Association between Echocardiographic Parameters of Cardiac Structure and Function and Mild Cognitive Impairment

Association between Echocardiographic Parameters of Cardiac Structure and Function and Mild Cognitive Impairment | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Association between Echocardiographic Parameters of Cardiac Structure and Function and Mild Cognitive Impairment Kai Zhang, Xiaoxia Liu, Siyu Huang, Xinrui Liu, Meiqi Zhao, Chao Xue, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5294926/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 05 Feb, 2025 Read the published version in BMC Cardiovascular Disorders → Version 1 posted 10 You are reading this latest preprint version Abstract Background: Cardiovascular diseases (CVDs) marked with cardiac morphological or hemodynamical abnormalities are associated with mild cognitive impairment (MCI). The links between cardiac structure and function and MCI are not well understood. We aimed to explore the association between echocardiographic parameters of cardiac structure and function and MCI in CVD patients. Methods: We conducted an age-, gender-, and education level-matched case-control study in general CVD participants with a 1:3 ratio of MCI (Montreal Cognitive Assessment [MoCA] score <26 and Mini-Mental State Examination [MMSE] score ≥24) and cognitively normal participants at a tertiary hospital in Beijing, China. The echocardiographic cardiac parameters and cognitive status were retrieved through the clinical electronic database from May 2021 to August 2023. Principal component analysis (PCA), negative binomial, and conditional multivariate regression were performed. Results: A total of 1136 CVD participants (mean age, 61.1 ± 8.3 years) were included in the study, comprising 289 (25.3%) MCI and 847 cognitively normal participants. Compared to cognitively normal participants, MCI participants had a higher prevalence of left ventricular (LV) diastolic dysfunction (54.0% vs. 40.3%; P<0.001) and greater interventricular septal thickness (IVST) (1.04 ± 0.17 cm vs. 1.00 ± 0.20 cm; P=0.002). LV diastolic dysfunction (Beta [SE], 0.234 [0.045]; P<0.001) and IVST (Beta [SE], 0.034 [0.016]; P=0.036) were negatively correlated with the MoCA score of global cognitive function. LV diastolic dysfunction (OR, 2.03; 95% CI, 1.48-2.79; P<0.001) and IVST (OR, 1.14; 95% CI, 1.03-1.27; P=0.014) were positively associated with MCI, independent of diagnosed CVDs and the conventional MCI risk factors. Conclusions: General CVD patients with abnormal echocardiographic LV diastolic dysfunction and IVST were associated with cognitive decline, suggesting further cognitive assessment for MCI. Trial registration: Retrospectively registered. echocardiography cardiac structure and function mild cognitive impairment Figures Figure 1 Background With the fast-aging population in China, cognitive impairment (CI) has increasingly become a serious health concern and imposed a tremendous public health and socioeconomic burden 1 . Among the various modifiable risk factors that impact cognitive functions, CVDs are the most challenging one 2 . One in 3 patients at cardiology clinics have some degree of CI 3 . Clinical researches have consistently shown a strong co-occurring or interaction between the four types of general CVDs (i.e., hypertension, coronary heart disease [CHD], atrial fibrillation [AF], and chronic heart failure [CHF]) and declining neurological conditions linked to MCI and dementia 3 , 4 . Recent studies indicated that cardiac dysfunctions, marked by low LV ejection fraction, high LV mass index, and concentric remodeling, were associated with lower cognitive function 5 – 9 . However, other researchers draw inconsistent results indicating that none of abnormalities of cardiac structure and function was significantly associated with cognitive impairment 10 . The relationship of heart-brain cognition, although incompletely understood, might facilitate the development of assessment biomarkers for neurovascular health 11 , 12 . MCI, a condition with objective cognitive impairment but minor effect on daily activities, is a transitional stage from normal cognitive function to dementia and has pivotal clinical significance 1 , 13 . Early detection and intervention for patients with MCI may help slow cognitive decline and reduce medical costs 14 . CVD guidelines highlight the importance of considering cognitive impairment as an essential comorbidity of cardiac diseases in disease management 15 – 17 . However, most cardiologists have not fully recognized MCI due to the subtle or hidden clinical symptoms and a lack of specialized knowledge in neuropsychology 18 , 19 . It is increasingly clear that we need to explore whether cognitive decline in CVDs can be evaluated by some essential cardiovascular biomarkers (e.g., body fluid, imaging, genetics), which might have potential value for MCI assessment. To address this unmet need, our group recently performed a matched case-control study to analyze the association between cardiac structure and function by echocardiography and MCI among general CVD participants at a tertiary hospital. We hypothesized that individuals with MCI would have higher prevalence of abnormalities in cardiac structure and function independent of diagnosed CVDs and the conventional MCI risk factors. Methods Study design and participants This study was a matched case-control study for CVD participants at Beijing Anzhen Hospital, Capital Medical University, China, where more than 70% of CVD patients were from different provinces other than Beijing. The data collection spanned the period from May 2021 to August 2023. Patients were eligible if they were hospitalized because of CVD conditions, had a prior or concurrent diagnosis of hypertension, and/or CHD, and/or AF, and/or CHF, and underwent neuropsychological tests of MoCA and MMSE within a 14-day window. Patients with serious infection, congenital cardiac conditions, significant valve disease, terminal malignancy, or subsequent acute cardiac events within 30 days of the hospital were excluded from the study. The exclusion criteria also included age < 18 years, no record of echocardiography or cognitive function assessment, diagnosed dementia, and psychiatric illness. Propensity score matching was done by a 1:3 ratio of pairing MCI participants with age-, gender- and education-matched controls of cognitively normal CVD participants. This study followed the Declaration of Helsinki and was approved by the Institutional Ethics Review Board at Beijing Anzhen Hospital (No.2022-17-1). Informed consent for this study was waived from all participants because it was retrospective. Measurements of echocardiographic parameters Echocardiographic examinations were performed by trained cardiac sonographers using a GE Vivid E9 ultrasound machine with an M5S probe (2–4 MHz; GE Healthcare, Chicago, IL, USA) or a Philips IE33 ultrasound machine with an S5-1 probe (2.5–3.5 MHz; Philips Healthcare, Andover, MA, USA) at the Echocardiography Medical Center office of Beijing Anzhen Hospital. All participants underwent standard transthoracic echocardiography within a 14-day window of hospitalization to assess cardiac structural and functional status because of the need for CVD management. The quantitative and qualitative diagnosis of cardiac structural and functional abnormalities were produced according to the echocardiographic guideline 20 and checked by an independent board-certified cardiac sonographer blinded to the clinical data. LV ejection fraction was calculated using the biplane Simpsons method in the apical four-chamber and two-chamber views. Early diastolic transmitral flow velocity (E), late atrial diastolic transmitral flow velocity (A), and early diastolic mitral annular velocity (e') were measured through pulsed-wave Doppler. LV diastolic dysfunction was ascertained by the ratio of E and A (E/A), ratio of E and e' (E/e'), tricuspid regurgitation velocity, and left atrial (LA) volume index according to the guideline 20 . Other cardiac abnormalities included LV hypertrophy, enhanced IVST, valvular dysfunction, atrial enlargement, regional wall motion abnormality, and aortic sinus enlargement. LV mass index was calculated by the LV end-diastolic dimension, IVST, LV posterior wall thickness, and body surface area (using Stevenson formula). Relative wall thickness (RWT) was calculated from LV posterior wall thickness and LV end-diastolic dimension. Assessment of cognitive ability We only conducted a cognitive assessment after hospital admission for CVDs and only for subjects who met the cognitive complaints (i.e., self-reported problems with memory or other aspects of cognition) 21 , 22 . Two validated tools, namely MoCA 23 , 24 and MMSE 24 , were performed by trained neuropsychological assessors for screening MCI. MoCA is a cognitive screening tool that has been extensively verified for detecting MCI with high sensitivity and specificity 23 – 26 . The total score of MoCA is 30 points. For those with ≤ 12 years of formal education, 1 point is added to the final score. MMSE is a 30-point questionnaire widely used for detecting dementia, while it has lower sensitivity in detecting MCI compared to MoCA 25 , 26 . All participants completed Chinese version of the MOCA and the MMSE under the guidance of trained professional investigators in the present study. MCI was defined by MoCA score of < 26 points and MMSE score of ≥ 24 points 27 . Normal cognition was defined by MoCA score of ≥ 26 points and MMSE score of ≥ 24 points. Covariates measurement Potential confounders were identified by reviewing the literature and consulting clinical experts 4 , 28 , 29 . All covariates from standard demographic and clinical data of each participant, including age, sex, education level, smoking status, hypertension, AF, CHD, CHF, stroke, chronic kidney disease, diabetes mellitus, and hyperlipidemia, were ascertained from self-reported medical history or physicians’ diagnosis in the clinical electronic database based on the International Classification of Diseases (ICD)-10 codes 30 —I10, I48.9, I20.9, I25.1, I50.9, I63, N18.9, E14, and E78.5. Participants’ height and weight were measured to compute body mass index (BMI). After a 15-minute rest, blood pressure (BP) was measured twice at a 2-minute interval and was ascertained by the mean of them. Laboratory parameters included fasting plasma glucose (FPG), estimated glomerular filtration rate (eGFR), total cholesterol, and low- density lipoprotein cholesterol (LDL-C). Missing data The missing data for all variables was < 5%, excluding echocardiographic parameters of A peak and E/A (10.5%). Most participants (90.8%) with missing data of A peak and E/A ratio were those suffering from AF, for whom it was not possible to measure the A peak and E/A ratio because of LA systolic dysfunction. Sample size calculation This study used a 1:3 case-control design. We assumed the control group had a probability of 0.5 for each cardiac structural and functional abnormality (50/50 chance) and an odds ratio (OR) of 1.5 to have the same abnormality in the case versus control groups. Therefore, a sample size pair of 272 cases and 816 controls (after Fleiss correction for continuity) will have an 80% power to detect the difference (OR = 1.5) between the case and control with a 2-sided significant level of 5%. Statistical analyses All data were analyzed using R software ( http://www.R-project.org ; Version 4.3.2). The Kolmogorov-Smirnov Test was used to verify normal distribution of numerical data. Continuous variables were described as mean ± standard deviation (normal distribution) or median and interquartile range (skewed distribution). The two-sample t-test or Mann-Whitney U-test was used for group comparisons of continuous variables, depending on the normality of distribution. Categorical variables were presented as the number of cases and percentages and were compared using the chi-square test. P-value < 0.05 was defined as statistically significant. PCA was used to explore the correlation of cardiac structure and function with MoCA score and MCI. All variables have been normalized before PCA. MoCA score is a count data. Therefore, variables that influenced the MoCA score were analyzed using either the Poisson or negative binomial regression dependent on the magnitude of dispersion strength (Supplement 1). Conditional univariate and multivariate logistic regression models were conducted to evaluate the association between MCI and echocardiographic parameters of cardiac structure and function. The echocardiographic parameters with p-value < 0.100 (i.e., at least marginally significant) in the univariate regression and the covariates described above were incorporated into the fully adjusted conditional multivariate logistic regression model. In further analysis, nonlinear transformations of continuous variables were conducted to explore potential nonlinear correlations. Results Study population characteristics In this study, 1136 participants with CVDs were enrolled. The mean and standard deviation [SD] age of participants were 61.1 ± 8.3 years. Most (77.20%) of them were males. Among them, 289 participants were identified with MCI and 837 participants had normal cognitive function. Demographic, clinical, cardiac, and cognitive characteristics are shown in Table 1 . MCI group had higher systolic BP (132.3 ± 16.4 mmHg vs. 129.9 ± 15.8 mmHg, P = 0.024), higher diastolic BP (79.1 ± 11.2 mmHg vs. 77.6 ± 11.3 mmHg, P = 0.044) and higher rates of smoking status (50.2% vs. 43.1%, P = 0.037), AF (31.5% vs. 19.8%, P < 0.001), and CHF (9.3% vs. 4.5%, P = 0.002). Table 1 Baseline characteristics of different cognitive function group in patients with CVDs Clinical variables Normal (n = 847) MCI (n = 289) p-value Demographic features Age, mean ± SD, years 61.2 ± 8.3 61.1 ± 8.3 0.951 Sex, male, n (%) 654 (77.2) 223 (77.2) 0.986 Education, n (%) 0.991 12years 331 (39.1) 114 (39.4) BMI, mean ± SD, kg/m 2 26.1 ± 3.4 26.3 ± 3.3 0.280 Smoking, n (%) 365 (43.1) 145 (50.2) 0.037 Medical History Diabetes mellitus, n (%) 262 (30.9) 96 (33.2) 0.470 Hyperlipidemia, n (%) 621 (73.3) 206 (71.3) 0.502 Hypertension, n (%) 537 (63.4) 197 (68.2) 0.144 Atrial fibrillation, n (%) 168 (19.8) 91 (31.5) < 0.001 Coronary artery disease, n (%) 583 (68.8) 181 (62.6) 0.052 Chronic heart failure, n (%) 38 (4.5) 27 (9.3) 0.002 Stroke, n (%) 61 (7.2) 25 (8.7) 0.422 Chronic kidney disease, n (%) 22 (2.6) 11 (3.8) 0.291 Clinical measures Systolic BP, mean ± SD, mmHg 129.9 ± 15.8 132.3 ± 16.4 0.026 Diastolic BP, mean ± SD, mmHg 77.6 ± 11.3 79.1 ± 11.2 0.044 FPG, mean ± SD, mg/dl 115.6 ± 40.8 118.6 ± 45.5 0.299 Total cholesterol, mean ± SD, mg/dl 159.6 ± 41.1 164.0 ± 42.3 0.128 Triglycerides, mean ± SD, mg/dl 150.0 ± 86.9 149.6 ± 118.5 0.960 HDL-C, mean ± SD, mg/dl 43.2 ± 11.2 43.4 ± 11.5 0.735 LDL-C, mean ± SD, mg/dl 88.5 ± 33.8 90.1 ± 32.8 0.471 eGFR, mean ± SD, mL/min/1.73m 2 87.1 ± 16.3 87.4 ± 16.0 0.791 Echocardiography measures Global cardiac status, n (%) 0.095 Normal 51 (6.0) 10 (3.5) Other cardiac abnormality 786 (92.8) 277 (95.8) 0.068 LV diastolic dysfunction 341 (40.3) 156 (54.0) < 0.001 Cardiac structure LA diameter, mean ± SD, cm 3.79 ± 0.50 3.85 ± 0.55 0.093 LA enlargement, n (%) 416 (49.1) 148 (51.2) 0.538 Biatrial enlargement, n (%) 83 (9.8) 46 (15.9) 0.005 IVST, mean ± SD, cm 1.00 ± 0.17 1.04 ± 0.20 0.002 Relative wall thickness, mean ± SD 0.39 ± 0.06 0.40 ± 0.07 0.263 LV mass index, mean ± SD, g/m 2 88.30 ± 21.87 90.88 ± 22.99 0.089 LV hypertrophy, n (%) 44 (5.2) 18 (6.2) 0.504 Aortic sinus diameter, mean ± SD, cm 3.41 ± 0.39 3.40 ± 0.37 0.940 Aortic regurgitation, n (%) 236 (27.9) 88 (30.4) 0.400 Cardiac systolic function LV ejection fraction, median (IQR), % 64 (60–66) 62 (60–66) 0.025* Cardiac diastolic function E, mean ± SD, m/s 0.74 ± 0.24 0.77 ± 0.26 0.095 A, mean ± SD, m/s 0.86 ± 0.20 0.85 ± 0.20 0.538 E/A, mean ± SD 0.87 ± 0.33 0.91 ± 0.43 0.140 Global Cognitive Function MoCA, median (IQR) 28 (27-28.5) 23 (22–25) < 0.001* MMSE, median (IQR) 30 (29–30) 28 (27–29) < 0.001* Data are mean ± standard deviation, median (interquartile range), or number of cases (%). P-values reflect the results of t-test, Mann-Whitney U-test, or χ 2 . BMI, body mass index; BP, blood pressure; FPG, fasting plasma glucose; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; eGFR, estimated glomerular filtration rate; LV, left ventricular; LA, left atrial; IVST, interventricular septal thickness; E, early diastolic transmitral flow velocity; A, late atrial diastolic transmitral flow velocity; MoCA, Montreal Cognitive Assessment; MMSE, Mini-Mental State Examination. *P-value of Mann-Whitney U-test due to skewed distribution. The cardiac structural and functional parameters are significantly different between the two groups. LV diastolic dysfunction (54.0% vs. 40.3%, P < 0.001) and biatrial enlargement (15.9% vs. 9.8%, P = 0.005) were more common in the MCI group. IVST (1.04 ± 0.20 cm vs. 1.00 ± 0.17 cm, P = 0.002) was greater in the MCI group. Correlation between global cognitive function and cardiac structural/functional measurements As shown in Figure, PCA indicated that the total MoCA score was negatively correlated with LV diastolic dysfunction, LA enlargement, aortic regurgitation (AR), LV mass index, aortic sinus diameter, IVST, and LV hypertrophy. LV diastolic dysfunction, LV hypertrophy, IVST, aortic sinus diameter, LV mass index, AR, LA enlargement, and LA diameter were positively correlated with MCI. Table 2 shows the correlation between the total MoCA score and cardiac structure and function in the negative binomial regression model. After adjusting for confounders, LV diastolic dysfunction (Beta [standard error, SE], 0.234 [0.045]; P < 0.001) and higher IVST (Beta [SE], 0.034 [0.016]; P = 0.036) demonstrated a negative correlation with the global cognitive score of MoCA. Table 2 Negative binomial regression for the correlation of echocardiographic cardiac parameters with global cognitive score of MoCA Cardiac variables 30 - global cognitive score Beta (SE) p-value adj. Beta (SE)* adj. p-value* LV diastolic dysfunction 0.244 (0.045) < 0.001 0.234 (0.045) < 0.001 LA enlargement -0.055 (0.045) 0.221 -0.052 (0.046) 0.264 Biatrial enlargement 0.090 (0.094) 0.341 0.066 (0.095) 0.489 IVST, per 1 mm 0.028 (0.016) 0.079 0.034 (0.016) 0.036 LV mass index, per 10 g/m 2 0.007 (0.013) 0.567 0.005 (0.014) 0.688 LV hypertrophy -0.011 (0.100) 0.911 -0.080 (0.101) 0.426 Aortic sinus diameter, per 1 mm -0.001 (0.006) 0.806 -0.001 (0.006) 0.839 Aortic regurgitation 0.032 (0.048) 0.506 0.020 (0.049) 0.682 LV ejection fraction, per 1% -0.004 (0.003) 0.208 -0.002 (0.003) 0.512 E/A, per 0.1 0.009 (0.006) 0.153 0.005 (0.006) 0.408 *Negative binomial regression was adjusted for age, sex, education, hypertension, atrial fibrillation, coronary artery disease, chronic heart failure, stroke, chronic kidney disease, diabetes mellitus, hyperlipidemia, smoking, systolic blood pressure, diastolic blood pressure, estimated glomerular filtration rate, fasting plasma glucose, total cholesterol, low-density lipoprotein cholesterol, and body mass index. SE, standard error; LV, left ventricular; LA, left atrial; IVST, interventricular septal thickness; E/A, ratio of the early diastolic transmitral flow velocity and late atrial diastolic transmitral flow velocity. Association between MCI and cardiac structure/function In the conditional univariate logistic regression analysis (Table 3 ), biatrial enlargement (OR, 1.76; 95% CI, 1.18–2.62; P = 0.005), IVST (OR, 1.14; 95% CI, 1.06–1.24; P < 0.001), LV diastolic dysfunction (OR, 1.74; 95% CI, 1.33–2.28; P < 0.001) were positively associated with MCI. Table 3 Association of echocardiographic measures of cardiac structure and function with MCI in conditional logistic regression models Cardiac variables Univariate model Multivariate model* n OR (95% CI) p-value n OR (95% CI) p-value Cardiac structure LA diameter, per 1 mm 1136 1.02 (0.99–1.05) 0.074 1091 0.99 (0.95–1.02) 0.431 LA enlargement 1136 1.09 (0.83–1.43) 0.540 Biatrial enlargement 1136 1.76 (1.18–2.62) 0.005 1091 1.58 (0.95–2.62) 0.077 IVST, per 1 mm 1128 1.14 (1.06–1.24) < 0.001 1091 1.14 (1.03–1.27) 0.014 Relative wall thickness, per 0.1 unit 1128 1.13 (0.92–1.39) 0.252 LV mass index, per 10 g/m 2 1115 1.06 (0.99–1.12) 0.077 1091 0.97 (0.88–1.07) 0.555 LV hypertrophy 1136 1.25 (0.70–2.23) 0.444 Aortic sinus diameter, per 1 mm 1136 1.00 (0.96–1.04) 0.955 Aortic sinus enlargement 1136 1.12 (0.77–1.62) 0.571 Aortic regurgitation 1136 1.13 (0.84–1.51) 0.437 Cardiac systolic function LV ejection fraction, per 1% 1132 0.99 (0.98–1.01) 0.506 Cardiac diastolic function LV diastolic dysfunction 1136 1.74 (1.33–2.28) < 0.001 1091 2.03 (1.48–2.79) < 0.001 E, per 0.1 m/s 1134 1.04 (0.99–1.10) 0.108 A, per 0.1 m/s 909 0.99 (0.91–1.06) 0.722 E/A, per 0.1 unit 909 1.03 (0.98–1.07) 0.224 *The conditional multivariate logistic regression model was adjusted for hypertension, atrial fibrillation, coronary artery disease, chronic heart failure, stroke, chronic kidney disease, diabetes mellitus, hyperlipidemia, smoking, systolic blood pressure, diastolic blood pressure, estimated glomerular filtration rate, fasting plasma glucose, total cholesterol, low-density lipoprotein cholesterol, and body mass index. OR, odds ratio; CI, confidence interval; LA, left atrial; IVST, interventricular septal thickness; LV, left ventricular; E, early diastolic transmitral flow velocity; A, late atrial diastolic transmitral flow velocity. In the fully-adjusted conditional multivariate logistic regression model (Table 3 ), LV diastolic dysfunction (OR, 2.03; 95% CI, 1.48–2.79, P < 0.001) and IVST (OR, 1.14; 95% CI, 1.03–1.27; P = 0.014) were independently associated with MCI, after adjusting for the diagnosis CVDs and other conventional MCI risk factors. The association between MCI and biatrial enlargement lost significance in adjusted model. Upon further analysis, nonlinear transformations of continuous variables in univariate and multivariate logistic regression models did not produce significant effects on outcomes (Supplement 2). Discussion In this matched case-control study for general CVDs, we observed the association between MCI and abnormal cardiac structure and function as measured by echocardiography. PCA and negative binomial regression revealed that LV diastolic dysfunction and greater IVST were correlated with the co-occurring MCI and lower MoCA score. After adjusting for diagnosed CVDs and other MCI-relevant covariates, the incidence of LV diastolic dysfunction and greater IVST were strongly correlated with lower MoCA scores and associated with the co-occurring MCI in general CVD participants. Our finding implied that more attention should be paid to neurocognitive decline in general CVD patients with abnormal echocardiographic cardiac structure and function. Cardiac structural and functional damages are the intermediate stages or consequences of CVD development and exacerbation 31 . Echocardiography is a first-line tool for assessing cardiac structure and function and plays a significant role in diagnosing and managing CVDs 15 – 17 . Our findings extended some previous studies that indicated the association between mitigating cognitive decline and cardiac structural and functional abnormalities measured by echocardiography 6 – 9 . The Rotterdam study of 3,291 elder participants, without clinical CVDs and stroke, found that LV diastolic dysfunction was associated with dementia 32 . With the CARDIA Study data, Rouch et al. indicated that midlife LV diastolic function and its 25-year change from early to middle adulthood were linked to lower cognitive function 2 . In a secondary analysis of Atherosclerosis Risk in Communities Study, Faulkner et al. found that worse LV diastolic function was associated with poorer performance in language, memory, and attention, although the links were weak 33 . However, none of the above three studies emphasized CVD patients the same as our target population, who were proven more likely to develop CI. Sacre et al. conducted a study among participants from the Nurse-led Intervention for Less Chronic Heart Failure Study and reported a significantly positive association between LV diastolic dysfunction and MCI among CHF patients, supporting our results 7 . However, Sacre et al. used data from only CHF patients and did not conduct multivariable logistic regression with all echocardiographic variables included. Inconsistent with our results, Eggermont et al. found that LV diastolic dysfunction was unrelated to memory and executive function among elder CVD participants 10 . Our study had more participants (n = 1136) than this study (n = 117 in Eggermont et al.). In addition, we utilized the MoCA to evaluate overall cognitive function and controlled for potential confounders that could influence cognitive function, which might facilitate obtaining more reliable conclusions. CVDs may lead to CI through a series of potential mechanisms, including decreases in cardiac output and cerebral perfusion, ischemic brain injury (e.g., thromboembolism, transient ischemic attack), inflammation, and so on 3 . LV diastolic dysfunction is associated with reduced cardiac output, which may cause cerebral hypoperfusion and subsequently lead to cerebral structural and functional abnormalities. Cerebral hypoperfusion may attenuate the clearance of amyloid-beta and facilitate the phosphorylation and aggregation of tau 34 , a significant pathological feature of dementia 35 . Although it is not feasible to screen all CVD patients for cognitive impairment, those with abnormal cardiac structure and function, such as LV diastolic dysfunction, might be considered to assess their cognitive function, which ensures some CVD patients at high risks of cognitive impairment be screened and managed appropriately, and timely to delay the progression of cognitive decline. While we may not be able to predict neurological impairment based solely on cardiac changes, however, adding cardiac parameters may improve our understanding of neurological impairment development in CVD patients. Our data also showed that greater IVST was positively associated with MCI. Consistent with our results, an earlier initial study, which enrolled 22 participants with Alzheimer’s disease (AD) and 23 age-matched control individuals, suggested that IVST was significantly greater in the AD group 36 . Furthermore, there is an association between IVST/LVMI and surrogate outcomes of global cognitive function or specific cognitive domains 8 , 37 . Apart from these, few studies have explored the association between IVST and MCI. Given our matched case-control study design, large-scale cohort studies are needed in the future. In this study, we collected detailed clinical measures and included more echocardiographic parameters from more than 1000 participants, which allowed us to study the echocardiographic parameters linked to MCI more comprehensively. In addition, given many echocardiographic variables were continuous variables with strong collinearity, we found that PCA, a dimension reduction method, helped explore important variables correlated with MCI in our study. Furthermore, the MoCA score is a count data, so Poisson or negative binomial models are appropriate methods to determine the effect of echocardiographic measures of cardiac structure and function on MCI. Nonlinearity is probably another crucial point, but it has been far less explored in previous studies. We considered that pure linear correlation might not be robust for all variables and, therefore, applied nonlinear transformations on several numerical variables. Although it did not produce significant effects on outcomes in our study, considering its clinical importance, future studies should also consider exploring the nonlinear correlations to reach a more accurate conclusion. Study limitations Several limitations, including inherent defects in case-control study design, should be acknowledged and guided for future research. First, MCI was identified based on MoCA and MMSE rather than comprehensive neuropsychological evaluation. Therefore, the results may be interpreted with caution. Second, given some missing data, some individuals were excluded from different steps of statistical analysis, which might lead to selection-biased results. Third, as a single-center study, the generalizability of the results might be affected. Fortunately, most of our participants were from different provinces rather than Beijing. Therefore, the effect may be less significant. Nonetheless, cohort designs across broader geographical regions and populations are needed to validate the relationship and associated degree between echocardiographic variables and MCI. Last, although we have made efforts to recognize and control for confounders by reviewing extensive literature, some unknown and residual confounders may still exist. Conclusions The current study indicated the association between cognitive impairment and abnormal cardiac structure and function as measured by echocardiography. It revealed LV diastolic dysfunction and IVST were positively associated with MCI in general CVD patients, suggesting further attention to cognitive assessment in this population. Declarations Ethics approval and consent to participate This study followed the Declaration of Helsinki and was approved by the Institutional Ethics Review Board at Beijing Anzhen Hospital (No.2022-17-1). Informed consent for this study was waived from all participants because it was retrospective. Consent for publication Not applicable. Availability of data and materials The data underlying this article will be shared on reasonable request to the corresponding author. Competing interests The authors declare that they have no competing interests. Funding The project is an investigator-initiated study. Professor Changsheng Ma received funding from the Capital Medical University affiliated with Beijing Anzhen Hospital Science and Technology Innovation Special Fund Project (No.2022-17-1) established by Capital Medical University affiliated with Beijing Anzhen Hospital. Changsheng Ma received funding from the Beijing Municipal Science and Technology Commission (BMSTC) under grant number Z241100007724008. Authors' contributions K.Z. designed the study and supervised the project. X.L. conducted the data analysis and interpretation. S.H. and X.L. performed the literature review and wrote the initial draft of the manuscript. M.Z. and C.X. prepared the figures and tables. S.X. and J.D. contributed to the methodology and provided critical revisions to the manuscript. C.M. offered valuable insights and guided the research direction. All authors reviewed and approved the final manuscript. K.Y. is the corresponding author and provided oversight for the overall project. Acknowledgements Not applicable. Appendix A. Supplementary data Supplementary data to this article can be found online. References Liu Y, Ma W, Li M, Han P, Cai M, Wang F et al . Relationship Between Physical Performance and Mild Cognitive Impairment in Chinese Community-Dwelling Older Adults. 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Associations of Echocardiography Markers and Vascular Brain Lesions: The ARIC Study. J Am Heart Assoc 2018; 7 :e8992. Jaggi A, Conole ELS, Raisi-Estabragh Z, Gkontra P, Mccracken C, Szabo L et al . A structural heart-brain axis mediates the association between cardiovascular risk and cognitive function. Imaging Neuroscience 2024; 2 :1-18. Petersen RC, Lopez O, Armstrong MJ, Getchius TSD, Ganguli M, Gloss D et al . Practice guideline update summary: Mild cognitive impairment: Report of the Guideline Development, Dissemination, and Implementation Subcommittee of the American Academy of Neurology. Neurology 2018; 90 :126-35. Gaugler J, James B, Johnson T, Reimer J, Scales K, Tom S et al . 2023 Alzheimer's disease facts and figures. Alzheimers Dement 2023; 19 :1598-695. Mcdonagh TA, Metra M, Adamo M, Gardner RS, Baumbach A, Böhm M et al . 2021 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure: Developed by the Task Force for the diagnosis and treatment of acute and chronic heart failure of the European Society of Cardiology (ESC). With the special contribution of the Heart Failure Association (HFA) of the ESC. Eur J Heart Fail 2022; 24 : Lawton JS, Tamis-Holland JE, Bangalore S, Bates ER, Beckie TM, Bischoff JM et al . 2021 ACC/AHA/SCAI Guideline for Coronary Artery Revascularization: Executive Summary: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. Circulation 2022; 145 : Vahanian A, Beyersdorf F, Praz F, Milojevic M, Baldus S, Bauersachs J et al . 2021 ESC/EACTS Guidelines for the management of valvular heart disease. Eur Heart J 2022; 43 :561-632. Oud FMM, Spies PE, Braam RL, van Munster BC. Recognition of cognitive impairment and depressive symptoms in older patients with heart failure. Neth Heart J 2021; 29 :377-82. Gu SZ, Beska B, Chan D, Neely D, Batty JA, Adams-Hall J et al . Cognitive Decline in Older Patients With Non- ST Elevation Acute Coronary Syndrome. J Am Heart Assoc 2019; 8 :e11218. Mitchell C, Rahko PS, Blauwet LA, Canaday B, Finstuen JA, Foster MC et al . Guidelines for Performing a Comprehensive Transthoracic Echocardiographic Examination in Adults: Recommendations from the American Society of Echocardiography. J Am Soc Echocardiogr 2019; 32 :1-64. Jacob L, Haro JM, Koyanagi A. Physical multimorbidity and subjective cognitive complaints among adults in the United Kingdom: a cross-sectional community-based study. Sci Rep 2019; 9 :12417. Hill NL, Bhargava S, Brown MJ, Kim H, Bhang I, Mullin K et al . Cognitive complaints in age-related chronic conditions: A systematic review. PLoS One 2021; 16 :e253795. Nasreddine ZS, Phillips NA, Bédirian V, Charbonneau S, Whitehead V, Collin I et al . The Montreal Cognitive Assessment, MoCA: a brief screening tool for mild cognitive impairment. J Am Geriatr Soc 2005; 53 :695-9. Langa KM, Levine DA. The diagnosis and management of mild cognitive impairment: a clinical review. JAMA 2014; 312 :2551-61. Pinto TCC, Machado L, Bulgacov TM, Rodrigues-Júnior AL, Costa MLG, Ximenes RCC et al . Is the Montreal Cognitive Assessment (MoCA) screening superior to the Mini-Mental State Examination (MMSE) in the detection of mild cognitive impairment (MCI) and Alzheimer's Disease (AD) in the elderly? Int Psychogeriatr 2019; 31 :491-504. Jia X, Wang Z, Huang F, Su C, Du W, Jiang H et al . A comparison of the Mini-Mental State Examination (MMSE) with the Montreal Cognitive Assessment (MoCA) for mild cognitive impairment screening in Chinese middle-aged and older population: a cross-sectional study. BMC Psychiatry 2021; 21 :485. Chen Y, Liang N, Li X, Yang S, Wang Y, Shi N. Diagnosis and Treatment for Mild Cognitive Impairment: A Systematic Review of Clinical Practice Guidelines and Consensus Statements. Front Neurol 2021; 12 :719849. Leszek J, Mikhaylenko EV, Belousov DM, Koutsouraki E, Szczechowiak K, Kobusiak-Prokopowicz M et al . The Links between Cardiovascular Diseases and Alzheimer's Disease. Curr Neuropharmacol 2021; 19 :152-69. Samieri C, Perier M, Gaye B, Proust-Lima C, Helmer C, Dartigues J et al . Association of Cardiovascular Health Level in Older Age With Cognitive Decline and Incident Dementia. JAMA 2018; 320 :657-64. World Health Organization. International Statistical Classification of Diseases and Related Health Problems 10th Revision. 2019; Santos M, Shah AM. Alterations in cardiac structure and function in hypertension. Curr Hypertens Rep 2014; 16 :428. de Bruijn RFAG, Portegies MLP, Leening MJG, Bos MJ, Hofman A, van der Lugt A et al . Subclinical cardiac dysfunction increases the risk of stroke and dementia: the Rotterdam Study. Neurology 2015; 84 :833-40. Faulkner KM, Dickson VV, Fletcher J, Katz SD, Shah AM, Gottesman RF et al . Cognitive Impairment is Associated with Abnormal Cardiac Hemodynamics in Heart Failure with Preserved Ejection Fraction. J Card Fail 2019; 25 :S4. Korte N, Nortley R, Attwell D. Cerebral blood flow decrease as an early pathological mechanism in Alzheimer's disease. Acta Neuropathol 2020; 140 :793-810. Drummond E, Pires G, Macmurray C, Askenazi M, Nayak S, Bourdon M et al . Phosphorylated tau interactome in the human Alzheimer's disease brain. Brain 2020; 143 :2803-17. Belohlavek M, Jiamsripong P, Calleja AM, Mcmahon EM, Maarouf CL, Kokjohn TA et al . Patients with Alzheimer disease have altered transmitral flow: echocardiographic analysis of the vortex formation time. J Ultrasound Med 2009; 28 :1493-500. Arangalage D, Ederhy S, Dufour L, Joffre J, Van der Vynckt C, Lang S et al . Relationship between cognitive impairment and echocardiographic parameters: a review. J Am Soc Echocardiogr 2015; 28 :264-74. Additional Declarations No competing interests reported. Supplementary Files Supplement.docx Cite Share Download PDF Status: Published Journal Publication published 05 Feb, 2025 Read the published version in BMC Cardiovascular Disorders → Version 1 posted Editorial decision: Revision requested 25 Nov, 2024 Reviews received at journal 20 Nov, 2024 Reviews received at journal 10 Nov, 2024 Reviewers agreed at journal 02 Nov, 2024 Reviewers agreed at journal 29 Oct, 2024 Reviewers invited by journal 29 Oct, 2024 Editor invited by journal 28 Oct, 2024 Editor assigned by journal 25 Oct, 2024 Submission checks completed at journal 25 Oct, 2024 First submitted to journal 19 Oct, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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University, Office of Beijing Cardiovascular Diseases Prevention","correspondingAuthor":false,"prefix":"","firstName":"Xiaoxia","middleName":"","lastName":"Liu","suffix":""},{"id":373297669,"identity":"dff81625-6c86-4051-8d3d-5600ee8fd4e4","order_by":2,"name":"Siyu Huang","email":"","orcid":"","institution":"National Clinical Research Center for Mental Disorders \u0026 National Center for Mental Disorders, Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Siyu","middleName":"","lastName":"Huang","suffix":""},{"id":373297671,"identity":"1efa858b-c973-4bf4-aa66-3dfd2062da51","order_by":3,"name":"Xinrui Liu","email":"","orcid":"","institution":"Capital Medical University, Office of Beijing Cardiovascular Diseases Prevention","correspondingAuthor":false,"prefix":"","firstName":"Xinrui","middleName":"","lastName":"Liu","suffix":""},{"id":373297672,"identity":"803df841-4f85-4ff5-b65f-373ebb25f71c","order_by":4,"name":"Meiqi Zhao","email":"","orcid":"","institution":"Capital Medical University, Office of Beijing Cardiovascular Diseases Prevention","correspondingAuthor":false,"prefix":"","firstName":"Meiqi","middleName":"","lastName":"Zhao","suffix":""},{"id":373297673,"identity":"beafa94a-0c40-450a-a68e-2cb00ff03b69","order_by":5,"name":"Chao Xue","email":"","orcid":"","institution":"Echocardiography Medical Center, Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Chao","middleName":"","lastName":"Xue","suffix":""},{"id":373297674,"identity":"6af91d41-6957-4fc6-881f-21350daabe49","order_by":6,"name":"Shijun Xia","email":"","orcid":"","institution":"Capital Medical University, Office of Beijing Cardiovascular Diseases Prevention","correspondingAuthor":false,"prefix":"","firstName":"Shijun","middleName":"","lastName":"Xia","suffix":""},{"id":373297675,"identity":"40beb380-da7e-4b7f-be82-e91492cc8e59","order_by":7,"name":"Jianzeng Dong","email":"","orcid":"","institution":"Capital Medical University, Office of Beijing Cardiovascular Diseases Prevention","correspondingAuthor":false,"prefix":"","firstName":"Jianzeng","middleName":"","lastName":"Dong","suffix":""},{"id":373297676,"identity":"71440b2b-30bc-45d6-995c-ecb798af1c1e","order_by":8,"name":"Yu Kong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvElEQVRIiWNgGAWjYBACCQb+Bwc+VNjw8LM3EK2Fh/HgjDNpMpI9B4jXwnyYs+WwjcENByK1SM7IPXCYseE8D8MNBsYPH3OI0CItkZdwuHDHbR7G2Q3MkjO3EaFFTiLB4PDMM7d5mGUOsDHzEq2Ft+0cD5tEApFapCVyQFoO8PAQrUWy51kCMJCTeSR4DjYT5xeJ48mHP3yosLO3P9588MNHYrQwCCTAWIwNxKgHAv4DRCocBaNgFIyCkQsAn+c6RtVzZAoAAAAASUVORK5CYII=","orcid":"","institution":"Capital Medical University, Office of Beijing Cardiovascular Diseases Prevention","correspondingAuthor":true,"prefix":"","firstName":"Yu","middleName":"","lastName":"Kong","suffix":""},{"id":373297678,"identity":"3918f568-8597-4e9f-9af5-d26c49eccfb3","order_by":9,"name":"Changsheng Ma","email":"","orcid":"","institution":"Capital Medical University, Office of Beijing Cardiovascular Diseases Prevention","correspondingAuthor":false,"prefix":"","firstName":"Changsheng","middleName":"","lastName":"Ma","suffix":""}],"badges":[],"createdAt":"2024-10-19 14:08:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5294926/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5294926/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12872-025-04528-8","type":"published","date":"2025-02-05T15:57:27+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":68699100,"identity":"06421441-f338-45db-8834-aaceae611fec","added_by":"auto","created_at":"2024-11-11 07:09:41","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":142850,"visible":true,"origin":"","legend":"\u003cp\u003eVariables correlation circle of principal component analysis of the association between (A) the MoCA score and cardiac structure and function and (B) MCI and cardiac structure and function. All variables have been normalized. MCI, mild cognitive impairment; MR, mitral regurgitation; TR, tricuspid regurgitation; AR, aortic regurgitation; AoSD, aortic sinus diameter; LAD, left atrial diameter; LAE, left atrial enlargement; BAE, biatrial enlargement; IVST, interventricular septum thickness; RWT, relative wall thickness; LV, left ventricular; LVMI, left ventricular mass index; LVH, left ventricular hypertrophy; EF, ejection fraction; E, early diastolic transmitral flow velocity; A, late atrial diastolic transmitral flow velocity.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5294926/v1/096343393d6e805878fe851d.png"},{"id":75931350,"identity":"60aaddab-56ac-44f0-8954-e39223439797","added_by":"auto","created_at":"2025-02-10 16:14:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1419236,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5294926/v1/65c68a12-adda-46f5-b09c-c0b50a9b29c4.pdf"},{"id":68697820,"identity":"44a3a1c4-1330-4045-ac43-ecf31dbbab62","added_by":"auto","created_at":"2024-11-11 07:01:41","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":449043,"visible":true,"origin":"","legend":"","description":"","filename":"Supplement.docx","url":"https://assets-eu.researchsquare.com/files/rs-5294926/v1/d30ca44fbe12454aacc0aba3.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association between Echocardiographic Parameters of Cardiac Structure and Function and Mild Cognitive Impairment","fulltext":[{"header":"Background","content":"\u003cp\u003eWith the fast-aging population in China, cognitive impairment (CI) has increasingly become a serious health concern and imposed a tremendous public health and socioeconomic burden\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Among the various modifiable risk factors that impact cognitive functions, CVDs are the most challenging one\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. One in 3 patients at cardiology clinics have some degree of CI\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Clinical researches have consistently shown a strong co-occurring or interaction between the four types of general CVDs (i.e., hypertension, coronary heart disease [CHD], atrial fibrillation [AF], and chronic heart failure [CHF]) and declining neurological conditions linked to MCI and dementia\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Recent studies indicated that cardiac dysfunctions, marked by low LV ejection fraction, high LV mass index, and concentric remodeling, were associated with lower cognitive function\u003csup\u003e\u003cspan additionalcitationids=\"CR6 CR7 CR8\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. However, other researchers draw inconsistent results indicating that none of abnormalities of cardiac structure and function was significantly associated with cognitive impairment\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. The relationship of heart-brain cognition, although incompletely understood, might facilitate the development of assessment biomarkers for neurovascular health\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMCI, a condition with objective cognitive impairment but minor effect on daily activities, is a transitional stage from normal cognitive function to dementia and has pivotal clinical significance\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Early detection and intervention for patients with MCI may help slow cognitive decline and reduce medical costs\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. CVD guidelines highlight the importance of considering cognitive impairment as an essential comorbidity of cardiac diseases in disease management\u003csup\u003e\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. However, most cardiologists have not fully recognized MCI due to the subtle or hidden clinical symptoms and a lack of specialized knowledge in neuropsychology \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. It is increasingly clear that we need to explore whether cognitive decline in CVDs can be evaluated by some essential cardiovascular biomarkers (e.g., body fluid, imaging, genetics), which might have potential value for MCI assessment.\u003c/p\u003e \u003cp\u003eTo address this unmet need, our group recently performed a matched case-control study to analyze the association between cardiac structure and function by echocardiography and MCI among general CVD participants at a tertiary hospital. We hypothesized that individuals with MCI would have higher prevalence of abnormalities in cardiac structure and function independent of diagnosed CVDs and the conventional MCI risk factors.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and participants\u003c/h2\u003e \u003cp\u003e This study was a matched case-control study for CVD participants at Beijing Anzhen Hospital, Capital Medical University, China, where more than 70% of CVD patients were from different provinces other than Beijing. The data collection spanned the period from May 2021 to August 2023. Patients were eligible if they were hospitalized because of CVD conditions, had a prior or concurrent diagnosis of hypertension, and/or CHD, and/or AF, and/or CHF, and underwent neuropsychological tests of MoCA and MMSE within a 14-day window. Patients with serious infection, congenital cardiac conditions, significant valve disease, terminal malignancy, or subsequent acute cardiac events within 30 days of the hospital were excluded from the study. The exclusion criteria also included age\u0026thinsp;\u0026lt;\u0026thinsp;18 years, no record of echocardiography or cognitive function assessment, diagnosed dementia, and psychiatric illness.\u003c/p\u003e \u003cp\u003e Propensity score matching was done by a 1:3 ratio of pairing MCI participants with age-, gender- and education-matched controls of cognitively normal CVD participants. This study followed the Declaration of Helsinki and was approved by the Institutional Ethics Review Board at Beijing Anzhen Hospital (No.2022-17-1). Informed consent for this study was waived from all participants because it was retrospective.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMeasurements of echocardiographic parameters\u003c/h3\u003e\n\u003cp\u003eEchocardiographic examinations were performed by trained cardiac sonographers using a GE Vivid E9 ultrasound machine with an M5S probe (2\u0026ndash;4 MHz; GE Healthcare, Chicago, IL, USA) or a Philips IE33 ultrasound machine with an S5-1 probe (2.5\u0026ndash;3.5 MHz; Philips Healthcare, Andover, MA, USA) at the Echocardiography Medical Center office of Beijing Anzhen Hospital.\u003c/p\u003e \u003cp\u003eAll participants underwent standard transthoracic echocardiography within a 14-day window of hospitalization to assess cardiac structural and functional status because of the need for CVD management. The quantitative and qualitative diagnosis of cardiac structural and functional abnormalities were produced according to the echocardiographic guideline\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e and checked by an independent board-certified cardiac sonographer blinded to the clinical data. LV ejection fraction was calculated using the biplane Simpsons method in the apical four-chamber and two-chamber views. Early diastolic transmitral flow velocity (E), late atrial diastolic transmitral flow velocity (A), and early diastolic mitral annular velocity (e') were measured through pulsed-wave Doppler. LV diastolic dysfunction was ascertained by the ratio of E and A (E/A), ratio of E and e' (E/e'), tricuspid regurgitation velocity, and left atrial (LA) volume index according to the guideline\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Other cardiac abnormalities included LV hypertrophy, enhanced IVST, valvular dysfunction, atrial enlargement, regional wall motion abnormality, and aortic sinus enlargement. LV mass index was calculated by the LV end-diastolic dimension, IVST, LV posterior wall thickness, and body surface area (using Stevenson formula). Relative wall thickness (RWT) was calculated from LV posterior wall thickness and LV end-diastolic dimension.\u003c/p\u003e\n\u003ch3\u003eAssessment of cognitive ability\u003c/h3\u003e\n\u003cp\u003eWe only conducted a cognitive assessment after hospital admission for CVDs and only for subjects who met the cognitive complaints (i.e., self-reported problems with memory or other aspects of cognition)\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Two validated tools, namely MoCA\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e and MMSE\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e, were performed by trained neuropsychological assessors for screening MCI. MoCA is a cognitive screening tool that has been extensively verified for detecting MCI with high sensitivity and specificity\u003csup\u003e\u003cspan additionalcitationids=\"CR24 CR25\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. The total score of MoCA is 30 points. For those with \u0026le;\u0026thinsp;12 years of formal education, 1 point is added to the final score. MMSE is a 30-point questionnaire widely used for detecting dementia, while it has lower sensitivity in detecting MCI compared to MoCA\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAll participants completed Chinese version of the MOCA and the MMSE under the guidance of trained professional investigators in the present study. MCI was defined by MoCA score of \u0026lt;\u0026thinsp;26 points and MMSE score of \u0026ge;\u0026thinsp;24 points\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Normal cognition was defined by MoCA score of \u0026ge;\u0026thinsp;26 points and MMSE score of \u0026ge;\u0026thinsp;24 points.\u003c/p\u003e\n\u003ch3\u003eCovariates measurement\u003c/h3\u003e\n\u003cp\u003ePotential confounders were identified by reviewing the literature and consulting clinical experts\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. All covariates from standard demographic and clinical data of each participant, including age, sex, education level, smoking status, hypertension, AF, CHD, CHF, stroke, chronic kidney disease, diabetes mellitus, and hyperlipidemia, were ascertained from self-reported medical history or physicians\u0026rsquo; diagnosis in the clinical electronic database based on the International Classification of Diseases (ICD)-10 codes\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e\u0026mdash;I10, I48.9, I20.9, I25.1, I50.9, I63, N18.9, E14, and E78.5. Participants\u0026rsquo; height and weight were measured to compute body mass index (BMI). After a 15-minute rest, blood pressure (BP) was measured twice at a 2-minute interval and was ascertained by the mean of them. Laboratory parameters included fasting plasma glucose (FPG), estimated glomerular filtration rate (eGFR), total cholesterol, and low- density lipoprotein cholesterol (LDL-C).\u003c/p\u003e\n\u003ch3\u003eMissing data\u003c/h3\u003e\n\u003cp\u003eThe missing data for all variables was \u0026lt;\u0026thinsp;5%, excluding echocardiographic parameters of A peak and E/A (10.5%). Most participants (90.8%) with missing data of A peak and E/A ratio were those suffering from AF, for whom it was not possible to measure the A peak and E/A ratio because of LA systolic dysfunction.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eSample size calculation\u003c/h2\u003e \u003cp\u003eThis study used a 1:3 case-control design. We assumed the control group had a probability of 0.5 for each cardiac structural and functional abnormality (50/50 chance) and an odds ratio (OR) of 1.5 to have the same abnormality in the case versus control groups. Therefore, a sample size pair of 272 cases and 816 controls (after Fleiss correction for continuity) will have an 80% power to detect the difference (OR\u0026thinsp;=\u0026thinsp;1.5) between the case and control with a 2-sided significant level of 5%.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStatistical analyses\u003c/h3\u003e\n\u003cp\u003eAll data were analyzed using R software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.R-project.org\u003c/span\u003e\u003cspan address=\"http://www.R-project.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e; Version 4.3.2). The Kolmogorov-Smirnov Test was used to verify normal distribution of numerical data. Continuous variables were described as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (normal distribution) or median and interquartile range (skewed distribution). The two-sample t-test or Mann-Whitney U-test was used for group comparisons of continuous variables, depending on the normality of distribution. Categorical variables were presented as the number of cases and percentages and were compared using the chi-square test. P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was defined as statistically significant.\u003c/p\u003e \u003cp\u003ePCA was used to explore the correlation of cardiac structure and function with MoCA score and MCI. All variables have been normalized before PCA.\u003c/p\u003e \u003cp\u003eMoCA score is a count data. Therefore, variables that influenced the MoCA score were analyzed using either the Poisson or negative binomial regression dependent on the magnitude of dispersion strength (Supplement 1).\u003c/p\u003e \u003cp\u003eConditional univariate and multivariate logistic regression models were conducted to evaluate the association between MCI and echocardiographic parameters of cardiac structure and function. The echocardiographic parameters with p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.100 (i.e., at least marginally significant) in the univariate regression and the covariates described above were incorporated into the fully adjusted conditional multivariate logistic regression model. In further analysis, nonlinear transformations of continuous variables were conducted to explore potential nonlinear correlations.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStudy population characteristics\u003c/h2\u003e \u003cp\u003eIn this study, 1136 participants with CVDs were enrolled. The mean and standard deviation [SD] age of participants were 61.1\u0026thinsp;\u0026plusmn;\u0026thinsp;8.3 years. Most (77.20%) of them were males. Among them, 289 participants were identified with MCI and 837 participants had normal cognitive function.\u003c/p\u003e \u003cp\u003eDemographic, clinical, cardiac, and cognitive characteristics are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. MCI group had higher systolic BP (132.3\u0026thinsp;\u0026plusmn;\u0026thinsp;16.4 mmHg vs. 129.9\u0026thinsp;\u0026plusmn;\u0026thinsp;15.8 mmHg, P\u0026thinsp;=\u0026thinsp;0.024), higher diastolic BP (79.1\u0026thinsp;\u0026plusmn;\u0026thinsp;11.2 mmHg vs. 77.6\u0026thinsp;\u0026plusmn;\u0026thinsp;11.3 mmHg, P\u0026thinsp;=\u0026thinsp;0.044) and higher rates of smoking status (50.2% vs. 43.1%, P\u0026thinsp;=\u0026thinsp;0.037), AF (31.5% vs. 19.8%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and CHF (9.3% vs. 4.5%, P\u0026thinsp;=\u0026thinsp;0.002).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of different cognitive function group in patients with CVDs\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical variables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal (n\u0026thinsp;=\u0026thinsp;847)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMCI (n\u0026thinsp;=\u0026thinsp;289)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDemographic features\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61.2\u0026thinsp;\u0026plusmn;\u0026thinsp;8.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61.1\u0026thinsp;\u0026plusmn;\u0026thinsp;8.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.951\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex, male, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e654 (77.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e223 (77.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.986\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.991\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;6 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u0026ndash;12 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e496 (58.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e168 (58.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;12years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e331 (39.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e114 (39.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.1\u0026thinsp;\u0026plusmn;\u0026thinsp;3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.3\u0026thinsp;\u0026plusmn;\u0026thinsp;3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.280\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e365 (43.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e145 (50.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMedical History\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes mellitus, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e262 (30.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96 (33.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.470\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHyperlipidemia, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e621 (73.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e206 (71.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.502\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e537 (63.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e197 (68.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.144\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAtrial fibrillation, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e168 (19.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e91 (31.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoronary artery disease, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e583 (68.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e181 (62.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic heart failure, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38 (4.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27 (9.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStroke, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61 (7.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (8.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.422\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic kidney disease, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22 (2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (3.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.291\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eClinical measures\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystolic BP, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e129.9\u0026thinsp;\u0026plusmn;\u0026thinsp;15.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e132.3\u0026thinsp;\u0026plusmn;\u0026thinsp;16.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiastolic BP, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e77.6\u0026thinsp;\u0026plusmn;\u0026thinsp;11.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79.1\u0026thinsp;\u0026plusmn;\u0026thinsp;11.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFPG, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, mg/dl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e115.6\u0026thinsp;\u0026plusmn;\u0026thinsp;40.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e118.6\u0026thinsp;\u0026plusmn;\u0026thinsp;45.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.299\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal cholesterol, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, mg/dl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e159.6\u0026thinsp;\u0026plusmn;\u0026thinsp;41.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e164.0\u0026thinsp;\u0026plusmn;\u0026thinsp;42.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.128\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglycerides, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, mg/dl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e150.0\u0026thinsp;\u0026plusmn;\u0026thinsp;86.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e149.6\u0026thinsp;\u0026plusmn;\u0026thinsp;118.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.960\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, mg/dl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43.2\u0026thinsp;\u0026plusmn;\u0026thinsp;11.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43.4\u0026thinsp;\u0026plusmn;\u0026thinsp;11.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.735\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, mg/dl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e88.5\u0026thinsp;\u0026plusmn;\u0026thinsp;33.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90.1\u0026thinsp;\u0026plusmn;\u0026thinsp;32.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.471\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, mL/min/1.73m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e87.1\u0026thinsp;\u0026plusmn;\u0026thinsp;16.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e87.4\u0026thinsp;\u0026plusmn;\u0026thinsp;16.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.791\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEchocardiography measures\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlobal cardiac status, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51 (6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther cardiac abnormality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e786 (92.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e277 (95.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLV diastolic dysfunction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e341 (40.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e156 (54.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiac structure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLA diameter, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.79\u0026thinsp;\u0026plusmn;\u0026thinsp;0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.093\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLA enlargement, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e416 (49.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e148 (51.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.538\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiatrial enlargement, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e83 (9.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46 (15.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIVST, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRelative wall thickness, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.39\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.263\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLV mass index, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, g/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e88.30\u0026thinsp;\u0026plusmn;\u0026thinsp;21.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90.88\u0026thinsp;\u0026plusmn;\u0026thinsp;22.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLV hypertrophy, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44 (5.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (6.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.504\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAortic sinus diameter, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.940\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAortic regurgitation, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e236 (27.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e88 (30.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.400\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiac systolic function\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLV ejection fraction, median (IQR), %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64 (60\u0026ndash;66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62 (60\u0026ndash;66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.025*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiac diastolic function\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, m/s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.74\u0026thinsp;\u0026plusmn;\u0026thinsp;0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.77\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, m/s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.86\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.538\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE/A, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.87\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.140\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGlobal Cognitive Function\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMoCA, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28 (27-28.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 (22\u0026ndash;25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMMSE, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30 (29\u0026ndash;30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (27\u0026ndash;29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eData are mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, median (interquartile range), or number of cases (%). P-values reflect the results of t-test, Mann-Whitney U-test, or \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eBMI, body mass index; BP, blood pressure; FPG, fasting plasma glucose; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; eGFR, estimated glomerular filtration rate; LV, left ventricular; LA, left atrial; IVST, interventricular septal thickness; E, early diastolic transmitral flow velocity; A, late atrial diastolic transmitral flow velocity; MoCA, Montreal Cognitive Assessment; MMSE, Mini-Mental State Examination.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e*P-value of Mann-Whitney U-test due to skewed distribution.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe cardiac structural and functional parameters are significantly different between the two groups. LV diastolic dysfunction (54.0% vs. 40.3%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and biatrial enlargement (15.9% vs. 9.8%, P\u0026thinsp;=\u0026thinsp;0.005) were more common in the MCI group. IVST (1.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20 cm vs. 1.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17 cm, P\u0026thinsp;=\u0026thinsp;0.002) was greater in the MCI group.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation between global cognitive function and cardiac structural/functional measurements\u003c/h2\u003e \u003cp\u003eAs shown in Figure, PCA indicated that the total MoCA score was negatively correlated with LV diastolic dysfunction, LA enlargement, aortic regurgitation (AR), LV mass index, aortic sinus diameter, IVST, and LV hypertrophy. LV diastolic dysfunction, LV hypertrophy, IVST, aortic sinus diameter, LV mass index, AR, LA enlargement, and LA diameter were positively correlated with MCI.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the correlation between the total MoCA score and cardiac structure and function in the negative binomial regression model. After adjusting for confounders, LV diastolic dysfunction (Beta [standard error, SE], 0.234 [0.045]; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and higher IVST (Beta [SE], 0.034 [0.016]; P\u0026thinsp;=\u0026thinsp;0.036) demonstrated a negative correlation with the global cognitive score of MoCA.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNegative binomial regression for the correlation of echocardiographic cardiac parameters with global cognitive score of MoCA\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCardiac variables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e30 - global cognitive score\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBeta (SE)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eadj. Beta (SE)*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eadj. p-value*\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLV diastolic dysfunction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.244 (0.045)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.234 (0.045)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLA enlargement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.055 (0.045)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.052 (0.046)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.264\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiatrial enlargement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.090 (0.094)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.066 (0.095)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.489\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIVST, per 1 mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.028 (0.016)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.034 (0.016)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLV mass index, per 10 g/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.007 (0.013)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.567\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.005 (0.014)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.688\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLV hypertrophy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.011 (0.100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.911\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.080 (0.101)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.426\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAortic sinus diameter, per 1 mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.001 (0.006)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.806\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.001 (0.006)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.839\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAortic regurgitation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.032 (0.048)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.506\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.020 (0.049)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.682\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLV ejection fraction, per 1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.004 (0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.002 (0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.512\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE/A, per 0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.009 (0.006)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.005 (0.006)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.408\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e*Negative binomial regression was adjusted for age, sex, education, hypertension, atrial fibrillation, coronary artery disease, chronic heart failure, stroke, chronic kidney disease, diabetes mellitus, hyperlipidemia, smoking, systolic blood pressure, diastolic blood pressure, estimated glomerular filtration rate, fasting plasma glucose, total cholesterol, low-density lipoprotein cholesterol, and body mass index.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eSE, standard error; LV, left ventricular; LA, left atrial; IVST, interventricular septal thickness; E/A, ratio of the early diastolic transmitral flow velocity and late atrial diastolic transmitral flow velocity.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eAssociation between MCI and cardiac structure/function\u003c/h2\u003e \u003cp\u003eIn the conditional univariate logistic regression analysis (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), biatrial enlargement (OR, 1.76; 95% CI, 1.18\u0026ndash;2.62; P\u0026thinsp;=\u0026thinsp;0.005), IVST (OR, 1.14; 95% CI, 1.06\u0026ndash;1.24; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), LV diastolic dysfunction (OR, 1.74; 95% CI, 1.33\u0026ndash;2.28; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were positively associated with MCI.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation of echocardiographic measures of cardiac structure and function with MCI in conditional logistic regression models\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCardiac variables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eUnivariate model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eMultivariate model*\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCardiac structure\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLA diameter, per 1 mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.02 (0.99\u0026ndash;1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.99 (0.95\u0026ndash;1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.431\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLA enlargement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.09 (0.83\u0026ndash;1.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiatrial enlargement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.76 (1.18\u0026ndash;2.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.58 (0.95\u0026ndash;2.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.077\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIVST, per 1 mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.14 (1.06\u0026ndash;1.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.14 (1.03\u0026ndash;1.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRelative wall thickness, per 0.1 unit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.13 (0.92\u0026ndash;1.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLV mass index, per 10 g/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.06 (0.99\u0026ndash;1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.97 (0.88\u0026ndash;1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.555\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLV hypertrophy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.25 (0.70\u0026ndash;2.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.444\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAortic sinus diameter, per 1 mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00 (0.96\u0026ndash;1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.955\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAortic sinus enlargement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.12 (0.77\u0026ndash;1.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.571\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAortic regurgitation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.13 (0.84\u0026ndash;1.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCardiac systolic function\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLV ejection fraction, per 1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.99 (0.98\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.506\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCardiac diastolic function\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLV diastolic dysfunction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.74 (1.33\u0026ndash;2.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.03 (1.48\u0026ndash;2.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE, per 0.1 m/s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.04 (0.99\u0026ndash;1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA, per 0.1 m/s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.99 (0.91\u0026ndash;1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.722\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE/A, per 0.1 unit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.03 (0.98\u0026ndash;1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e*The conditional multivariate logistic regression model was adjusted for hypertension, atrial fibrillation, coronary artery disease, chronic heart failure, stroke, chronic kidney disease, diabetes mellitus, hyperlipidemia, smoking, systolic blood pressure, diastolic blood pressure, estimated glomerular filtration rate, fasting plasma glucose, total cholesterol, low-density lipoprotein cholesterol, and body mass index.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eOR, odds ratio; CI, confidence interval; LA, left atrial; IVST, interventricular septal thickness; LV, left ventricular; E, early diastolic transmitral flow velocity; A, late atrial diastolic transmitral flow velocity.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn the fully-adjusted conditional multivariate logistic regression model (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), LV diastolic dysfunction (OR, 2.03; 95% CI, 1.48\u0026ndash;2.79, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and IVST (OR, 1.14; 95% CI, 1.03\u0026ndash;1.27; P\u0026thinsp;=\u0026thinsp;0.014) were independently associated with MCI, after adjusting for the diagnosis CVDs and other conventional MCI risk factors. The association between MCI and biatrial enlargement lost significance in adjusted model. Upon further analysis, nonlinear transformations of continuous variables in univariate and multivariate logistic regression models did not produce significant effects on outcomes (Supplement 2).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this matched case-control study for general CVDs, we observed the association between MCI and abnormal cardiac structure and function as measured by echocardiography. PCA and negative binomial regression revealed that LV diastolic dysfunction and greater IVST were correlated with the co-occurring MCI and lower MoCA score. After adjusting for diagnosed CVDs and other MCI-relevant covariates, the incidence of LV diastolic dysfunction and greater IVST were strongly correlated with lower MoCA scores and associated with the co-occurring MCI in general CVD participants. Our finding implied that more attention should be paid to neurocognitive decline in general CVD patients with abnormal echocardiographic cardiac structure and function.\u003c/p\u003e \u003cp\u003eCardiac structural and functional damages are the intermediate stages or consequences of CVD development and exacerbation\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Echocardiography is a first-line tool for assessing cardiac structure and function and plays a significant role in diagnosing and managing CVDs\u003csup\u003e\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Our findings extended some previous studies that indicated the association between mitigating cognitive decline and cardiac structural and functional abnormalities measured by echocardiography\u003csup\u003e\u003cspan additionalcitationids=\"CR7 CR8\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. The Rotterdam study of 3,291 elder participants, without clinical CVDs and stroke, found that LV diastolic dysfunction was associated with dementia\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. With the CARDIA Study data, Rouch et al. indicated that midlife LV diastolic function and its 25-year change from early to middle adulthood were linked to lower cognitive function\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. In a secondary analysis of Atherosclerosis Risk in Communities Study, Faulkner et al. found that worse LV diastolic function was associated with poorer performance in language, memory, and attention, although the links were weak\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. However, none of the above three studies emphasized CVD patients the same as our target population, who were proven more likely to develop CI. Sacre et al. conducted a study among participants from the Nurse-led Intervention for Less Chronic Heart Failure Study and reported a significantly positive association between LV diastolic dysfunction and MCI among CHF patients, supporting our results\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. However, Sacre et al. used data from only CHF patients and did not conduct multivariable logistic regression with all echocardiographic variables included.\u003c/p\u003e \u003cp\u003eInconsistent with our results, Eggermont et al. found that LV diastolic dysfunction was unrelated to memory and executive function among elder CVD participants\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Our study had more participants (n\u0026thinsp;=\u0026thinsp;1136) than this study (n\u0026thinsp;=\u0026thinsp;117 in Eggermont et al.). In addition, we utilized the MoCA to evaluate overall cognitive function and controlled for potential confounders that could influence cognitive function, which might facilitate obtaining more reliable conclusions.\u003c/p\u003e \u003cp\u003eCVDs may lead to CI through a series of potential mechanisms, including decreases in cardiac output and cerebral perfusion, ischemic brain injury (e.g., thromboembolism, transient ischemic attack), inflammation, and so on\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. LV diastolic dysfunction is associated with reduced cardiac output, which may cause cerebral hypoperfusion and subsequently lead to cerebral structural and functional abnormalities. Cerebral hypoperfusion may attenuate the clearance of amyloid-beta and facilitate the phosphorylation and aggregation of tau\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e, a significant pathological feature of dementia\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Although it is not feasible to screen all CVD patients for cognitive impairment, those with abnormal cardiac structure and function, such as LV diastolic dysfunction, might be considered to assess their cognitive function, which ensures some CVD patients at high risks of cognitive impairment be screened and managed appropriately, and timely to delay the progression of cognitive decline. While we may not be able to predict neurological impairment based solely on cardiac changes, however, adding cardiac parameters may improve our understanding of neurological impairment development in CVD patients.\u003c/p\u003e \u003cp\u003eOur data also showed that greater IVST was positively associated with MCI. Consistent with our results, an earlier initial study, which enrolled 22 participants with Alzheimer\u0026rsquo;s disease (AD) and 23 age-matched control individuals, suggested that IVST was significantly greater in the AD group\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Furthermore, there is an association between IVST/LVMI and surrogate outcomes of global cognitive function or specific cognitive domains\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Apart from these, few studies have explored the association between IVST and MCI. Given our matched case-control study design, large-scale cohort studies are needed in the future.\u003c/p\u003e \u003cp\u003eIn this study, we collected detailed clinical measures and included more echocardiographic parameters from more than 1000 participants, which allowed us to study the echocardiographic parameters linked to MCI more comprehensively. In addition, given many echocardiographic variables were continuous variables with strong collinearity, we found that PCA, a dimension reduction method, helped explore important variables correlated with MCI in our study. Furthermore, the MoCA score is a count data, so Poisson or negative binomial models are appropriate methods to determine the effect of echocardiographic measures of cardiac structure and function on MCI. Nonlinearity is probably another crucial point, but it has been far less explored in previous studies. We considered that pure linear correlation might not be robust for all variables and, therefore, applied nonlinear transformations on several numerical variables. Although it did not produce significant effects on outcomes in our study, considering its clinical importance, future studies should also consider exploring the nonlinear correlations to reach a more accurate conclusion.\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eStudy limitations\u003c/h2\u003e \u003cp\u003eSeveral limitations, including inherent defects in case-control study design, should be acknowledged and guided for future research. First, MCI was identified based on MoCA and MMSE rather than comprehensive neuropsychological evaluation. Therefore, the results may be interpreted with caution. Second, given some missing data, some individuals were excluded from different steps of statistical analysis, which might lead to selection-biased results. Third, as a single-center study, the generalizability of the results might be affected. Fortunately, most of our participants were from different provinces rather than Beijing. Therefore, the effect may be less significant. Nonetheless, cohort designs across broader geographical regions and populations are needed to validate the relationship and associated degree between echocardiographic variables and MCI. Last, although we have made efforts to recognize and control for confounders by reviewing extensive literature, some unknown and residual confounders may still exist.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe current study indicated the association between cognitive impairment and abnormal cardiac structure and function as measured by echocardiography. It revealed LV diastolic dysfunction and IVST were positively associated with MCI in general CVD patients, suggesting further attention to cognitive assessment in this population.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study followed the Declaration of Helsinki and was approved by the Institutional Ethics Review Board at Beijing Anzhen Hospital (No.2022-17-1). Informed consent for this study was waived from all participants because it was retrospective.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data underlying this article will be shared on reasonable request to the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe project is an investigator-initiated study. Professor Changsheng Ma received funding from the Capital Medical University affiliated with Beijing Anzhen Hospital Science and Technology Innovation Special Fund Project (No.2022-17-1) established by Capital Medical University affiliated with Beijing Anzhen Hospital. Changsheng Ma received funding from the Beijing Municipal Science and Technology Commission (BMSTC) under grant number Z241100007724008.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eK.Z. designed the study and supervised the project. X.L. conducted the data analysis and interpretation. S.H. and X.L. performed the literature review and wrote the initial draft of the manuscript. M.Z. and C.X. prepared the figures and tables. S.X. and J.D. contributed to the methodology and provided critical revisions to the manuscript. C.M. offered valuable insights and guided the research direction. All authors reviewed and approved the final manuscript. K.Y. is the corresponding author and provided oversight for the overall project.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAppendix A. Supplementary data\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSupplementary data to this article can be found online.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLiu Y, Ma W, Li M, Han P, Cai M, Wang F\u003cem\u003e et al\u003c/em\u003e. Relationship Between Physical Performance and Mild Cognitive Impairment in Chinese Community-Dwelling Older Adults. \u003cem\u003eClin Interv Aging\u003c/em\u003e 2021;\u003cstrong\u003e16\u003c/strong\u003e:119-27.\u003c/li\u003e\n\u003cli\u003eRouch L, Hoang T, Xia F, Sidney S, Lima JAC, Yaffe K. 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Cerebral blood flow decrease as an early pathological mechanism in Alzheimer\u0026apos;s disease. \u003cem\u003eActa Neuropathol\u003c/em\u003e 2020;\u003cstrong\u003e140\u003c/strong\u003e:793-810.\u003c/li\u003e\n\u003cli\u003eDrummond E, Pires G, Macmurray C, Askenazi M, Nayak S, Bourdon M\u003cem\u003e et al\u003c/em\u003e. Phosphorylated tau interactome in the human Alzheimer\u0026apos;s disease brain. \u003cem\u003eBrain\u003c/em\u003e 2020;\u003cstrong\u003e143\u003c/strong\u003e:2803-17.\u003c/li\u003e\n\u003cli\u003eBelohlavek M, Jiamsripong P, Calleja AM, Mcmahon EM, Maarouf CL, Kokjohn TA\u003cem\u003e et al\u003c/em\u003e. Patients with Alzheimer disease have altered transmitral flow: echocardiographic analysis of the vortex formation time. \u003cem\u003eJ Ultrasound Med\u003c/em\u003e 2009;\u003cstrong\u003e28\u003c/strong\u003e:1493-500.\u003c/li\u003e\n\u003cli\u003eArangalage D, Ederhy S, Dufour L, Joffre J, Van der Vynckt C, Lang S\u003cem\u003e et al\u003c/em\u003e. Relationship between cognitive impairment and echocardiographic parameters: a review. \u003cem\u003eJ Am Soc Echocardiogr\u003c/em\u003e 2015;\u003cstrong\u003e28\u003c/strong\u003e:264-74.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"echocardiography, cardiac structure and function, mild cognitive impairment","lastPublishedDoi":"10.21203/rs.3.rs-5294926/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5294926/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eCardiovascular diseases (CVDs) marked with cardiac morphological or hemodynamical abnormalities are associated with mild cognitive impairment (MCI). The links between cardiac structure and function and MCI are not well understood. We aimed to explore the association between echocardiographic parameters of cardiac structure and function and MCI in CVD patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eWe conducted an age-, gender-, and education level-matched case-control study in general CVD participants with a 1:3 ratio of MCI (Montreal Cognitive Assessment [MoCA] score \u0026lt;26 and Mini-Mental State Examination [MMSE] score ≥24) and cognitively normal participants at a tertiary hospital in Beijing, China. The echocardiographic cardiac parameters and cognitive status were retrieved through the clinical electronic database from May 2021 to August 2023. Principal component analysis (PCA), negative binomial, and conditional multivariate regression were performed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e A total of 1136 CVD participants (mean age, 61.1 ± 8.3 years) were included in the study, comprising 289 (25.3%) MCI and 847 cognitively normal participants. Compared to cognitively normal participants, MCI participants had a higher prevalence of left ventricular (LV) diastolic dysfunction (54.0% vs. 40.3%; P\u0026lt;0.001) and greater interventricular septal thickness (IVST) (1.04 ± 0.17 cm vs. 1.00 ± 0.20 cm; P=0.002). LV diastolic dysfunction (Beta [SE], 0.234 [0.045]; P\u0026lt;0.001) and IVST (Beta [SE], 0.034 [0.016]; P=0.036) were negatively correlated with the MoCA score of global cognitive function. LV diastolic dysfunction (OR, 2.03; 95% CI, 1.48-2.79; P\u0026lt;0.001) and IVST (OR, 1.14; 95% CI, 1.03-1.27; P=0.014) were positively associated with MCI, independent of diagnosed CVDs and the conventional MCI risk factors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eGeneral CVD patients with abnormal echocardiographic LV diastolic dysfunction and IVST were associated with cognitive decline, suggesting further cognitive assessment for MCI.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTrial registration: \u003c/strong\u003eRetrospectively registered.\u003c/p\u003e","manuscriptTitle":"Association between Echocardiographic Parameters of Cardiac Structure and Function and Mild Cognitive Impairment","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-11 07:01:36","doi":"10.21203/rs.3.rs-5294926/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-11-25T07:03:05+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-20T22:42:52+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-10T09:09:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"297049874193480569860405582595285214756","date":"2024-11-02T15:11:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"14624436708615946330901953729908715719","date":"2024-10-29T14:22:23+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-10-29T13:04:22+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-10-28T12:28:45+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-10-25T09:35:56+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-10-25T09:33:44+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cardiovascular Disorders","date":"2024-10-19T14:04:09+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0cd745d6-70ea-4f2c-81ff-8df8422875a1","owner":[],"postedDate":"November 11th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-02-10T16:11:07+00:00","versionOfRecord":{"articleIdentity":"rs-5294926","link":"https://doi.org/10.1186/s12872-025-04528-8","journal":{"identity":"bmc-cardiovascular-disorders","isVorOnly":false,"title":"BMC Cardiovascular Disorders"},"publishedOn":"2025-02-05 15:57:27","publishedOnDateReadable":"February 5th, 2025"},"versionCreatedAt":"2024-11-11 07:01:36","video":"","vorDoi":"10.1186/s12872-025-04528-8","vorDoiUrl":"https://doi.org/10.1186/s12872-025-04528-8","workflowStages":[]},"version":"v1","identity":"rs-5294926","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5294926","identity":"rs-5294926","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}