Abstract
Background and purpose
Lower left atrial (LA) function is associated with higher dementia risk and may be mechanistically linked through vascular brain injury, an established correlate for higher dementia risk. Using data from the Atherosclerosis Risk in Communities study, we assessed the cross‐sectional association between LA function and brain magnetic resonance imaging (MRI) markers of vascular brain injury.
Methods
We included 1488 participants who were free of prevalent dementia, stroke, or atrial fibrillation and who underwent a two‐dimensional echocardiogram and brain MRI in 2011–2013 (mean [± standard deviation] age 76 [± 5] years, 60% female, 27% Black). LA function measures (reservoir, conduit, contractile strain) were assessed in quartiles. Brain MRI measures included cerebral microbleeds, brain infarcts, and white matter hyperintensity (WMH) volume. Logistic regression was used for dichotomous outcomes. Linear regression was used for WMH volume.
Results
Overall, 343 (23%) and 344 participants (23%) had ≥1 cerebral microbleed or brain infarct. After multivariable adjustments, the lowest LA reservoir and conduit strain quartiles (vs. highest quartile) were associated with higher odds of the presence of ≥1 cerebral microbleed (odds ratios [95% confidence intervals] 1.78 [1.42–2.22] and 1.52 [1.22–1.90]). Compared to the highest quartile, participants in the lowest LA conduit strain quartile had 1.51 (95% confidence interval 1.22–1.88) times higher odds of having ≥1 brain infarct. Lower LA contractile strain was associated with lower odds of brain infarcts. No association with WMH volume was noted.
Conclusions
We found that LA reservoir and conduit strain were associated with cerebral microbleeds and brain infarcts. Lower LA function may be linked to dementia risk via vascular brain injury. Prospective studies are needed to confirm these findings.
Keywords: echocardiogram, left atrial function, vascular brain injury
INTRODUCTION
Atrial fibrillation (AF) and clinical ischemic stroke are associated with an increased risk of dementia [1, 2]. Recent evidence indicates that atrial myopathy, which is characterized by left atrial (LA) dysfunction and enlargement, is also associated with elevated dementia risk, independently of AF and stroke [3]. Currently, mechanisms underlying this association are unclear, but may include vascular brain injury (e.g., subclinical brain infarcts, cerebral microbleeds, and white matter hyperintensity [WMH] volume). As several measures of vascular brain injury have been associated with higher dementia risk [4, 5, 6], it is possible that atrial myopathy is linked to higher dementia risk via vascular brain injury. However, the relationship between atrial myopathy and vascular brain injury has not been adequately explored.
Prior studies have characterized the neuroimaging correlates of atrial myopathy; however, most studies defined atrial myopathy with electrocardiogram (ECG) markers [7, 8, 9, 10, 11]. Only few studies have used echocardiogram‐defined atrial myopathy measures [12, 13, 14]. These studies suggest atrial myopathy and vascular brain injury may be related but had limitations including their small sample sizes and the limited number of brain MRI measures that were assessed [12, 13, 14]. Additional research with echocardiogram‐defined atrial myopathy and diverse brain imaging parameters is warranted. Using data from the Atherosclerosis Risk in Communities (ARIC) study, we assessed the cross‐sectional association of atrial myopathy (measured by two dimensional [2D] echocardiograms) with vascular brain injury.
METHODS
Study population
The ARIC study is a community‐based cohort of predominately Black and White adults. At inception (1987–1989), 15,792 participants aged 45–64 years were recruited from four US communities: Forsyth County, North Carolina; Jackson, Mississippi; Washington County, Maryland; and the suburbs of Minneapolis, Minnesota [15]. Participants have attended several additional follow‐up visits and are followed continuously for hospitalizations and death.
For this analysis, data from Visit 5 (2011–2013) were used since echocardiograms and brain MRIs were performed at this visit. We excluded participants with prevalent dementia, AF, or stroke, missing covariates, those whose race was other than Black or White, and non‐White participants in the MN and MD centers due to small numbers (Figure 1). After exclusions, 1488 participants were included.
FIGURE 1.
Study participant exclusion flowchart. LA, left atrial; MRI, magnetic resonance imaging.
The study has had continuous Institutional Review Board approval, and participants provided written informed consent at each visit.
Echocardiogram measures
There are three main components to LA function: (i) reservoir function, when blood fills the left atrium during systole; (ii) conduit function, when blood is being transferred into the left ventricle during early diastole; and (iii) contractile function, when the left atrium contracts and augments ventricular filling during late diastole [16]. In this analysis, LA function measures (LA reservoir, conduit, and contractile strain) were obtained from 2D‐echocardiograms at Visit 5 [17]. Briefly, echocardiograms were performed by trained sonographers using Philips iE33 Ultrasound systems with Vision 2011. Studies were transferred from each field center to a secure server at the Echocardiography Reading Center (Brigham and Women's Hospital, Boston, MA, USA). LA function was measured with speckle tracking vendor‐dependent software, using R‐R gating with an auto‐strain algorithm (QLAB Advanced Quantification Software 13.0, Philips Ultrasound, Inc.). Speckles were tracked frame by frame during a cardiac cycle. The absolute values of LA conduit and contractile strain were used.
A single investigator, who was blinded to participants’ clinical characteristics and outcomes, performed all strain analyses. Reproducibility of strain measures was analyzed by a second blinded investigator using a sample of 40 randomly selected participants. Among this random sample of 40 participants, intra‐reader and inter‐reader variability was assessed. For inter‐reader and intra‐reader variability, the intraclass correlation coefficients were 0.91 and 0.98, while the coefficients of variation were 11% and 8%, respectively. LA strain measures were analyzed by a single physician.
Brain MRI measures
The ARIC brain MRI imaging protocol has been described previously [18]. Briefly, a subset of Visit 5 participants who had no brain MRI contraindications were invited to undergo a brain MRI if they: (i) had undergone a prior ARIC brain MRI scan in 2004–2006; (ii) had evidence of cognitive impairment and/or declines on longitudinally administered tests; or (iii) were selected from an age‐stratified random sample of cognitively normal participants to approximate the age distribution of cognitively impaired participants. Sampling weights were assigned based on inverse sampling fractions and the probability of completing the examination [18].
Using standardized protocols, 3‐T Siemens scanners were used at each study site. Scans were read centrally at the ARIC MRI Reading Center (Mayo Clinic, Rochester, MN, USA). Total intracranial volume was measured using FreeSurfer version 5.1. WMH burden was measured using an algorithm that was developed at the Mayo Clinic, Rochester [19]. Cerebral microbleeds were identified as lesions ≤10 mm in maximum diameter on gradient‐echo T2‐weighted imaging sequences. Microbleeds were divided into lobar or subcortical microbleeds depending on the location [20]. Brain infarcts were identified and measured by a trained imaging technician and confirmed by radiologists. Lacunar infarcts were defined as subcortical T2 fluid‐attenuated inversion recovery (FLAIR) lesions with central hypointensity >3 mm and hyperintensity ≤20 mm in maximum diameter located in the caudate, lenticular nucleus, internal capsule, thalamus, brainstem, deep cerebral white matter, centum semiovale, or corona radiata [21]. Cortical infarcts were characterized on T2 FLAIR and included lesions involving the cortical matter that were >10 mm (large) or 5–10 mm (small) [22].
Covariates
Covariates were obtained from Visit 5, with the exception of race/center (Visit 1), education (Visit 1), and Apolipoprotein E ε4 allele (APOE ɛ4; Visit 2 or 3). Age, sex, race, education, and smoking status were self‐reported. Technicians recorded current medication use via review of medication bottles. Body mass index was derived from height and weight. Blood pressure was measured three times and the mean of the final two measurements was used. Diabetes was defined as a fasting glucose level ≥126 mg/dL, a non‐fasting glucose level ≥200 mg/dL, antidiabetic medication use in the past 2 weeks, or a self‐reported physician diabetes diagnosis. High‐density lipoprotein (HDL) cholesterol was measured enzymatically and low‐density lipoprotein (LDL) cholesterol was calculated using the Friedewald formula [23]. APOE ɛ4 genotyping was performed using the TaqMan assay (Applied Biosystems) [24]. Coronary heart disease was defined by self‐reported physician diagnoses at Visit 1, myocardial infarction diagnosis by ECG, or adjudicated cases after Visit 1 [25]. Heart failure was identified by the Gothenburg criteria (Visit 1 only), heart failure medication use within the past 2 weeks, or International Classification of Diseases codes from hospitalization records during follow‐up [26].
Left atrial volume index, left ventricular (LV) ejection fraction, and LV mass index were obtained from 2D echocardigrams. LA size was measured at the end of systole using biplane disk summation and indexed to body surface area to derive LA volume index. LV ejection fraction was calculated as 100 × (LV end‐diastolic volume – LV end‐systolic volume)/LV end‐diastolic volume. LV mass index was calculated from LV linear measures and indexed to body surface area [27].
Statistical analysis
Participant characteristics are described using frequencies and percentages for categorical variables and means and standard deviations (SDs) are used for continuous variables. LA function measures were assessed in quartiles. Cerebral microbleeds were assessed as the presence of (i) any microbleeds, (ii) subcortical microbleeds, and (iii) lobar microbleeds. For brain infarcts, we analyzed the presence of (i) any infarcts, (ii) lacunar infarcts, and (iii) cortical infarcts. Because WMH volume was highly skewed, log base 2 transformation was applied for normality. Logistic regression was used for dichotomous outcomes and linear regression was used for WMH volume.
Sampling weights were incorporated in all analyses to account for selection into the brain MRI study. Model 1 adjusted for age, sex, race/center, education, APOE ɛ4 (0 or ≥1 allele), and total intracranial volume (for WMH volume outcome only). Model 2 further adjusted for systolic blood pressure, antihypertensive medications, body mass index, diabetes, HDL cholesterol, LDL cholesterol, smoking status (current or former/never), coronary heart disease, heart failure, and anticoagulant use. Model 3 additionally adjusted for LA volume index, LV ejection fraction, and LV mass index. SAS software (v9.4; Cary, NC) was used.
RESULTS
At Visit 5, participants had a mean ± SD age of 76 ± 5 years, 60% were female, and 27% identified as Black individuals. Participant characteristics stratified by LA reservoir quartile are presented in Table 1. Those in the lowest quartile were older, had lower educational attainment, a higher prevalence of cardiovascular risk factors and disease, ≥1 APOE ε4 allele, and more markers of vascular brain injury. Characteristics stratified by other LA function measures are shown in Table S1.
TABLE 1.
Participant characteristics by left atrial reservoir strain in the Atherosclerosis Risk in Communities Study, 2011–2013 (n = 1488).
| LA reservoir strain | ||||
|---|---|---|---|---|
| ≤27.44% (n = 372) | 27.45–31.87% (n = 372) | 31.88–37.61% (n = 372) | ≥37.62% (n = 372) | |
| Demographics | ||||
| Age, years | 77.6 ± 5.5 | 76.1 ± 5.2 | 75.5 ± 4.7 | 74.7 ± 5.0 |
| Female sex | 242 (65.1) | 222 (59.7) | 216 (58.1) | 216 (58.1) |
| Black race | 115 (30.9) | 116 (31.2) | 84 (22.6) | 88 (23.7) |
| Less than high school education | 64 (17.2) | 42 (11.3) | 36 (9.7) | 34 (9.1) |
| Physiological indicators | ||||
| Body mass index, kg/m2 | 28.7 ± 5.7 | 28.5 ± 5.5 | 28.1 ± 4.9 | 27.6 ± 5.0 |
| Systolic blood pressure, mmHg | 133.0 ± 19.2 | 130.8 ± 18.5 | 129.2 ± 16.6 | 128.7 ± 16.7 |
| HDL cholesterol, mg/dL | 52.9 ± 13.6 | 52.8 ± 14.4 | 52.3 ± 14.2 | 55.1 ± 13.9 |
| LDL cholesterol, mg/dL | 103.5 ± 33.7 | 107.6 ± 34.7 | 104.8 ± 32.3 | 109.5 ± 35.4 |
| Diabetes | 127 (34.1) | 121 (32.5) | 111 (29.8) | 94 (25.3) |
| Coronary heart disease | 40 (10.8) | 28 (7.5) | 29 (7.8) | 25 (6.7) |
| Heart failure | 61 (16.4) | 24 (6.5) | 20 (5.4) | 13 (3.5) |
| ≥1 Apolipoprotein E ε4 allele | 103 (27.7) | 109 (29.3) | 87 (23.4) | 117 (31.5) |
| Antihypertensive medication use | 302 (81.2) | 282 (75.8) | 269 (72.3) | 245 (65.9) |
| Anticoagulant use | 7 (1.9) | 8 (2.2) | 5 (1.3) | 4 (1.1) |
| Current smokers | 15 (4.0) | 26 (7.0) | 15 (4.0) | 17 (4.6) |
| Echocardiogram measures | ||||
| LA volume index, mL/m2 | 28.8 ± 9.7 | 26.2 ± 7.2 | 24.2 ± 6.6 | 23.4 ± 5.8 |
| LV ejection fraction, % | 64.3 ± 8.1 | 66.3 ± 5.4 | 66.1 ± 5.5 | 67.1 ± 4.9 |
| LV mass index, g/m2 | 85.8 ± 23.9 | 76.5 ± 16.6 | 75.4 ± 15.5 | 72.1 ± 14.5 |
| Brain MRI measures | ||||
| Any microbleeds | 112 (30.1) | 89 (23.9) | 78 (21.0) | 64 (17.2) |
| Subcortical microbleeds | 90 (24.2) | 73 (19.6) | 60 (16.1) | 54 (14.5) |
| Lobar microbleeds | 38 (10.2) | 30 (8.1) | 24 (6.5) | 26 (7.0) |
| Any infarcts | 98 (26.3) | 90 (24.2) | 84 (22.6) | 72 (19.4) |
| Cortical infarcts | 43 (11.6) | 38 (10.2) | 29 (7.8) | 30 (8.1) |
| Lacunar infarcts | 69 (18.5) | 57 (15.3) | 62 (16.7) | 50 (13.4) |
| WMH volume, cm3 | 20.4 ± 19.6 | 16.5 ± 15.1 | 14.7 ± 14.9 | 14.4 ± 15.3 |
| Total intracranial volume, cm3 | 1373.7 ± 156.7 | 1371.4 ± 153.2 | 1379.3 ± 150.8 | 1398.2 ± 151.9 |
Note: Data are expressed as mean ± SD or n (%).
Abbreviations: HDL, high‐density lipoprotein; LA, left atrial; LDL, low‐density lipoprotein; LV, left ventricular; MRI, magnetic resonance imaging; WMH, white matter hyperintensity.
LA function and cerebral microbleeds
In this analysis, 343 participants (23%) had ≥1 cerebral microbleed. For LA reservoir strain, there was evidence of a dose–response association, with lower LA reservoir strain associated with higher odds of ≥1 cerebral microbleed. Compared to those in the highest quartile, participants in the lowest LA reservoir strain quartile had 1.78 (95% confidence interval [CI] 1.42–2.22) times higher odds of the presence of ≥1 cerebral microbleed after full model adjustments (Table 2). Similarly, lower LA conduit strain was associated with higher odds of the presence of cerebral microbleeds (lowest vs. highest quartile odds ratio [OR] 1.52 [95% CI 1.22–1.90]). No consistent association with LA contractile strain was observed.
TABLE 2.
Association of left atrial function measures and cerebral microbleeds or brain infarcts in the Atherosclerosis Risk in Communities Study, 2011–2013 (n = 1488).
| Cerebral microbleeds | ||||
|---|---|---|---|---|
| LA reservoir strain (%) | ≤27.44 (n = 372) | 27.45–31.87 (n = 372) | 31.88–37.61 (n = 372) | ≥37.62 (n = 372) |
| N, ≥1 microbleed a | 112 | 89 | 78 | 64 |
| OR (95% CI) | ||||
| Model 1 | 1.98 (1.61–2.44) | 1.36 (1.10–1.68) | 1.32 (1.08–1.62) | Reference |
| Model 2 | 1.92 (1.55–2.37) | 1.35 (1.09–1.67) | 1.31 (1.06–1.60) | Reference |
| Model 3 | 1.78 (1.42–2.22) | 1.31 (1.06–1.62) | 1.28 (1.04–1.57) | Reference |
| LA conduit strain (%) | ≤10.7 (n = 372) | 10.71–14.30 (n = 372) | 14.31–17.97 (n = 371) | ≥17.98 (n = 373) |
| N, ≥1 microbleed a | 106 | 86 | 87 | 64 |
| OR (95% CI) | ||||
| Model 1 | 1.67 (1.36–2.06) | 1.22 (0.99–1.51) | 1.43 (1.17–1.75) | Reference |
| Model 2 | 1.63 (1.32–2.01) | 1.22 (0.99–1.51) | 1.40 (1.14–1.72) | Reference |
| Model 3 | 1.52 (1.22–1.90) | 1.19 (0.96–1.47) | 1.39 (1.13–1.70) | Reference |
| LA contractile strain (%) | ≤14.43 (n = 373) | 14.44–17.61 (n = 372) | 17.62–21.06 (n = 372) | ≥21.07 (n = 371) |
| N, ≥1 microbleed a | 92 | 89 | 91 | 71 |
| OR (95% CI) | ||||
| Model 1 | 1.16 (0.94–1.42) | 1.33 (1.09–1.62) | 1.24 (1.02–1.52) | Reference |
| Model 2 | 1.11 (0.90–1.38) | 1.32 (1.08–1.62) | 1.24 (1.01–1.51) | Reference |
| Model 3 | 1.03 (0.82–1.28) | 1.28 (1.04–1.57) | 1.21 (0.99–1.49) | Reference |
| Brain infarcts | ||||
| LA reservoir strain (%) | ≤27.44 (n = 372) | 27.45–31.87 (n = 372) | 31.88–37.61 (n = 372) | ≥37.62 (n = 372) |
| N, ≥1 infarct a | 98 | 90 | 84 | 72 |
| OR (95% CI) | ||||
| Model 1 | 1.47 (1.19–1.80) | 1.24 (1.01–1.51) | 1.21 (0.99–1.47) | Reference |
| Model 2 | 1.28 (1.04–1.58) | 1.18 (0.96–1.45) | 1.16 (0.95–1.41) | Reference |
| Model 3 | 1.08 (0.86–1.34) | 1.11 (0.90–1.36) | 1.10 (0.90–1.34) | Reference |
| LA conduit strain (%) | ≤10.70 (n = 372) | 10.71–14.30 (n = 372) | 14.31–17.97 (n = 371) | ≥17.98 (n = 373) |
| N, ≥1 infarct a | 109 | 79 | 87 | 69 |
| OR (95% CI) | ||||
| Model 1 | 1.94 (1.58–2.38) | 1.35 (1.10–1.66) | 1.67 (1.37–2.04) | Reference |
| Model 2 | 1.71 (1.38–2.10) | 1.26 (1.02–1.55) | 1.57 (1.28–1.92) | Reference |
| Model 3 | 1.51 (1.22–1.88) | 1.18 (0.96–1.46) | 1.54 (1.25–1.88) | Reference |
| LA contractile strain (%) | ≤14.43 (n = 373) | 14.44–17.61 (n = 372) | 17.62–21.06 (n = 372) | ≥21.07 (n = 371) |
| N, ≥1 infarct a | 99 | 71 | 77 | 97 |
| OR (95% CI) | ||||
| Model 1 | 0.96 (0.80–1.17) | 0.67 (0.55–0.82) | 0.78 (0.64–0.94) | Reference |
| Model 2 | 0.88 (0.72–1.07) | 0.68 (0.56–0.84) | 0.77 (0.63–0.93) | Reference |
| Model 3 | 0.79 (0.64–0.97) | 0.66 (0.54–0.80) | 0.75 (0.61–0.91) | Reference |
Note: Model 1, adjusted for age, sex, race/center, education, Apolipoprotein E ε4 allele. Model 2, adjusted for variables in Model 1 plus systolic blood pressure, body mass index, diabetes, high‐density lipoprotein cholesterol, low‐density lipoprotein cholesterol, antihypertensive medications, smoking status, coronary heart disease, heart failure, and anticoagulant use. Model 3, adjusted for variables in Model 2 plus LA volume index, left ventricular (LV) ejection fraction, LV mass index.
Abbreviations: CI, confidence interval; LA, left atrial; OR, odds ratio.
Participants with ≥1 microbleed or infarct present.
When assessing cerebral microbleed subtypes, 277 participants (19%) had ≥1 subcortical microbleed, while 118 (8%) had ≥1 lobar microbleed. Compared to participants in the highest quartiles, those in the lowest LA reservoir and conduit strain quartiles had higher odds of having ≥1 subcortical microbleed (Table S2; Model 3: OR 1.60 [95% CI 1.26–2.03] and 1.55 [95% CI 1.23–1.96], respectively). No association was noted with LA contractile strain.
Lower LA reservoir and contractile strain were associated with higher odds of ≥1 lobar microbleed being present (lowest vs. highest quartile ORs 1.50 [95% CI 1.05–2.14] and 1.64 [95% CI 1.16–2.30], respectively). There was no association between LA conduit strain and lobar microbleeds.
LA function and brain infarcts
Overall, 344 participants (23%) had ≥1 brain infarct. After full model adjustments, participants in the lowest LA conduit strain had 1.51 (95% CI 1.22–1.88) times higher odds of the presence of ≥1 brain infarct compared to those in the highest quartile (Table 2). After adjusting for demographics and vascular risk factors, participants in the lowest LA reservoir strain quartile had 1.28 (95% CI 1.04–1.58) times higher odds of the presence of ≥1 brain infarct; however, this association was attenuated after additionally adjusting for LA size and LV measures. Lower LA contractile strain was associated with lower odds of brain infarcts (lowest vs. highest quartile OR 0.79 [95% CI 0.64–0.97]).
For brain infarct subtypes, 238 participants (16%) had ≥1 lacunar infarct and 140 (9%) had ≥1 cortical infarct. Lower LA conduit strain was associated with higher odds of the presence of ≥1 lacunar infarct after full model adjustments (Table S3; lowest vs. highest quartile OR 1.57 [95% CI 1.22–2.03]). Participants in the lowest LA reservoir strain quartile had higher odds of the presence of ≥1 lacunar infarct compared to those in the highest quartile after adjusting for demographics and vascular risk factors (OR 1.29 [95% CI 1.01–1.65]), although this association was attenuated after further adjusting for echocardiogram measures. No consistent association between LA contractile strain and lacunar infarcts was observed.
Lower LA conduit strain was associated with ≥1 cortical infarct after adjusting for demographics and vascular risk factors (lowest vs. highest quartile OR 1.41 [95% CI 1.06–1.88]); however, this association was attenuated after additional adjustments. Lower LA contractile strain was associated with lower odds of cortical infarcts after full model adjustments (lowest vs. highest quartile OR 0.72 [95% CI 0.54–0.97]). No association between LA reservoir strain and cortical infarcts was noted.
LA function and WMH volume
Among all participants, mean WMH volume was 16.5 cm3. In the fully adjusted models, no association between LA function measures and WMH volume was observed (Table 3).
TABLE 3.
Association of left atrial function measures and white matter hyperintensity volume a in the Atherosclerosis Risk in Communities Study, 2011–2013 ( n = 1488).
| LA reservoir strain (%) | ≤27.44 (n = 372) | 27.45–31.87 (n = 372) | 31.88–37.61 (n = 372) | ≥37.62 (n = 372) |
| β (95% CI) | ||||
| Model 1 | 0.14 (0.01, 0.28) | 0.16 (0.03, 0.29) | 0.07 (−0.06, 0.19) | Reference |
| Model 2 | 0.08 (−0.05, 0.22) | 0.13 (−0.004, 0.25) | 0.06 (−0.06, 0.18) | Reference |
| Model 3 | 0.03 (−0.11, 0.17) | 0.11 (−0.02, 0.24) | 0.05 (−0.07, 0.17) | Reference |
| LA conduit strain (%) | ≤10.70 (n = 372) | 10.71–14.30 (n = 372) | 14.31–17.97 (n = 371) | ≥17.98 (n = 373) |
| β (95% CI) | ||||
| Model 1 | 0.09 (−0.05, 0.22) | 0.05 (−0.08, 0.18) | 0.05 (−0.08, 0.17) | Reference |
| Model 2 | 0.02 (−0.12, 0.15) | −0.01 (−0.14, 0.12) | −0.01 (−0.13, 0.11) | Reference |
| Model 3 | −0.02 (−0.15, 0.12) | −0.03 (−0.15, 0.10) | −0.02 (−0.14, 0.11) | Reference |
| LA contractile strain (%) | ≤14.43 (n = 373) | 14.44–17.61 (n = 372) | 17.62–21.06 (n = 372) | ≥21.07 (n = 371) |
| β (95% CI) | ||||
| Model 1 | 0.17 (0.04, 0.30) | 0.10 (−0.02, 0.23) | 0.09 (−0.04, 0.22) | Reference |
| Model 2 | 0.14 (0.01, 0.27) | 0.12 (−0.01, 0.25) | 0.08 (−0.04, 0.21) | Reference |
| Model 3 | 0.11 (−0.02, 0.24) | 0.11 (−0.02, 0.24) | 0.07 (−0.05, 0.20) | Reference |
Note: Model 1, adjusted for age, sex, race/center, education, Apolipoprotein E ε4 allele, total intracranial volume. Model 2, adjusted for variables in Model 1 plus systolic blood pressure, body mass index, diabetes, high‐density lipoprotein cholesterol, low‐density lipoprotein cholesterol, antihypertensive medications, smoking status, coronary heart disease, heart failure, anticoagulant use. Model 3, adjusted for variables in Model 2 plus LA volume index, left ventricular (LV) ejection fraction, LV mass index.
Abbreviation: CI, confidence interval; LA, left atrial.
Analyzed as log2(white matter hyperintensity volume).
DISCUSSION
In this analysis of a US community‐based cohort, echocardiographmeasures of atrial myopathy were cross‐sectionally associated with vascular brain injury after adjustment for demographics, vascular risk factors, and measures of LA size, LV size and function. Several measures of lower LA function were associated with higher odds of cerebral microbleeds. Additionally, lower LA conduit strain was associated with a higher odds of brain infarcts. Overall, our findings suggest a link between atrial myopathy and vascular brain injury, which could partly explain the known association between atrial myopathy and risk of dementia.
Very little is known about the relationship between atrial myopathy and cerebral microbleeds. ECG‐defined atrial myopathy, such as elevated PTFV1 (P‐wave terminal force in lead V1), has shown an association with cerebral microbleeds [10, 11]. For echocardiogram‐defined atrial myopathy, the Multi‐Ethnic Study of Atherosclerosis (MESA) reported that, among participants with microbleeds (n = 313), lower LA reservoir strain was associated with more microbleeds. However, when assessing their entire analytical sample (n = 908), no association between LA reservoir strain and the presence of microbleeds was observed [14]. In contrast, our study found that lower LA function, measured by 2D echocardiograms, was associated with cerebral microbleeds, including both lobar and subcortical microbleeds. These differing results may be due to: (i) the mean LA reservoir strain in MESA (24%) being lower than that of our study (32%); (ii) our study population being slightly older (mean age 76 vs. 72 years in MESA); or (iii) MESA being more racially and ethnically diverse than our study.
It is likely that LA dysfunction leads to brain infarcts through blood stasis and microembolism, and the results from our analysis are in line with prior studies that have shown an association between LA function and brain infarcts [12, 13]. We further advance the field by evaluating subtypes of infarcts. In our study, lower LA conduit strain was associated with the presence of lacunar infarcts. For cortical infarcts, which are often considered to be embolic [28], the association between LA conduit strain and cortical infarcts was attenuated after adjusting for echocardiogram measures, suggesting that the relationship may be explained by LA size or LV function. Alternatively, given that there were fewer cortical infarcts, precision was relatively poor for this analysis. Additional research should evaluate this question among a larger sample.
Contrary to our hypothesis, lower LA contractile strain was associated with lower odds of the presence of brain infarcts. These unexpected results may reflect the influence that age has on the phases of LA function, given that our sample was older (mean age 76 years). A study of 120 healthy individuals aged 20–80 years found that those in older age groups had lower LA reservoir and conduit functions, but higher booster pump (contractile) function [29]. Other studies have similarly reported that advancing age simultaneously results in a lower conduit and higher booster (contractile) function [30, 31]. Therefore, it may be plausible that the decrease in conduit function, which reflects diastolic dysfunction, results in an increase in contractile function to compensate for the decrease in early diastolic filling [29, 31].
Previous studies suggest atrial myopathy may be associated with WMH volume, but the data are conflicting. Reduced LA emptying fraction, a measure of LA function derived from LA volumes, was associated with greater WMH volume [12]. However, when LA function was measured by strain, no association with WMH volume was observed [13, 14], which is consistent with our findings. It is possible that these different results may be due to the sensitivity of LA strain measures in identifying LA dysfunction earlier than LA volume measures [32].
Of note, prior evidence has shown that LV echocardiogram measures are associated with cerebrovascular disease [28, 33]. It has been suggested that changes in LA strain often occur prior to LV structural and functional changes [34], highlighting the importance of measuring LA function. However, of the few studies that analyzed echocardiogram‐defined LA function and markers of vascular brain injury, sample sizes were small and these studies did not assess subtypes of cerebral microbleeds or brain infarcts [12, 13, 14]. Therefore, our results add to the literature by indicating an association between LA function and vascular brain injury, independent of LV mass and function, in a larger sample of individuals.
Different mechanisms could explain the link between atrial myopathy and vascular brain injury. Because the left atrium plays a vital role in LV filling, lower LA function could reduce LV filling, which could then result in decreased cardiac output [35]. As up to 20% of cardiac output is consumed by the brain [36], reduced cardiac output could lead to brain injury. Another possible mechanism could be through shared pathways, such as inflammation [37, 38, 39]. In the Framingham Offspring Cohort, different inflammatory biomarkers, such as those that represent systemic inflammation or vascular inflammation/endothelial dysfunction, may underlie cerebral microbleeds and brain infarcts [38]. Several of these biomarkers (e.g., tumor necrosis factor‐alpha, C‐reactive protein) have also been reported to be altered in atrial myopathy [37]. Finally, although associations remained after adjusting for covariates, it is possible that shared risk factors (e.g., hypertension, smoking, diabetes) may explain this association.
The strengths of this study include the relatively large sample of Black and White men and women, the comprehensive range of LA function measures assessed by echocardiogram, and the availability of an array of brain MRI measures. However, the study also has some limitations. First, the study was a cross‐sectional analysis, which does not allow us to assess the temporality of the association between atrial myopathy and brain MRI measures. Relatedly, causal inference is limited due to the observational design. Second, multiple exposures were assessed in this analysis and adjustment for multiple comparisons was not performed; therefore, these results should be interpreted as exploratory. Third, participants who attended study visits and had a brain MRI were probably a healthier subset of the cohort, which may lead to selection bias. To address this, we used sampling weights to account for selection into the brain MRI study. Fourth, our results may not be generalizable to younger individuals as our sample consists of older adults.
In conclusion, among participants free of clinical stroke, dementia, and AF, measures of lower LA function were associated with cerebral microbleeds and brain infarcts. These findings suggest that reduced LA function may be a risk factor for vascular brain injury. Furthermore, it is possible that vascular brain injury may be a mechanism that links LA function and dementia. Future prospective research is needed to confirm these findings.
AUTHOR CONTRIBUTIONS
Wendy Wang: Conceptualization; writing – original draft; formal analysis; data curation. Pamela L. Lutsey: Writing – review and editing. Riccardo M. Inciardi: Data curation; writing – review and editing. Jorge L. Reyes: Writing – review and editing. Thomas H. Mosley: Writing – review and editing. Michelle C. Johansen: Writing – review and editing. Rebecca F. Gottesman: Writing – review and editing. Alvaro Alonso: Writing – review and editing. Clifford R. Jack Jr: Writing – review and editing. Scott D. Solomon: Writing – review and editing. Amil M. Shah: Writing – review and editing. Bruce A. Wasserman: Writing – review and editing. Lin Yee Chen: Writing – review and editing; supervision; conceptualization.
CONFLICT OF INTEREST STATEMENT
Amil Shah reports funding from Novartis and Philips Ultrasound, and consulting fees from Philips Ultrasound outside the submitted work. Scott Solomon reports funding from Actelion, Alnylam, Amgen, AstraZeneca, Bellerophon, Bayer, BMS, Celladon, Cytokinetics, Eidos, Gilead, GSK, Ionis, Lilly, Mesoblast, MyoKardia, NIH/NHLBI, Neurotronik, Novartis, NovoNordisk, Respicardia, Sanofi Pasteur, Theracos, and US2, an AI grant to institution, outside the submitted work and personal fees from Abbott, Action, Akros, Alnylam, Amgen, Arena, AstraZeneca, Bayer, Boeringer‐Ingelheim, BMS, Cardior, Cardurion, Corvia, Cytokinetics, Daiichi‐ Sankyo, GSK, Lilly, Merck, Myokardia, Novartis, Roche, Theracos, Quantum Genomics, Cardurion, Janssen, Cardiac Dimensions, Tenaya, SanofiPasteur, Dinaqor, Tremeau, CellProThera, Moderna, American Regent, Sarepta, Lexicon, Anacardio, Akros, and PureHealth consulting outside the submitted work. No other disclosures are reported.
Supporting information
Table S1.
ACKNOWLEDGEMENTS
The ARIC study has been funded in whole or in part with federal funds from the National Heart, Lung, and Blood Institute (NHLBI), the National Institutes of Health (NIH), Department of Health and Human Services, under contract numbers (75N92022D00001, 75N92022D00002, 75N92022D00003, 75N92022D00004, 75N92022D00005). This work was also supported by the NHLBI (T32HL007779 [Wendy Wang], R01HL126637 [Lin Yee Chen], R01HL141288 [Lin Yee Chen], R01HL158022 [Lin Yee Chen], K24HL155813 [Lin Yee Chen], R01HL137338 [Alvaro Alonso], K24HL148521 [Alvaro Alonso], K24HL159246 [Pamela L. Lutsey], R01HL135008 [Amil M. Shah], R01HL143224 [Amil M. Shah], R01HL150342 [Amil M. Shah], R01HL148218 [Amil M. Shah], K24HL152008 [Amil M. Shah]), National Institute of Neurological Disorders and Stroke (NINDS) (RF1NS127266 [Lin Yee Chen], RF1NS135615 [Lin Yee Chen]), National Institute of Aging (NIA) (R01AG054491 [Bruce A. Wasserman]), and the NINDS Intramural Research Program (Rebecca F. Gottesman). The authors thank the staff and participants of the ARIC study for their important contributions.
Wang W, Lutsey PL, Inciardi RM, et al. Association of left atrial function with vascular brain injury: The Atherosclerosis Risk in Communities study. Eur J Neurol. 2025;32:e16549. doi: 10.1111/ene.16549
DATA AVAILABILITY STATEMENT
The data that support the findings of this study are available from the corresponding author upon reasonable request.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Table S1.
Data Availability Statement
The data that support the findings of this study are available from the corresponding author upon reasonable request.