Relationship between extracranial carotid artery calcification and amyloid-β deposition in the brain among the very old: A retrospective study

Abstract

Background

Extracranial carotid artery calcification (ECAC) is a known risk factor for the development of cardiovascular diseases (CVD) and has been associated with an increased risk of dementia and Alzheimer's disease. However, the relationship between ECAC and amyloid-β (Aβ) deposition in older adults has not been fully understood.

Objective

This study aims to determine the relationship between ECAC and Aβ deposition in very old adults.

Methods

90 participants (87.2 ± 2.7 years old, 96% whites, 63% males) without dementia from an observational study were included in the cross-sectional analysis. Aβ deposition in the brain was assessed using the ¹¹C-labeled Pittsburgh compound-B positron emission tomography (PiB-PET) scans performed in 2009 and 2011. Carotid artery plaque and ECAC status were measured via high-resolution carotid ultrasonography in 2011.

Results

Among the participants, 82 (91%) had carotid plaques, and 46 (51%) were globally Aβ positive by PiB-PET. ECAC was less frequently observed in the Aβ positive group compared to the Aβ negative group (90% versus 81%, p = 0.3). The odds of Aβ positivity in those with ECAC were 62% lower than in those without ECAC. However, the association was not statistically significant (OR = 0.38, 95% CI: [0.08, 1.76 ], p = 0.2).

Conclusions

In the population whose average age is older than 85 without dementia, there is no statistically significant association between ECAC and Aβ deposition after adjusting for confounding factors. The relationship between ECAC and dementia in older adults warrants further analysis in a larger and more racially and ethnically diverse population.

Keywords

aging population Alzheimer's disease Aβ deposition carotid artery calcification PiB-PET

Introduction

Carotid artery calcification, a well-known marker of atherosclerosis, plays an important role in the progression of cardiovascular diseases (CVDs).¹ Carotid artery calcification is frequently observed in older adults. The prevalence of carotid plaque among older adults is noteworthy, with 50% to 60% of carotid plaques calcified.^1,2 Carotid artery calcification can be assessed using duplex ultrasound and computerized tomography (CT).³ Previous population-based observational studies showed a positive association between carotid atherosclerosis, calcification, and the future development of dementia in older adults.^4,5

The intracranial accumulation of amyloid-β (Aβ) is a prominent hypothesis in Alzheimer's disease (AD) and serves as a key pathological biomarker in the diagnosis of the disease.^6–9 In the animal study, mild chronic cerebral hypoperfusion was found to induce the Aβ deposition¹⁰ as well as reduce the clearance of Aβ.¹¹ The clinical study focusing on the dementia population found a strong association between large vessel atherosclerosis and the increased frequency of neuritic plaques.¹² What's more, one review summarized the clinical study suggested that impaired blood-brain barrier and reduced cerebral blood flow in individuals with significant atherosclerosis may promote Aβ accumulation within a hypoxic and inflammatory environment.¹³ However, in the study focusing on patients with unilateral atherosclerotic stenosis, the relationship between hypoperfusion and an increase in Aβ deposition was small.¹⁴ The relationship between carotid calcification and intracranial neuropathologic changes also remains inadequately characterized with inconsistent results.^15–17

The study aims to determine the cross-sectional association between extracranial carotid artery calcification (ECAC) and intracranial Aβ deposition as measured using positron emission tomography (PET) imaging with carbon-11 labeled Pittsburgh Compound-B ([¹¹C]PiB).¹⁸ We hypothesized that in non-dementated individuals aged 85 and above without dementia, ECAC would be associated with the intracranial Aβ deposition status (positive/negative).

Methods

Study population

The Ginkgo Evaluation of Memory Study (GEMS) was a multicenter, double-blind, randomized clinical trial (NCT00010803) conducted from 2000 to 2008¹⁹ with detailed trial design and results reported elsewhere.²⁰ In 2009, the GEMS Imaging Sub-Study commenced at the Pittsburgh site, enrolling 193 dementia-free participants who underwent [¹¹C]PiB PET imaging.²¹ Between 2010 and 2011, the participants were invited for carotid artery ultrasound scans, and 98 individuals (51%) with baseline [¹¹C]PiB scans underwent the ultrasound exam. During the same period, 100 participants underwent a repeat [¹¹C]PiB scan as part of a 2-year follow-up study.²⁰ A total of 90 participants who completed baseline and follow-up [¹¹C]PiB scans, carotid ultrasound scans, complete demographic information, and medical history were included in the final analysis (Figure 1). This study was done in accord with the ethical standards of the University of Pittsburgh Institutional Review Board and the study received IRB approval before study initiation. All participants completed the informed consent process before any study procedures began.

Figure 1.

Analytic sample flow chart for GEMS imaging study. GEMS: Ginkgo Evaluation of Memory Study.

Demographic information and cardiovascular risk factors

During the enrollment period of GEMS from 2000 to 2002, demographic information (date of birth, sex, race, and years of education) and self-reported health history, including hypertension, diabetes, and smoking status, were collected.²² Hypertension status was reviewed at the start of the imaging sub-study in 2010.²³ The hyperlipidemia status and statin-using condition were not collected during the visits. The participants' age was calculated using the date of the carotid ultrasound scan (2010 to 2011) and the date of birth.²² BMI (body weight divided by height square) was calculated using the enrollment baseline physical examination data (2000 to 2002). Apolipoprotein E4 (APOE4) genotype was assessed by the blood test at the enrollment baseline visit.²²

Carotid artery ultrasound scan

Participants underwent a bilateral carotid scan with a high-resolution B-mode ultrasound system (Siemens Antares; Siemens Medical Systems, Malvern, PA, USA) between 2010 and 2011.²³ The scans were conducted by a certified sonographer following a standard protocol within the Ultrasound Research Lab at the University of Pittsburgh. Scan images were systematically acquired and recorded for subsequent review.²⁴ During the carotid exam, using a standard worksheet, sonographers recorded plaque number, grade, and calcification information from five carotid artery segments, including the proximal and distal common carotid artery, bulb, internal carotid artery and external carotid artery from both sides. Plaque grading (a surrogate measure of plaque size) within each segment was determined based on established criteria: Grade 0 means no observable plaque, grade 1 is one small plaque (less than 30% of the vessel diameter), grade 2 is one medium plaque (30–49% of the vessel diameter) or multiple small plaques, and grade 3 has one large plaque (greater than or equal to 50% of the vessel diameter) or multiple plaques with at least one medium plaque.²⁵

In the subsequent analysis, external carotid artery scans were excluded. The total number of plaques was calculated by adding all the visible plaque numbers across all segments in both carotid arteries. The plaque index was calculated by adding plaque grades from all carotid segments visualized during the scan.²⁶ The calcification status was recorded as a categorical variable, indicating whether participants had calcified plaque in any segment visualized.

PET imaging

All PET imaging was performed using a Siemens/CTI ECAT HR + scanner (Siemens Medical Systems, Malvern, PA) operating in 3D mode, collecting 63 image planes over a 15.2-cm axial field of view with a reconstructed image resolution of approximately 6 mm full-width at half-maximum (FWHM).²⁷ [¹¹C]PiB PET scans were analyzed using the Computational Analysis of PET by AIBL (CapAIBL) package (Commonwealth Scientific and Industrial Research Organisation, Australian Government, Canberra, Australia) developed for the Australian Imaging, Biomarker, and Lifestyle (AIBL) study.²⁸ CapAIBL is a well-validated analysis methodology for computing regional and global amyloid load indices from a variety of PET Aβ radiotracers²⁹ and scaling them to a common 100-point Centiloid (CL) scale.^30,31 A key feature of CapAIBL is that it does not require co-registered structural magnetic resonance (MR) images from individual participants and can be used successfully in populations characterized by frequent MR contraindications, or when MR images are unavailable, of poor quality, or acquired with parameters not optimized for other analysis techniques. In the present study, one participant's PET imaging was excluded due to image quality concerns. As the distribution of global Aβ quantification data was left-skewed, the data were transformed as a categorical variable. In the following analysis, participants with equal or greater than 20 global CL values were considered Aβ positive, and participants with lower than 20 global CL values were defined as Aβ negative.^32,33

Statistical analysis

The descriptive status of participants' characteristics, including sex, age, race, education level, APOE4 status, and cardiovascular risk factors were summarized. The normality of the continuous variables was evaluated before univariate analysis using the graphical approach. The Wilcoxon rank sum test and two sample student T-tests were performed to detect the difference in baseline characteristics and Aβ deposition status between groups as appropriate. Fisher's exact test and Pearson's Chi-squared test were used to assess the difference in the categorical variables between groups. Since the Aβ quantification data were shown to be highly skewed using the graphical approach, the transformed categorical variables were used in the future analysis. Multivariable logistic regression models were constructed to analyze the relationship between Aβ positivity and the existence of carotid artery calcification, adjusting for carotid plaque index, baseline demographic characteristics, and other confounding factors. Sensitivity analyses were performed between the GEMS Imaging sub-study participants who were included in the analysis and the participants who did not have the complete PET and carotid ultrasound scan data. All the statistical analyses were conducted with R studio (v 4.4.0; R Core Team 2024).

Results

Among all the participants who were enrolled in the image sub-study, 90 participants who had a complete carotid ultrasound scan, along with both satisfied baseline and follow-up [¹¹C] PiB scans were included in the analysis. Of these, 57 individuals (63%) were male, and the mean age of all participants was 87.2 years (ranging from 82 to 96) (Table 1). The majority were white (96%), with an average of 14.9 years of education. Among these participants, 13 (15%) were APOE4 carriers, 59 (66%) had hypertension, and 82 participants (91%) had carotid plaques. The hyperlipidemia status and statin-using condition were not collected during the visits. Compared to participants with carotid plaques, participants without carotid plaques had lower BMI at baseline (p < 0.01). Other baseline characteristics were similar between participants with or without carotid plaques (Table 1). In the subset of 82 participants with carotid plaques, comparisons were made based on calcification status (Table 2).

Table 1.

Baseline characteristics of all participants in the 2011 PiB-PET analytic sample.

Characteristic	Overall, N = 90^a	No plaque, N = 8^a	Plaque present, N = 82^a	p-value^b
Age at scan	87.2 (2.7)	86.9 (2.9)	87.2 (2.7)	0.8
Sex (Male)	57 (63%)	5 (62%)	52 (63%)	>0.9
Race (White)	86 (96%)	8 (100%)	78 (95%)	>0.9
Years of education	14.9 (2.7)	13.8 (1.8)	15.0 (2.8)	0.1
ApoE4 carrier (n = 85)	13 (15%)	2 (25%)	11 (14%)	0.6
Hypertension	59 (66%)	5 (63%)	54 (66%)	>0.9
Diabetes^c (n = 88)	5 (6%)	0 (0%)	5 (6%)	>0.9
BMI^c,d (n = 89)	26.9 (3.8)	24.3 (2.3)	27.2 (3.9)	<0.01**
Smoking^c (n = 89)	1 (1%)	0 (0%)	1 (1%)	>0.9
2011 Aβ load	26.4 (3.2, 74.9)	41.3 (−0.9, 102)	26.4 (3.8, 72.6)	0.7
2011 Aβ positive	46 (51%)	4 (50%)	42 (51%)	>0.9

n (%); Mean (SD); Median (interquartile range, IQR); ^bFisher's exact test; Welch Two Sample t-test; Wilcoxon rank sum test; ^cdata were collected from baseline GEMS visit in 2000–2002; ^dBody Mass Index.

GEMS: Ginkgo Evaluation of Memory Study.

*p<0.05, **p<0.01.

Table 2.

Univariate analysis of participants' characteristics by calcification status (N = 82).

Characteristic	No calcification, N = 12^a	Having calcification, N = 70^a	p-value^b
Age	88.5 (3.4)	87.0 (2.5)	0.2
Sex (Male)	6 (50%)	46 (66%)	0.3
Race (White)	11 (92%)	67 (96%)	0.5
Years of Education	14.2 (2.9)	15.1 (2.8)	0.3
ApoE4 carrier (N = 77)	1 (10%)	10 (15%)	>0.9
Hypertension	8 (67%)	46 (67%)	>0.9
Diabetes (N = 80)	1 (8.3%)	4 (5.9%)	0.6
BMI (N = 81)	27.6 (4.1)	27.1 (3.9)	0.7
Smoking status (N = 81)	0 (0%)	1 (1.4%)	>0.9
2011 Aβ positive	8(67%)	34 (49%)	0.2
Ultrasound scan characteristics
Right side plaque (+)	10 (83%)	64 (91%)	0.3
Left side plaque (+)	7 (58%)	67 (96%)	<0.01**
Right size plaque number	1 (0.8,1.3)	3 (2,4)	<0.01**
Left side plaque number	2 (0,4)	2.5 (2,5)	0.04*
Total plaque number	1 (1,5.3)	6 (4,8)	<0.01**
Right side plaque index	1 (0.8, 1.3)	2.5 (1, 3.8)	0.01*
Left side plaque index	1 (0, 2)	2 (1.3, 4)	0.01*
Plaque index	1(1, 3.3)	5 (3, 6.8)	<0.01**
Maximum plaque grade	1 (1, 1)	2 (1, 2)	0.03*

n (%); Mean (SD); Median (IQR); ^bFisher's exact test; Pearson's Chi-squared test; Welch Two Sample t-test; Wilcoxon rank sum test.

*p<0.05, **p<0.01.

Baseline characteristics, including APOE4 carrier rate, were similar between participants with or without calcified plaques. When comparing the ultrasound scan characteristics, the group with calcification had a higher maximum plaque grade, both sides plaque index, both sides plaque number, total plaque index, total plaque number, and prevalence of left-side plaques (Table 2). Among 43 participants with negative Aβ status in the 2009 scan, only 3 progressed to Aβ positive in the follow-up scan in 2011.

Considering the scan timeline, univariate analyses were performed based on Aβ positivity results in 2011. Baseline characteristics, including APOE4 carrier rate, were similar between Aβ-negative and positive participants. When comparing the ultrasound scan results, the prevalence of left-side calcification was significantly lower in individuals with Aβ positive status than in those with negative status (62% to 83%, p = 0.04) (Table 3). This significant association persisted in the logistic regression model after adjusting for left side plaque index (OR = 0.25, 95% CI: [0.08, 0.75], p = 0.02), and remained robust after further adjustment for age, sex, education years, APOE4 carrier status, Body mass index (BMI), diabetes, and hypertension (OR = 0.22, 95% CI: [0.06, 0.79], p = 0.03). However, the prevalence of ECAC from both sides showed no statistically significant differences between Aβ positive and negative groups (81% versus 90%, p = 0.3). Logistic regression models showed no statistically significant association between Aβ deposition and carotid artery calcification after adjusting for plaque index (OR = 0.34, 95% CI:[0.08, 1.26], p = 0.1), the association remains insignificant when adjusted for age, sex, education years, APOE4 carrier status, BMI, diabetes and hypertension (OR = 0.38, 95% CI: [0.08, 1.76], p = 0.2) as well (Table 4).

Table 3.

Univariate analysis of the participants' characteristics using the 2011 Aβ status (N = 82).

Characteristic	Aβ negative, N = 40^a	Aβ positive, N = 42^a	p-value^b
Age	87.0 (2.4)	87.5 (2.9)	0.6
Sex (male)	26(65%)	26 (62%)	0.8
Race (White)	38 (95%)	40 (95%)	>0.9
Years of Education	15.3 (2.9)	14.7 (2.6)	0.3
BMI	27.6 (3.8)	26.8 (4.0)	0.5
ApoE4 carrier (N = 77)	3 (7%)	8 (21%)	0.1
Hypertension	29 (73%)	25 (60%)	0.2
Diabetes (N = 80)	3 (7%)	2 (6%)	>0.9
Smoking status (N = 81)	1 (3%)	0 (0%)	0.5
Ultrasound scan characteristics
Right side calcification (+)	29 (73%)	30 (71%)	0.9
Left side calcification (+)	33 (83%)	26 (62%)	0.04*
Any calcification (+)	36 (90%)	34 (81%)	0.3
Right size plaque number	2 (1, 3.3)	3 (1.3, 5)	0.2
Left side plaque number	3 (1, 4)	3.5 (1, 5.8)	0.6
Total plaque number	5.5(3, 7.3)	6 (3,11)	0.4
Right side plaque index	2 (1, 3)	2 (1, 4)	0.2
Left side plaque index	2 (1, 3)	2 (1, 4)	0.5
Plaque index	4.5 (2, 6)	4.5 (3, 7)	0.4
Maximum plaque grade	1 (1, 2)	2 (1, 2)	0.2

n (%); Mean (SD); Median (IQR);^bFisher's exact test; Pearson's Chi-squared test; Welch Two Sample t-test; Wilcoxon rank sum test.

*p<0.05.

Table 4.

Multivariable logistic regression model of Aβ positivity and carotid artery calcification in 2011 (N = 77).

	model 1			model 2			model 3
Characteristic	OR^a	95% CI^a	p-value	OR^a	95% CI^a	p-value	OR^a	95% CI^a	p-value
Age				1.05	0.87, 1.27	0.6	1.00	0.82, 1.22	>0.9
Sex (ref = “Male”)				0.81	0.30, 2.17	0.7	0.73	0.25, 2.07	0.6
Years of Education				0.97	0.80, 1.16	0.7	0.93	0.76, 1.13	0.5
ApoE4 carrier				3.81	0.97, 19.2	0.1	2.76	0.63, 14.7	0.2
BMI							0.95	0.83, 1.08	0.4
Diabetes							0.54	0.04, 4.72	0.6
Hypertension							0.43	0.14, 1.21	0.1
Plaque index	1.13	0.98, 1.34	0.1	1.11	0.94, 1.32	0.2	1.11	0.94, 1.33	0.2
Calcification status (ref = “No”)	0.34	0.08, 1.26	0.1	0.45	0.09, 1.96	0.3	0.38	0.08, 1.76	0.2

OR: Odds Ratio, CI: Confidence Interval.

Model 1: logistic regression model adjusted by plaque index.

Model 2: model 1 + age, sex, years of education, and ApoE4 status.

Model 3: model 1 + age, sex, years of education, ApoE4 status, BMI, diabetes and hypertension status.

Discussion

In this retrospective study, we investigated the association between calcified carotid plaques and Aβ deposition in a non-demented population with a mean age of over 85. Although a previous study suggested that ECAC is associated with a higher risk of dementia in the population with a mean age of 69,⁵ we did not observe a statistically significant association between ECAC and Aβ status (positive/negative). To the best of our knowledge, this is the first study to explore the relationship between these two critical biomarkers in the oldest-old population.

We observed that participants with ECAC had higher plaque numbers and higher plaque indexes than those without ECAC, the latter reflecting the presence of larger plaques.¹ However, neither univariate nor multivariable analysis revealed a statistically significant association between bilateral ECAC, plaque index, and Aβ deposition in older adults without dementia. These findings are consistent with previous studies on the relationship between intracranial carotid calcification and Aβ deposition.^34–36 The observation may suggest the existence of alternative pathophysiological functions of ECAC in the intracranial Aβ accumulation and future cognitive decline in the oldest old adults.

Multiple studies have demonstrated the relationship between carotid calcification and future cognitive decline in both the generally healthy population and patients with carotid stenosis.^5,16 However, the direct relationship between carotid calcification and intracranial pathological changes has not been well characterized. The Rotterdam study focusing on the aging population, whose average was 70 years old, found that there was no association between intracranial internal carotid calcification and Aβ burden.³⁷ The meta-analysis focusing on the relationship between carotid occlusive disease and cerebral Aβ burden did not find a statistically significant relationship.³⁸ One clinical-pathologic brain sample study found that extracranial atherosclerosis was associated with neurofibrillary tangle accumulation but not Aβ accumulation, suggesting that the potential mechanism linking ECAC and AD might involve neurofibrillary tangles.³⁴ One study focusing on ischemic stroke patients also showed that the intracranial carotid calcification was associated with white matter hyperintensity.¹⁷ Those results showed that there might be more complex intracranial pathological changes in the patients who had atherosclerosis. However, as long-term hypoperfusion and ischemic events were the risk factors for future dementia, controversial results also showed that calcified plaque might be negatively associated with ischemic events.^2,12,39 Since our cross-sectional study did not include the duration of the carotid plaque existence or the degree of stenosis, the severity of the hypoperfusion was unclear in these populations. Moreover, the Aβ positivity could increase with age in the non-dementia aging population. In the population who were older than 80, the prevalence of Aβ could reach 41%;⁴⁰ in our generally healthy population older than 82 years without dementia, with a high prevalence of carotid calcification, detecting a relationship between ECAC and Aβ deposition could be challenging.⁴¹

We found that compared with Aβ positive patients, the prevalence of left-side calcification was significantly higher in the Aβ negative patients, the odds of Aβ positivity in those with left side calcification were 28% lower than in those without left side calcification after multiple adjustment (OR = 0.22, 95% CI: 0.06–0.79, p = 0.03). This result suggests the potentially different effect between left-side and right-side atherosclerosis. In the population with atherosclerosis, the plaque size and composition were found to be asymmetrically distributed. One study with an average age of 72 years found that left-sided carotid plaques were more vulnerable compared with right-sided.⁴² Also, in the study focusing on patients with a history of ischemic events, the plaque burden was significantly higher on the left side compared with the right side.⁴³ Those results stated that in the old population, the left-side calcification might have the function of stabilizing the plaque and reducing the effect of atherosclerosis in the intracranial Aβ deposition.

The strengths of the current study include that it is the first study to examine the association of carotid artery calcification with Aβ deposition using [¹¹C]PiB PET scans in a population whose mean age was over 85. Moreover, the focus on non-demented individuals older than 85 years allows for the exploration of potential vascular biomarkers before the onset of dementia. In addition, the reprocessed [¹¹C] PiB PET results, which used the PET-only CapAIBL methods, standardized the data between different scans and gave us more consistency in comparison.³¹

This study has several limitations worth noting. Carotid plaque calcification was assessed using ultrasonography, which does not measure the degree of calcification, a factor that could provide valuable insight given its relationship with plaque stability.^1,44 Although ultrasound is non-invasive and cost-effective, it can be challenging in cases of severe calcification³ and subject to measurement variability.²⁶ Considering that hyperlipidemia and cholesterol metabolism disorder could be the critical risk factors for both atherosclerosis and carotid calcification.⁴⁵ In the demographic information measurement, the lack of hyper-cholesterol status could also be the unreached confounding factor. Since the original study was started in 2000, the remaining elderly participants were limited, which induced the survival bias. The final study population likely represents a particularly resilient subset of individuals from the original GEMS cohort, as those with significant carotid calcification may have been excluded due to mortality or cognitive decline. Furthermore, the 2009 imaging study only enrolled non-demented participants, potentially excluding individuals with significant carotid calcification who had already developed cognitive symptoms. Consequently, the association between carotid plaques and amyloid deposition observed in our relatively healthy oldest-old population might not reflect the true relationship that would be seen in a more representative cohort where both pathologies could coexist. Based on the power calculation, at least 213 participants were needed to reach 80% power.⁴⁶ Additionally, the homogeneity of the participants, predominantly whites (96%) and healthier than the general population, introduces the selection bias, limiting the generalizability of our findings. Since the disparity in AD prevention and diagnosis exists in population,⁴⁷ future research design needs to enroll participants from a more diverse population. Moreover, participants included in the analysis had a lower prevalence of Aβ positivity and higher last follow-up age than those who did not complete all the scans in the sensitivity analysis, which also suggested the selection bias of the study (Table 5).

Table 5.

Characteristics between 90 participants in the analysis with carotid ultrasound scan versus those not included in the analysis from the 2009 GEMS imaging sub-study. (N = 193).

Characteristic	Not in the final analysisN = 103 ^a	Participants in the final analysisN = 90 ^a	p-value ^b
Age in 2009	85.7 (2.9)	85.3 (2.6)	0.4
Age at the last follow-up	91.5 (4.0)	93.6 (3.1)	<0.01**
Sex (Male)	57 (55%)	57 (63%)	0.3
Race (White)	99 (97%)	86 (96%)	0.7
Years of education	14.6 (2.6)	14.9 (2.7)	0.4
ApoE4 carrier (N = 179)	24 (26%)	13 (15%)	0.1
Hypertension	73 (71%)	59 (66%)	0.4
Diabetes (N = 188)	6 (6%)	5 (6%)	0.9
BMI in 2000(N = 191)	26.8 (3.8)	26.9 (3.8)	0.8
Smoking status in 2000 (N = 189)	5 (5%)	1 (1%)	0.2
PiB positivity in 2009 (N = 192)	67 (66%)	43 (48%)	0.01*

Mean (SD); n (%); ^bWelch Two Sample t-test; Pearson's Chi-squared test.

GEMS: Ginkgo Evaluation of Memory Study.

*p<0.05; **p<0.01.

In conclusion, in the oldest-old population, carotid artery calcification was not significantly associated with Aβ deposition. Together with previous research, the relationship between carotid atherosclerosis, carotid calcification, and Aβ development warrants further investigation with a larger research population and longer follow-up.

Footnotes

Acknowledgements

We gratefully acknowledge the use of the CapAIBL tool in this research, which was developed by Pierrick Bourgeat and made available through a collaborative agreement with the Australian government's Commonwealth Scientific and Industrial Research Organisation (CSIRO). We sincerely thank Dr Bourgeat for his invaluable contribution to the development of CapAIBL and to CSIRO for facilitating access to this tool, which has been instrumental in advancing our research.

ORCID iDs

Jiatong Li

Emma Barinas-Mitchell

Beth E Snitz

Brian Lopresti

Alex DelBene

Akira Sekikawa

Oscar L Lopez

Ethical considerations

This study received approval from the local institutional review board of the University of Pittsburgh (STUDY20020079) before study initiation.

Consent to participate

In accordance with the Declaration of Helsinki, all participants completed the written informed consent process before any study procedures commenced.

Author contributions

Akira Sekikawa (Conceptualization; Writing–review & editing); Jiatong Li (Conceptualization; Data curation; Formal analysis; Writing – original draft); Emma Barinas-Mitchell (Writing – review & editing); Yuefang Chang (Methodology; Writing – review & editing); Beth E Snitz (Writing–review & editing); Brian Lopresti (Methodology; Writing – review & editing); Alex DelBene (Methodology); Oscar L Lopez (Conceptualization; Writing – review & editing).

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by NIH grants U01 AT000162, P50 AG005133, R37 AG025516, P01 AG025204, and partly by R01-AG074971.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data availability statement

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.

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