Asymptomatic carotid artery stenosis is associated with increased Alzheimer’s disease and non-Alzheimer’s disease dementia risk

Introduction

Alzheimer’s disease (AD) is the most common form of dementia.1 As of 2022, 6.55 million people in the USA had AD alone.2 Non-Alzheimer’s dementias comprise a large group of dementias that include vascular, Lewy-Body, frontotemporal and other dementias.3 There is a growing appreciation that vascular disease and associated vascular risk factors contribute to dementia risk.4 5

Vascular contributions to cognitive impairment and dementia (VCID) encompass a broad group of systemic vascular processes impacting brain health.4–6 Research in VCID has predominantly focused on cerebral small vessel disease (cSVD)7 8 and vascular risk factors including age, hypertension (HTN), hyperlipidaemia (HLD) and smoking.2 8–10 In contrast to cSVD, extracranial carotid atherosclerotic disease (ECAD) is characterised by large vessel atherosclerosis at the common carotid artery bifurcation and the internal carotid artery, leading to arterial stenosis.

Emboli from ECAD can cause cerebrovascular accidents, which have immediate effects on brain tissue and cognition and are associated with a well-defined long-term risk for dementia.11 Patients with ECAD who have no recent history of stroke or transient ischaemic attack (TIA) attributed to ECAD are considered asymptomatic. The prevalence of asymptomatic ECAD (aECAD) is approximately 7.5% for moderate ECAD (defined as 50%–70% stenosis) and 3% for severe ECAD (>70% stenosis) in individuals over 60 years of age, with higher predominance in men.12 Current treatment for aECAD primarily focuses on managing comorbidities (eg, smoking, HTN, HLD and diabetes) and on stroke prevention.13 However, there is no clinical evaluation or treatment goals targeting dementia outcomes. This is due to the lack of a well-established association between aECAD and dementia.

Few studies have focused on evaluating the impact of carotid disease on AD.14–20 Of these studies, five reported a positive association between carotid disease and AD,14–19 and one not confirming this association.20 Key limitations of these studies include (1) most rely on small cohorts; (2) use of non-clinical indicators of carotid atherosclerosis, such as carotid intima–media thickness ratio and common carotid plaque counting; (3) they do not control for cardiovascular confounders such as HTN, smoking, diabetes and heart disease and (4) inclusion of patients with both symptomatic and asymptomatic carotid disease. These studies, including the Rotterdam study,15 contributed significantly to the AD field by highlighting the importance of vascular disease and vascular risk factors in AD physiology. However, whether asymptomatic carotid stenosis is associated with AD and non-AD dementia risk remains unclear.

Defining the association between aECAD and AD as well as other forms of dementia is crucial considering the availability of effective treatments for aECAD that are currently not used for reducing the risk of AD and non-AD dementias. Furthermore, a deeper understanding of the cognitive effects of ECAD could inform clinical trials aimed at modifying the management of carotid disease. Cognition is a critical outcome to document in the aECAD population, as cognitive dysfunction results in increased disability, dependence, decreased medication adherence, higher healthcare costs and higher caregiving needs.2

This study evaluated the hypothesis that aECAD is associated with an elevated risk of AD and non-AD dementias leveraging longitudinal data with a US-based population insurance claims records.

Methods

Study design and variables

Similar to previous studies,22 23 a subset of 7 133 677 patients diagnosed with non-melanoma skin cancer was selected for medical informatic analysis from the Mariner database as this medical condition is not associated with neurodegenerative disease or cardiovascular diseases, its treatment does not require chemotherapy or surgical anaesthesia which can impact the risk of AD or non-AD dementias, and it occurs with sufficient frequency in the general population to enable robust analyses. Additionally, patients diagnosed with melanoma, the more aggressive form of skin cancer, were specifically excluded.

The outcome variable was determined by identifying the first diagnosis of AD and non-AD dementias using ICD-9-CM and ICD-10-CM codes in the patient’s medical claims data. Detailed ICD procedural codes used to identify the diagnosis of AD and non-AD dementias are reported in online supplemental table 1.

For the aECAD group, patients were defined as those with three or more ICD code diagnosis claims for aECAD who did not undergo carotid endarterectomy (CEA) or carotid artery stenting (CAS) surgical interventions. The ICD procedural codes for aECAD, CEA and CAS are provided in online supplemental table 1. Conversely, the control group included subjects without any aECAD or CEA or CAS ICD procedural codes. Patients younger than 55 years old, with a history of AD, dementia, brain cancer or neurosurgery were excluded from the study (ICD codes for these conditions are provided in online supplemental table 1).

To ensure a robust population, an active enrolment criterion of a minimum of 6 months before and 5 years after the diagnosis of aECAD was required, accounting for potential factors such as patients leaving, dying or switching insurance providers (figure 1). The index dates were defined as the first record of aECAD diagnosis for the treatment group and 6 months after the first patient claim record in the database for the control group. The 6 months prior to the index date were used to calculate baseline comorbidities in both groups. A start date of 1 year after the index date was selected to survey records for AD and non-AD dementia diagnoses to focus on long-term impact on disease progression.

Request permission

Figure 1

Study design and patient breakdown.

Results

Of the 17 133 677 patients in the Mariner’s data subset, 767 354 met the inclusion and exclusion criteria and the claims enrolment period requirements for our study (figure 1). Of the 125 926 patients who met the propensity score matching criteria, 62 963 patients were assigned to the treatment group and 62 963 were assigned to the control group. Patients were evaluated for a diagnosis of AD or non-AD dementia 12 months after the index date (figure 1).

Demographics and comorbidities for the unadjusted populations are summarised in table 1. In the unadjusted cohort, 207 159 individuals (mean (SD) age, 67.6 (7.1) years) were diagnosed with aECAD (defined by at least three consecutive aECAD diagnosis codes), whereas 560 195 individuals (mean (SD) age, 63.7 5.0) years) did not have any diagnosis of aECAD (table 1). The majority of patient records in the study were from individuals aged 70–74 years (table 1). There were significant differences in comorbidities between patients with aECAD and those without (asthma, 1.47% vs 9.71%; chronic obstructive pulmonary disease, 23.91% vs 26.70%; chronic kidney disease 10.60% vs 15.90%; congestive heart failure, 9.15% vs 6.72%; coronary artery disease, 45.00% vs 27.16%; deep vein thrombosis, 0.18% vs 2.02%; diabetes, 37.44% vs 35.85%; HTN, 75.64% vs 70.87%; obesity, 10.35% vs 24.98%; pulmonary heart disease, 4.08% vs 6.98% and tobacco use, 14.69% vs 25.28%) (table 1).

Logistic regression analysis for potential confounders associated with aECAD identified the following confounding factors and comorbidities: age, gender, region, CCI score, asthma, chronic obstructive pulmonary disease, chronic kidney disease, congestive heart failure, coronary artery disease, deep vein thrombosis, diabetes, HTN, obesity, pulmonary heart disease and tobacco use (online supplemental table 1).

Propensity score matching was used to create groups that controlled for identified confounding factors between the aECAD and control groups. Demographic characteristics and comorbidities of propensity score-matched populations are detailed in table 1. In the matched dataset, 62 963 individuals (mean (SD) age, 67.3 (2.8) years) comprised the aECAD group and were matched with an equal number of control individuals (mean (SD) age, 67.3 (2.8) years) without aECAD (table 1). Patient demographics and comorbidities, as well as CCI scores, did not exhibit statistically significant differences between the matched groups (table 1).

Analysis of unadjusted population data indicated that a diagnosis of aECAD was associated with a significantly higher incidence of AD compared with controls (9803 (4.51%) vs 12 814 (2.17%); relative risk (RR), 2.07; 95% CI 2.02 to 2.13; p<0.001) and a significant increased risk of developing non-AD dementia (7777 (3.58%) vs 8498 (1.44%); RR 2.49; 95% CI 2.41 to 2.56; p<0.001) (figure 2a and b). Results of the χ² analysis in the propensity score-matched populations indicated that aECAD diagnosis was associated with a significantly higher incidence of AD (2384 (3.79%) vs 1962 (3.12%); RR 1.22; 95% CI 1.15 to 1.29; p<0.001) and non-AD dementia (1879 (2.98%) vs 1271 (2.02%); RR 1.48; 95% CI 1.38 to 1.59; p<0.001) compared with controls (figure 2a and c).

Request permission

The propensity score-matched population was used to generate Kaplan-Meier survival curves for AD-free survival and non-AD dementia-free survival to assess the rate and proportion of the population developing these conditions over a period of 5 years (figure 3a,b). Changes in disease incidence rates between patients diagnosed with aECAD and controls mirrored the χ² analysis results. aECAD diagnosis was associated with a higher incidence and faster progression to AD (figure 3a) and non-AD dementia (figure 3b) over 5 years; CIs did not overlap in the 5-year analysis and were divergent. AD-free survival over 5 years was also stratified by age (figure 4), exhibiting a more evident increased risk in patients aged 60–74 years (figure 4a–c), where the greater risk was more apparent in those aged 70–74 years (figure 4c). In contrast, patients older than 75 years of age did not exhibit a significant difference in AD risk (figure 4d).

Request permission

Request permission

Discussion

The impact of AD and non-AD dementias is growing worldwide, and its incidence is expected to triple over the next three decades.2 Vascular diseases have emerged as crucial contributors to dementia risk.4 5 8 24 The majority of current research related to vascular contributions to dementia focuses on cSVD and associated cardiovascular risk factors,4–6 8 with few studies investigating the specific contribution of ECAD to dementia and none of them differentiating between asymptomatic and symptomatic ECAD.14–19 This study addressed the limitations of prior research by using US-based Mariner insurance claims of a large clinical population of 7 133 677 individuals to evaluate the specific impact of aECAD on the risk of developing AD and non-AD dementias. This approach used clinically defined carotid stenosis as a marker of carotid atherosclerosis (overcoming limitations of indirect indicators, such as carotid intima–media thickness ratio and common carotid plaque counting) and assured the exclusion of symptomatic patients with stroke or TIA. Distinguishing between symptomatic and asymptomatic carotid disease is crucial, as stroke and TIA are independent risk factors for dementia25 26 and, therefore, key confounders.

Propensity score matching was used to mitigate potential confounders between aECAD cohort and controls. Regression analysis identified confounding factors associated with aECAD including established aECAD risk factors, such as age, HTN, HLD, smoking, statin therapy and coronary artery disease, as well as unexpected factors, such as deep vein thrombosis, asthma and pulmonary heart disease. This strategy for identifying potential confounders promotes unbiased selection. Both the unmatched and propensity score-matched aECAD cohorts exhibited similarities to those in classic carotid studies, such as the Carotid Revascularization Endarterectomy versus Stenting Trial (CREST trial, including high incidence of HTN and heart diseases.27 Interestingly, we observed slightly higher rates of diabetes and lower rates of smoking in the aECAD cohorts, which likely reflect changing demographics of the USA.

Within the propensity score-matched 125 926 individuals, aECAD was associated with a 22% increased risk of developing AD and a 48% increased risk of developing non-AD dementias. Higher non-AD dementia risk compared with AD may be due to the inclusion of vascular dementia in this category. Furthermore, the matching approach indicated that aECAD was associated with increased risk of dementia independently of general cardiovascular diseases, suggesting that distinct mechanisms related to aECAD risk impact dementia risk.

Potential mechanisms by which aECAD influences dementia pathophysiology include decreased cerebral perfusion, which is considered an early contributor to AD pathology including neurofibrillary tangle (NFT) and β-amyloid (Aβ) accumulation.28 29 Supporting this, previous studies have shown that aECAD is associated with cerebral hypoperfusion30 and increased NFT accumulation.31 Additionally, we hypothesise that subclinical microembolic events, which occur in 10%–15% of patients with aECAD32 and do not result in TIA or stroke, may contribute to endothelial dysfunction, blood–brain barrier impairment and neuroinflammation, all mechanisms that contributor to AD pathophysiology.33 Endothelial inflammation is another mechanism that may contribute to AD risk in individuals with aECAD. Chronic endothelial inflammation, often observed in patients with aECAD,34 35 creates a proinflammatory environment that can exacerbate vascular and neural damage. The resulting endothelial dysfunction contributes to a cycle of inflammation, oxidative stress and vascular impairment, all of which are implicated in the progression of dementia.36

Findings from the survival analysis indicated that the largest difference in AD risk between aECAD and non-aECAD cohorts occurred in the age group of 70–74 years (figure 4), slightly younger than the typical peak age range for AD incidence, which peaks at 80 years old.37 This earlier peak aligns with evidence suggesting that combined vascular disease may accelerate AD onset and progression38 39 emphasising the importance of early aECAD diagnosis and highlighting a key window for intervention that could mitigate AD risk.

Outcomes from this study have broad implications across medical disciplines.40 Currently, aECAD evaluation and treatment do not routinely consider neurodegenerative outcomes such as AD or non-AD dementia. Our data suggest that these outcomes are relevant and that aECAD should be regularly considered as a risk factor in AD and dementia evaluation. The intervention and treatment of AD and dementia risk factors, including aECAD, could represent a new target for risk modification and contribute to delay the onset and progression of AD and dementia. Delaying AD onset by 5 years would reduce total health and long-term care spending for people with Alzheimer’s by 33%.2 This 5-year delay is also highly relevant considering that a difference between mild and moderate dementia is loss of independent living.2

Limitations of this study include the retrospective analysis of insurance claims, potentially excluding patients receiving service outside the database. Furthermore, insurance claims do not include indicators of carotid disease severity, therefore, the analyses reported here likely include a mixed population of subjects with both moderate and severe stenosis. Additionally, this study relied on clinical diagnoses for aECAD, AD and non-AD dementia included in the claim insurance data which may result in less reliable diagnoses than in prospective studies. To mitigate this limitation and increase the likelihood that patients have clinically relevant carotid stenosis, three consecutive diagnoses of aECAD were required for inclusion of aECAD patients in the study. Moreover, the inclusion of 62 963 patients in both propensity-matched cohorts mitigates these limitations.

Future retrospective research could confirm, validate and address the limitations of this study by using comprehensive datasets that include both patient diagnosis, medication data, laboratory values and imaging data that quantify the severity of the carotid disease. Additionally, well-designed prospective trials will be needed to confirm the role of aECAD in dementia risk.

In summary, this study evaluated whether asymptomatic carotid stenosis is associated with dementia risk using matched cohorts of a total of 125 926 patients from insurance claim records. The outcomes of this study indicated that aECAD is associated with an increased risk of AD and non-AD dementia over 5 years independent from other cardiovascular risks. As roughly 10% of the population over the age of 65 have aECAD, these are clinically relevant results and should prompt a closer evaluation of aECAD-specific effects on brain health and how aECAD-specific interventions can impact AD onset and progression.