-education also exhibited a higher risk for objective cognitive disorders [21]. For instance, cognitive reserve has been found it can possibly influence the progression from SCD to MCI even dementia [25 –27]. However, it remains unclear how these four factors (APOE ɛ4, age, sex, years of education) in SCD population confer greater risk of cognitive decline or the progression from SCD to AD.

Herein, a cross-sectional study was conducted to illustrate the possible influencing factors of Aβ deposition and gray matter (GM) volume in the SCD population and explore whether interactive effects exhibit between these factors.

MATERIALS AND METHODS

Patients and assessment

In this cross-sectional study, 281 subjects ranged in age from 41 to 80 years old were obtained. All these 281 subjects were diagnosed as SCD with worries, which were assessed by clinicians and the SCD questionnaire (Are you feeling memory loss? Do you feel worried about it?). Worry-free SCD subjects were not included in this work. Simultaneously, none of them appeared objective cognitive impairment. Cognitive normal and MCI and dementia population were excluded. Neuropsychological tests, including Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment-Basic (MoCA_B) Chinese version [28], Auditory Verbal Learning Test_N5, Auditory Verbal Learning Test_N7 [29], Animal Fluency Test, Boston Naming Test, Trail Making Test A and Trail Making Test B [30], were evaluated. This work was approved by the ethical review committee of Huashan Hospital, Fudan University and written informed consent was obtained from each participant or their guardians.

Demographic and APOE genotyping

Age and years of education were both measured as continuous and dichotomous variables. 60 years of age (one SCD plus feature) [4] and 12 years of education (mean, 12.324 years; median, 12) were used as cutoff values of age and education. APOE ɛ4 allele status and self-reported sex information of all subjects were collected. Venous blood samples were collected to perform PCR to obtain APOE allele status. Without being distinguished the homozygous and heterozygous APOE ɛ4 allele status, subjects with at least one ɛ4 allele were called APOE ɛ4 carriers, then the rest of subjects were called APOE ɛ4 non-carriers.

¹⁸F-AV45 PET and MRI scanning and preprocessing

Brain ¹⁸F-Florbetapir (¹⁸F-AV45) PET scans were following a routine procedure on a dedicated PET/CT scanner (Biograph mCT Flow scanner, Siemens, Germany) within 50 min post intravenous injection of ∼370 MBq (∼10 mCi) of ¹⁸F-AV45. Magnetic resonance images (MRI) were collected on an MRI scanner (PRISMA 3.0T, Siemens). All MRI T1-weighted images were obtained through a Magnetization Prepared Rapid Gradient Echo sequence on sagittal scans at different locations.

Statistical Parametric Mapping (SPM) 12 toolbox in MATLAB R 2018b was used to perform preprocessing. Computational Anatomy Toolbox 12 was used to segment the T1-weighted MRIs. Meanwhile, MRIs were used as guide images to co-register with PET images and to perform partial volume correction, normalization, and smoothing.

Statistical analysis

SPM 12 was used to perform two-sample t tests and analysis of variance (ANOVA) of voxel-wise outcome. Two-simple t tests were used to test the Aβ deposition and GM volume difference among APOE genotypes (ɛ4 carrier versus ɛ4 non-carrier), age (≥60 versus < 60), sex, and years of education (> 12 versus≤12). For the purpose of understanding the main and interaction effects of these factors on Aβ deposition and GM atrophy, ANOVA were performed, adjusted for the effects of remaining demographic variables not of interests. Cerebellar crus was used as a reference region for Aβ deposition and GM corrected for effect of total intracranial volume. The results of neuropsychological assessment were also compared by two-simple t tests. All probability p values were set as p < 0.05. Statistical analysis was performed using SPSS version 26 (SPSS Inc, Chicago, IL).

For voxel-wise analyses, the significance level was set at p < 0.05 with cluster-level False Discovery Rate (FDR) correction, and the cluster-defining voxel threshold at default of 0.001.

RESULTS

Demographic results

Among the 281 SCD subjects with an average age of 63.86 (standard deviation (SD), 7.53), 194 (69.04%) were female. 56 subjects carried APOE ɛ4 risk genes (homozygous and heterozygous conditions were not considered).

The neuropsychological assessment results

In the neuropsychological assessment, average MMSE score is 28.04 (SD, 1.68) and average MoCA_B is 25.38 (SD, 2.82). When grouped by age and grouped by years of education, all SCD subjects almost showed all significant differences. On the contrary, there is almost no significant difference between carriers, noncarriers, males, females (Table 1).

Table 1

Inter-group comparisons between E4-carrier and E4-noncarrier, age≥60 and age < 60, females and males and education years > 12 and≤12

APOE ɛ4 Status			Age
	Carrier	Non-carrier	p	≥60	< 60	p
	(n = 56)	(n = 225)		(n = 200)	(n = 81)
APOE ɛ4 (%)	–	–	–	16.50	28.40	0.024*
Age, y	63.75 (8.78)	63.89 (7.20)	0.913	–	–	–
Sex (female%)	71.43	68.44	0.666	66.00	76.54	0.083
Education, y	12.66 (3.32)	12.24 (3.07)	0.368	12.04 (3.10)	13.02 (3.08)	0.017^*
MMSE	27.91 (1.86)	28.07 (1.63)	0.534	27.87 (1.79)	28.44 (1.28)	0.003^*
MoCA_B	24.93 (2.77)	25.50 (2.83)	0.178	24.84 (2.93)	26.73 (2.01)	0.000^*
AVLT_N5	5.20 (2.75)	5.50 (2.47)	0.423	4.95 (2.33)	6.65 (2.62)	0.000^*
AVLT_N7	21.15 (3.92)	21.42 (3.08)	0.572	21.11 (3.51)	22.03 (2.39)	0.034^*
AFT	16.18 (4.73)	16.92 (4.12)	0.242	16.59 (4.23)	17.25 (4.31)	0.238
BNT	23.38 (3.15)	24.16 (3.24)	0.105	23.84 (3.19)	24.40 (3.33)	0.201
STT_A	47.86 (18.46)	48.08 (15.84)	0.927	50.36 (17.18)	42.08 (12.29)	0.000^*
STT_B	125.59 (46.30)	121.68 (34.58)	0.556	129.15 (37.98)	105.44 (28.94)	0.000^*
Sex			Education, years
	Female	Male	p	> 12	≤12	p
	(n = 194)	(n = 87)		(n = 125)	(n = 156)
APOE ɛ4 (%)	20.62	18.39	0.666	20.00	19.87	0.979
Age, y	63.09 (7.60)	65.59 (7.08)	0.010*	63.16 (8.30)	64.42 (6.82)	0.172
Sex (Female%)	–	–	–	62.40	74.34	0.031^*
Education, y	12.10 (3.09)	12.83 (3.16)	0.070	–	–	–
MMSE	28.06 (1.66)	27.99 (1.72)	0.753	28.42 (1.42)	27.73 (1.80)	0.000^*
MoCA_B	25.46 (2.90)	25.22 (2.65)	0.510	26.18 (2.21)	24.75 (3.10)	0.000^*
AVLT_N5	5.64 (2.46)	5.00 (2.64)	0.052	5.95 (2.55)	5.03 (2.45)	0.003^*
AVLT_N7	21.37 (3.41)	21.37 (2.90)	0.989	21.68 (3.01)	21.12 (3.43)	0.151
AFT	16.46 (4.06)	17.46 (4.60)	0.070	17.65 (4.65)	16.08 (3.79)	0.002^*
BNT	23.28 (3.22)	25.57 (2.67)	0.000^*	24.88 (2.87)	23.31 (3.34)	0.000^*
STT_A	49.26 (16.61)	45.36 (15.58)	0.065	43.67 (11.71)	51.50 (18.60)	0.000^*
STT_B	122.01 (36.68)	123.47 (38.47)	0.762	114.67 (35.38)	128.69 (37.53)	0.002^*

Including basic demographic characteristics as well as scale results. MMSE, Mini-Mental State Examination; MoCA_B, Montreal Cognitive Assessment-Basic Chinese version; AVLT_N5, Auditory Verbal Learning Test_N5; AVLT_N7, Auditory Verbal Learning Test_N7; AFT, Animal Fluency Test; BNT, Boston Naming Test; STT_A, Trail Making Test A; STT_B, Trail Making Test B. ^*p values are significant at the 0.05 level.

Voxel-based analysis results

Among four factors (APOE ɛ4, age, sex, years of education), the comparison of ¹⁸F-AV45 voxel results showed statistically significant differences excepting for the years of education group. The results can be seen in Fig. 1.

Fig. 1

Inter-group comparison of Aβ deposition among 4 grouping factors: A) APOE ɛ4 risk gene status; B) age; C) sex; and D) years of education.

As we can see in Fig. 1, APOE ɛ4 carriers and age≥60 group showed elevated Aβ deposition mainly in frontal, lateral temporal, cingulate, parietal, and precuneus. Aβ deposition in both hemispheres was asymmetric, with slightly stronger deposition in the left hemisphere. However, in female group, elevated Aβ can be detected mostly in medial temporal, precentral and postcentral lobes, occipital, and cingulate gyrus. And there was no statistical difference of Aβ deposition between education years > 12 and≤12.

Voxel-wise 2×2 ANOVA analysis was performed with SPM12, correcting the effects of remaining two factors on the results. There was only an interaction effect between APOE ɛ4 risk gene and age (Fig. 2A). The results of six stratified analyses showed in Fig. 2B. In age under 60 group, no significantly statistical discrepancy was found. However, in age over 60 group, APOE ɛ4 carriers showed higher Aβ deposition in frontal, lateral temporal, cingulate, parietal, and precuneus. In non-carrier group, people over 60 years of age showed only limited Aβ deposition compared to people under 60 years of age. By contrast, carriers over 60 showed remarkable Aβ deposition than carriers under 60. There was no difference between carriers under 60 and non-carriers over 60. The most remarkable difference was between carriers over 60 and non-carriers under 60. The results of voxel-wise 3×2 ANOVA analysis was showed in Supplementary Figure 1.

Fig. 2

A) The 2×2 ANOVA analysis of gene and age of interaction effect on amyloid-β deposition, adjusted for sex and education years; B) The stratified comparison of age and sex.

Together, we found the atrophy of hippocampal, amygdala, precentral, and occipital gray matter volume in age≥60 (Fig. 3A) and the atrophy of hippocampal, frontal, and occipital in females (Fig. 3B).

Fig. 3

Inter-group comparison of gray matter atrophy for grouping factors: A) age; B) sex.

DISCUSSION

In this cross-sectional study, we found that in the SCD population, APOE ɛ4 gene, age, and sex affected Aβ deposition, and only an interaction between gene and age existed. Voxel analysis showed reduced GM volume in hippocampus, frontal, occipital lobes, and amygdala in age over 60 group and reduced GM volume of hippocampus, occipital lobes, and precentral and postcentral lobes in female group. However, years of education only affected cognitive outcomes, which is independent of Aβ and GM volume.

Several published works [20 , 31] have documented that some demographic factors and APOE ɛ4 risk gene were related to the progression from SCD to MCI or dementia. However, these studies cannot exclaim why these factors can possibly influence this progression. In this paper, we found APOE ɛ4 carriers exhibited a higher Aβ level than APOE ɛ4 non-carriers. With regard to sex, the number of SCD was significantly higher in women than in men in this study, which is consistent with previous studies [32 –34]. At the same time, one previous work found that SCD can be stronger predictor of dementia in female [24] and female SCD showed higher relevance with progression from SCD to MCI and dementia. Although the present study is cross-sectional and lacks follow-up data, we found higher Aβ deposition and atrophy in the hippocampus, occipital lobes as well as frontal lobes in female, and these differences in AD-related biomarkers may be relevant to the subsequent conversion of SCD to MCI and dementia.

In our cohort, because 60 years old is a SCD plus feature [4], we chose 60 as a cutoff value. The onset SCD at 60 years and older is a feature that increases the risk of cognitive decline. In this study, the participants included were recruited from communities, and this is the first time for most participants to report the self-perceived declined cognition. Although some participants reported that the first time self-perceived declined-cognition up to 1 to 3 years, but the degree of declined cognition might not disturb or worry them at that time as they did not seek any medical advice. Besides, SCD subjects are also not very accurate in self-grasping the exact time point of memory decline [5]. That’s why SCD research requires systematic documentation of longitudinal time points. In this work, the recorded time of first report SCD time was determined. Thus, here we use the age at the evaluation with SCD to split the population into two groups to analysis the age effect in SCD. And the interference of some non-AD pathogenic factors in SCD subjects under 60 years of age can be well distinguished. In this work, we found elevated Aβ and GM atrophy in older group, which seems can explain why relevant studies showed that older SCD can easily develop to dementia than younger population. Nevertheless, years of education cannot affect the Aβ deposition or GM volume in SCD population. No significantly statistical discrepancy can be detected between education years > 12 and≤12 except for cognitive assessment results. Since Aβ deposition was uniformly distributed across years of education (correlation efficient, r = 0.077, p = 0.203), we chose the mean (12, and also the median) of years of education as the cutoff value. Nearly all of cognitive tests were better in higher educated group. Therefore, the results of the scale, just like other works [35, 36], can be influenced by the years of education. Years of education may affect the understanding and execution of the content of the scale and thus affect the final scores of the scales, so it is important to correct for the effect of education when using the results of the scales.

One previous study [20] found an interaction between APOE ɛ4 and sex on progression from SCD to AD. In this study, there was only an interactive effect on Aβ deposition between APOE ɛ4 and age in SCD population. The effect of this interaction is only more pronounced when the risk gene is carried and at an older age. Aβ levels were not significantly higher in the younger population (carrier) or in the older population (non-carrier).

This study found an asymmetry deposition of Aβ, with the left hemisphere depositing significantly more than the right hemisphere. One study [37] found that amyloid deposition is symmetrical in the AD phase. Only in the early stages of AD is this amyloid asymmetry present. For example, it has been found that during the MCI phase, there is an asymmetry in amyloid deposition in frontal, occipital, and temporal cortices, while this asymmetry disappeared in AD patients. In SCD period, an early clinical stage of AD, we also observed the asymmetry feature, which could also directly indicate the importance of this period.

In addition to Aβ and tau protein deposition, neurodegenerative manifestations are also important indicators in the typical AD population [38]. In MRI scans of the AD population [39 –44], GM atrophy in some brain regions is often observed. Neurodegeneration appearances are more severe in the AD stage. This neurodegeneration is an important indicator of AD, and in MCI studies atrophy of GM volumes were found [43, 45]. Studies of SCD [46 –48] also found that SCD is closely linked to lower AD-relevant cortical. Hence, in the present study we also investigated the effect of these factors on GM volumes in the SCD population. As a pre-clinical stage between the normal cognitive and MCI populations, some structural changes in the high-risk factor groups may have occurred. We found that age and sex were related to GM volume in SCD population.

The population in this study was a group of people recruited from communities via advertisement and recognized as SCD using questionnaire with two questions. However, it is worth noting that SCD populations are highly heterogeneous, which may be associated with a number of non-AD pathogenic factors, including self-physical condition, medication history, or emotional factors [49]. Meanwhile, AD biomarker patterns summarized by different sources (community or memory clinic) of SCD populations may also have contrasting characteristics. Recruit methods of SCD are associated with AD biomarker pattern of SCD [50 –52]. It has been found that different recruitment methods affect the demographic characteristics of the study population, and for volunteers recruited through the community this group tends to have a higher level of education and a family history of AD compared to population-based studies, while the recruited subjects also tend to be younger [53, 54]. For study subjects from memory clinics cognitive impairment tends to be more severe and also tend to be carriers of risk genes [55, 56], which may be a category of SCD with a higher prevalence of AD. Having the action of active seeking medical help is considered another potential plus feature of SCD [20]. This SCD population may be more closely related to subsequent objective cognitive impairment [57, 58].

Limitations and strengths

This paper also has several limitations. First of all, this is a cross-sectional study and lacks follow-up data to further acquire the occurrence incidence of MCI and AD in this cohort. Further follow-up of the SCD population is needed to confirm whether these three groups (APOE ɛ4 carrier, age above 60, female) are more likely to develop MCI and AD. We would continue to analyze the neuroimaging characteristic of this population once the follow-up study is completed. Secondly, we cannot further classify the SCD population to reversible SCD, irreversible SCD, and progressive SCD [4]. Whether the same factors differently modulate Aβ deposition and GM volume in different types of SCD cannot be concluded. Thirdly, these subjects were community sources. The SCD characteristics in our cohort can only reflect the SCD characteristics of community sources and cannot represent the characteristics of the overall SCD or SCD subjects from other recruitment methods. Finally, different types of APOE genes were unevenly distributed, so this study only considered the status of risk genes (carrier versus non-carrier). Unlike other study [59], it can consider whether the genotype of the risk gene carriers was homozygous or heterozygous. Future work needs to be conducted in a population of uniform gene distribution and collect longitudinal data.

On the other hand, this work has its own advantages. All subjects completed APOE ɛ4 gene testing, ¹⁸F-AV45 PET, and MRI. We were able to explore the relationship between the factors and AD biomarkers. Almost previous SCD studies did not perform these examinations and only analyzed the relationship between these factors and the conversion from SCD to MCI and AD at the epidemiological level.

Conclusions

In conclusion, we found that APOE ɛ4 gene, age, and sex can influence the Aβ deposition in SCD. APOE ɛ4 carriers, subjects age above 60, and female were related with higher Aβ deposition. At the same time, there is only the interactive effect between age and APOE ɛ4 gene. Moreover, age and sex can influence the GM volume in SCD. These results showed the close relationship between these factors and AD biomarkers of SCD population.

Footnotes

ACKNOWLEDGMENTS

The authors would like to thank all patients who took part in this study and all funding that supported this study.

FUNDING

This work was supported by the National Natural Science Foundation of China [grant numbers 81971674, 81801752, 82171473, 82201583] and National Key R&D Program of China [grant number 2022ZD0213800].

CONFLICT OF INTEREST

The authors have no conflict of interest to disclose.

DATA AVAILABILITY

The data supporting the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.

The supplementary material is available in the electronic version of this article: .

References

Breijyeh

, Karaman

(2020) Comprehensive review on Alzheimer’s disease: Causes and treatment, Molecules 25, 5789.

Verlinden

VJA

, van der Geest

, de Bruijn

, Hofman

, Koudstaal

, Ikram

(2016) Trajectories of decline in cognition and daily functioning in preclinical dementia, Alzheimers Dement 12, 144–153.

Amieva

, Le Goff

, Millet

, Orgogozo

, Peres

, Barberger-Gateau

, Jacqmin-Gadda

, Dartigues

(2008) Prodromal Alzheimer’s disease: Successive emergence of the clinical symptoms, Ann Neurol 64, 492–498.

Jessen

, Amariglio

, Buckley

, van der Flier

, Han

, Molinuevo

, Rabin

, Rentz

, Rodriguez-Gomez

, Saykin

, Sikkes

SAM

, Smart

, Wolfsgruber

, Wagner

(2020) The characterisation of subjective cognitive decline, Lancet Neurol 19, 271–278.

Jessen

, Amariglio

, van Boxtel

, Breteler

, Ceccaldi

, Chetelat

, Dubois

, Dufouil

, Ellis

, van der Flier

, Glodzik

, van Harten

, de Leon

, McHugh

, Mielke

, Molinuevo

, Mosconi

, Osorio

, Perrotin

, Petersen

, Rabin

, Rami

, Reisberg

, Rentz

, Sachdev

, de la Sayette

, Saykin

, Scheltens

, Shulman

, Slavin

, Sperling

, Stewart

, Uspenskaya

, Vellas

, Visser

, Wagner

, Subjective Cognitive Decline Initiative Working Group (2014) A conceptual framework for research on subjective cognitive decline in preclinical Alzheimer’s disease, Alzheimers Dement 10, 844–852.

van Harten

, Smits

, Teunissen

, Visser

, Koene

, Blankenstein

, Scheltens

, van der Flier

(2013) Preclinical AD predicts decline in memory and executive functions in subjective complaints, Neurology 81, 1409–1416.

Wolfsgruber

, Polcher

, Koppara

, Kleineidam

, Frolich

, Peters

, Hull

, Ruther

, Wiltfang

, Maier

, Kornhuber

, Lewczuk

, Jessen

, Wagner

(2017) Cerebrospinal fluid biomarkers and clinical progression in patients with subjective cognitive decline and mild cognitive impairment, J Alzheimers Dis 58, 939–950.

Hessen

, Eckerstrom

, Nordlund

, Selseth Almdah

, Stalhammar

, Bjerke

, Eckerstrom

, Gothlin

, Fladby

, Reinvang

, Wallin

(2017) Subjective cognitive impairment is a predominantly benign condition in memory clinic patients followed for 6 years: The Gothenburg-Oslo MCI Study, Dement Geriatr Cogn Dis Extra 7, 1–14.

Winblad

, Amouyel

, Andrieu

, Ballard

, Brayne

, Brodaty

, Cedazo-Minguez

, Dubois

, Edvardsson

, Feldman

, Fratiglioni

, Frisoni

, Gauthier

, Georges

, Graff

, Iqbal

, Jessen

, Johansson

, Jonsson

, Kivipelto

, Knapp

, Mangialasche

, Melis

, Nordberg

, Rikkert

, Qiu

, Sakmar

, Scheltens

, Schneider

, Sperling

, Tjernberg

, Waldemar

, Wimo

, Zetterberg

(2016) Defeating Alzheimer’s disease and other dementias: A priority for European science and society, Lancet Neurol 15, 455–532.

10.

Jansen

, Ossenkoppele

, Knol

, Tijms

, Scheltens

, Verhey

, Visser

, Amyloid Biomarker Study

, Aalten

, Aarsland

, Alcolea

, Alexander

, Almdahl

, Arnold

, Baldeiras

, Barthel

, van Berckel

, Bibeau

, Blennow

, Brooks

, van Buchem

, Camus

, Cavedo

, Chen

, Chetelat

, Cohen

, Drzezga

, Engelborghs

, Fagan

, Fladby

, Fleisher

, van der Flier

, Ford

, Forster

, Fortea

, Foskett

, Frederiksen

, Freund-Levi

, Frisoni

, Froelich

, Gabryelewicz

, Gill

, Gkatzima

, Gomez-Tortosa

, Gordon

, Grimmer

, Hampel

, Hausner

, Hellwig

, Herukka

, Hildebrandt

, Ishihara

, Ivanoiu

, Jagust

, Johannsen

, Kandimalla

, Kapaki

, Klimkowicz-Mrowiec

, Klunk

, Kohler

, Koglin

, Kornhuber

, Kramberger

, Van Laere

, Landau

, Lee

, de Leon

, Lisetti

, Lleo

, Madsen

, Maier

, Marcusson

, Mattsson

, de Mendonca

, Meulenbroek

, Meyer

, Mintun

, Mok

, Molinuevo

, Mollergard

, Morris

, Mroczko

, Van der Mussele

, Na

, Newberg

, Nordberg

, Nordlund

, Novak

, Paraskevas

, Parnetti

, Perera

, Peters

, Popp

, Prabhakar

, Rabinovici

, Ramakers

, Rami

, Resende de Oliveira

, Rinne

, Rodrigue

, Rodriguez-Rodriguez

, Roe

, Rot

, Rowe

, Ruther

, Sabri

, Sanchez-Juan

, Santana

, Sarazin

, Schroder

, Schutte

, Seo

, Soetewey

, Soininen

, Spiru

, Struyfs

, Teunissen

, Tsolaki

, Vandenberghe

, Verbeek

, Villemagne

, Vos

, van Waalwijk van Doorn

, Waldemar

, Wallin

, Wiltfang

, Wolk

, Zboch

, Zetterberg

(2015) Prevalence of cerebral amyloid pathology in persons without dementia: A meta-analysis, JAMA 313, 1924–1938.

11.

Jack

Jr. , Knopman

, Jagust

, Petersen

, Weiner

, Aisen

, Shaw

, Vemuri

, Wiste

, Weigand

, Lesnick

, Pankratz

, Donohue

, Trojanowski

(2013) Tracking pathophysiological processes in Alzheimer’s disease: An updated hypothetical model of dynamic biomarkers, Lancet Neurol 12, 207–216.

12.

Jack

Jr , Holtzman

(2013) Biomarker modeling of Alzheimer’s disease, Neuron 80, 1347–1358.

13.

Bateman

, Xiong

, Benzinger

, Fagan

, Goate

, Fox

, Marcus

, Cairns

, Xie

, Blazey

, Holtzman

, Santacruz

, Buckles

, Oliver

, Moulder

, Aisen

, Ghetti

, Klunk

, McDade

, Martins

, Masters

, Mayeux

, Ringman

, Rossor

, Schofield

, Sperling

, Salloway

, Morris

, Dominantly

Inherited Alzheimer Network

(2012) Clinical and biomarker changes in dominantly inherited Alzheimer’s disease, N Engl J Med 367, 795–804.

14.

Fagan

, Xiong

, Jasielec

, Bateman

, Goate

, Benzinger

, Ghetti

, Martins

, Masters

, Mayeux

, Ringman

, Rossor

, Salloway

, Schofield

, Sperling

, Marcus

, Cairns

, Buckles

, Ladenson

, Morris

, Holtzman

, Dominantly

Inherited Alzheimer Network

(2014) Longitudinal change in CSF biomarkers in autosomal-dominant Alzheimer’s disease, Sci Transl Med 6, 226ra230.

15.

Eravuchira

, Banchelli

, D’Andrea

, De Angelis

, Matteini

, Gannot

(2020) Hollow core photonic crystal fiber-assisted Raman spectroscopy as a tool for the detection of Alzheimer’s disease biomarkers, J Biomed Opt 25, 1–10.

16.

Blennow

, Zetterberg

, Rinne

, Salloway

, Wei

, Black

, Grundman

, Liu

, Investigators

AAB

(2012) Effect of immunotherapy with bapineuzumab on cerebrospinal fluid biomarker levels in patients with mild to moderate Alzheimer disease, Arch Neurol 69, 1002–1010.

17.

Uenaka

, Nakano

, Willis

, Friedrich

, Ferguson-Sells

, Dean

, Ieiri

, Siemers

(2012) Comparison of pharmacokinetics, pharmacodynamics, safety, and tolerability of the amyloid beta monoclonal antibody solanezumab in Japanese and white patients with mild to moderate Alzheimer disease, Clin Neuropharmacol 35, 25–29.

18.

Poirier

, Davignon

, Bouthillier

, Kogan

, Bertrand

, Gauthier

(1993) Apolipoprotein E polymorphism and Alzheimer’s disease, Lancet 342, 697–699.

19.

Zerbinatti

, Wozniak

, Cirrito

, Cam

, Osaka

, Bales

, Zhuo

, Paul

, Holtzman

, Bu

(2004) Increased soluble amyloid-beta peptide and memory deficits in amyloid model mice overexpressing the low-density lipoprotein receptor-related protein, Proc Natl Acad Sci U S A 101, 1075–1080.

20.

Muller-Gerards

, Weimar

, Abramowski

, Tebrugge

, Jokisch

, Dragano

, Erbel

, Jockel

, Moebus

, Winkler

, Heinz

Nixdorf Recall Study Investigative Group

(2019) Subjective cognitive decline, APOE epsilon4, and incident mild cognitive impairment in men and women, Alzheimers Dement (Amst) 11, 221–230.

21.

Wang

, Wang

, Hu

, Qu

, Wang

, Shen

, Xu

, Dong

, Tan

, Yu

(2021) Association of subjective cognitive decline with risk of cognitive impairment and dementia: A systematic review and meta-analysis of prospective longitudinal studies, J Prev Alzheimers Dis 8, 277–285.

22.

Stewart

, Dufouil

, Godin

, Ritchie

, Maillard

, Delcroix

, Crivello

, Mazoyer

, Tzourio

(2008) Neuroimaging correlates of subjective memory deficits in a community population, Neurology 70, 1601–1607.

23.

Tomita

, Sugawara

, Kaneda

, Okubo

, Iwane

, Takahashi

, Kaneko

, Yasui-Furukori

(2014) Sex-specific effects of subjective memory complaints with respect to cognitive impairment or depressive symptoms, Psychiatry Clin Neurosci 68, 176–181.

24.

Heser

, Kleineidam

, Wiese

, Oey

, Roehr

, Pabst

, Kaduszkiewicz

, van den Bussche

, Brettschneider

, Konig

, Weyerer

, Werle

, Fuchs

, Pentzek

, Mosch

, Bickel

, Maier

, Scherer

, Riedel-Heller

, Wagner

(2019) Subjective cognitive decline may be a stronger predictor of incident dementia in women than in men, J Alzheimers Dis 68, 1469–1478.

25.

Stern

(2002) What is cognitive reserve? Theory and research application of the reserve concept, J Int Neuropsychol Soc 8, 448–460.

26.

Bessi

, Mazzeo

, Padiglioni

, Piccini

, Nacmias

, Sorbi

, Bracco

(2018) From subjective cognitive decline to Alzheimer’s disease: The predictive role of neuropsychological assessment, personality traits, and cognitive reserve. A 7-year follow-up study, J Alzheimers Dis 63, 1523–1535.

27.

Mazzeo

, Padiglioni

, Bagnoli

, Bracco

, Nacmias

, Sorbi

, Bessi

(2019) The dual role of cognitive reserve in subjective cognitive decline and mild cognitive impairment: A 7-year follow-up study, J Neurol 266, 487–497.

28.

Shalom

, Cohen

(2016) Statin exposure and the risk for herpes zoster: Implications for public health, Br J Dermatol 175, 1137–1138.

29.

Zhao

, Lv

, Zhou

, Hong

, Guo

(2012) Short-term delayed recall of auditory verbal learning test is equivalent to long-term delayed recall for identifying amnestic mild cognitive impairment, PLoS One 7, e51157.

30.

Zhao

, Guo

, Li

, Zhou

, Wang

, Hong

(2013) The Shape Trail Test: Application of a new variant of the Trail making test, PLoS One 8, e57333.

31.

Gino

, Mendes

, Maroco

, Ribeiro

, Schmand

, de Mendonca

, Guerreiro

(2010) Memory complaints are frequent but qualitatively different in young and elderly healthy people, Gerontology 56, 272–277.

32.

Fernandez-Blazquez

, Avila-Villanueva

, Maestu

, Medina

(2016) Specific features of subjective cognitive decline predict faster conversion to mild cognitive impairment, J Alzheimers Dis 52, 271–281.

33.

Hao

, Wang

, Zhang

, Xing

, Guo

, Hu

, Mu

, Chen

, Cao

, Zhi

, Liu

, Li

, Yang

, Li

, Du

, Sun

, Wang

, Liu

, Zhao

, Li

, Yu

, Wang

, Jia

, Han

(2017) Prevalence, risk factors, and complaints screening tool exploration of subjective cognitive decline in a large cohort of the Chinese population, J Alzheimers Dis 60, 371–388.

34.

Lee

, Kang

, Lee

, Kim

, Yu

, Lee

, Kim

, Lewis

, Won

, Jessen

, Myung

(2020) Subjective cognitive decline and subsequent dementia: A nationwide cohort study of 579,710 people aged 66 years in South Korea, Alzheimers Res Ther 12, 52.

35.

Takeda

, Fukuzaki

(2021) Development of a neuropsychological test to evaluate cognitive flexibility, Yonago Acta Med 64, 162–167.

36.

, Jia

, Yang

(2016) Mini-Mental State Examination in elderly Chinese: A population-based normative study, J Alzheimers Dis 53, 487–496.

37.

Yoon

, Kim

, Jeong

, Kim

, Park

, Chun

(2021) Asymmetric amyloid deposition as an early sign of progression in mild cognitive impairment due to Alzheimer disease, Clin Nucl Med 46, 527–531.

38.

Jack

Jr. , Bennett

, Blennow

, Carrillo

, Dunn

, Haeberlein

, Holtzman

, Jagust

, Jessen

, Karlawish

, Liu

, Molinuevo

, Montine

, Phelps

, Rankin

, Rowe

, Scheltens

, Siemers

, Snyder

, Sperling

, Contributors (2018) NIA-AA Research Framework: Toward a biological definition of Alzheimer’s disease, Alzheimers Dement 14, 535–562.

39.

, Schuff

, Kramer

, Ganzer

, Zhu

, Jagust

, Miller

, Reed

, Mungas

, Yaffe

, Chui

, Weiner

(2004) Higher atrophy rate of entorhinal cortex than hippocampus in AD, Neurology 62, 422–427.

40.

Cavedo

, Boccardi

, Ganzola

, Canu

, Beltramello

, Caltagirone

, Thompson

, Frisoni

(2011) Local amygdala structural differences with 3T MRI in patients with Alzheimer disease, Neurology 76, 727–733.

41.

Guo

, Wang

, Li

, Qi

, Jin

, Yao

, Chen

(2010) Voxel-based assessment of gray and white matter volumes in Alzheimer’s disease, Neurosci Lett 468, 146–150.

42.

de Jong

, van der Hiele

, Veer

, Houwing

, Westendorp

, Bollen

, de Bruin

, Middelkoop

, van Buchem

, van der Grond

(2008) Strongly reduced volumes of putamen and thalamus in Alzheimer’s disease: An MRI study, Brain 131, 3277–3285.

43.

Duarte

, Hayasaka

, Du

, Schuff

, Jahng

, Kramer

, Miller

, Weiner

(2006) Volumetric correlates of memory and executive function in normal elderly, mild cognitive impairment and Alzheimer’s disease, Neurosci Lett 406, 60–65.

44.

Tabatabaei-Jafari

, Walsh

, Shaw

, Cherbuin

, Alzheimer’s Disease Neuroimaging Initiative (2017) The cerebellum shrinks faster than normal ageing in Alzheimer’s disease but not in mild cognitive impairment, Hum Brain Mapp 38, 3141–3150.

45.

Spampinato

, Rumboldt

, Hosker

, Mintzer

, Alzheimer’s Disease Neuroimaging Initiative (2011) Apolipoprotein E and gray matter volume loss in patients with mild cognitive impairment and Alzheimer disease, Radiology 258, 843–852.

46.

Schultz

, Oh

, Koscik

, Dowling

, Gallagher

, Carlsson

, Bendlin

, LaRue

, Hermann

, Rowley

, Asthana

, Sager

, Johnson

, Okonkwo

(2015) Subjective memory complaints, cortical thinning, and cognitive dysfunction in middle-aged adults at risk for AD, Alzheimers Dement (Amst) 1, 33–40.

47.

Meiberth

, Scheef

, Wolfsgruber

, Boecker

, Block

, Traber

, Erk

, Heneka

, Jacobi

, Spottke

, Walter

, Wagner

, Hu

, Jessen

(2015) Cortical thinning in individuals with subjective memory impairment, J Alzheimers Dis 45, 139–146.

48.

Hafkemeijer

, Altmann-Schneider

, Oleksik

, van de Wiel

, Middelkoop

, van Buchem

, van der Grond

, Rombouts

(2013) Increased functional connectivity and brain atrophy in elderly with subjective memory complaints, Brain Connect 3, 353–362.

49.

Comijs

, Deeg

, Dik

, Twisk

, Jonker

(2002) Memory complaints; the association with psycho-affective and health problems and the role of personality characteristics. A 6-year follow-up study, J Affect Disord 72, 157–165.

50.

Archer

, Newson

, Coulthard

(2015) Subjective memory complaints: Symptoms and outcome in different research settings, J Alzheimers Dis 48(Suppl 1), S109–114.

51.

Rodriguez-Gomez

, Abdelnour

, Jessen

, Valero

, Boada

(2015) Influence of sampling and recruitment methods in studies ofsubjective cognitive decline, J Alzheimers Dis 48 Suppl1, S99–S107.

52.

Abdelnour

, Rodriguez-Gomez

, Alegret

, Valero

, Moreno-Grau

, Sanabria

, Hernandez

, Rosende-Roca

, Vargas

, Mauleon

, Sanchez

, Espinosa

, Ortega

, Perez-Cordon

, Diego

, Gailhajanet

, Guitart

, Sotolongo-Grau

, Ruiz

, Tarraga

, Boada

(2017) Impact of recruitment methods in subjective cognitive decline, J Alzheimers Dis 57, 625–632.

53.

Ganguli

, Lytle

, Reynolds

, Dodge

(1998) Random versus volunteer selection for a community-based study.M, J Gerontol A Biol Sci Med Sci 53, 39–46.

54.

Brodaty

, Mothakunnel

, de Vel-Palumbo

, Ames

, Ellis

, Reppermund

, Kochan

, Savage

, Trollor

, Crawford

, Sachdev

(2014) Influence of population versus convenience sampling on sample characteristics in studies of cognitive aging, Ann Epidemiol 24, 63–71.

55.

Andersen

, Engstad

, Straume

, Viitanen

, Halvorsen

, Hykkerud

, Sjobrend

(2010) Recruitment methods in Alzheimer’s disease research: General practice versus population based screening by mail, BMC Med Res Methodol 10, 35.

56.

Tsuang

, Kukull

, Sheppard

, Barnhart

, Peskind

, Edland

, Schellenberg

, Raskind

, Larson

(1996) Impact of sample selection on APOE epsilon 4 allele frequency: A comparison of two Alzheimer’s disease samples, J Am Geriatr Soc 44, 704–707.

57.

Jessen

, Wiese

, Bachmann

, Eifflaender-Gorfer

, Haller

, Kolsch

, Luck

, Mosch

, van den Bussche

, Wagner

, Wollny

, Zimmermann

, Pentzek

, Riedel-Heller

, Romberg

, Weyerer

, Kaduszkiewicz

, Maier

, Bickel

, German Study on Aging, Cognition and Dementia in Primary Care Patients Study Group (2010) Prediction of dementia by subjective memory impairment: Effects of severity and temporal association with cognitive impairment, Arch Gen Psychiatry 67, 414–422.

58.

Koppara

, Wagner

, Lange

, Ernst

, Wiese

, Konig

, Brettschneider

, Riedel-Heller

, Luppa

, Weyerer

, Werle

, Bickel

, Mosch

, Pentzek

, Fuchs

, Wolfsgruber

, Beauducel

, Scherer

, Maier

, Jessen

(2015) Cognitive performance before and after the onset of subjective cognitive decline in old age, Alzheimers Dement (Amst) 1, 194–205.

59.

Yan

, Zheng

, Paranjpe

, Li

, Wang

, Benzinger

TLS

, Lu

, Zhou

(2021) Sex modifies APOE epsilon4 dose effect on brain tau deposition in cognitively impaired individuals, Brain 144, 3201–3211.

18 F-AV45 PET and MRI Reveal the Influencing Factors of Alzheimer’s Disease Biomarkers in Subjective Cognitive Decline Population

Abstract

Background:

Objective:

Methods:

Results:

Conclusion:

Keywords

INTRODUCTION

MATERIALS AND METHODS

Patients and assessment

Demographic and APOE genotyping

18F-AV45 PET and MRI scanning and preprocessing

Statistical analysis

RESULTS

Demographic results

The neuropsychological assessment results

Voxel-based analysis results

DISCUSSION

Limitations and strengths

Conclusions

Footnotes

ACKNOWLEDGMENTS

FUNDING

CONFLICT OF INTEREST

DATA AVAILABILITY

References

¹⁸F-AV45 PET and MRI scanning and preprocessing