Atrial Cardiomyopathy Predicts the Functional Outcome and Mortality in Stroke Patients

Xinjing Liu; Yuying Wang; Lan Ding; Ruiyao Hu; Yige Zhang; Wan Zhang; Lulu Pei; Yuan Cao; Hui Fang; Kai Liu; Shilei Sun; Jun Wu; Ferdinando S Buonanno; Mingming Ning; Yuming Xu; Bo Song

doi:10.5551/jat.64756

Abstract

Aim: Atrial cardiomyopathy (ACM) is characterized by atrial dysfunction. This study aims to assess the prognostic significance of ACM in patients with noncardioembolic stroke (NCS).

Methods: Patients with NCS within seven days of onset were prospectively enrolled between January 2019 and December 2020. ACM was defined as either an N-terminal pro-brain natriuretic peptide (NT-pro BNP) ＞250 pg/ml or a P-terminal force in precordial lead V1 (PTFV1) ≥ 5000µV·ms. A poor functional outcome was determined as a score of 3-6 on the modified Rankin Scale (mRS) within a 2-year follow-up period. Logistic regression and Cox regression analyses were employed to examine the relationship between ACM and the long-term prognosis of patients with NCS.

Results: A total of 1,346 patients were enrolled, of whom 299 (22.2%) patients were diagnosed with ACM. A total of 207(15.4%) patients experienced a poor functional outcome, and 58 (4.3%) patients died. A multivariate logistic regression analysis indicated that ACM was significantly associated with a poor functional outcome in NCS patients [adjusted odds ratio (aOR): 2.01; 95% confidence interval (CI): 1.42-2.87; p＜0.001]. Additionally, a multivariate Cox regression analysis showed that an NT-pro BNP ＞250 pg/ml was significantly associated with an increased risk of all-cause mortality [adjusted hazard ratio (aHR), 2.51; 95% CI: 1.42-4.43; p=0.001].

Conclusions: ACM may serve as a novel predictor of a poor long-term functional outcome in patients with NCS. Elevated NT-pro BNP levels (＞250 pg/ml) were found to be associated with a higher risk of all-cause mortality. These findings warrant further validation in multicenter studies.

Xinjing Liu, Yuying Wang and Lan Ding contributed equally to this work.

Introduction

Acute ischemic stroke (AIS) is a leading cause of long-term disability and mortality worldwide^{1, 2)}. Identifying accurate and early prognostic indicators for AIS is crucial for physicians as it enables them to tailor the most effective treatment strategies and mitigate the long-term consequences of stroke.

Atrial cardiomyopathy (ACM) refers to a spectrum of structural, architectural, contractile, and electrophysiological changes in the atrium³⁾. This emerging concept helps to clarify the relationship between arrhythmias and ischemic stroke (IS), thus suggesting that the increased risk of thrombosis is more closely linked to atrial dysfunction than arrhythmias alone⁴⁾. Although various studies have established that ACM-related biomarkers correlate with adverse events in cardiovascular diseases, the predictive value of ACM for patients with AIS remains underexplored^5-7). Two markers often used to diagnose ACM are the P-terminal force in precordial lead V1 (PTFV1) and N-terminal pro-brain natriuretic peptide (NT-pro BNP)^{3, 8)}. Although these markers are considered to be potential predictors of AIS, their efficacy is a matter of debate. For instance, a meta-analysis concluded that PTFV1 is an independent predictor of IS⁹⁾; however, a subsequent prospective study questioned its reliability¹⁰⁾. Similarly, while NT-pro BNP has been identified as useful for predicting the functional outcomes and mortality in AIS patients^{11, 12)}, other studies have found that elevated levels of NT-pro BNP do not necessarily correlate with the 3-month mortality in AIS patients who do not show any obvious myocardial damage¹³⁾. Additionally, some evidence suggests that composite indicators of the myocardial function may offer a more nuanced prognostic value than single markers¹⁴⁾. Thus, whether ACM, the composite indicator of atrial dysfunction, can predict the prognosis of NCS remains inconclusive and thus this possibility is worthy of further study.

Given the existing ambiguities, it is necessary to determine whether ACM, as a composite indicator of atrial dysfunction, can reliably predict the long-term functional outcomes and mortality in patients with NCS. This study aims to address this research gap.

Materials and Methods

Study Population

This study had a prospective observational cohort design, leveraging data from the Ischemic Cerebral Disease Database of the First Affiliated Hospital of Zhengzhou University, as previously described^{15, 16)}. Comprehensive baseline data, including demographic details, clinical and imaging features, biochemical profiles, and follow-up records, were collected electronically.

Briefly, this study consecutively enrolled patients with AIS within 7 days of onset from the Department of the First Affiliated Hospital of Zhengzhou University from January 2019 to December 2020. Stroke was diagnosed according to the World Health Organization diagnostic criteria¹⁷⁾. Patients with the following characteristics were excluded from this study: refusal to participate; incomplete clinical data; cardioembolic stroke; pre-stroke mRS score ≥ 2; severe liver and renal insufficiency; malignant tumors; vasculitis; immunotherapy; and severe heart failure (HF). The flowchart is shown in Fig.1.

Fig.1.

Patients’ flowchart of the cohort

Ethics Statement

The study protocol was approved by the Ethics Committee of the First Affiliated Hospital of Zhengzhou University. Written informed consent was obtained from the participants for the publication of any potentially identifiable information included in this article.

Clinical Data Collection

Baseline demographic characteristics such as sex, age, smoking history, medical history (hypertension, diabetes mellitus, coronary heart disease, ischemic stroke, and hyperlipidemia), National Institutes of Health Stroke Scale (NIHSS) score within 24 hours of admission, and hospital treatment were extracted from the database. IS etiology was classified according to the Trial of ORG 10172 in Acute Stroke Treatment (TOAST) classification by two experienced neurologists¹⁸⁾.

PTFV1 was calculated by multiplying the duration (ms) and depth (µV) of the negative terminal part of the P-wave in lead V1 by two standardized trained residents. The median of three consecutive cardiac cycles was measured in the ECG or 24-hour Holter ECG monitoring of the patients after admission. The inter-rater reliability was 92%. The NT-pro BNP level was tested within 24 h of admission. Smoking was defined as continuous smoking of at least one cigarette per day for at least six months. ACM was defined when any of the following conditions were satisfied: (1) NT-Pro BNP ＞250 pg/ml¹⁹⁾; (2) PTFV1 ≥ 5,000µV·ms²⁰⁾.

Follow-Up and Outcome

Patients were followed-up at 3 months, 6 months, 1 year and 2 years after discharge by face-to-face or telephone interviews. Poor functional outcomes were defined as an mRS score ≥ 3. All-cause death, recurrent stroke events, and therapeutic regimens were also recorded.

Statistical Analysis

Statistical analyses were performed using the SPSS software program (version 26.0 (SPSS Inc., Chicago, IL, USA) and the R software program, version 4.1.0, with statistical significance set at p＜0.05 (two-sided). Categorical variables were expressed as frequencies and percentages and were analyzed using the chi-square test. Continuous variables with a normal distribution were expressed as the mean and standard deviation (SD) and were analyzed using Student’s t-test and the corrected t-test. Non-normally distributed continuous variables were expressed as medians and interquartile ranges and were analyzed using the Wilcoxon rank-sum test. Logistic regression and Cox regression analyses were used to evaluate the associations of risk factors with poor functional outcomes and all-cause death separately. Kaplan-Meier curves and log-rank tests were used to compare the cumulative risk of death among the different groups. Statistical associations were presented as hazard ratios (HR), odds ratios (OR), and with their 95% confidence intervals (CI). In the multivariate analysis, variables with p＜0.1 as determined by a univariate analysis and previously considered important, were included. A receiver operating characteristic curve (ROC) was constructed, and the area under the curve (AUC) was calculated to evaluate the discrimination ability for a poor functional outcome and mortality. Net reclassification improvement (NRI) and Integrated Discrimination Improvement (IDI) analyses were performed to evaluate the improvement in the prediction model when ACM was added.

Results

Baseline Characteristics

A total of 2,045 NCS patients were consecutively recruited within 7 days of the onset of symptoms, and 699 (34.2%) patients were excluded during the study, including 127 (8.6%) patients who were lost to follow-up over the 2-year period. A comparison of the baseline characteristics of the included and excluded patients is shown in Supplemental Table 1. Notably, there were no significant differences in the baseline characteristics between the two groups. Finally, 1,346 patients were included in the analysis. The mean age was 59.92 years and 947 (70.4%) were male. A total of 299 (22.2%) patients were diagnosed with ACM. Out Of the 1,346 patients analyzed, 207 (15.4%) suffered from poor functional outcomes, and 58 (4.3%) died during the 2-year follow-up period. The baseline characteristics of the patients are shown in Table 1.

Supplemental Table 1.Comparisons of characteristics of patients included and excluded

	Excluded	Included
Characteristics	N= 699	N= 1346	p -value
Age(y) mean±SD	59.12±11.01	59.92±12.24	0.134
Gender (Male), n (%)	472 (67.5)	947 (70.4)	0.205
Smoking, n (%)	258 (36.9)	504 (37.4)	0.850
Hypertension, n (%)	491 (70.2)	981 (72.9)	0.227
Diabetes mellitus, n (%)	240 (34.3)	500 (37.1)	0.228
Coronary heart disease, n (%)	80 (11.4)	197 (14.6)	0.053
Hyperlipidemia, n (%)	121 (17.3)	198 (14.7)	0.141
Previous stroke, n (%)	187 (26.8)	382 (28.4)	0.467
Admission NIHSS (IQR)	3 [1;5]	3 [1;6]	0.069
LVEF (IQR)	63 [61;65]	63 [61;65]	0.298

SD, standard deviation; NIHSS, National Institutes of Health Stroke Scale; IQR, interquartile range; LVEF, Left Ventricular Ejection Fraction.

Table 1.Baseline characteristics of the noncardioembolic stroke patients in relation to a poor functional outcome

	Total	Good functional outcome	Poor functional outcome
Characteristics	N= 1,346	N= 1,139 (84.6%)	N= 207 (15.4%)	p -value
Age(y) mean±SD	59.92±12.24	58.75±12.12	66.33±10.82	＜0.001
Male, n (%)	947 (70.4)	821 (72.1)	126 (60.9)	0.002
Smoking, n (%)	504 (37.4)	449 (39.4)	55 (26.6)	0.001
Hypertension, n (%)	981 (72.9)	825 (72.4)	156 (75.4)	0.431
Diabetes mellitus, n (%)	500 (37.1)	421 (37.0)	79 (38.2)	0.802
CHD, n (%)	197 (14.6)	159 (14.0)	38 (18.4)	0.124
Hyperlipidemia, n (%)	198 (14.7)	173 (15.2)	25 (12.1)	0.291
Previous stroke, n (%)	382 (28.4)	293 (25.7)	89 (43.0)	＜0.001
Admission NIHSS (IQR)	3 [1;6]	3 [1;5]	5 [2;9]	＜0.001
Heart rate (IQR)	78 [70;80]	78 [70;80]	78 [72;80]	0.541
LVEF (IQR)	63 [61;65]	63 [62;65]	63 [61;64]	＜0.001
PTFV1 ≥ 5000 μV·ms, n (%)	113 (8.4)	82 (7.2)	31 (15.0)	＜0.001
NT-pro BNP ＞250pg/ml, n (%)	222 (16.5)	156 (13.7)	66 (31.9)	＜0.001
ACM, n (%)	299 (22.2)	214 (18.8)	85 (41.1)	＜0.001

SD, standard deviation; NIHSS, National Institutes of Health Stroke Scale; IQR, interquartile range; CHD, coronary heart disease; LVEF, left ventricular ejection fraction; PTFV1, P-terminal force in precordial lead V1; NT-pro BNP, N-terminal pro-brain natriuretic peptide; ACM, atrial cardiomyopathy.

The Predictive Value of PTFV1/NT-pro BNP/ACM for the Functional Outcomes in Noncardioembolic Stroke Patients

The findings of a univariate analysis of the functional outcomes is shown in Table 1. Supplemental Fig.1 illustrates the incidence of ACM in different functional outcomes based on different marker levels. As shown in Table 1, 9 variables were statistically significant (p＜0.1) according to the univariate analysis, including PTFV1, NT-pro BNP, and ACM. Compared those with a good functional outcome, the patients with a poor functional outcome had a higher prevalence of atrial cardiomyopathy based on PTFV1(15.0% vs. 7.2%, p＜0.001), NT-pro BNP (31.9% vs. 13.7 %, p＜0.001), and ACM (41.1 % vs. 18.8 %, p＜0.001).

Supplemental Fig.1. The prevalence of ACM based on different markers in patients grouped by functional outcomes

PTFV1, P-terminal force in precordial lead V1; NT-pro BNP, N-Terminal pro-brain natriuretic peptide; ACM, atrial cardiomyopathy.

After adjusting for variables such as age, sex, smoking, previous stroke, admission NIHSS, and the left ventricular ejection fraction (LVEF), ACM emerged as an independent predictor of a poor functional outcome (OR, 2.01; 95%CI: 1.42-2.87; p＜0.001) , as detailed in Table 2. Furthermore, both PTFV1 (OR: 1.90, 95%CI: 1.17-3.08, p=0.010) and NT-pro BNP (OR: 1.77, 95%CI: 1.21-2.59, p=0.004) were significantly associated with poor functional outcomes. Meanwhile, age (OR:1.04, 95%CI: 1.02-1.06, p＜0.001), previous stroke (OR: 2.03, 95%CI: 1.46-2.83, p＜0.001), and the NIHSS score on admission (OR: 1.15, 95%CI: 1.11-1.19, p＜0.001) also appeared to be independent factors as well.

Table 2.A multivariate logistic regression analysis of the relationship between the variables and a poor functional outcome

	Multivariate Analysis
Model	Variables	OR	95% CI	p -value
Model 1	Age	1.05	1.03-1.07	＜0.001
	Sex	1.16	0.80-1.68	0.443
	Smoking	0.75	0.50-1.12	0.151
	Previous stroke	2.01	1.45-2.80	＜0.001
	Admission NIHSS	1.15	1.11-1.19	＜0.001
	LVEF	0.97	0.93-1.01	0.136
	PTFV1	1.90	1.17-3.08	0.010
Model 2	Age	1.05	1.03-1.07	＜0.001
	Sex	1.14	0.79-1.65	0.480
	Smoking	0.76	0.51-1.13	0.177
	Previous stroke	1.99	1.43-2.78	＜0.001
	Admission NIHSS	1.16	1.12-1.20	＜0.001
	LVEF	0.96	0.92-1.00	0.078
	NT-pro BNP	1.77	1.21-2.59	0.004
Model 3	Age	1.04	1.02-1.06	＜0.001
	Sex	1.16	0.80-1.69	0.437
	Smoking	0.74	0.50-1.11	0.143
	Previous stroke	2.03	1.46-2.83	＜0.001
	Admission NIHSS	1.15	1.11-1.19	＜0.001
	LVEF	0.97	0.93-1.01	0.161
	ACM	2.01	1.42-2.87	＜0.001

OR, odds ratio; CI, confidence interval; NIHSS, National Institutes of Health Stroke Scale; LVEF, left ventricular ejection fraction; PTFV1, P-terminal force in precordial lead V1; NT-pro BNP, N-Terminal pro-brain natriuretic peptide; ACM, atrial cardiomyopathy.

Evaluation of the Prognostic Value of ACM in Patients with Noncardioembolic Stroke

The ROC curve for a poor functional outcome is shown in Fig.2. The conventional model used for comparisons included variables such as age, sex, the NIHSS score on admission, and a history of stroke. In terms of predicting poor functional outcomes, adding either NT-pro BNP (model 2: AUC,0.76 vs 0.75; p=0.025) or ACM (model 3: AUC,0.77 vs 0.75; p=0.007) to the conventional model enhanced its discriminatory power; however, adding PTFV1 did not produce the same effect. As shown in Table 3, after ACM was added to the conventional model, IDI was 1.33% (95%CI: 0.43%-2.23%, p=0.004), and NRI was 34.63% (95%CI: 20.25%-49.01%, p＜0.001), thus indicating that ACM significantly improved risk stratification for a poor functional outcome in NCS. PTFV1（NRI: 15.55%, p=0.003; IDI: 0.60%, p=0.065）and NT-pro BNP（NRI: 16.89%, p=0.017; IDI: 0.80%, p=0.031）were separately added to the conventional model to evaluate their incremental ability to predict the functional outcomes.

Fig.2. ROC curves of different models for predicting a poor functional outcome

The conventional model included age, sex, previous stroke, and the NIHSS score on admission. Model 1: conventional model + PTFV1; Model 2: conventional model + NT-pro BNP; Model 3: conventional model + ACM. PTFV1, P-terminal force in precordial lead V1; NT-pro BNP, N-Terminal pro-brain natriuretic peptide; ACM, atrial cardiomyopathy.

Table 3.Reclassification and discrimination statistics for a poor functional outcome and mortality by atrial cardiomyopathy

Poor functional outcome	C statistic		NRI (continuous), %		IDI, %
Poor functional outcome	Estimate (95%CI)	p -value	Estimate (95%CI)	p -value	Estimate (95%CI)	p -value
Conventional model	0.75 (0.72-0.79)		reference		reference
Model 1	0.76 (0.72-0.80)	0.127	15.55 (5.38-25.73)	0.003	0.60 (0.00-1.24)	0.065
Model 2	0.76 (0.73-0.80)	0.025	16.89 (2.94-30.84)	0.017	0.80 (0.00-1.52)	0.031
Model 3	0.77 (0.74-0.81)	0.007	34.63 (20.25 – 49.01)	＜0.001	1.33 (0.43 – 2.23)	0.004

The conventional model included age, sex, previous stroke, and admission NIHSS score; NRI, net reclassification improvement; IDI, integrated discrimination improvement; CI, confidence interval.

Survival Analysis of ACM in Patients with Noncardioembolic Stroke

Kaplan-Meier survival curves and log-rank tests were used to analyze the relationship between ACM markers and survival probability. As shown in Fig.3, the survival probability of patients with NCS in the high NT-pro BNP group was significantly lower than that in the low NT-pro BNP group (p＜0.001). Patients with NCS with ACM had a significantly higher risk of death than those without ACM (p＜0.001). PTFV1 ≥ 5000µV·ms was not associated with mortality in patients with NCS. A Cox regression analysis showed that NT-pro BNP (aHR: 2.51, 95%CI: 1.42-4.43, p=0.001) and ACM (aHR: 2.22, 95%CI: 1.28 to 3.86, p=0.005) were independently associated with an increased risk of death after adjusting for age, smoking, history of coronary heart disease, previous stroke, and the NIHSS score on admission (Table 4).

Fig.3. Survival curves of noncardioembolic stroke patients with and without atrial cardiopathy

NT-pro BNP, N-Terminal pro-brain natriuretic peptide.

Table 4.A Cox regression analysis of the relationship between the variables and mortality

Variables	Univariate Analysis		Model 1		Model 2
Variables	HR (95%CI)	p -value	aHR (95%CI)	p -value	aHR (95%CI)	p -value
Age	1.05 (1.03 - 1.08)	＜0.001	1.03 (1.00-1.06)	0.020	1.03 (1.01-1.06)	0.012
Sex	1.36 (0.79 - 2.32)	0.264
Smoking	0.53 (0.29 - 0.96)	0.036	0.64 (0.35-1.19)	0.161	0.65 (0.35-1.19)	0.162
Hypertension	1.29 (0.70 - 2.40)	0.415
Diabetes mellitus	0.96 (0.56 - 1.64)	0.879
CHD	1.70 (0.92 - 3.14)	0.093	1.13 (0.60-2.15)	0.704	1.16 (0.61-2.20)	0.649
Hyperlipidemia	0.54 (0.21 - 1.34)	0.183
Previous stroke	2.57 (1.53 - 4.30)	＜0.001	2.23 (1.32-3.76)	0.003	2.24 (1.33-3.79)	0.002
Admission NIHSS	1.12 (1.07 - 1.17)	＜0.001	1.13 (1.07-1.19)	＜0.001	1.13 (1.08-1.19)	＜0.001
Heart rate	1.02 (1.00 - 1.04)	0.107
LVEF	0.96 (0.91 - 1.02)	0.182
NT-pro BNP	3.97 (2.36 – 6.67)	＜0.001	2.51 (1.42-4.43)	0.001
PTFV1	1.52 (0.69 - 3.35)	0.298
ACM	3.37 (2.01 - 5.63)	＜0.001			2.22 (1.28-3.86)	0.005

HR, hazard ratio; aHR, adjusted hazard ratio; CI, confidence interval; NIHSS, National Institutes of Health Stroke Scale; LVEF, left ventricular ejection fraction; PTFV1, P-terminal force in precordial lead V1; NT-pro BNP, N-terminal pro-brain natriuretic peptide; ACM, atrial cardiomyopathy.

Discussion

In this cohort study, we utilized a combination of NT-pro BNP and PTFV1 markers to comprehensively evaluate the prognostic significance of atrial cardiomyopathy (ACM) in patients with non-cardioembolic stroke (NCS). Our study demonstrated that ACM is positively associated with poor functional outcomes and higher mortality rates.

ACM, which is usually defined by biomarkers and atrial imaging, is considered to represent a state of atrial dysfunction²¹⁾. Existing evidence suggests that ACM may elevate the risk of embolism through various mechanisms such as left atrial systolic dysfunction, endothelial damage, and hypercoagulability³⁾. While previous large-scale studies have indicated the significance of ACM markers in gauging the risk of adverse cardiac events, such as atrial fibrillation (AF)^{22, 23)}, myocardial infarction (MI)^{24, 25)}, and HF²⁶⁾, their role in stroke prognosis remains underexplored. Several small-sample retrospective studies have found that some ACM-related markers are associated with death and disability after IS^{11, 12)}. However, some results remain controversial in different research contexts¹³⁾. Evidence has shown that patients with AIS with ventricular systolic dysfunction might experience a further deterioration of cerebral ischemia and a higher mortality risk^{27, 28)}. In recent years, an increasing number of studies have suggested that ACM may be the underlying cause of cryptogenic stroke (CS)^{29, 30)}. Theoretically, cardiac dysfunction caused by ACM may mediate a poor prognosis in patients with AIS. At present, there are no studies on the predictive value of ACM for long-term outcomes in patients with AIS. Our study makes a unique contribution by prospectively validating ACM’s predictive value of ACM for long-term outcomes in patients with NCSs. We observed that the prevalence of ACM in our cohort was notably lower than what has been previously reported (22.2% vs. 38.3%)¹⁹⁾, a difference that could be attributed to varying definitions and subtypes of stroke examined in different studies.

In this study, ACM combined two indicators, NT-pro BNP and PTFV1, which are associated with stroke risk and adverse cardiovascular events, to better understand their impact. NT-pro BNP is an inactive hormone, the concentration of which gradually increases in patients with ventricular dysfunction. Similarly, abnormal PTFV1 levels can indicate left atrial fibrosis and cardiac systolic dysfunction, even before the atrium enlarges. Theoretically, these factors are related to the prognosis of cerebrovascular disease. Previous studies have shown a significant association between elevated NT-pro BNP levels and poor functional outcomes in AIS at 3 months³¹⁾, but this association weakens when patients with acute myocardial infarction are excluded¹³⁾. Similarly, PTFV1 has increasingly been identified as a predictor of stroke and cardiovascular death^{32, 33)}. Our study uniquely demonstrated that abnormal PTFV1 levels could also predict poor functional outcomes in patients with NCS, which may be explained by the presence of left atrial dilatation and left ventricular diastolic dysfunction⁸⁾. Unlike previous studies, our study excluded cardioembolic stroke, which could balance the confounding factors of the heart and be more representative of cerebrovascular diseases. In addition, ACM combined with NT-pro BNP and PTFV1 provided more information than ACM alone and this could help clinicians quickly stratify patients at high risk and actively formulate individualized treatments.

At the same time, we need to recognize that ACM is a prognostic predictor of stroke, not a cause of a poor prognosis, and that ACM itself is a result of the disease. The potential relationship between ACM and a poor stroke prognosis therefore remains to be further explored.

Our study is associated with several limitations. First, this was a single-center study. Some selection bias was unavoidable, although our hospital is a comprehensive stroke center with a wide referral network. Second, the exact criteria for diagnosing ACM lack uniformity across different fields. This study used a combination of two simple, efficient, and easily accessible biological and electrocardiogram indicators to define ACM, as has been done in most previous studies³⁴⁾. Finally, the exclusion of a proportion of patients who did not have NT-pro BNP and PTFV1 measurements could introduce some bias, although the baseline characteristics of the included and excluded groups did not differ significantly. Given these limitations, future multi-center studies with larger sample sizes are warranted for a more comprehensive understanding of ACM’s predictive value of ACM in patients with NCS.

Conclusions

In summary, our findings suggest that ACM serves as a valuable prognostic indicator of both poor functional outcomes and all-cause mortality in patients with non-cardioembolic stroke. These insights underscore the clinical relevance of ACM in the brain health context. However, to confirm these conclusions, further investigations in larger, more diverse populations are warranted.

Author Contribution

Bo Song and Yuming Xu conceived of and designed the study. Ding, Hu, Zhang, and Zhang collected data. Liu and Wang analyzed the data. Bo Song and Yuming Xu supervised the study. Lulu Pei, Yuan Cao, Hui Fang, Kai Liu, Shilei Sun, and Jun Wu were involved in the data interpretation. Xinjing Liu drafted the manuscript. Ferdinando Buonanno and MingMing Ning were responsible for the revision. All authors have contributed to the manuscript and approved the submitted version.

Conflicts of Interest

The authors have no financial conflicts of interest.

Funding Information

This work was supported by the National Natural Science Foundation of China (No. 82171324) and Henan Training Project for Leading Talents (No. YXKC2020019).

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