Association between Atrial Fibrillation and Risk of Alzheimer’s Disease and Vascular Dementia: A Systematic Review and Meta-analysis

Abstract

Background and Objectives:

Recent research suggests that atrial fibrillation (AF) may influence the risk of developing Alzheimer’s disease (AD) and vascular dementia (VaD). However, existing studies have provided inconsistent results, with some showing a significant association between AF and the risk of AD and VaD, while others do not. The objective of this study is to conduct a meta-analysis to investigate the association between AF and the risk of AD and VaD.

Methods:

A comprehensive search was conducted in several databases, including PubMed, Web of Science, Embase, and Google Scholar, covering research published before December 2023. Odds ratios (ORs) or relative risks (RRs) and 95% confidence intervals (CIs) were calculated using Stata 12.0 software to assess the association between AF and the risk of AD or VaD.

Results:

The meta-analysis revealed a significant association between AF and an increased risk of AD, using a random effects model (OR/RR: 1.23, 95% CI: 1.13–1.34, I² = 81.3%, P < 0.001). Similarly, a significant association was found between AF and an increased risk of VaD, using a random effects model (OR/RR: 1.80, 95% CI: 1.57–2.07, I² = 82.1%, P < 0.001).

Conclusion:

In summary, our comprehensive meta-analysis provides compelling evidence of a significant association between AF and an elevated risk of AD and VaD. The findings are corroborated by robust cross-sectional and longitudinal cohort studies, which further validate the observed link. However, further large-scale prospective studies are necessary to comprehensively investigate the relationship between AF and the risk of AD and VaD.

Introduction

Dementia is a widespread and debilitating disease that affects approximately 47.5 million individuals globally.[1] The high prevalence of dementia has significant social and economic implications, placing a burden on both the health-care system and society as a whole. Therefore, it is crucial to prioritize research and prevention efforts in the field of dementia. Atrial fibrillation (AF) is a prevalent cardiac arrhythmia, characterized by irregular and often rapid heart rates.[2] Current estimates place the prevalence of AF in the adult population between 2% and 4%.[2] There is growing evidence suggesting that AF may play a significant role in cognitive dysfunction.[3] Previous studies have indicated that AF is a risk factor for cognitive impairment and dementia, particularly in patients with a history of stroke. AF-related brain infarcts may contribute to a decline in cognitive function as part of the vascular contributions to cognitive impairment and dementia.[4] A meta-analysis by Kwok et al.[3] showed that AF confers an increased risk of dementia not only in patients with a history of stroke, but also in broader populations. Recent observational studies have reinforced the association between AF and the risk of developing dementia, particularly highlighting that anticoagulant therapy in individuals with AF is linked to a reduced incidence of cognitive decline or dementia.[5] Alzheimer’s disease (AD), the most prevalent form of dementia, is distinguished by neurodegeneration associated with the abnormal aggregation of β-amyloid and tau proteins.[6] Vascular dementia (VaD), the second leading cause of dementia, is related to cerebrovascular conditions such as infarcts, microbleeds, and cerebral amyloid angiopathy.[7] AF has been identified as a risk factor for dementia,[8] as it promotes cerebral hypoperfusion and a proinflammatory state.[9,10] Recent data suggests that there is a biological process, not exclusive to AF, in which the underlying mechanisms of cardiovascular diseases contribute to the formation of amyloid-β.[11] Animal studies have shown that long-term AF leads to a decrease in cardiac output, which may precede chronic cerebral hypoperfusion (CCH) and subsequent hypoxia.[3] These events impair the clearance of amyloid-β peptides and enhance their accumulation in cerebral vessels, thereby increasing the risk of AD. This supports the implication of AF not only in VaD, but also in AD.

Recent research has shown that AF may also influence the risk of developing AD[12,13,14] and VaD.[12,15,16] However, previous studies have reported inconsistent results, with some finding a significant association between AF and the risk of AD[12,13,14] and VaD,[12,15,16] while others did not find a significant association.[17,18,19] Previous meta-analyses mainly focused on the association between AF and the risk of dementia and cognitive impairment.[20,21] This study aims to investigate the association between AF and the risk of specific subtypes of dementia (AD and VaD) through a meta-analysis. This research will contribute to our understanding of the relationship between AF and dementia, providing valuable insights for future prevention and treatment strategies.

Methods

The present study was conducted on the basis of the Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) guidelines.[22] The PRISMA checklist is attached in Supplementary Table 1.

Table 1.

Characteristics of included studies regarding the association between AF and the risk of AD

Study (year)	Country	Study type	Sample size	Mean age (years)	Gender (male%)	Median follow-up time	AF ascertainment	Dementia diagnosis	Adjustment	Result OR/RR (95%CI)
Ott et al. (1997)[23]	The Netherlands	Cross sectional	6584	NR	NR	NA	ECG	Criteria with a subdiagnosis of AD based on NINCDS-ADRDA	Age, sex, ECG, MI, SBP and DBP, peripheral atherosclerosis, diabetes mellitus, educational level, antihypertension agent, β-blocker, digoxin, verapamil, anticoagulation agent, and thyroid drug treatment	AD without CVD: 1.8 (0.9–3.5); AD with CVD: 2.9 (1.1–7.5)
Bunch et al. (2010)[12]	USA	Cohort	37,025	60.7	60.1	5 years	Medical records, ICD-9 codes	Medical records, ICD-9 codes	NR	2.04 (1.40–2.97)
Marengoni et al. (2011)[17]	Sweden	Cohort	685	78	NR	6 years	ICD codes, history, medical records	DSM-III	Age, gender, education, baseline MMSE score, hypertension, antithrombotic medications, and+genotype	0.8 (0.4–1.5)
Cacciatore et al. (2012)[18]	Italy	Cohort	358	73.7 (7.1)	40.5	10 years	ECG	Criteria from NINCDS-ADRDA	NR	0.705 (0.354–1.394)
Kawabata- Yoshihara et al. (2012)[19]	Brazil	Cross sectional	1524	72.2	39.6	NA	ECG	DSM-IV, diagnostic tool developed by the 10/66 Dementia Research Group and validated for low-income countries, CSI-D, modified version of the CERAD 10-word list, community-directed version of the geriatric mental state	Age	1.6 (0.3–7.9)
Rusane et al. (2014)[13]	Finland	Cohort	1510	50.3	37.6	25.5 years	ICD codes	MMSE, Finnish version of CERAD	Gender, education, midlife SBP, cholesterol, BMI, ApoE, midlife smoking, physical activity, diabetes or impaired glucose tolerance, and stroke at late life	2.54 (1.04–6.16)
de Bruijn et al. (2015)[24]	The Netherlands	Cohort	6514	68.6	41.1	20 years	ECG	Cambridge Examination for Mental Disorders of the Elderly	Diabetes mellitus, smoking, total cholesterol and high-density lipoprotein cholesterol levels, lipid-lowering medication, SBP and DBP, BP–lowering medication, BMI, educational level, ever use of oral anticoagulant medication, CHD, heart failure, and ApoE μ4 carrier status	1.18 (0.91–1.54)
Di Nisio et al. (2015)[25]	Italy	Cross sectional	309	77.5	40.8	NA	ECG	DSM-IV, NINDS-ADRDA, NINDS-AIREN criteria	NR	2.688 (1.227–5.951)
Dugger et al. (2016)[26]	USA	Cross sectional	17,008	NR	NR	NA	NR	UDS, composed of standardized clinical evaluations at ADCs funded by the National Institute of Aging	NR	0.917 (0.800–1.052)
Alonso et al. (2017)[27]	USA	Cross sectional	6432	76.2	41.3	NA	ECG during study visit or any prior visit, or ICD-9 code at any point	NIA-AA and NINDS-AIREN criteria	NR	1.417 (1.147–1.744)
Ding et al. (2018)[5]	Sweden	Cohort	2658	73.1 (10.5)	37.1	9 years	ICD codes	DSM-IV, NINCDS-ADRDA, NINDS-AIREN	Age, sex, education, ever smoking, excessive alcohol consumption, physical inactivity, BMI, diabetes, hypertension, high cholesterol, CHD, and heart failure	1.33 (0.92–1.94)
Kim et al. (2019)[14]	Korea	Cohort	262,611	70.7	44.2	8 years	NA	KDSQ, ICD-10 codes	Age, sex, and clinical variables, including hypertension, diabetes mellitus, previous MI, heart failure, peripheral artery disease, dyslipidemia, osteoporosis, CKD, COPD, liver disease, history of malignant neoplasm, economic status, cardiovascular medications, BMI, SBP and DBP, blood glucose level, total cholesterol, and alcohol and smoking habits	Including stroke 1.39 (1.29–1.50) Censored for stroke 1.32 (1.22–1.44)
Kauko et al. (2023)[28]	Finland	Cohort	218,192	61.7 (17.1)	47	NR	ICD codes	ICD codes	Sex, birth year, smoking status, obesity, hyperlipidemia, CKD, hypertension, diabetes, and alcoholism	1.03 (0.97–1.09)
Li et al. (2023)[16]	Taiwan, China	Cohort	136,774	66.7	54.1	6.6 years	ICD codes	ICD codes	Age, sex, underlying diseases, and medications	1.15 (1.10–1.20)
Ou et al. (2023)[29]	China	Cohort	502,493	56.98	45.8	9.175 years	ICD codes	ICD codes	Education, alcohol intake, BMI, MET- min/wk, ApoE μ4, Townsend Deprivation Index, pacemaker, cholesterol-lowering medication, and insulin use	1.267 (1.129–1.421)
Zhang et al. (2023)[30]	China	Cohort	433,746	56.91	45.53	12.6 years	ICD codes	ICD codes	Age, sex, ethnicity, education, BMI, LDL-C, SBP, DBP, smoking, alcohol use, physical activity, depressed mood, hypertension, diabetes, CHD, statin use, and ApoE4 status	1.08 (0.96–1.21)

AD: Alzheimer’s disease, ADCs: Alzheimer’s Disease Centers, AF: atrial fibrillation, ApoE: apolipoprotein E, BMI: body mass index, BP: blood pressure, CERAD: Consortium to Establish a Registry for Alzheimer’s Disease, CHD: coronary heart disease, CKD: chronic kidney disease, COPD: chronic obstructive pulmonary disease, CSI-D: community screening instrument for dementia, CVD: cardiovascular disease, DBP: diastolic blood pressure, DSM: Diagnostic and Statistical Manual of mental disorders, ECG: electrocardiogram, ICD: International Statistical Classification of Diseases and Related Health Problems, KDSQ: Korean Dementia Screening Questionnaire, LDL-C: low-density lipoprotein cholesterol, MET: metabolic equivalent of task, MI: myocardial infarction, MMSE: Mini-Mental State Examination, NA: not applicable, NIA-AA: National Institute on Aging-Alzheimer’s Association, NR: not reported, NINCDS-ADRDA: National Institute of Neurological and Communicative Disorders and the Stroke-Alzheimer’s Disease and Related Disorders Association, NINDS-AIREN: National Institute of Neurological Disorders and Stroke-Association Internationale pour la Recherche et l’Enseignement en Neurosciences, SBP: systolic blood pressure, UDS: uniform data set

Supplementary Table 1.

PRISMA 2020 Checklist

Section and Topic	Item #	Checklist item	Location where item is reported
TITLE
Title	1	Identify the report as a systematic review.	Page 1
ABSTRACT
Abstract	2	See the PRISMA 2020 for Abstracts checklist.	Pages 1-2
INTRODUCTION
Rationale	3	Describe the rationale for the review in the context of existing knowledge.	Page 2
Objectives	4	Provide an explicit statement of the objective (s) or question (s) the review addresses.	Page 2
METHODS
Eligibility criteria	5	Specify the inclusion and exclusion criteria for the review and how studies were grouped for the syntheses.	Page 3
Information sources	6	Specify all databases, registers, websites, organisations, reference lists and other sources searched or consulted to identify studies. Specify the date when each source was last searched or consulted.	Page 3
Search strategy	7	Present the full search strategies for all databases, registers and websites, including any filters and limits used.	Page 3
Selection process	8	Specify the methods used to decide whether a study met the inclusion criteria of the review, including how many reviewers screened each record and each report retrieved, whether they worked independently, and if applicable, details of automation tools used in the process.	Page 3
Data collection process	9	Specify the methods used to collect data from reports, including how many reviewers collected data from each report, whether they worked independently, any processes for obtaining or confirming data from study investigators, and if applicable, details of automation tools used in the process.	Page 3
Data items	10a	List and define all outcomes for which data were sought. Specify whether all results that were compatible with each outcome domain in each study were sought (e.g. for all measures, time points, analyses), and if not, the methods used to decide which results to collect.	Page 3
	10b	List and define all other variables for which data were sought (e.g. participant and intervention characteristics, funding sources). Describe any assumptions made about any missing or unclear information.	Page 3
Study risk of bias assessment	11	Specify the methods used to assess risk of bias in the included studies, including details of the tool (s) used, how many reviewers assessed each study and whether they worked independently, and if applicable, details of automation tools used in the process.	Not applicable
Effect measures	12	Specify for each outcome the effect measure (s) (e.g. risk ratio, mean difference) used in the synthesis or presentation of results.	Page 3
Synthesis methods	13a	Describe the processes used to decide which studies were eligible for each synthesis (e.g. tabulating the study intervention characteristics and comparing against the planned groups for each synthesis (item #5)).	Page 3
	13b	Describe any methods required to prepare the data for presentation or synthesis, such as handling of missing summary statistics, or data conversions.	Page 3
	13c	Describe any methods used to tabulate or visually display results of individual studies and syntheses.	Page 3
	13d	Describe any methods used to synthesize results and provide a rationale for the choice (s). If meta-analysis was performed, describe the model (s), method (s) to identify the presence and extent of statistical heterogeneity, and software package (s) used.	Page 3
	13e	Describe any methods used to explore possible causes of heterogeneity among study results (e.g. subgroup analysis, meta-regression).	Page 3
	13f	Describe any sensitivity analyses conducted to assess robustness of the synthesized results.	Page 3
Reporting bias assessment	14	Describe any methods used to assess risk of bias due to missing results in a synthesis (arising from reporting biases).	Page 3
Certainty assessment	15	Describe any methods used to assess certainty (or confidence) in the body of evidence for an outcome.	Page 3
RESULTS
Study selection	16a	Describe the results of the search and selection process, from the number of records identified in the search to the number of studies included in the review, ideally using a flow diagram.	Pages 3-4
	16b	Cite studies that might appear to meet the inclusion criteria, but which were excluded, and explain why they were excluded.	Page 4
Study characteristics	17	Cite each included study and present its characteristics.	Page 4
Risk of bias in studies	18	Present assessments of risk of bias for each included study.	Not applicable
Results of individual studies	19	For all outcomes, present, for each study: (a) summary statistics for each group (where appropriate) and (b) an effect estimate and its precision (e.g. confidence/credible interval), ideally using structured tables or plots.	Pages 4-5
Results of syntheses	20a	For each synthesis, briefly summarise the characteristics and risk of bias among contributing studies.	Pages 4-5
	20b	Present results of all statistical syntheses conducted. If meta-analysis was done, present for each the summary estimate and its precision (e.g. confidence/credible interval) and measures of statistical heterogeneity. If comparing groups, describe the direction of the effect.	Pages 4-5
	20c	Present results of all investigations of possible causes of heterogeneity among study results.	Pages 4-5
	20d	Present results of all sensitivity analyses conducted to assess the robustness of the synthesized results.	Pages 4-5
Reporting biases	21	Present assessments of risk of bias due to missing results (arising from reporting biases) for each synthesis assessed.	Pages 4-5
Certainty of evidence	22	Present assessments of certainty (or confidence) in the body of evidence for each outcome assessed.	Pages 4-5
DISCUSSION
Discussion	23a	Provide a general interpretation of the results in the context of other evidence.	Page 5
	23b	Discuss any limitations of the evidence included in the review.	Page 6
	23c	Discuss any limitations of the review processes used.	Page 6
	23d	Discuss implications of the results for practice, policy, and future research.	Page 6
OTHER INFORMATION
Registration and protocol	24a	Provide registration information for the review, including register name and registration number, or state that the review was not registered.	Not applicable
	24b	Indicate where the review protocol can be accessed, or state that a protocol was not prepared.	Not applicable
	24c	Describe and explain any amendments to information provided at registration or in the protocol.	Not applicable
Support	25	Describe sources of financial or non-financial support for the review, and the role of the funders or sponsors in the review.	Page 6
Competing interests	26	Declare any competing interests of review authors.	Pages 6-7
Availability of data, code and other materials	27	Report which of the following are publicly available and where they can be found: template data collection forms; data extracted from included studies; data used for all analyses; analytic code; any other materials used in the review.	Page 7

From: Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 2021;372:n71. doi: 10.1136/bmj.n71

Search strategy

For this meta-analysis, a comprehensive search was conducted for literature published until December 2023 in the following databases: PubMed, Web of Science, Embase, and Google Scholar. The search terms used were: (“atrial fibrillation”) AND (“Alzheimer’s disease” OR “Alzheimer’s Dementia” OR “AD” OR “vascular dementia” OR “VaD”).

Inclusion criteria and exclusion criteria

The study included research that met the following inclusion criteria: 1) studies that explored AF and 2) studies that investigated the risk of AD or VaD. The exclusion criteria were as follows: 1) reviews, case reports, and meta-analyses; 2) studies that did not investigate the association between AF and the risk of AD or VaD; 3) studies that did not provide sufficient information to calculate odds ratios (ORs) or relative risks (RRs) and their 95% confidence intervals (CIs) for the association between AF and the risk of AD or VaD.

Data extraction

The following information was extracted from the included studies: author, publication year, study country, study type, sample size, mean age of the population, gender distribution, follow-up time, method of AF ascertainment, dementia diagnosis, adjustments made, and results.

Meta-analysis

Stata 12.0 software was utilized to calculate ORs/RRs and corresponding 95% CIs to assess the association between AF and the risk of AD or VaD. A random effects model was employed when there was high heterogeneity (as indicated by a P value of ≤0.05 for the Q test and an I² value of ≥50%), while a fixed effects model was used for low heterogeneity (as indicated by a P value >0.05 for the Q test and an I² value of <50%). Meta-regression analysis was conducted to explore potential sources of heterogeneity, and subgroup analyses were performed based on different races and study types. Sensitivity analysis was employed to evaluate the stability of the findings. The presence of publication bias was assessed using funnel plots, Begg’s test, and Egger’s test.

Results

Study characteristics

After applying the inclusion and exclusion criteria, a total of 17 studies[5,12,13,14,15,16,17,18,19,23,24,25,26,27,28,29,30] were included in the analysis. The process of study selection is illustrated in Figure 1, and the characteristics of the included studies are presented in Tables 1 and 2. Among the 17 studies, 16[5,12,13,14,16,17,18,19,23,24,25,26,27,28,29,30] investigated the association between AF and the risk of AD, comprising a combined total of 1,903,618 participants. In addition, 13 studies[12,14,15,16,18,19,23,25,26,27,28,29,30] explored the association between AF and the risk of VaD, comprising a combined total of 1,902,729 participants.

Figure 1.

The process of study selection. AD: Alzheimer’s disease, AF: atrial fibrillation, CI: confidence interval, OR: odds ratio, RR: relative risk

Table 2.

Characteristics of included studies regarding the association between AF and the risk of VaD

Study (year)	Country	Study type	Sample size	Mean age (years)	Gender (male%)	Median follow-up time	AF ascertainment	Dementia diagnosis	Adjustment	Result OR/RR (95%CI)
Ott et al. (1997)[23]	The Netherlands	Cross sectional	6584	NA	NA	NA	ECG	Criteria with subdiagnosis of VaD in accordance with NINDS-AIREN	Age, sex, ECG, MI, SBP and DBP, peripheral atherosclerosis, diabetes mellitus, educational level, antihypertensive agent, β-blocker, digoxin, verapamil, anticoagulation agent, and thyroid drug treatment	1.5 (0.4–4.9)
Bunch et al. (2010)[12]	USA	Cohort	37,025	60.7	60.1	5 years	Medical records, ICD-9 codes	Medical records, ICD-9 codes	NR	1.73 (1.25–2.40)
Cacciatore et al. (2012)[18]	Italy	Cohort	358	73.7 (7.1)	40.5	10 years	ECG	Criteria of the California Alzheimer’s Disease Diagnostic and Treatment Centers	NR	0.573 (0.286–1.135)
Kawabata- Yoshihara et al. (2012)[19]	Brazil	Cross sectional	1524	72.2	39.6	NA	ECG	DSM-IV, diagnostic tool developed by the 10/66 Dementia Research Group and validated for low-income countries, CSI-D, modified version of the CERAD 10- word list, community-directed version of the geriatric mental state	Age	1.0 (0.1–7.8)
Di Nisio et al. (2015)[25]	Italy	Cross sectional	309	77.5	40.8	NA	ECG	DSM-IV, NINDS-ADRDA, NINDS-AIREN criteria	NR	2.271 (1.126–4.560)
Habeych et al. (2015)[15]	USA	Case–control	17,062	NR	NR	NA	NR	ICD-9 codes	NR	1.7 (1.1–2.6)
Dugger et al. (2016)[26]	USA	Cross sectional	17,008	NR	NR	NA	NR	UDS, composed of standardized clinical evaluations at ADCs funded by the National Institute of Aging	NR	2.24 (1.29–3.88)
Alonso et al. (2017)[27]	USA	Cross sectional	6432	76.2	41.3	NA	ECG during study visit or any prior visit, or ICD-9 code at any point	NIA-AA and NINDS-AIREN criteria	NR	1.732 (1.139–2.561)
Kim et al. (2019)[14]	Korea	Cohort	262,611	70.7	44.2	8 years	NA	KDSQ, ICD-10 codes	Age, sex, and clinical variables, including hypertension, diabetes mellitus, previous MI, heart failure, peripheral artery disease, dyslipidemia, osteoporosis, CKD, COPD, liver disease, history of malignant neoplasm, economic status, cardiovascular medications, BMI, SBP and DBP, blood glucose level, total cholesterol, and alcohol and smoking habits	Including stroke 2.46 (2.20–2.75) Censored for stroke 1.63 (1.40–1.90)
Kauko et al. (2023)[28]	Finland	Cohort	218,192	61.7 (17.1)	47	NR	ICD codes	ICD codes	Sex, birth year, smoking status, obesity, hyperlipidemia, CKD, hypertension, diabetes, and alcoholism	1.58 (1.42–1.77)
Li et al. (2023)[16]	Taiwan, China	Cohort	136,774	66.7	54.1	6.6 years	ICD codes	ICD codes	Age, sex, underlying diseases, and medications	1.56 (1.43–1.70)
Ou et al. (2023)[29]	China	cohort	502,493	56.98	45.8	9.175 years	ICD codes	ICD codes	Education, alcohol intake, BMI, MET- min/wk, ApoE μ4, Townsend Deprivation Index, pacemaker, cholesterol-lowering medication, and insulin use	2.225 (1.945–2.546)
Zhang et al. (2023)[30]	China	Cohort	433,746	56.91	45.53	12.6 years	ICD codes	ICD codes	Age, sex, ethnicity, education, BMI, LDL-C, SBP, DBP, smoking, alcohol use, physical activity, depressed mood, hypertension, diabetes, CHD, statin use, and ApoE4 status	2.06 (1.80–2.36)

ADCs: Alzheimer’s Disease Centers, AF: atrial fibrillation, ApoE: apolipoprotein E, BMI: body mass index, CERAD: Consortium to Establish a Registry for Alzheimer’s Disease, CHD: coronary heart disease, CKD: chronic kidney disease, COPD: chronic obstructive pulmonary disease, CSI-D: community screening instrument for dementia, DBP: diastolic blood pressure, DSM: Diagnostic and Statistical Manual of Mental Disorders, ECG: electrocardiogram, ICD: International Statistical Classification of Diseases and Related Health Problems, KDSQ: Korean Dementia Screening Questionnaire, LDL-C: low-density lipoprotein cholesterol, MET: metabolic equivalent of task, MI: myocardial infarction, NA: not applicable, NIA-AA: National Institute on Aging-Alzheimer’s Association, NR: not reported, SBP: systolic blood pressure, UDS: uniform data set, VaD: vascular dementia

Association between AF and the risk of AD

The meta-analysis revealed a significant association between AF and an increased risk of AD, as determined by the random effects model (OR/RR: 1.23, 95% CI: 1.13–1.34, I² = 81.3%, P < 0.001) [Figure 2]. Subgroup analysis further demonstrated that AF was associated with a higher risk of AD in both Caucasian and Asian populations (Caucasian: OR/RR: 1.28, 95% CI: 1.08–1.52; Asian: OR/RR: 1.24, 95% CI: 1.13–1.36) [Supplementary Figure 1 ^{(285.7KB, tif)} ]. Moreover, subgroup analysis indicated that AF was associated with an increased risk of AD in both cross-sectional and cohort studies (cross-sectional studies: OR: 1.52, 95% CI: 1.04–2.21; cohort studies: RR: 1.22, 95% CI: 1.11–1.34) [Supplementary Figure 2 ^{(286.8KB, tif)} ]. Meta-regression analysis did not identify publication year, age, gender, or follow-up time as sources of heterogeneity among the included studies (publication year: P = 0.059; age: P = 0.303; gender: P = 0.356; follow-up time: P = 0.141). Sensitivity analysis showed no change in the direction of the effect when any study was excluded from the meta-analysis [Supplementary Figure 3 ^{(214.8KB, tif)} ]. In addition, the funnel plot, Begg’s test, and Egger’s test indicated no significant risk of publication bias [Supplementary Figure 4 ^{(128KB, tif)} ] (Begg’s test: P = 0.705; Egger’s test: P = 0.211).

Figure 2.

Forest plot for the association between AF and the risk of AD. AD: Alzheimer’s disease, AF: atrial fibrillation, CI: confidence interval, OR: odds ratio, RR: relative risk

Association between AF and the risk of VaD

The meta-analysis conducted in this study reveals a significant association between AF and an increased risk of VaD, as indicated by the random effects model used (OR/RR: 1.80, 95% CI: 1.57–2.07, I² = 82.1%, P < 0.001) [Figure 3]. Subgroup analysis further demonstrates that AF is associated with a higher risk of VaD in both Caucasian and Asian populations (Caucasian: OR/RR: 1.62, 95% CI: 1.36–1.93; Asian: OR/RR: 1.96, 95% CI: 1.61–2.37) [Supplementary Figure 5 ^{(231.9KB, tif)} ]. Moreover, subgroup analysis suggests that AF is linked to an increased risk of VaD in both cross-sectional and cohort studies (cross-sectional studies: OR: 1.91, 95% CI: 1.44–2.54; cohort: RR: 1.78, 95% CI: 1.51–2.10) [Supplementary Figure 6 ^{(261.7KB, tif)} ]. Analysis through meta-regression indicates that publication year, age, gender, and follow-up time do not contribute to heterogeneity among the included studies (publication year: P = 0.468; age: P = 0.466; gender: P = 0.691; follow-up time: P = 0.523). Sensitivity analysis demonstrates that the exclusion of any study does not alter the direction of the effect [Supplementary Figure 3 ^{(214.8KB, tif)} ]. Furthermore, no significant risk of publication bias is detected through the funnel plot, Begg’s test, and Egger’s test [Supplementary Figure 8 ^{(113.8KB, tif)} ] (Begg’s test: P = 0.913; Egger’s test: P =0.772).

Figure 3.

Forest plot for the association between AF and the risk of VaD. AF: atrial fibrillation, CI: confidence interval, OR: odds ratio, RR: relative risk, VaD: vascular dementia

Discussion

This meta-analysis, consisting of 17 studies, indicates that AF is a risk factor for both AD and VaD. Our findings are consistent with a recent meta-analysis[31] that included 13 studies on the association between AF and AD, as well as five studies on the association between AF and VaD. This analysis displayed an increased risk of AD (OR/RR: 1.4, 95% CI: 1.1–1.6) and VaD (OR/RR: 1.7, 95% CI: 1.2–2.3) among older individuals with AF. Another recent meta-analysis revealed that individuals with AF have a 30% higher chance of developing AD compared to those without AF.[32] The recent study[32] included only six literature sources published before October 2018. Another meta-analysis[31] published in 2021 found a significant association between AF and an increased risk of both VaD (adjusted OR: 1.7, 95% CI: 1.2–2.3) and AD (adjusted OR: 1.4, 95% CI: 1.2–1.6). This present study serves as an updated analysis of the previous meta-analysis, incorporating subgroup analyses for different races and study types, as well as meta-regression analysis to identify potential indicators that may influence the association between AF and the risk of AD. The significant risk of AD and VaD in elderly AF patients has notable implications for the management of this arrhythmia. Current AF treatment guidelines emphasize the involvement of patients in making decisions regarding their disease management.[33,34] However, individuals with dementia may face unique challenges in this process, and clinicians and caregivers should acknowledge these difficulties. Unfortunately, AF guidelines do not adequately address these issues, aside from recommending the avoidance of oral anticoagulation in dementia patients without reliable caregiver support.[33,34] It is crucial to identify effective strategies that can slow cognitive decline and prevent dementia in patients with AF to enhance outcomes in this population.

The study findings indicate a significant association between AF and an increased risk of AD. Both conditions share common risk factors including age, type 2 diabetes mellitus, and cardiovascular diseases.[35] However, limited neuropathologic evidence exists to support the link between AF and AD.[36] To address this gap, a cross-sectional study conducted on stroke-free individuals with AF demonstrated a correlation between AF and a reduction in hippocampal volume, which is a neuropathologic characteristic of AD.[37] Furthermore, an autopsy-based study on 328 participants revealed a higher prevalence of neuritic plaques and neurofibrillary tangles, both associated with AD, among those with permanent AF (RR: 1.47, 95% CI: 0.96–2.28) compared to non-AF subjects (RR: 1.40, 95% CI: 0.79–2.49).[38]

On the contrary, another study suggested that heart failure and AF were associated with less severe AD neuropathology. Various proposed mechanisms attempt to explain the connection between these diseases. One mechanism suggests that persistent AF reduces cardiac output, resulting in CCH and hypoxia. This, in turn, can impact the permeability of the blood–brain barrier, leading to impaired clearance of β-amyloid peptide and its accumulation in the brain – a key pathologic feature of AD.[38,39] In addition, chronic reduction in cerebral perfusion in AF may cause local acidosis and increased oxidative stress in the brain, which could affect the functioning of tau protein. This, in turn, leads to hyperphosphorylation and the formation of tau oligomers – critical elements in AD.[40] Another factor linking these diseases is inflammation. AF is associated with systemic inflammation,[41] and studies have indicated a connection between AF and inflammatory markers, which may contribute to the development of AD.[42] Furthermore, inflammatory pathways have been observed in the brains of individuals with AD, suggesting inflammation’s role in the pathogenesis of the disease.[43] In addition, infectious diseases may also play a role in the pathophysiology of both AF and AD, potentially serving as a link between the two.[44,45] VaD, which results from cerebrovascular disorders, is a cognitive impairment condition. AF is considered a risk factor for VaD due to its association with cerebrovascular accidents.[46] Moreover, reduced cardiac output in AF patients may lead to white matter lesions, which are correlated with decreased cognition.[47] However, further research is necessary to explore the underlying mechanisms connecting AF with the risk of AD and VaD.

The study has several limitations that should be considered. Firstly, the presence of high heterogeneity may have influenced the overall findings. Although efforts were made to identify potential sources of heterogeneity through meta-regression and subgroup analyses, the specific factors that contribute to this variation are still unclear. Secondly, it is important to note that this meta-analysis primarily included population-based cross-sectional and cohort-based/case–control studies. Therefore, the results should be interpreted within the context of observational research and its inherent limitations. For example, some studies had a small percentage of patients with a history of stroke, which could have influenced the overall outcomes.

Conclusions

In summary, our comprehensive meta-analysis provides compelling evidence of a significant association between AF and an elevated risk of AD and VaD. The findings are corroborated by robust cross-sectional and longitudinal cohort studies, which further validate the observed link. However, further large-scale prospective studies are needed to comprehensively investigate these associations.

Data availability

Data could be obtained from the corresponding author.

Conflicts of interest

There are no conflicts of interest.

Supplementary Figure 1

Subgroup analysis for the association between AF and the risk of AD in different races. AD: Alzheimer’s disease, AF: atrial fibrillation, CI: confidence interval, OR: odds ratio, RR: relative risk

Supplementary Figure 2

Subgroup analysis for the association between AF and the risk of AD in different study types. AD: Alzheimer’s disease, AF: atrial fibrillation, CI: confidence interval, OR: odds ratio, RR: relative risk

Supplementary Figure 3

Sensitivity analysis for the association between AF and the risk of AD in different races. AD: Alzheimer’s disease, AF: atrial fibrillation

Supplementary Figure 4

Funnel plot for the association between AF and the risk of AD in different races. AD: Alzheimer’s disease, AF: atrial fibrillation

Supplementary Figure 5

Subgroup analysis for the association between AF and the risk of VaD in different races. AF: atrial fibrillation, CI: confidence interval, OR: odds ratio, RR: relative risk, VaD: vascular dementia

Supplementary Figure 6

Subgroup analysis for the association between AF and the risk of VaD in different study types. AF: atrial fibrillation, CI: confidence interval, OR: odds ratio, RR: relative risk, VaD: vascular dementia

Supplementary Figure 7

Sensitivity analysis for the association between AF and the risk of VaD. AF: atrial fibrillation, VaD: vascular dementia

Supplementary Figure 8

Funnel plot for the association between AF and the risk of VaD. AF: atrial fibrillation, VaD: vascular dementia

Funding Statement

Nil.