Associations of Vascular Risk with Cognition,Brain Glucose Metabolism,and Clinical Progression in Cognitively Intact Elders

Abstract

Background:

Increasing evidence supports an important role of vascular risk in cognitive decline and dementia.

Objective:

This study aimed to examine whether vascular risk was associated with cognitive decline, cerebral hypometabolism, and clinical progression in cognitively intact elders.

Methods:

Vascular risk was assessed by the Framingham Heart Study general Cardiovascular disease (FHS-CVD) risk score. The cross-sectional and longitudinal associations of FHS-CVD risk score with cognition and brain glucose metabolism were explored using multivariate linear regression and linear mixed effects models, respectively. The risk of clinical progression conversion was assessed using Kaplan-Meier survival curves and multivariate Cox proportional hazard models.

Results:

A total of 491 cognitively intact elders were included from Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Participants with high FHS-CVD risk scores had lower baseline Mini-Mental State Examination (MMSE) (p = 0.009), executive function (EF) (p < 0.001), memory function (MEM) (p < 0.001) scores, and F18-fluorodeoxyglucose positron emission tomography (FDG-PET) uptake (p < 0.001) than those with low FHS-CVD risk scores. In longitudinal analyses, individuals with higher FHS-CVD risk scores had greater longitudinal declines in MMSE (p = 0.043), EF (p = 0.029) scores, and FDG-PET uptake (p = 0.035). Besides, individuals with a higher vascular risk had an increased risk of clinical progression (p = 0.004).

Conclusion:

These findings indicated effects of vascular risk on cognitive decline, cerebral hypometabolism, and clinical progression. Early detection and management of vascular risk factors might be useful in the prevention of dementia.

Keywords

Clinical progression cognition dementia FDG-PET vascular risk

INTRODUCTION

Dementia is one of the greatest global public health challenges in modern societies [1]. Alzheimer’s disease (AD) is the most common form of dementia which is characterized by a progressive decline in cognitive function. AD is driven by a constellation of genetic and environmental risk factors. Accumulating evidence has indicated a vascular contribution to AD [2 –4], with multiple vascular risk factors associated with an increased risk of AD, including hypertension [5], diabetes [6], smoking [7], and hypercholestero-lemia [8]. Besides, it has been reported that only 24% of demented patients have pure AD pathology, whereas vascular alterations can be discovered in more than 50% of patients with clinically diagnosed AD, highlighting the importance of vascular risk factors in the mechanisms underlying AD and dementia [9, 10].

In the 2018 National Institute on Aging-Alzhei-mer’s Association (NIA-AA) Research Framework [11], cognitive decline was used in staging the severity of AD. Several vascular risk factors have been reported to be associated with cognitive function [12 –15]. Available evidence supports that amyloid-β (Aβ) burden represents the earliest evidence of AD neuropathologic change [16, 17]. Besides, F18-fluorodeoxyglucose positron emission tomography (FDG-PET) brain metabolism is a biomarker of neurodegeneration [11]. Recently, FDG-PET has been reported to be an independent biomarker for AD biological diagnosis [18]. Studies on the associations of vascular risk with Aβ burden and FDG-PET could be useful for understanding the role of vascular factors in AD.

Considering that the effects of single vascular risk factors on AD are controversial, the Framingham Heart Study general Cardiovascular Disease (FHS-CVD) risk score was a multivariable measure of vascular risk [19]. It represents a quantifiable expression of the factors acting on functional and anatomical changes of macrovessels and microvessels. Therefore, the FHS-CVD risk score can be used to assess general vascular risk. Previous studies had investigated whether FHS-CVD risk score was related to brain atrophy and hypometabolism [20 –22], and several studies investigated the association between FHS-CVD risk score and cognitive decline, but with controversial findings [14 , 23]. Furthermore, there were inconsistent findings regarding the influence of Aβ burden on cognition or neurodegeneration [21 , 25]. In addition, a previous study found that the FHS-CVD risk score could predict the conversion from mild cognitive impairment (MCI) to AD [26], but the association of the FHS-CVD risk score with clinical progression in cognitively intact elders is still unknown.

In this study, our first goal was to investigate the cross-sectional and longitudinal associations of general vascular risk assessed by the FHS-CVD risk score with cognition and brain glucose metabolism as well as whether these associations depended on Aβ burden, in a cohort of cognitively intact elders. Second, we aimed to test the predictive value of the FHS-CVD risk score for the development of clinical progression in cognitively intact elders.

METHODS

ADNI database

Data used in this study were obtained from Alz-heimer’s Disease Neuroimaging Initiative (ADNI) database. The ADNI database was established as an ongoing, longitudinal multicenter study launched in 2003, aiming at developing AD-related biomarkers for early detection and treatment of AD. ADNI was approved by institutional review boards of all participating institutions, and written informed consent was obtained from all participants or their guardians according to the Declaration of Helsinki. More details can be found in previous studies [27, 28] and at http://www.adni-info.org.

Participants

Cognitively intact elders who had available data to calculate the FHS-CVD risk score were included from ADNI-1/GO/2 cohorts at baseline. All participants had a Mini-Mental State Examination (MMSE) score from 24 to 30 and a global Clinical Dementia Rating (CDR) score of 0 at baseline. All participants received an MRI scan at 1.5 Tesla signal strength, among whom 50% received an FDG PET scan at bas-eline, and all the subjects had to be able to complete a 3-year imaging study [27]. The inclusive and exclusive criteria for participants in ADNI were described in a previous study [27]. A total of 491 ADNI participants were included in the present study, including 139 Apolipoprotein E (APOE) ɛ4 carriers and 352 non-carriers.

FHS-CVD risk score

FHS-CVD risk score is calculated based on age, gender, medicine for hypertension, systolic blood pr-essure (SBP), total cholesterol (Total-C), high-den-sity lipoprotein cholesterol (HDL-C), smoking status, and medical history of diabetes at baseline [19]. FHS-CVD risk score is a sex-specific risk score with different points in each variable for males and females. The score for age ranges from 0 to 12; SBP –3 to 7; Total-C 0 to 5; HDL-C –2 to 2; smoking 0 to 3; and diabetes 0 to 4 in females. And in males, the score for age ranges from 0 to 15; SBP –2 to 5; Total-C 0 to 4; HDL-C –2 to 2; smoking 0 to 4; and diabetes 0 to 3. The total FHS-CVD risk score ranged from –5 to 33 for females and ranged from –4 to 33 for males. The detailed method of calculating the FHS-CVD risk score has been described in a previous study [19].

FDG-PET image processing

Global brain glucose metabolism of participants was measured by FDG-PET. In ADNI database, the FDG-PET scans were obtained and the mean FDG-PET uptake was averaged over right and left angular gyri, right and left inferior temporal regions, and bilateral posterior cingulate as regions of interest (metaROIs) which were sensitive to AD-related cha-nges in metabolism [29, 30]. PET images were spatially normalized in statistical parametric mapping (SPM) to the MNI PET template. The mean counts were extracted from the 5 metaROIs for each subject’s FDG scans at each time point, computing the intensity values with SPM subroutines [30]. Then, each metaROI mean was intensity-normalized by dividing it by pons/vermis reference region mean. Preprocessing steps were performed to mitigate inter-scanner variability, including dynamic co-registrat-ion of images acquired in consecutive time frames, averaging, reorientation along the anterior-posterior commissure and filtering with a scanner specific filter function to produce images of a uniform isotropic resolution of 8 mm full width at half maximum Gaussian el [18, 30]. A detailed description of FDG-PET image acquisition and processing can be found at http://adni.loni.usc.edu/data-samples/pet/.

Cognitive measures and clinical progression

MMSE, executive function (EF), and memory function (MEM) scores were all included to assess cognition using the neuropsychological battery from the ADNI database. The MMSE score indicates the global cognitive function of participants. EF score in ADNI database was assessed using Category Fluency, WAIS-R Digit Symbol, Trails A & B, Digit Span Backwards, and clock drawing. MEM score in the ADNI database was assessed using relevant items of the Rey Auditory Verbal Learning Test (RAVLT), ADAS-Cog, Logical Memory, and MMSE. Participants were defined as having clinical progression if their clinical status (cognitive normals progressed to MCI or dementia) changed [31].

Measurements of Aβ burden

In our study, we used cerebrospinal fluid (CSF) Aβ₄₂ levels to represent Aβ burden. In ADNI, CSF procedural protocols have been described in a previous study [28]. In brief, baseline CSF Aβ₄₂ was measured at the ADNI Biomarker Core Laboratory by the INNOBIA AlzBio3 immunoassay (Fujirebio, Belgium) with a within-batch precision value of 5.1–7.8%.

Statistical analyses

Participants were classified into high and low FHS-CVD risk score groups according to a threshold of >20% for predicted risk in 10 years which required more aggressive risk factor modification (e.g., FHS-CVD risk score of 17 for women and FHS-CVD risk score of 14 for men) [19]. Baseline demographic factors of this study were compared by Chi-square tests for categorical variables and Kruskal-Wallis test for continuous variables. In case of skewed distribution (Kolmogorov-Smirnov test <0.05) for dependent variables, log10-transformed was performed to approximate a normal distribution. Data were missing for EF score (n = 15, 3.1%), MEM score (n = 15, 3.1%), FDG-PET (n = 128, 26.1%), and CSF Aβ₄₂ (n = 135, 27.5%) at baseline. In our cross-sectional study, the associations of FHS-CVD risk score with cognitive scores, FDG-PET uptake and CSF Aβ₄₂ were explored using multivariate linear regression analyses (using low FHS-CVD risk score group as a reference). The longitudinal associations of baseline FHS-CVD risk score with cognitive scores, FDG-PET uptake and CSF Aβ₄₂ were explored using linear mixed effects models. The longitudinal study eventually included 476 participants for MMSE score, 475 participants for EF score, 476 participants for MEM score, 202 participants for FDG-PET, and 176 participants for CSF Aβ₄₂ (at least one follow-up during 4 years). As age and gender had been incorporated into the FHS-CVD risk score model, the above analyses were adjusted for years of education and APOE ɛ4 status at baseline. All outcome variables in linear mixed-effects models were standardized to z scores to facilitate comparisons between modalities. To investigate the effects of Aβ burden on the associations of the FHS-CVD risk score with cognition and brain glucose met-abolism, we additionally adjusted for baseline CSF Aβ₄₂. Finally, Kaplan-Meier survival curves were plotted with log-rank tests to compare the probabilities of conversion to clinical progression between low and high FHS-CVD risk score groups, and multivariate Cox proportional hazard models were further used to test the predictive ability of FHS-CVD risk score for clinical progression (adjusted for years of education and APOE ɛ4 status at baseline). As for the clinical progression cohort, 476 participants had finished follow-up exam over 4 years. R version 3.5.1 was used for statistical analyses and figure preparation. And p < 0.05 was considered significant in these analyses.

RESULTS

Characteristics of participants

The baseline demographic characteristics are shown in Table 1. In brief, 491 cognitively intact individuals were included from the ADNI with a mean age of 74.18±5.79 at baseline. Participants were divided into high and low vascular risk groups according to the FHS-CVD risk score, and there were 241 participants (49.08%) having high vascular risk. The longitudinal distributions of the samples were concluded in Supplementary Table 1. The average duration of follow-up for MMSE score cohort was 1.36±1.27 years, EF score cohort 1.41±1.31 years, MEM score cohort 1.41±1.31 years, FDG-PET imaging cohort 1.26±1.17 years, CSF Aβ₄₂ cohort 1.16±1.20 years, and clinical progression cohort 1.35±1.26 years.

Table 1

Demographic information

	Total	Low FHS-CVD	High FHS-CVD	p
NO. (%)	491 (100)	250 (50.92)	241 (49.08)	–
Age (y), mean (SD)	74.18 (5.79)	72.62 (5.39)	75.79 (5.75)	<0.001
Female, NO. (%)	249 (50.71)	210 (84.00)	39 (16.18)	<0.001
Education (y), mean (SD)	16.42 (2.70)	15.98 (2.75)	16.88 (2.58)	<0.001
APOE ɛ4 carrier, NO. (%)	139 (28.31)	78 (31.20)	61 (25.31)	0.148
MMSE score, mean (SD)	29.06 (1.14)	29.18 (1.02)	28.94 (1.23)	0.033
Total-C, mean (SD), mmol/L	3.82 (0.76)	3.96 (0.73)	3.67 (0.76)	<0.001
HDL-C, mean (SD), mmol/L	1.46 (0.33)	1.57 (0.30)	1.34 (0.32)	<0.001
SBP, mean (SD), mmHg	133.5 (15.98)	128.5 (14.70)	138.6 (15.65)	<0.001
Hypertension treatment, NO. (%)	205 (41.75)	78 (31.20)	127 (52.70)	<0.001
Smoking, NO. (%)	198 (40.33)	64 (25.60)	134 (55.60)	<0.001
Diabetes, NO. (%)	34 (6.92)	9 (3.60)	25 (10.37)	0.003
FHS-CVD risk score, mean (SD)	15.48 (4.10)	12.41 (2.82)	18.66 (2.46)	<0.001
^*EF score, mean (SD) (n = 476)	0.78 (0.76)	0.88 (0.75)	0.68 (0.76)	0.004
^*MEM score, mean (SD) (n = 476)	1.03 (0.56)	1.19 (0.49)	0.87 (0.58)	<0.001
^*FDG-PET, mean (SD) (n = 363)	1.31 (0.12)	1.33 (0.11)	1.29 (0.12)	<0.001
^*CSF Aβ₄₂, mean (SD), pg/ml (n = 356)	200.2 (50.89)	199.5 (49.17)	200.9 (52.81)	0.496

p values of between-group comparisons were obtained using the Chi-square tests for categorical variables and Kruskal-Wallis test for continuous variables. APOE, apolipoprotein E; MMSE, Mini-Mental State Examination; Total-C, total cholesterol; HDL-C, high-density lipoprotein cholesterol; SBP, systolic blood pressure; FHS-CVD, Framingham Heart Study general cardiovascular disease; EF, executive function; MEM, memory function; FDG-PET, F18-fluorodeoxyglucose positron emission tomography; CSF, cerebrospinal fluid; Aβ, amyloid-β. ^*Data were missing for EF (n = 15, 3.1%), MEM (n = 15, 3.1%), FDG-PET (n = 128, 26.1%), CSF Aβ₄₂ (n = 135, 27.5%).

Associations of FHS-CVD risk score with cognitive scores and FDG-PET at baseline

After adjusting for educational level and APOE ɛ4 status, we found that participants with high FHS-CVD risk scores had lower MMSE (p = 0.009), EF (p < 0.001), and MEM (p < 0.001) scores at baseline than those with low FHS-CVD risk scores (Table 2). In addition, we found participants with high FHS-CVD risk scores had lower FDG-PET uptake than those with low FHS-CVD risk scores (p < 0.001) (Table 2).

Table 2

Associations of FHS-CVD risk score with baseline cognitive scores and brain glucose metabolism

FHS -CVD risk score	MMSE		EF		MEM		FDG-PET
	β	p	β	p	β	p	β	p
Low	Reference		Reference		Reference		Reference
High	–0.0037	0.009	–0.0439	<0.001	–0.3586	<0.001	–0.0435	<0.001

Cross-sectional associations of FHS-CVD risk score with cognitive scores and brain glucose metabolism at baseline were tested by multiple linear regression models. All models were adjusted for education and APOE ɛ4 status at baseline. FHS-CVD, Framingham Heart Study general cardiovascular disease; MMSE, Mini-Mental State Examination; EF, executive function; MEM, memory function; FDG-PET, F18-fluorodeoxyglucose positron emission tomography; APOE, apolipoprotein E.

Longitudinal associations of baseline FHS-CVD risk score with cognitive scores and FDG-PET

The longitudinal associations of FHS-CVD risk score with cognitive scores and FDG-PET uptake are shown in Fig. 1. We found that individuals with high FHS-CVD risk score showed faster decline in MMSE (p = 0.043) and EF (p = 0.029) scores than those with low FHS-CVD risk score (Fig. 1A, B). Besides, compared to low FHS-CVD risk score group, a higher FHS-CVD risk score was associated with a faster decline in FDG-PET uptake (p = 0.035) (Fig. 1D). But there was no significant association of FHS-CVD risk score with MEM score over 4 years (p = 0.372) (Fig. 1C).

Fig. 1

Longitudinal associations of baseline FHS-CVD risk score with cognitive scores and FDG-PET. Compared to low FHS-CVD risk score group, individuals with higher FHS-CVD risk scores showed faster declines in MMSE score (A), EF score (B), and FDG-PET uptake (D). No significant association of the FHS-CVD risk score with MEM score (C) over time was found.

Effects of Aβ burden on associations of FHS-CVD risk score with FDG-PET and cognitive scores

We found no associations between the FHS-CVD risk score and CSF Aβ₄₂ levels (p = 0.780) at baseline after comparing the high and low FHS-CVD risk score groups. Longitudinally, CSF Aβ₄₂ levels were also not found to be associated with the baseline FHS-CVD risk score over 4 years (p = 0.567). Besides, we found that the cross-sectional and longitudinal associations between the FHS-CVD risk score with FDG-PET uptake and cognition scores were not affected after additionally adjusting for baseline CSF Aβ₄₂ (Supplementary Tables 2 and 3).

FHS-CVD risk score and risk of clinical progression

In the clinical progression cohort, 57 participants eventually had clinical progression (only 2 progressed to dementia) with an average time to progression of 2.12±1.15 years, including 19 participants (average time to progression of 2.05±0.96 years) in the low risk group and 38 participants (average time to progression of 2.16±1.24 years) in the high risk group. Kaplan–Meier survival curves showed that individuals with high FHS-CVD risk scores had faster rates of progression than those with low FHS-CVD risk scores (p = 0.005) (Fig. 2). This result was further confirmed by multivariate cox proportional hazard models. Participants with higher FHS-CVD risk scores were at an increased risk of clinical progression compared to those with lower risk scores (HR = 2.28, 95% CI: 1.31–3.99, p = 0.004) (Supplementary Table 4) after adjustment for education and APOE ɛ4 status at baseline.

Fig. 2

Association between baseline FHS-CVD risk score and clinical progression. The progression rate of individuals with high FHS-CVD risk scores was much higher than that of individuals with low FHS-CVD risk scores (p = 0.005).

DISCUSSION

In cognitively normal older individuals, our main findings are as follows: 1) increased FHS-CVD risk scores were associated with cognitive decline and cerebral hypometabolism both cross-sectionally and longitudinally; 2) patients with higher FHS-CVD risk scores were at an increased risk of clinical progression in cognitively intact elders.

Previous studies have demonstrated that single vascular risk factors could contribute to cognitive decline [32]. Higher general vascular risk assessed by the FHS-CVD risk score was associated with memory impairment and deterioration of executive function [14 , 21]. In the Framingham Offspring Study, the associations between similar multivariable vascular risk algorithms and the risk of cognitive decline in healthy adults were also reported [33, 34]. Our results indicated that vascular burden could impair cognitive function, which was consistent with the above studies. In longitudinal analysis, higher vascular risk was found to be associated with a faster decline in EF score. However, we did not find any association between vascular risk and MEM score. This is consistent with previous studies showing that many patients with vascular cognitive impairment or vascular dementia might exhibit impaired executive functions but not memory deficits especially in early stage [35, 36]. Neuroimaging studies also showed that new lacunes could cause a steeper decline in cognitive functions, especially in executive function [37]. Additionally, our study was the first to find that cognitively intact elders with increased FHS-CVD risk scores were more likely to have clinical progression to MCI or dementia, further indicating an important role of vascular risk in cognitive decline and dementia development.

Several potential mechanisms in which vascular burden leads to cognitive decline have been identified. Diminished cerebral glucose metabolism was proved to occur before neurodegenerative changes and cerebral atrophy [38], and it could predict cognitive decline years before clinical diagnosis of AD [39]. Decreased FDG-PET uptake was considered as a biomarker of brain hypometabolism. The Harvard Aging Brain Study found that higher FHS-CVD risk scores were associated with widespread cerebral hypometabolism [21]. Our study also found that FHS-CVD risk score was associated with a faster decline in FDG-PET uptake, which was consistent with previous studies. This also suggested that an increased FHS-CVD risk score might play an important role in the early stages of AD. In addition, impaired FDG-PET uptake could also reflect dysfunction in blood-brain barrier (BBB) transport systems [40, 41]. BBB breakdown has been proved to be an early biomarker of human cognitive dysfunction, independent of Aβ and tau [42]. Studies indicated a significant association of cerebrovascular abnormalities (especially BBB dysfunction) with the onset and progression of AD [43]. Moreover, a higher FHS-CVD risk score also reflected accelerated vascular aging, which would damage intracranial vessels and further lead to reduced cerebral blood flow (CBF). Previous studies showed that reduced CBF would cause cognitive decline [44]. Besides, we found no association between FHS-CVD risk scores and CSF Aβ₄₂ levels, which was in line with previous studies [14], suggesting separate pathophysiological amyloid and vascular pathways to enhance neurodegeneration and cognitive decline [24, 25]. Vascular burden leading to cognitive decline is not affected by Aβ burden, which is consistent with previous longitudinal studies showing that amyloid-positive individuals did not show clear evidence of cognitive decline [45, 46]. To sum up, an increased FHS-CVD risk score played an important part in cognitive decline and dementia.

Previous studies on single vascular risk factors failed to demonstrate a clear and reliable association with cognitive impairment [47]. FHS-CVD risk score is an easy and reliable tool for a global assessment of vascular burden. Most of the items considered in the scale are potentially treatable. Findings from this work have potential implications for early detection and integrated management of vascular risk factors as well as for developing primary and secondary prevention strategies. Although promising, our findings should be considered preliminary, and further independent evaluation is necessary.

Several limitations should be mentioned. Firstly, vascular risk interventions may modify the associations, but it is challenging to collect data on vascular risk factors in longitudinal observational studies and therefore we did not investigate vascular risk interventions. Secondly, ethnic homogeneity of our mainly Caucasian sample limits the generalizability of these results to other populations with different ethnic backgrounds. Moreover, after multiple corrections, the longitudinal associations of baseline FHS-CVD risk score with cognitive scores and FDG-PET became non-significant. Considering that there was loss to follow-up to a certain extent in the longitudinal data which might reduce the trend of the longitudinal associations, our results were still of certain suggestive value. Further validation should be performed in large-scale cohort studies with adequate follow-up. Lastly, the role of FHS-CVD risk score in the cognitive function of middle-aged or elderly people is still controversial, but most subjects in our study were elderly people. Thus, more young and multiracial participants should be involved in future studies and further stratified analyses should be conducted. Moreover, further studies are required to investigate whether a reduction in the FHS-CVD risk score is a new therapeutic target for decreasing the risk of cognitive deterioration and dementia.

In summary, we found that a higher FHS-CVD risk score was associated with poorer cognitive performance, lower brain glucose metabolism as well as an increased risk of clinical progression in cognitively normal elders. This finding indicates that early detection and integrated management of vascular burden might be effective in combating cognitive decline and protecting brain health.

Footnotes

ACKNOWLEDGMENTS

This study was supported by grants from the Nat-ional Natural Science Foundation of China (8101274) and Qingdao Applied Basic Research Project (18-2-2-43-jch).

Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Gr-ant 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 Assoc-iation; 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 Ins-titutes 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 ().

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

References

Livingston

, Sommerlad

, Orgeta

, Costafreda

, Huntley

, Ames

, Ballard

, Banerjee

, Burns

, Cohen-Mansfield

, Cooper

, Fox

, Gitlin

, Howard

, Kales

, Larson

, Ritchie

, Rockwood

, Sampson

, Samus

, Schneider

, Selbæk

, Teri

, Mukadam

(2017) Dementia prevention, intervention, and care. Lancet 390, 2673–2734.

Stewart

, Prince

, Mann

(1999) Vascular risk factors and Alzheimer’s disease. Aust N Z J Psychiatry 33, 809–813.

Liesz

(2019) The vascular side of Alzheimer’s disease. Science 365, 223–224.

Shi

, Perry

, Smith

, Friedland

(2000) Vascular abnormalities: The insidious pathogenesis of Alzheimer’s disease. Neurobiol Aging 21, 357–361.

Gabin

, Tambs

, Saltvedt

, Sund

, Holmen

(2017) Association between blood pressure and Alzheimer disease measured up to 27 years prior to diagnosis: The HUNT Study. Alzheimers Res Ther 9, 37.

Zhang

, Chen

, Hua

, Liao

, Wang

, Xiong

, Cao

(2017) An updated meta-analysis of cohort studies: Diabetes and risk of Alzheimer’s disease. Diabetes Res Clin Pract 124, 41–47.

Niu

, Qu

, Li

, Wang

, Li

, Lv

, Gao

, Song

, Li

(2018) Smoking and risk for Alzheimer disease: A meta-analysis based on both case-control and cohort study. J Nerv Ment Dis 206, 680–685.

Solomon

, Kivipelto

, Wolozin

, Zhou

, Whitmer

(2009) Midlife serum cholesterol and increased risk of Alzheimer’s and vascular dementia three decades later. Dement Geriatr Cogn Disord 28, 75–80.

Sweeney

, Montagne

, Sagare

, Nation

, Schneider

, Chui

, Harrington

, Pa

, Law

, Wang

DJJ

, Jacobs

, Doubal

, Ramirez

, Black

, Nedergaard

, Benveniste

, Dichgans

, Iadecola

, Love

, Bath

, Markus

, Salman

, Allan

, Quinn

, Kalaria

, Werring

, Carare

, Touyz

, Williams

SCR

, Moskowitz

, Katusic

, Lutz

, Lazarov

, Minshall

, Rehman

, Davis

, Wellington

, Gonzalez

, Yuan

, Lockhart

, Hughes

, Chen

CLH

, Sachdev

, O’Brien

, Skoog

, Pantoni

, Gustafson

, Biessels

, Wallin

, Smith

, Mok

, Wong

, Passmore

, Barkof

, Muller

, Breteler

MMB

, Roman

, Hamel

, Seshadri

, Gottesman

, van Buchem

, Arvanitakis

, Schneider

, Drewes

, Hachinski

, Finch

, Toga

, Wardlaw

, Zlokovic

(2019) Vascular dysfunction-The disregarded partner of Alzheimer’s disease. Alzheimers Dement 15, 158–167.

10.

Azarpazhooh

, Avan

, Cipriano

, Munoz

, Sposato

, Hachinski

(2018) Concomitant vascular and neurodegenerative pathologies double the risk of dementia. Alzheimers Dement 14, 148–156.

11.

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.

12.

Moheet

, Mangia

, Seaquist

(2015) Impact of diabetes on cognitive function and brain structure. Ann N Y Acad Sci 1353, 60–71.

13.

Walker

, Power

, Gottesman

(2017) Defining the relationship between hypertension, cognitive decline, and dementia: A review. Curr Hypertens Rep 19, 24.

14.

Bos

, Vos

SJB

, Schindler

, Hassenstab

, Xiong

, Grant

, Verhey

, Morris

, Visser

, Fagan

(2019) Vascular risk factors are associated with longitudinal changes in cerebrospinal fluid tau markers and cognition in preclinical Alzheimer’s disease. Alzheimers Dement 15, 1149–1159.

15.

Aleman

, Muller

, de Haan

, van der Schouw

(2005) Vascular risk factors and cognitive function in a sample of independently living men. Neurobiol Aging 26, 485–490.

16.

Xiong

, Jasielec

, Weng

, Fagan

, Benzinger

, Head

, Hassenstab

, Grant

, Sutphen

, Buckles

, Moulder

, Morris

(2016) Longitudinal relationships among biomarkers for Alzheimer disease in the Adult Children Study. Neurology 86, 1499–1506.

17.

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

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

18.

, Xu

, Li

, Guo

, Cui

, Chen

, Huang

, Dong

, Tan

, Yu

, Alzheimer’s Disease Neuroimaging Initiative (2019) FDG-PET as an independent biomarker for Alzheimer’s biological diagnosis: A longitudinal study. Alzheimers Res Ther 11, 57.

19.

D’Agostino

Sr. , Vasan

, Pencina

, Wolf

, Cobain

, Massaro

, Kannel

(2008) General cardiovascular risk profile for use in primary care: The Framingham Heart Study. Circulation 117, 743–753.

20.

Song

, Xu

, Dintica

, Pan

, Qi

, Buchman

, Bennett

, Xu

(2020) Associations between cardiovascular risk, structural brain changes, and cognitive decline. J Am Coll Cardiol 75, 2525–2534.

21.

Rabin

, Schultz

, Hedden

, Viswanathan

, Marshall

, Kilpatrick

, Klein

, Buckley

, Yang

, Properzi

, Rao

, Kirn

, Papp

, Rentz

, Johnson

, Sperling

, Chhatwal

(2018) Interactive associations of vascular risk and beta-amyloid burden with cognitive decline in clinically normal elderly individuals: Findings from the Harvard Aging Brain Study. JAMA Neurol 75, 1124–1131.

22.

Meusel

, Greenwood

, Maione

, Tchistiakova

, MacIntosh

, Anderson

(2019) Cardiovascular risk and encoding-related hippocampal connectivity in older adults. BMC Neurosci 20, 37.

23.

Aljondi

, Szoeke

, Steward

, Gorelik

, Desmond

(2020) The effect of midlife cardiovascular risk factors on white matter hyperintensity volume and cognition two decades later in normal ageing women. Brain Imaging Behav 14, 51–61.

24.

Bos

, Verhey

, Ramakers

, Jacobs

HIL

, Soininen

, Freund-Levi

, Hampel

, Tsolaki

, Wallin

Å K

, van Buchem

, Oleksik

, Verbeek

, Olde Rikkert

, van der Flier

, Scheltens

, Aalten

, Visser

, Vos

SJB

(2017) Cerebrovascular and amyloid pathology in predementia stages: The relationship with neurodegeneration and cognitive decline. Alzheimers Res Ther 9, 101.

25.

Vemuri

, Knopman

(2016) The role of cerebrovascular disease when there is concomitant Alzheimer disease. Biochim Biophys Acta 1862, 952–956.

26.

Viticchi

, Falsetti

, Buratti

, Sajeva

, Luzzi

, Bartolini

, Provinciali

, Silvestrini

(2017) Framingham risk score and the risk of progression from mild cognitive impairment to dementia. J Alzheimers Dis 59, 67–75.

27.

Petersen

, Aisen

, Beckett

, Donohue

, Gamst

, Harvey

, Jack

Jr. , Jagust

, Shaw

, Toga

, Trojanowski

, Weiner

(2010) Alzheimer’s Disease Neuroimaging Initiative (ADNI): Clinical characterization. Neurology 74, 201–209.

28.

Shaw

, Vanderstichele

, Knapik-Czajka

, Clark

, Aisen

, Petersen

, Blennow

, Soares

, Simon

, Lewczuk

, Dean

, Siemers

, Potter

, Lee

, Trojanowski

(2009) Cerebrospinal fluid biomarker signature in Alzheimer’s Disease Neuroimaging Initiative subjects. Ann Neurol 65, 403–413.

29.

, Li

, Suckling

, Feng

, Pan

, Wang

, Song

, Zhu

, Li

, Wang

, Tan

, Dong

, Tan

, Mok

, Aisen

, Weiner

(2019) Frequency and longitudinal clinical outcomes of Alzheimer’s AT(N) biomarker profiles: A longitudinal study. Alzheimers Dement 15, 1208–1217.

30.

Landau

, Harvey

, Madison

, Koeppe

, Reiman

, Foster

, Weiner

, Jagust

(2011) Associations between cognitive, functional, and FDG-PET measures of decline in AD and MCI. Neurobiol Aging 32, 1207–1218.

31.

Jack

Jr. , Shiung

, Gunter

, O’Brien

, Weigand

, Knopman

, Boeve

, Ivnik

, Smith

, Cha

, Tangalos

, Petersen

(2004) Comparison of different MRI brain atrophy rate measures with clinical disease progression in AD. Neurology 62, 591–600.

32.

Debette

, Seshadri

, Beiser

, Au

, Himali

, Palumbo

, Wolf

, DeCarli

(2011) Midlife vascular risk factor exposure accelerates structural brain aging and cognitive decline. Neurology 77, 461–468.

33.

Gupta

, Preis

, Beiser

, Devine

, Hankee

, Seshadri

, Wolf

, Au

(2015) Mid-life cardiovascular risk impacts memory function: The Framingham Offspring Study. Alzheimer Dis Assoc Disord 29, 117–123.

34.

Nishtala

, Preis

, Beiser

, Devine

, Hankee

, Seshadri

, Wolf

, Au

(2014) Midlife cardiovascular risk impacts executive function: Framingham Offspring Study. Alzheimer Dis Assoc Disord 28, 16–22.

35.

Desmond

(2004) The neuropsychology of vascular cognitive impairment: Is there a specific cognitive deficit? J Neurol Sci 226, 3–7.

36.

Kalaria

(2018) The pathology and pathophysiology of vascular dementia. Neuropharmacology 134, 226–239.

37.

Jokinen

, Gouw

, Madureira

, Ylikoski

, van Straaten

, van der Flier

, Barkhof

, Scheltens

, Fazekas

, Schmidt

, Verdelho

, Ferro

, Pantoni

, Inzitari

, Erkinjuntti

(2011) Incident lacunes influence cognitive decline: The LADIS study. Neurology 76, 1872–1878.

38.

Hunt

, Schonknecht

, Henze

, Seidl

, Haberkorn

, Schroder

(2007) Reduced cerebral glucose metabolism in patients at risk for Alzheimer’s disease. Psychiatry Res 155, 147–154.

39.

Mosconi

, Tsui

, Herholz

, Pupi

, Drzezga

, Lucignani

, Reiman

, Holthoff

, Kalbe

, Sorbi

, Diehl-Schmid

, Perneczky

, Clerici

, Caselli

, Beuthien-Baumann

, Kurz

, Minoshima

, de Leon

(2008) Multicenter standardized 18F-FDG PET diagnosis of mild cognitive impairment, Alzheimer’s disease, and other dementias. J Nucl Med 49, 390–398.

40.

Montagne

, Zhao

, Zlokovic

(2017) Alzheimer’s disease: A matter of blood-brain barrier dysfunction? J Exp Med 214, 3151–3169.

41.

Sweeney

, Sagare

, Zlokovic

(2018) Blood-brain barrier breakdown in Alzheimer disease and other neurodegenerative disorders. Nat Rev Neurol 14, 133–150.

42.

Nation

, Sweeney

, Montagne

, Sagare

, D’Orazio

, Pachicano

, Sepehrband

, Nelson

, Buennagel

, Harrington

, Benzinger

TLS

, Fagan

, Ringman

, Schneider

, Morris

, Chui

, Law

, Toga

, Zlokovic

(2019) Blood-brain barrier breakdown is an early biomarker of human cognitive dysfunction. Nat Med 25, 270–276.

43.

, Zhao

, Xu

, Mao

(2020) Vascular risk factors and Alzheimer’s disease: Blood-brain barrier disruption, metabolic syndromes, and molecular links. J Alzheimers Dis 73, 39–58.

44.

Iadecola

(2013) The pathobiology of vascular dementia. Neuron 80, 844–866.

45.

Mormino

, Betensky

, Hedden

, Schultz

, Amariglio

, Rentz

, Johnson

, Sperling

(2014) Synergistic effect of β-amyloid and neurodegeneration on cognitive decline in clinically normal individuals. JAMA Neurol 71, 1379–1385.

46.

Mormino

, Betensky

, Hedden

, Schultz

, Ward

, Huijbers

, Rentz

, Johnson

, Sperling

(2014) Amyloid and APOE ɛ4 interact to influence short-term decline in preclinical Alzheimer disease. Neurology 82, 1760–1767.

47.

Silvestrini

, Viticchi

, Altamura

, Luzzi

, Balucani

, Vernieri

(2012) Cerebrovascular assessment for the risk prediction of Alzheimer’s disease. J Alzheimers Dis 32, 689–698.