Value of APOB/APOA1 ratio in prediction of calcific aortic valve disease

Value of APOB/APOA1 ratio in prediction of calcific aortic valve disease | 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 Value of APOB/APOA1 ratio in prediction of calcific aortic valve disease Wang yuxing, Yu ming, Yang song, Mei jiajie, Liu zhenzhu, Geng zhao hong, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5364924/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Calcific Aortic Valve Disease (CAVD) is a prevalent heart valve disease. The ratio of two apolipoproteins with distinct functions (APOB/APOA1) has been proposed as a novel assessment index for the evaluation of cardiovascular diseases. The aim of this article is to discuss the role of lipids parameters such as APOB/APOA1 in CAVD and the risk factors for CAVD, to develop a predictive model for CAVD, and to evaluate the sensitivity and specificity of this model. Method: Patients who initially presented to the Department of Cardiology of the Second Affiliated Hospital of Dalian Medical University between 1 January 2023 and 31 December 2023 were retrospectively identified and included in the study. Patients were divided into an aortic valve calcification group (111 cases) and a control group (201 cases) based on CT findings. The patients' clinical data, laboratory examination results, and chest CT images were collected and analyzed. A variety of statistical methods were used to analyses risk factors for CAVD in order to construct a CAVD prediction model and to assess its sensitivity and specificity. Results：Lipid parameters APOA1, APOB/APOA1, cumulative LDL exposure and non-HDL/HDL were significantly associated with aortic valve calcification. Age, history of diabetes, DBP, APOB/APOA1, Cys-c and NLR are identified as independent risk factors for CAVD, and the combination of the above indexes in the prediction of aortic valve calcification was 0.796, corresponding to a sensitivity of 0.769 and a specificity of 0.755. Conclusion: APOA1, APOB/APOA1, cumulative LDL exposure, and Non-HDL/HDL have been demonstrated to be associated withCAVD. Furthermore, age, history of diabetes mellitus, DBP, APOB/APOA1, Cys-c, and NLR have been identified as valid predictors of CAVD. lipid aortic valve calcification ROC curve Figures Figure 1 Introduction Calcific Aortic Valve Disease (CAVD) is a prevalent form of valvular heart disease, ranking as the third most common cardiovascular disorder after coronary heart disease and hypertension [ 1 ] . The early presentation of CAVD is atherosclerosis of the aortic valve leaflets, with a prevalence of calcification or sclerosis of the aortic valve of 20–30% in individuals over the age of 65 and 48% in those over the age of 85 [ 2 ] . Severe calcification of the aortic valve in advanced stages results in the development of aortic stenosis, which in turn leads to obstruction of the left ventricular outflow tract. This, in turn, leads to heart failure, for which surgical valve replacement is the only effective treatment. Liu Li [ 3 ] found that the prevalence of valvular calcification was 13.4% in a random sample of the elderly population in Beijing, 7.7% in those aged 60 years or older, 16.1% in those aged 70 years or older, and 25.7% in those aged 80 to 89 years. The notion that CAVD is merely a passive degenerative change associated with age has long been a widely held view. Recent studies have provided new insights into the pathogenesis of calcific aortic stenosis, indicating that it is an active progressive disease influenced by multiple risk factors. Epidemiological studies have demonstrated a strong correlation between age, dyslipidemia, and diabetes mellitus and the development of calcific aortic stenosis. Histopathological studies have revealed that calcific aortic stenosis involves inflammation, abnormal lipid metabolism, matrix remodeling, and calcification. CAVD shares numerous similarities with atherosclerosis, both in terms of risk factors and pathological changes. Indeed, it has been postulated that CAVD represents an additional manifestation of atherosclerosis [ 4 ] . Lipids play a significant role in the pathogenesis of calcific aortic valve disease. While the majority of lipid management guidelines identify low-density lipoprotein (LDL) as the causative lipid component and have demonstrated that LDL reduction can reduce the risk of cardiovascular events, attempts have been made to block or slow the progression of calcific aortic valve disease with statin therapy. The results of early studies in animal models have indicated that hypercholesterolemia can result in the development of aortic valve sclerosis and hemodynamic changes. Additionally, retrospective studies have suggested that statins may potentially slow the progression of valve calcification and stenosis. However, despite these findings, large-scale, prospective, controlled trials have not consistently demonstrated that statins are effective in slowing or preventing the onset and progression of calcific aortic stenosis [ 5 – 8 ] . Apolipoprotein B (APOB) has also been identified as a risk-predicting biomarker in a study conducted by the UK Biobank. This study revealed that APOB is responsible for the transportation of a vast array of potentially atherogenic cholesterol, including very low-density lipoprotein (VLDL), intermediate-density lipoprotein (IDL), and low-density lipoprotein (LDL). Conversely, high-density lipoprotein (HDL) is transported by apolipoprotein A1 (APOA1) [ 9 – 10 ] . In the context of various lipid abnormalities, a multitude of factors influence the equilibrium between pro- and anti-atherosclerosis. The most utilized indicators in the clinical assessment of lipids encompass total cholesterol (TC), triglyceride (TG), LDL, HDL, and, in recent years, APOA1 and APOB. The importance of this in dyslipidemia is evidenced by the fact that, despite a reduction in LDL levels, there is still a significant residual risk of cardiovascular events. This suggests that other components of lipids, in addition to LDL, may also play a role in the development of cardiovascular disease. The search for more sensitive and accurate lipid parameters for risk prediction of cardiovascular events could help in the prevention of such diseases, and therefore a series of non-traditional lipid parameters have been derived from lipids, including cumulative exposure to LDL, APOB/APOA1 ratio, non-HDL, residual cholesterol, and atherogenic index of plasma (AIP), which are all important factors in the development of cardiovascular disease. Therefore, the objective of this study is threefold: firstly, to examine the risk factors associated with CAVD; secondly, to investigate the relationship between lipids and CAVD; and thirdly, to construct a prediction model for CAVD and to evaluate its value. Methods This is a retrospective study, in which patients who first visited the Department of Cardiology of the Second Hospital of Dalian Medical University between 1 January 2023 and 31 December 2023 are being retrospectively collected. All patients underwent a comprehensive chest CT scan upon admission, and 312 patients were ultimately included in the study after the exclusion of individuals with comorbidities such as pregnancy, severe hepatic and renal insufficiency, hematological diseases, tumors, autoimmune disorders, inflammatory diseases, hyperthyroidism, hypothyroidism, rheumatic heart disease, cardiomyopathy, dilated cardiomyopathy, and myocarditis. Additionally, individuals with a history of coronary stenting, coronary artery bypass grafting, or long-term oral lipid-lowering medications, such as statins, were excluded. Agatston established a minimum threshold of CT for calcified areas at 130 Hu, defining the area with CT values exceeding this threshold as calcified. This was further divided into 111 cases in the calcified group and 201 cases in the control group based on the presence or absence of aortic valve calcification in CT. The general information of the two groups was also collected, including gender. The general data, including gender, age, height, weight, body mass index (BMI), body surface area (BSA), history of hypertension, history of diabetes, history of coronary heart disease, etc., were collected from the two groups. The results of blood tests, including those pertaining to lipids, were collated from the patients. Thereafter, lipid-derived parameters were calculated, along with the Agatston Calcification Score. The correlation between aortic valve calcification and lipids and their derived parameters was investigated by comparing the differences in lipids and their derived parameters between the two groups. A CAVD prediction model was constructed using Spearman's correlation analysis, binary logistic regression and the ROC curve, and the model was evaluated for sensitivity and specificity. This study was approved by the Ethics Committee of the Second Affiliated Hospital of Dalian Medical University. Table 1 calculation method of Agatston Calcification Score Agatston Calcification Score = ∑ area of calcification in each CT cross-section * coefficient corresponding to the maximum CT value of the calcified plaque Maximum CT value Modulus Calculate the area of calcification in each cross-section Calcification Score of the cross-section = plaque area * coefficient corresponding to maximum CT value Agatston Calcification Score= sum of calcification integrals for each cross-section 130-199 1 200-299 2 300-399 3 ≥400 4 Statistical analyses The statistical analysis of the obtained data was conducted using the SPSS 26.0 software package. The count data were expressed by a constitutive ratio, and a X 2 test was employed. The measurement data were tested for normality using a S-W test and a Q-Q plot. If they conformed to normality, they were expressed by the mean ± standard deviation (X ± SD). If the data did not conform to normality, they were expressed by M (Q25, Q75) and the nonparametric test was used. If the data conformed to normal distribution and met the chi-square, an independent samples t-test analysis was conducted. If the data met the normal distribution but did not meet the chi-square, an t’-test was performed. The correlation was analyzed using Spearman's correlation coefficient, and factors with a p-value less than 0.05 were subjected to binary logistic regression (stepwise). The indexes with a p-value less than 0.05 were included in the analysis and plotted on the receiver operating characteristic curve. A statistically significant difference was observed when p < 0.05. Results 1. The difference in age, history of diabetes mellitus, diastolic blood pressure (DBP) was statistically significant in the calcification group when compared to the control group. While the difference in smoking, alcohol consumption, history of hypertension, history of coronary heart disease, height, weight, BMI, BSA, systolic blood pressure (SBP) was not statistically significant. Table 2 Comparison of baseline information between the calcified and control groups Item Calcification group N=111 Control Group N=201 Test Value（t/t＇/X 2 ） P Meal（n, %） (70,63.1%) (109,54.2%) 2.282 0.131 Smoking（n, %） (44,39.6%) (88,43.8%) 0.502 0.478 Drinking（n, %） (21,18.9%) (33,16.4%) 0.313 0.576 Hypertensive（n, %） (76,68.5%) (125,62.2%) 1.230 0.267 Diabetes（n, %） (49,44.1%) (48,24.0%) 13.497 <0.001 Coronary Heart Disease（n, %） (67,60.4%) (100,49.8%) 3.236 0.072 Age（year） 68.09±9.20 60.97±11.06 -5.773 <0.001 Height（m） 1.66±0.13 1.67±0.08 0.414 0.679 Weight（kg） 72.04±13.97 73.23±13.41 0.741 0.459 BMI（kg/m 2 ） 27.03±14.04 26.27±3.83 -0.718 0.473 BSA（m 2 ） 1.78±0.22 1.80±0.21 0.735 0.463 SBP（mmHg） 136.69±20.05 138.55±18.64 0.819 0.414 DBP（mmHg） 81.59±12.47 86.68±11.73 3.591 <0.001 2．Compared with the control group, the differences in blood glucose, glycated hemoglobin A1c (HbA1c), LDL cumulative exposure, APOA1, APOB/APOA1, Non-HDL/HDL, urea, Cystatin c (Cys-c), Sodium, Chlorine, Calcium, neutrophil percentage (NEUT%), lymphocyte percentage (LY%), neutrophil-to-lymphocyte ratio (NLR), D-dimer were statistically significant, and the patients in the calcification group possessed higher blood glucose, HbA1c, LDL cumulative exposure, APOB/APOA1, Non-HDL/HDL, urea, Cys-c, WBC, NEUT%, NLR, and D-dimer. amount, APOB/APOA1, Non-HDL/HDL, urea, Cys-c, WBC, NEUT%, NLR, and D-dimer, while Sodium, Chlorine, Calcium, APOA1, and LY% were lower than in the control group. And the differences in TC, TG, LDL, HDL, APOB, Non-HDL, residual cholesterol, AIP, Creatinine, uric acid (UA), alkaline phosphatase (ALP), lactate dehydrogenase (LDH) potassium were not statistically significant. Table 3 Comparison of laboratory findings between patients in the calcified and control groups Item Calcification group Control Group test value（t/t＇/X 2 ） P Blood glucose（mmol/L） 6.66±2.72 5.88±1.71 -2.739 0.007 HbA1c（mmol/L） 6.82±1.66 6.24±1.22 -3.243 0.001 TC（mmol/L） 4.76±0.95 4.63±0.80 -1.244 0.215 TG（mmol/L） 1.51±0.81 1.49±0.57 -0.328 0.743 HDL（mmol/L） 1.07±0.28 1.11±0.27 1.458 0.146 LDL（mmol/L） 2.97±0.77 2.82±0.66 -1.771 0.078 Cumulative LDL exposure（mmol/L·year） 201.29±55.00 171.63±49.05 -4.894 <0.001 APOA1（mmol/L） 1.34±0.24 1.4±0.22 2.042 0.042 APOB（mmol/L） 0.88±0.22 0.84±0.18 -1.914 0.057 APOB/APOA1 0.68±0.23 0.62±0.17 -2.878 0.004 Non-HDL（mmol/L） 3.69±0.93 3.52±0.80 -1.718 0.087 Non-HDL/HDL 3.71±1.48 3.37±1.119 -2.252 0.025 Residual cholesterol（mmol/L） 0.73±0.29 0.70±0.27 -0.815 0.416 AIP 0.12±0.26 0.11±0.24 -0.408 0.683 Urea（mmol/L） 6.11±1.81 5.71±1.37 -2.038 0.043 Creatinine（mmol/L） 69.53±14.16 66.73±14.62 -1.626 0.105 UA（mmol/L） 352.21±88.47 339.83±86.00 -1.205 0.229 Cys-c（mmol/L） 1.11±0.23 1.01±0.16 -4.274 <0.001 ALP（U/L） 78.78±26.79 75.73±20.62 -1.120 0.263 LDH （mmol/L） 221.79±146.11 196.39±93.15 -1.654 0.100 Potassium（mmol/L） 3.97±0.37 3.96±0.31 -0.473 0.636 Sodium（mmol/L） 141.33±2.99 142.09±2.20 2.339 0.020 Chlorine（mmol/L） 106.32±2.78 107.06±2.14 2.429 0.016 Calcium（mmol/L） 2.22±0.13 2.26±0.12 2.599 0.010 WBC（x10 9 /L） 6.51±1.88 6.46±1.85 -0.196 0.845 NEUT% 66.01±8.15 62.31±8.59 -3.694 <0.001 LY% 25.00±7.43 29.15±7.35 4.735 <0.001 NLR 3.03±1.47 2.36±0.93 -4.906 <0.001 D-dimer(ug/ml) 0.64±0.31 0.57±0.22 -2.488 0.013 3．Spearman correlation results showed that aortic valve calcification correlated with age, history of coronary artery disease, history of diabetes mellitus, diastolic blood pressure, blood glucose, HbA1c, cumulative LDL exposure, APOA1, APOB/APOA1, Non-HDL/HDL, Cys-c, LDH, Sodium, Chlorine, Calcium, NEUT%, LY%, NLR, D-dimer. The Agatston score for aortic valve calcification correlated with age, history of coronary artery disease, history of diabetes mellitus, diastolic blood pressure, blood glucose, HbA1c, cumulative LDL exposure, Non-HDL/HDL, APOB/APOA1, urea, Cys-c, LDH, Sodium, Chlorine, NEUT%, LY%, NLR, and D-dimer. Table 4 Correlation analysis of aortic valve calcification and Agatston score Item correlation coefficient P correlation coefficient P Sex 0.086 0.066 0.091 0.055 Age 0.313 <0.001 0.332 <0.001 Height 0.027 0.319 0.024 0.334 Weight -0.013 0.407 -0.027 0.320 BMI -0.037 0.259 -0.053 0.176 BSA 0.004 0.472 -0.006 0.457 Smoking -0.040 0.240 -0.051 0.185 Drinking 0.032 0.289 0.038 0.255 Coronary Heart Disease 0.102 0.036 0.110 0.027 Hypertensive 0.063 0.134 0.057 0.160 Diabetes 0.208 <0.001 0.210 <0.001 SBP -0.063 0.133 -0.042 0.228 DBP -0.223 <0.001 -0.220 <0.001 Blood glucose 0.126 0.013 0.111 0.025 HbA1c 0.159 0.003 0.154 0.003 TC 0.048 0.199 0.060 0.145 TG -0.064 0.131 -0.060 0.145 HDL -0.084 0.068 -0.075 0.094 LDL 0.070 0.108 0.078 0.086 Cumulative LDL exposure 0.257 <0.001 0.275 <0.001 APOA1 -0.112 0.024 -0.093 0.051 APOB 0.078 0.084 0.087 0.064 APOB/APOA1 0.139 0.007 0.135 0.009 Non-HDL 0.074 0.098 0.083 0.071 Non-HDL/HDL 0.114 0.022 0.112 0.024 Residual Cholesterol 0.045 0.216 0.060 0.146 AIP -0.003 0.478 -0.009 0.437 Urea 0.091 0.054 0.106 0.031 Creatinine 0.090 0.056 0.082 0.075 UA 0.063 0.133 0.050 0.188 Cys-c 0.242 <0.001 0.234 <0.001 ALP 0.033 0.283 0.025 0.332 LDH 0.114 0.022 0.107 0.030 Potassium 0.013 0.408 0.031 0.295 Sodium -0.101 0.038 0.099 0.041 Chlorine -0.130 0.011 -0.138 0.008 Calcium -0.113 0.024 -0.088 0.062 WBC 0.007 0.453 -0.007 0.453 NEUT% 0.194 <0.001 0.218 <0.001 LY% -0.250 <0.001 -0.268 <0.001 NLR 0.241 <0.001 0.262 <0.001 D- dimer 0.100 0.040 0.102 0.037 4．The factors associated with aortic valve calcification were subjected to binary logistic regression, and the results demonstrated that age, history of diabetes mellitus, DBP, APOB/APOA1, Cys-c, and NLR were independent risk factors for aortic valve calcification. Upon standardization of the data, it was observed that an increase of one standard deviation in age, history of diabetes mellitus, DBP, APOB/APOA1, Cys-c, and NLR resulted in a 0.638, 0.387, -0.410, 0.357, 0.322, and 0.555 standard deviation increase in aortic valve calcification, respectively. Table 5 Binary logistic regression analysis of aortic valve calcification Item B P B P Constant -6.012 0.001 -0.776 <0.001 Age 0.058 0.001 0.638 0.001 Diabetes 0.835 0.005 0.387 0.005 DBP -0.034 0.007 -0.410 0.007 APOB/APOA1 1.827 0.026 0.357 0.026 Cys-c 1.673 0.032 0.322 0.032 NLR 0.464 <0.001 0.555 <0.001 5．The ROC analysis included the variables age, history of diabetes, DBP, APOB/APOA1, Cys-c, and NLR. The areas under the curve for these variables were 0.679, 0.600, 0.641, 0.583, 0.645, and 0.647, the area under the curve for the combined prediction of aortic valve calcification by the above indexes was 0.796, and the maximum Uden index of this prediction model was 0.522, corresponding to a sensitivity of 0.769 and a specificity of 0.754. Table 6 ROC curves for aortic valve calcification and various risk factors Item AUC P Lower limits Upper limits Combine model 0.796 0.743 0.850 <0.001 Age 0.679 0.616 0.742 <0.001 Diabetes 0.600 0.532 0.668 0.004 DBP 0.641 0.575 0.707 <0.001 APOB/APOA1 0.583 0.516 0.649 0.018 Cys-c 0.645 0.578 0.712 <0.001 NLR 0.647 0.582 0.712 <0.001 Discussion CAVD is a prevalent form of heart valve disease across the globe. It is characterized by the progressive fibrosis and calcification of the aortic valve. In its early stages, the disease presents with leaflet thickening and mild calcification. However, as the calcification progresses, the aortic valve develops stenosis, which impedes the heart's pumping function. If left untreated, the disease progresses rapidly, resulting in a poor prognosis. Surgical intervention remains the only viable treatment option [ 11 ] . The pathology of this condition is complex and shares numerous similarities with atherosclerosis. It involves several pathological processes, including chronic inflammation, disorders of lipid metabolism, fibrotic remodeling, and calcification [ 12 ] . Although lipids are primarily associated with the pathogenesis of CAVD, statins have not shown consistent results in improving aortic stenosis according to the literature [ 5 – 8 ] . Therefore, the search for more sensitive and accurate lipid parameters for the prediction of aortic valve calcification could help in the study of CAVD prevention and its mechanisms. Parameters commonly used clinically to assess lipids are TC, TG, LDL, HDL, APOA1, APOB, and their derivatives, including cumulative LDL exposure [ 13 ] , APOB/APOA1, non-HDL, residual cholesterol, AIP [ 14 ] . In this study, we found that cumulative LDL exposure, APOA1, APOB/APOA1 and non-HDL/HDL in lipid parameters were associated with aortic valve calcification. The APOB/APOA1 ratio is a sensitive indicator of the equilibrium between atherosclerosis-promoting and anti-atherosclerotic factors, with LDL, MDL, VLDL, and Lipoprotein α each containing 1 APOB molecule, and a change in any of these indices leading to an imbalance in the APOB/APOA1 ratio [ 15 – 16 ] . Prior research has demonstrated that the APOB/APOA1 ratio is a risk factor for cardiovascular disease and is associated with an unfavorable prognosis for cardiovascular disease [ 17 – 18 ] . APOB and APOA1 levels as predictors of cardiovascular events and all-cause mortality in patients with chronic kidney disease [ 19 ] . However, few studies have been reported on the APOB/APOA1 ratio and calcific aortic valve disease. APOB acts as a ligand for the surface receptor of LDL, transports cholesterol from the liver to the periphery and induces platelet activation, degranulation, and adhesion release to promote the inflammatory response; alternatively, natural polymorphic APOB danger-associated signaling 1 has been found to efficiently activate platelets and promote platelet-leukocyte interactions, which plays an important role in the promotion of inflammatory response by APOB [ 20 ] . ApoA1 is the main protein component in HDL, which can inhibit platelet activation, reduce clot strength and stability by inhibiting thromboxane A2 release, and bind with HDL receptor, which not only promotes reverse cholesterol transport and prevents cholesterol from being deposited abnormally and damaging the vascular endothelium, but also activates the activity of inducible nitric oxide synthase, thus maintaining endothelial cell integrity and acting as a protective agent [ 21 – 22 ] . ApoB promotes the inflammatory response whereas ApoA1 suppresses the systemic inflammatory state [ 23 ] . While the NLR ratio reflects the level of systemic inflammation, The APOB/APOA1 and NLR were significantly higher in the aortic valve calcification group than in the control group, which suggests that the APOB/APOA1 ratio is an indicator of the balance between lipid and inflammatory responses in patients with aortic valve calcification. A high APOB/APOA1 ratio suggests that the balance between ‘promotion’ and ‘inhibition’ is disrupted, which may explain the increased risk of aortic valve calcification with an elevated APOB/APOA1 ratio. Globally, there is a clear transition in the incidence of cardiovascular disease from the young to the old, with an exponential increase with age [ 24 ] . A previous large-scale survey demonstrated that the prevalence of aortic stenosis was approximately 0.4% in individuals younger than 45 years of age, 1.5% in those aged 65 years and older, and 3.4% in those aged 75 years and older [ 25 ] .As in previous studies, the results of the present study showed that the mean age of the aortic valve calcification group was significantly higher than that of the control group, suggesting an increase in the occurrence of calcific aortic valve disease with increasing age. The prevalence of diabetes is increasing year by year, and there are now more than 150 million people with diabetes globally. Diabetes is associated with the development of several cardiovascular diseases. Wang Xue's study identified coronary heart disease and diabetes mellitus as risk factors for calcific heart valve disease through the analysis of their medical history [ 26 ] . Cheng Na’ study finds diabetes is associated with the development of degenerative heart valve disease [ 27 ] . In conjunction with the results of this study it is illustrated that diabetes mellitus is positively associated with calcific aortic valve lesions and is a risk factor for the development of calcific aortic valve lesions. Blood pressure is strongly associated with the development and prognosis of many cardiovascular diseases [ 28 – 29 ] , A Mendelian randomization study [ 30 ] showed that both diastolic and systolic blood pressure were significantly associated with several cardiovascular diseases, including myocardial infarction, increasing the risk of these diseases. Zhang’s study found that low diastolic blood pressure is a risk factor for diastolic insufficiency of the heart [ 31 ] . Similar results were found in the study by Chen Yanmei [ 32 ] . In terms of pathological changes, a decrease in diastolic blood pressure results in a slowing of blood flow at the aortic valve, increasing the likelihood of stagnation. This promotes contact between blood components and the aortic valve, allowing for reactions with the valve. This results in the promotion of aortic valve calcification, which in turn leads to incomplete valve closure and the regurgitation of some ventricular blood during diastole. This further contributes to the reduction in diastolic blood pressure. Elevated diastolic blood pressure has been identified as a protective factor against calcific aortic valve disease (OR = 0.932, P < 0.05) [ 33 ] . In conjunction with the results of this study, diastolic blood pressure was negatively correlated with calcific aortic valve disease, suggesting that lower diastolic blood pressure promotes the development of calcific aortic valve disease. Cys-c is a class of low molecular weight non-glycosylated proteins and a member of the human cysteine protease inhibitor superfamily [ 34 ] . Cys-c is widely distributed in human tissue cells and blood, and the kidney is the only metabolic pathway for Cys-c, which is filtered in the glomerulus and reabsorbed and catabolized in the proximal tubule. Some studies have confirmed that Cys-c is more accurate and sensitive to the early and slight changes in glomerular filtration rate, and can be used to assess the early stage of renal function impairment [ 35 ] . And in recent years, Cys-c has been found to be valuable in the prediction of cardiovascular disease, with one study suggesting that Cys-c is independently associated with coronary artery calcification [ 36 ] . Elevated Cys-c associated with coronary atherosclerotic plaque formation in Vakili’s study [ 37 ] . Cho’s study finds Cys-c to be a valid marker for predicting cardiovascular disease progression or new onset [ 38 ] . Cys-c can inhibit tissue protease activity under physiological conditions, thus preventing the breakdown of cells by proteases. When vascular wall damage occurs, the increased release of inflammatory mediators leads to a disruption of the balance between hydrolyzing proteases and Cys-c in the vascular wall, which results in the disruption of the integrity of the arterial vasculature, thereby contributing to the formation of atherosclerosis [ 39 ] . In this study, Cys-c was found to be positively associated with CAVD, and is a risk factor for aortic valve calcification. Conclusions The lipid parameters APOA1, APOB/APOA1, cumulative LDL exposure, and Non-HDL/HDL have been demonstrated to be associated with the development of aortic valve calcification. Furthermore, age, history of diabetes mellitus, DBP, APOB/APOA1, Cys-c, and NLR have been identified as valid predictors of this condition, and thus may assist in guiding the clinical management of patients. Limitations It must be acknowledged that the present study is a single-center, small-sample, retrospective study, which may have resulted in the observed results being affected by other vascular calcifications. Consequently, it would be beneficial for future multicenter, large-sample, prospective studies to be conducted in order to confirm these findings. Abbreviations AIP Atherogenic Index of Plasma ALP Alkaline Phosphatase APOA1 Apolipoprotein A1 APOB Apolipoprotein B BMI Body Mass Index BSA Body Surface Area CAVD Calcific Aortic Valve Disease Cys-c Cystatin c DBP Diastolic Blood Pressure HbA1c Glycated Hemoglobin A1c HDL High-Density Lipoprotein IDL Intermediate-Density Lipoprotein LDH Lactate Dehydrogenase LDL Low-Density Lipoprotein LY% Lymphocyte Percentage NEUT% Neutrophil Percentage NLR Neutrophil-to-Lymphocyte Ratio SBP Systolic Blood Pressure TC Total Cholesterol TG Triglyceride UA Uric Acid VLDL Very Low-Density Lipoprotein Declarations Acknowledgements Not applicable. Author contributions conceptualization Wang yuxing: methodology; investigation; software; formal analysis; data curation; writing-original draft preparation; writing-review and editing; Yu ming: methodology; software; formal analysis; Yang song: investigation; data curation; Mei jiajie: formal analysis; Liu zhenzhu: formal analysis; Geng zhao hong: formal analysis; Xie wenli: formal analysis; Wang hongyan: supervision; Niu nan: supervision; resources; Qu peng: project administration; supervision Funding Not applicable. Clinical trial number Not applicable. Data availability The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Ethics approval has been approved by the Second Hospital of Dalian Medical University. Consent for publication We confirm that the manuscript has been read and approved for publication by all of the named authors. Competing interests The authors declare no competing interests. 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Shu S, Yang Y, Sun B, Su Z, Fu M, Xiong C, Zhang X, Hu S, Song J. Alerting trends in epidemiology for calcific aortic valve disease, 1990-2019: An age-period-cohort analysis for the Global Burden of Disease Study 2019. Eur Heart J Qual Care Clin Outcomes. 2023 Aug 7;9(5):459-473. doi: 10.1093/ehjqcco/qcad018. Yang Y, Wang Z, Chen Z, Wang X, Zhang L, Li S, Zheng C, Kang Y, Jiang L, Zhu Z, Gao R. Current status and etiology of valvular heart disease in China: a population-based survey. BMC Cardiovasc Disord. 2021 Jul 13;21(1):339. doi: 10.1186/s12872-021-02154-8. Wang X, Hou T, Du B. Analysis of risk factors associated with degenerative heart valve disease in the elderly. Disease Surv & Control,2009,(07): 417-418+396. Cheng N. Echocardiographic imaging characteristics and associated risk factors in elderly patients with degenerative heart valve disease. Chin J Gerontol, 2019,39(24):5920-5922. DOI:10.3969/j.issn.1005-9202.2019.24.006. Juraschek SP, Hu JR, Cluett JL, Ishak AM, Mita C, Lipsitz LA, Appel LJ, Beckett NS, Coleman RL, Cushman WC, Davis BR, Grandits G, Holman RR, Miller ER 3rd, Peters R, Staessen JA, Taylor AA, Thijs L, Wright JT Jr, Mukamal KJ. Orthostatic Hypotension, Hypertension Treatment, and Cardiovascular Disease: An Individual Participant Meta-Analysis. JAMA. 2023 Oct 17;330(15):1459-1471. doi: 10.1001/jama.2023.18497. Erratum in: JAMA. 2023 Nov 21;330(19):1915. doi: 10.1001/jama.2023.23332. He J, Ouyang N, Guo X, Sun G, Li Z, Mu J, Wang DW, Qiao L, Xing L, Ren G, Zhao C, Yang R, Yuan Z, Wang C, Shi C, Liu S, Miao W, Li G, Chen CS, Sun Y; CRHCP Study Group. Effectiveness of a non-physician community health-care provider-led intensive blood pressure intervention versus usual care on cardiovascular disease (CRHCP): an open-label, blinded-endpoint, cluster-randomised trial. Lancet. 2023 Mar 18;401(10380):928-938. doi: 10.1016/S0140-6736(22)02603-4. Epub 2023 Mar 2. Richardson TG, Sanderson E, Elsworth B, Tilling K, Davey Smith G. Use of genetic variation to separate the effects of early and later life adiposity on disease risk: mendelian randomisation study. BMJ. 2020 May 6;369:m1203. doi: 10.1136/bmj.m1203. Zhang TT, Zhang XY, Zhao ZW, Sun G, Niu YM, Lin J. Effect of low diastolic blood pressure on diastolic function in elderly patients with primary hypertension. J China Pharm Univ,2016,45(6):514-517,521. DOI:10.12007/j.issn.0258-4646.2016.06.009. Chen YM, Wu YT, Cao DY, Pei ZY, Dong L, Yao YQ. Correlation of diastolic blood pressure and blood pressure trough ratio with left ventricular diastolic function in elderly patients. Med Recapitulate, 2013,19(18):3395-3397. DOI:10.3969/j.issn.1006-2084.2013.18.045. Ge Xiao. Correlation study between serum ferritin level and calcific aortic stenosis. Shandong University,2023. Angelidis C, Deftereos S, Giannopoulos G, Anatoliotakis N, Bouras G, Hatzis G, Panagopoulou V, Pyrgakis V, Cleman MW. Cystatin C: an emerging biomarker in cardiovascular disease. Curr Top Med Chem. 2013;13(2):164-79. doi: 10.2174/1568026611313020006. Horio M, Imai E, Yasuda Y, Watanabe T, Matsuo S; Collaborators Developing the Japanese Equation for Estimated GFR. GFR estimation using standardized serum cystatin C in Japan. Am J Kidney Dis. 2013 Feb;61(2):197-203. doi: 10.1053/j.ajkd.2012.07.007. Epub 2012 Aug 11. Hou FJ, Wang ZZ, Wang YP, Yan RQ, Dai HY, Lin XR, Yu ZX, Guan J. Correlation study between cystatin C and coronary artery calcification. Cardiovasc Res, 2016,14(1):29-32. DOI:10.3969/j.issn.1672-5301.2016.01.008. Vakili H, Mohamadian A, Naderian M, Khaheshi I. Cystatin C may not be a precious predictor for coronary artery disease and its severity: an area of uncertainty. Acta Biomed. 2018 Jun 7;89(2):209-213. doi: 10.23750/abm.v89i2.5495. Cho YK, Kang YM, Yoo JH, Lee J, Lee SE, Yang DH, Kang JW, Park JY, Jung CH, Kim HK, Lee WJ. The impact of non-alcoholic fatty liver disease and metabolic syndrome on the progression of coronary artery calcification. Sci Rep. 2018 Aug 13;8(1):12004. doi: 10.1038/s41598-018-30465-y. Wang L, Han Q, Xie D, Diagnostic value of combined serum cystatin C, homocysteine, and triglyceride/high-density lipoprotein cholesterol ratio for coronary heart disease in the elderly [J].J Clin Exp Med, 2021, 20(9): 931-934. DOI:10.3969/j.issn.1671-4695.2021.09.010. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5364924","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":374698552,"identity":"19e36a59-eb27-4e0a-a128-f02f683bee65","order_by":0,"name":"Wang yuxing","email":"","orcid":"","institution":"The Second Hospital of Dalian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Wang","middleName":"","lastName":"yuxing","suffix":""},{"id":374698553,"identity":"94b2dea2-0b17-493d-8567-9b6acf5e98fd","order_by":1,"name":"Yu ming","email":"","orcid":"","institution":"The Second Hospital of Dalian Medical 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Hospital of Dalian Medical University","correspondingAuthor":true,"prefix":"","firstName":"Qu","middleName":"","lastName":"peng","suffix":""}],"badges":[],"createdAt":"2024-10-31 05:23:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5364924/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5364924/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":70401990,"identity":"a72d315c-198f-4ded-ba02-f5133201ddcb","added_by":"auto","created_at":"2024-12-02 22:27:27","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":90451,"visible":true,"origin":"","legend":"\u003cp\u003eROC curves for aortic valve calcification and various risk factors\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5364924/v1/48522b48cdb6c85a6abb2a31.png"},{"id":86272660,"identity":"d772094a-32fe-4565-8036-53eb3b9c53f4","added_by":"auto","created_at":"2025-07-08 17:50:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":831458,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5364924/v1/f126c304-d758-46ac-acad-f4718bbaafb7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Value of APOB/APOA1 ratio in prediction of calcific aortic valve disease","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCalcific Aortic Valve Disease (CAVD) is a prevalent form of valvular heart disease, ranking as the third most common cardiovascular disorder after coronary heart disease and hypertension \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. The early presentation of CAVD is atherosclerosis of the aortic valve leaflets, with a prevalence of calcification or sclerosis of the aortic valve of 20\u0026ndash;30% in individuals over the age of 65 and 48% in those over the age of 85 \u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Severe calcification of the aortic valve in advanced stages results in the development of aortic stenosis, which in turn leads to obstruction of the left ventricular outflow tract. This, in turn, leads to heart failure, for which surgical valve replacement is the only effective treatment. Liu Li \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e found that the prevalence of valvular calcification was 13.4% in a random sample of the elderly population in Beijing, 7.7% in those aged 60 years or older, 16.1% in those aged 70 years or older, and 25.7% in those aged 80 to 89 years.\u003c/p\u003e \u003cp\u003eThe notion that CAVD is merely a passive degenerative change associated with age has long been a widely held view. Recent studies have provided new insights into the pathogenesis of calcific aortic stenosis, indicating that it is an active progressive disease influenced by multiple risk factors. Epidemiological studies have demonstrated a strong correlation between age, dyslipidemia, and diabetes mellitus and the development of calcific aortic stenosis. Histopathological studies have revealed that calcific aortic stenosis involves inflammation, abnormal lipid metabolism, matrix remodeling, and calcification.\u003c/p\u003e \u003cp\u003eCAVD shares numerous similarities with atherosclerosis, both in terms of risk factors and pathological changes. Indeed, it has been postulated that CAVD represents an additional manifestation of atherosclerosis \u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Lipids play a significant role in the pathogenesis of calcific aortic valve disease. While the majority of lipid management guidelines identify low-density lipoprotein (LDL) as the causative lipid component and have demonstrated that LDL reduction can reduce the risk of cardiovascular events, attempts have been made to block or slow the progression of calcific aortic valve disease with statin therapy. The results of early studies in animal models have indicated that hypercholesterolemia can result in the development of aortic valve sclerosis and hemodynamic changes. Additionally, retrospective studies have suggested that statins may potentially slow the progression of valve calcification and stenosis. However, despite these findings, large-scale, prospective, controlled trials have not consistently demonstrated that statins are effective in slowing or preventing the onset and progression of calcific aortic stenosis \u003csup\u003e[\u003cspan additionalcitationids=\"CR6 CR7\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eApolipoprotein B (APOB) has also been identified as a risk-predicting biomarker in a study conducted by the UK Biobank. This study revealed that APOB is responsible for the transportation of a vast array of potentially atherogenic cholesterol, including very low-density lipoprotein (VLDL), intermediate-density lipoprotein (IDL), and low-density lipoprotein (LDL). Conversely, high-density lipoprotein (HDL) is transported by apolipoprotein A1 (APOA1) \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. In the context of various lipid abnormalities, a multitude of factors influence the equilibrium between pro- and anti-atherosclerosis. The most utilized indicators in the clinical assessment of lipids encompass total cholesterol (TC), triglyceride (TG), LDL, HDL, and, in recent years, APOA1 and APOB. The importance of this in dyslipidemia is evidenced by the fact that, despite a reduction in LDL levels, there is still a significant residual risk of cardiovascular events. This suggests that other components of lipids, in addition to LDL, may also play a role in the development of cardiovascular disease. The search for more sensitive and accurate lipid parameters for risk prediction of cardiovascular events could help in the prevention of such diseases, and therefore a series of non-traditional lipid parameters have been derived from lipids, including cumulative exposure to LDL, APOB/APOA1 ratio, non-HDL, residual cholesterol, and atherogenic index of plasma (AIP), which are all important factors in the development of cardiovascular disease.\u003c/p\u003e \u003cp\u003eTherefore, the objective of this study is threefold: firstly, to examine the risk factors associated with CAVD; secondly, to investigate the relationship between lipids and CAVD; and thirdly, to construct a prediction model for CAVD and to evaluate its value.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThis is a retrospective study, in which patients who first visited the Department of Cardiology of the Second Hospital of Dalian Medical University between 1 January 2023 and 31 December 2023 are being retrospectively collected. All patients underwent a comprehensive chest CT scan upon admission, and 312 patients were ultimately included in the study after the exclusion of individuals with comorbidities such as pregnancy, severe hepatic and renal insufficiency, hematological diseases, tumors, autoimmune disorders, inflammatory diseases, hyperthyroidism, hypothyroidism, rheumatic heart disease, cardiomyopathy, dilated cardiomyopathy, and myocarditis. Additionally, individuals with a history of coronary stenting, coronary artery bypass grafting, or long-term oral lipid-lowering medications, such as statins, were excluded. Agatston established a minimum threshold of CT for calcified areas at 130 Hu, defining the area with CT values exceeding this threshold as calcified. This was further divided into 111 cases in the calcified group and 201 cases in the control group based on the presence or absence of aortic valve calcification in CT. The general information of the two groups was also collected, including gender. The general data, including gender, age, height, weight, body mass index (BMI), body surface area (BSA), history of hypertension, history of diabetes, history of coronary heart disease, etc., were collected from the two groups. The results of blood tests, including those pertaining to lipids, were collated from the patients. Thereafter, lipid-derived parameters were calculated, along with the Agatston Calcification Score. The correlation between aortic valve calcification and lipids and their derived parameters was investigated by comparing the differences in lipids and their derived parameters between the two groups. A CAVD prediction model was constructed using Spearman\u0026apos;s correlation analysis, binary logistic regression and the ROC curve, and the model was evaluated for sensitivity and specificity. This study was approved by the Ethics Committee of the Second Affiliated Hospital of Dalian Medical University.\u003c/p\u003e\n\u003cp\u003eTable 1 calculation method of Agatston Calcification Score\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 75%;\"\u003e\n \u003cp\u003eAgatston Calcification Score = \u0026sum; area of calcification in each CT cross-section * coefficient corresponding to the maximum CT value of the calcified plaque\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13%;\"\u003e\n \u003cp\u003eMaximum CT value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003eModulus\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 75%;\"\u003e\n \u003cp\u003eCalculate the area of calcification in each cross-section\u003c/p\u003e\n \u003cp\u003eCalcification Score of the cross-section = plaque area * coefficient corresponding to maximum CT value\u003c/p\u003e\n \u003cp\u003eAgatston Calcification Score=\u0026nbsp;sum of calcification integrals for each cross-section\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13%;\"\u003e\n \u003cp\u003e130-199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13%;\"\u003e\n \u003cp\u003e200-299\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13%;\"\u003e\n \u003cp\u003e300-399\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13%;\"\u003e\n \u003cp\u003e\u0026ge;400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eStatistical analyses\u003c/h2\u003e\n \u003cp\u003eThe statistical analysis of the obtained data was conducted using the SPSS 26.0 software package. The count data were expressed by a constitutive ratio, and a X\u003csup\u003e2\u003c/sup\u003e test was employed. The measurement data were tested for normality using a S-W test and a Q-Q plot. If they conformed to normality, they were expressed by the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (X\u0026thinsp;\u0026plusmn;\u0026thinsp;SD). If the data did not conform to normality, they were expressed by M (Q25, Q75) and the nonparametric test was used. If the data conformed to normal distribution and met the chi-square, an independent samples t-test analysis was conducted. If the data met the normal distribution but did not meet the chi-square, an t\u0026rsquo;-test was performed. The correlation was analyzed using Spearman\u0026apos;s correlation coefficient, and factors with a p-value less than 0.05 were subjected to binary logistic regression (stepwise). The indexes with a p-value less than 0.05 were included in the analysis and plotted on the receiver operating characteristic curve. A statistically significant difference was observed when p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e1.\u0026nbsp;The difference in age, history of diabetes mellitus, diastolic blood pressure (DBP) was statistically significant in the calcification group when compared to the control group. While the difference in smoking, alcohol consumption, history of hypertension, history of coronary heart disease, height, weight, BMI, BSA, systolic blood pressure (SBP) was not statistically significant.\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;2\u0026nbsp;Comparison of baseline information between the calcified and control groups\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eItem\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eCalcification\u003c/p\u003e\n \u003cp\u003egroup\u003c/p\u003e\n \u003cp\u003eN=111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4414%;\"\u003e\n \u003cp\u003eControl\u003c/p\u003e\n \u003cp\u003eGroup\u003c/p\u003e\n \u003cp\u003eN=201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.5586%;\"\u003e\n \u003cp\u003eTest Value（t/t＇/X\u003csup\u003e2\u003c/sup\u003e）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eMeal（n, %）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e(70,63.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4414%;\"\u003e\n \u003cp\u003e(109,54.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.5586%;\"\u003e\n \u003cp\u003e2.282\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e0.131\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eSmoking（n, %）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e(44,39.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4414%;\"\u003e\n \u003cp\u003e(88,43.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.5586%;\"\u003e\n \u003cp\u003e0.502\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e0.478\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eDrinking（n, %）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e(21,18.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4414%;\"\u003e\n \u003cp\u003e(33,16.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.5586%;\"\u003e\n \u003cp\u003e0.313\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e0.576\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eHypertensive（n, %）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e(76,68.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4414%;\"\u003e\n \u003cp\u003e(125,62.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.5586%;\"\u003e\n \u003cp\u003e1.230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e0.267\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eDiabetes（n, %）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e(49,44.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4414%;\"\u003e\n \u003cp\u003e(48,24.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.5586%;\"\u003e\n \u003cp\u003e13.497\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eCoronary Heart Disease（n, %）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e(67,60.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4414%;\"\u003e\n \u003cp\u003e(100,49.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.5586%;\"\u003e\n \u003cp\u003e3.236\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e0.072\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eAge（year）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e68.09\u0026plusmn;9.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4414%;\"\u003e\n \u003cp\u003e60.97\u0026plusmn;11.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.5586%;\"\u003e\n \u003cp\u003e-5.773\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eHeight（m）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e1.66\u0026plusmn;0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4414%;\"\u003e\n \u003cp\u003e1.67\u0026plusmn;0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.5586%;\"\u003e\n \u003cp\u003e0.414\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e0.679\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eWeight（kg）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e72.04\u0026plusmn;13.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4414%;\"\u003e\n \u003cp\u003e73.23\u0026plusmn;13.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.5586%;\"\u003e\n \u003cp\u003e0.741\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e0.459\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eBMI（kg/m\u003csup\u003e2\u003c/sup\u003e）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e27.03\u0026plusmn;14.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4414%;\"\u003e\n \u003cp\u003e26.27\u0026plusmn;3.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.5586%;\"\u003e\n \u003cp\u003e-0.718\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e0.473\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eBSA（m\u003csup\u003e2\u003c/sup\u003e）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e1.78\u0026plusmn;0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4414%;\"\u003e\n \u003cp\u003e1.80\u0026plusmn;0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.5586%;\"\u003e\n \u003cp\u003e0.735\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e0.463\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eSBP（mmHg）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e136.69\u0026plusmn;20.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4414%;\"\u003e\n \u003cp\u003e138.55\u0026plusmn;18.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.5586%;\"\u003e\n \u003cp\u003e0.819\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e0.414\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eDBP（mmHg）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e81.59\u0026plusmn;12.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4414%;\"\u003e\n \u003cp\u003e86.68\u0026plusmn;11.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.5586%;\"\u003e\n \u003cp\u003e3.591\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e2．Compared with the control group, the differences in blood glucose, glycated hemoglobin A1c (HbA1c), LDL cumulative exposure, APOA1, APOB/APOA1, Non-HDL/HDL, urea, Cystatin c (Cys-c), Sodium, Chlorine, Calcium, neutrophil percentage (NEUT%), lymphocyte percentage (LY%), neutrophil-to-lymphocyte ratio (NLR), D-dimer were statistically significant, and the patients in the calcification group possessed higher blood glucose, HbA1c, LDL cumulative exposure, APOB/APOA1, Non-HDL/HDL, urea, Cys-c, WBC, NEUT%, NLR, and D-dimer. amount, APOB/APOA1, Non-HDL/HDL, urea, Cys-c, WBC, NEUT%, NLR, and D-dimer, while Sodium, Chlorine, Calcium, APOA1, and LY% were lower than in the control group. And the differences in TC, TG, LDL, HDL, APOB, Non-HDL, residual cholesterol, AIP, Creatinine, uric acid (UA), alkaline phosphatase (ALP), lactate dehydrogenase (LDH) potassium were not statistically significant.\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;3\u0026nbsp;Comparison of laboratory findings between patients in the calcified and control groups\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eItem\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eCalcification\u003c/p\u003e\n \u003cp\u003egroup\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4414%;\"\u003e\n \u003cp\u003eControl\u003c/p\u003e\n \u003cp\u003eGroup\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.5586%;\"\u003e\n \u003cp\u003etest value（t/t＇/X\u003csup\u003e2\u003c/sup\u003e）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eBlood glucose（mmol/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e6.66\u0026plusmn;2.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4414%;\"\u003e\n \u003cp\u003e5.88\u0026plusmn;1.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.5586%;\"\u003e\n \u003cp\u003e-2.739\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eHbA1c（mmol/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e6.82\u0026plusmn;1.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4414%;\"\u003e\n \u003cp\u003e6.24\u0026plusmn;1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.5586%;\"\u003e\n \u003cp\u003e-3.243\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eTC（mmol/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e4.76\u0026plusmn;0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4414%;\"\u003e\n \u003cp\u003e4.63\u0026plusmn;0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.5586%;\"\u003e\n \u003cp\u003e-1.244\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e0.215\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eTG（mmol/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e1.51\u0026plusmn;0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4414%;\"\u003e\n \u003cp\u003e1.49\u0026plusmn;0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.5586%;\"\u003e\n \u003cp\u003e-0.328\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e0.743\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eHDL（mmol/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e1.07\u0026plusmn;0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4414%;\"\u003e\n \u003cp\u003e1.11\u0026plusmn;0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.5586%;\"\u003e\n \u003cp\u003e1.458\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e0.146\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eLDL（mmol/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e2.97\u0026plusmn;0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4414%;\"\u003e\n \u003cp\u003e2.82\u0026plusmn;0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.5586%;\"\u003e\n \u003cp\u003e-1.771\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e0.078\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eCumulative LDL exposure（mmol/L\u0026middot;year）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e201.29\u0026plusmn;55.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4414%;\"\u003e\n \u003cp\u003e171.63\u0026plusmn;49.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.5586%;\"\u003e\n \u003cp\u003e-4.894\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eAPOA1（mmol/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e1.34\u0026plusmn;0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4414%;\"\u003e\n \u003cp\u003e1.4\u0026plusmn;0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.5586%;\"\u003e\n \u003cp\u003e2.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eAPOB（mmol/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e0.88\u0026plusmn;0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4414%;\"\u003e\n \u003cp\u003e0.84\u0026plusmn;0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.5586%;\"\u003e\n \u003cp\u003e-1.914\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eAPOB/APOA1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e0.68\u0026plusmn;0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4414%;\"\u003e\n \u003cp\u003e0.62\u0026plusmn;0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.5586%;\"\u003e\n \u003cp\u003e-2.878\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eNon-HDL（mmol/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e3.69\u0026plusmn;0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4414%;\"\u003e\n \u003cp\u003e3.52\u0026plusmn;0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.5586%;\"\u003e\n \u003cp\u003e-1.718\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eNon-HDL/HDL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e3.71\u0026plusmn;1.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4414%;\"\u003e\n \u003cp\u003e3.37\u0026plusmn;1.119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.5586%;\"\u003e\n \u003cp\u003e-2.252\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eResidual cholesterol（mmol/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e0.73\u0026plusmn;0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4414%;\"\u003e\n \u003cp\u003e0.70\u0026plusmn;0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.5586%;\"\u003e\n \u003cp\u003e-0.815\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e0.416\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eAIP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e0.12\u0026plusmn;0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4414%;\"\u003e\n \u003cp\u003e0.11\u0026plusmn;0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.5586%;\"\u003e\n \u003cp\u003e-0.408\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e0.683\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eUrea（mmol/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e6.11\u0026plusmn;1.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4414%;\"\u003e\n \u003cp\u003e5.71\u0026plusmn;1.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.5586%;\"\u003e\n \u003cp\u003e-2.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eCreatinine（mmol/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e69.53\u0026plusmn;14.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4414%;\"\u003e\n \u003cp\u003e66.73\u0026plusmn;14.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.5586%;\"\u003e\n \u003cp\u003e-1.626\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eUA（mmol/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e352.21\u0026plusmn;88.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4414%;\"\u003e\n \u003cp\u003e339.83\u0026plusmn;86.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.5586%;\"\u003e\n \u003cp\u003e-1.205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e0.229\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eCys-c（mmol/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e1.11\u0026plusmn;0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4414%;\"\u003e\n \u003cp\u003e1.01\u0026plusmn;0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.5586%;\"\u003e\n \u003cp\u003e-4.274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eALP（U/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e78.78\u0026plusmn;26.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4414%;\"\u003e\n \u003cp\u003e75.73\u0026plusmn;20.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.5586%;\"\u003e\n \u003cp\u003e-1.120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e0.263\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eLDH\u003c/p\u003e\n \u003cp\u003e（mmol/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e221.79\u0026plusmn;146.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4414%;\"\u003e\n \u003cp\u003e196.39\u0026plusmn;93.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.5586%;\"\u003e\n \u003cp\u003e-1.654\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e0.100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003ePotassium（mmol/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e3.97\u0026plusmn;0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4414%;\"\u003e\n \u003cp\u003e3.96\u0026plusmn;0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.5586%;\"\u003e\n \u003cp\u003e-0.473\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e0.636\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eSodium（mmol/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e141.33\u0026plusmn;2.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4414%;\"\u003e\n \u003cp\u003e142.09\u0026plusmn;2.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.5586%;\"\u003e\n \u003cp\u003e2.339\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eChlorine（mmol/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e106.32\u0026plusmn;2.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4414%;\"\u003e\n \u003cp\u003e107.06\u0026plusmn;2.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.5586%;\"\u003e\n \u003cp\u003e2.429\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eCalcium（mmol/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e2.22\u0026plusmn;0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4414%;\"\u003e\n \u003cp\u003e2.26\u0026plusmn;0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.5586%;\"\u003e\n \u003cp\u003e2.599\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eWBC（x10\u003csup\u003e9\u003c/sup\u003e/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e6.51\u0026plusmn;1.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4414%;\"\u003e\n \u003cp\u003e6.46\u0026plusmn;1.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.5586%;\"\u003e\n \u003cp\u003e-0.196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e0.845\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eNEUT%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e66.01\u0026plusmn;8.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4414%;\"\u003e\n \u003cp\u003e62.31\u0026plusmn;8.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.5586%;\"\u003e\n \u003cp\u003e-3.694\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eLY%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e25.00\u0026plusmn;7.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4414%;\"\u003e\n \u003cp\u003e29.15\u0026plusmn;7.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.5586%;\"\u003e\n \u003cp\u003e4.735\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eNLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e3.03\u0026plusmn;1.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4414%;\"\u003e\n \u003cp\u003e2.36\u0026plusmn;0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.5586%;\"\u003e\n \u003cp\u003e-4.906\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eD-dimer(ug/ml)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e0.64\u0026plusmn;0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4414%;\"\u003e\n \u003cp\u003e0.57\u0026plusmn;0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.5586%;\"\u003e\n \u003cp\u003e-2.488\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e3．Spearman correlation results showed that aortic valve calcification correlated with age, history of coronary artery disease, history of diabetes mellitus, diastolic blood pressure, blood glucose, HbA1c, cumulative LDL exposure, APOA1, APOB/APOA1, Non-HDL/HDL, Cys-c, LDH, Sodium, Chlorine, Calcium, NEUT%, LY%, NLR, D-dimer. The Agatston score for aortic valve calcification correlated with age, history of coronary artery disease, history of diabetes mellitus, diastolic blood pressure, blood glucose, HbA1c, cumulative LDL exposure, Non-HDL/HDL, APOB/APOA1, urea, Cys-c, LDH, Sodium, Chlorine, NEUT%, LY%, NLR, and D-dimer.\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;4\u0026nbsp;Correlation analysis of aortic valve calcification and Agatston score\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 24%;\"\u003e\n \u003cp\u003eItem\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 42%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 32%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22%;\"\u003e\n \u003cp\u003ecorrelation coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16%;\"\u003e\n \u003cp\u003ecorrelation coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16%;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24%;\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22%;\"\u003e\n \u003cp\u003e0.086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e0.066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16%;\"\u003e\n \u003cp\u003e0.091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16%;\"\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24%;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22%;\"\u003e\n \u003cp\u003e0.313\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16%;\"\u003e\n \u003cp\u003e0.332\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24%;\"\u003e\n \u003cp\u003eHeight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22%;\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e0.319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16%;\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16%;\"\u003e\n \u003cp\u003e0.334\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24%;\"\u003e\n \u003cp\u003eWeight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22%;\"\u003e\n \u003cp\u003e-0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e0.407\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16%;\"\u003e\n \u003cp\u003e-0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16%;\"\u003e\n \u003cp\u003e0.320\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24%;\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22%;\"\u003e\n \u003cp\u003e-0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e0.259\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16%;\"\u003e\n \u003cp\u003e-0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16%;\"\u003e\n \u003cp\u003e0.176\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24%;\"\u003e\n \u003cp\u003eBSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22%;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e0.472\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16%;\"\u003e\n \u003cp\u003e-0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16%;\"\u003e\n \u003cp\u003e0.457\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24%;\"\u003e\n \u003cp\u003eSmoking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22%;\"\u003e\n \u003cp\u003e-0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e0.240\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16%;\"\u003e\n \u003cp\u003e-0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16%;\"\u003e\n \u003cp\u003e0.185\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24%;\"\u003e\n \u003cp\u003eDrinking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22%;\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e0.289\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16%;\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16%;\"\u003e\n \u003cp\u003e0.255\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24%;\"\u003e\n \u003cp\u003eCoronary Heart Disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22%;\"\u003e\n \u003cp\u003e0.102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16%;\"\u003e\n \u003cp\u003e0.110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16%;\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24%;\"\u003e\n \u003cp\u003eHypertensive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22%;\"\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e0.134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16%;\"\u003e\n \u003cp\u003e0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16%;\"\u003e\n \u003cp\u003e0.160\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24%;\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22%;\"\u003e\n \u003cp\u003e0.208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16%;\"\u003e\n \u003cp\u003e0.210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24%;\"\u003e\n \u003cp\u003eSBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22%;\"\u003e\n \u003cp\u003e-0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e0.133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16%;\"\u003e\n \u003cp\u003e-0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16%;\"\u003e\n \u003cp\u003e0.228\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24%;\"\u003e\n \u003cp\u003eDBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22%;\"\u003e\n \u003cp\u003e-0.223\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16%;\"\u003e\n \u003cp\u003e-0.220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24%;\"\u003e\n \u003cp\u003eBlood glucose\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22%;\"\u003e\n \u003cp\u003e0.126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16%;\"\u003e\n \u003cp\u003e0.111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16%;\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24%;\"\u003e\n \u003cp\u003eHbA1c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22%;\"\u003e\n \u003cp\u003e0.159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16%;\"\u003e\n \u003cp\u003e0.154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16%;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24%;\"\u003e\n \u003cp\u003eTC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22%;\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e0.199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16%;\"\u003e\n \u003cp\u003e0.060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16%;\"\u003e\n \u003cp\u003e0.145\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24%;\"\u003e\n \u003cp\u003eTG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22%;\"\u003e\n \u003cp\u003e-0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e0.131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16%;\"\u003e\n \u003cp\u003e-0.060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16%;\"\u003e\n \u003cp\u003e0.145\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24%;\"\u003e\n \u003cp\u003eHDL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22%;\"\u003e\n \u003cp\u003e-0.084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e0.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16%;\"\u003e\n \u003cp\u003e-0.075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16%;\"\u003e\n \u003cp\u003e0.094\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24%;\"\u003e\n \u003cp\u003eLDL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22%;\"\u003e\n \u003cp\u003e0.070\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e0.108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16%;\"\u003e\n \u003cp\u003e0.078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16%;\"\u003e\n \u003cp\u003e0.086\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24%;\"\u003e\n \u003cp\u003eCumulative LDL exposure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22%;\"\u003e\n \u003cp\u003e0.257\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16%;\"\u003e\n \u003cp\u003e0.275\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24%;\"\u003e\n \u003cp\u003eAPOA1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22%;\"\u003e\n \u003cp\u003e-0.112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16%;\"\u003e\n \u003cp\u003e-0.093\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16%;\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24%;\"\u003e\n \u003cp\u003eAPOB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22%;\"\u003e\n \u003cp\u003e0.078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e0.084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16%;\"\u003e\n \u003cp\u003e0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16%;\"\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24%;\"\u003e\n \u003cp\u003eAPOB/APOA1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22%;\"\u003e\n \u003cp\u003e0.139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16%;\"\u003e\n \u003cp\u003e0.135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16%;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24%;\"\u003e\n \u003cp\u003eNon-HDL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22%;\"\u003e\n \u003cp\u003e0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e0.098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16%;\"\u003e\n \u003cp\u003e0.083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16%;\"\u003e\n \u003cp\u003e0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24%;\"\u003e\n \u003cp\u003eNon-HDL/HDL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22%;\"\u003e\n \u003cp\u003e0.114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16%;\"\u003e\n \u003cp\u003e0.112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16%;\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24%;\"\u003e\n \u003cp\u003eResidual Cholesterol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22%;\"\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e0.216\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16%;\"\u003e\n \u003cp\u003e0.060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16%;\"\u003e\n \u003cp\u003e0.146\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24%;\"\u003e\n \u003cp\u003eAIP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22%;\"\u003e\n \u003cp\u003e-0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e0.478\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16%;\"\u003e\n \u003cp\u003e-0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16%;\"\u003e\n \u003cp\u003e0.437\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24%;\"\u003e\n \u003cp\u003eUrea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22%;\"\u003e\n \u003cp\u003e0.091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16%;\"\u003e\n \u003cp\u003e0.106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16%;\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24%;\"\u003e\n \u003cp\u003eCreatinine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22%;\"\u003e\n \u003cp\u003e0.090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16%;\"\u003e\n \u003cp\u003e0.082\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16%;\"\u003e\n \u003cp\u003e0.075\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24%;\"\u003e\n \u003cp\u003eUA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22%;\"\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e0.133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16%;\"\u003e\n \u003cp\u003e0.050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16%;\"\u003e\n \u003cp\u003e0.188\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24%;\"\u003e\n \u003cp\u003eCys-c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22%;\"\u003e\n \u003cp\u003e0.242\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16%;\"\u003e\n \u003cp\u003e0.234\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24%;\"\u003e\n \u003cp\u003eALP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22%;\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e0.283\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16%;\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16%;\"\u003e\n \u003cp\u003e0.332\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24%;\"\u003e\n \u003cp\u003eLDH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22%;\"\u003e\n \u003cp\u003e0.114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16%;\"\u003e\n \u003cp\u003e0.107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16%;\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24%;\"\u003e\n \u003cp\u003ePotassium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22%;\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e0.408\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16%;\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16%;\"\u003e\n \u003cp\u003e0.295\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24%;\"\u003e\n \u003cp\u003eSodium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22%;\"\u003e\n \u003cp\u003e-0.101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16%;\"\u003e\n \u003cp\u003e0.099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16%;\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24%;\"\u003e\n \u003cp\u003eChlorine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22%;\"\u003e\n \u003cp\u003e-0.130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16%;\"\u003e\n \u003cp\u003e-0.138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16%;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24%;\"\u003e\n \u003cp\u003eCalcium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22%;\"\u003e\n \u003cp\u003e-0.113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16%;\"\u003e\n \u003cp\u003e-0.088\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16%;\"\u003e\n \u003cp\u003e0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24%;\"\u003e\n \u003cp\u003eWBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22%;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e0.453\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16%;\"\u003e\n \u003cp\u003e-0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16%;\"\u003e\n \u003cp\u003e0.453\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24%;\"\u003e\n \u003cp\u003eNEUT%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22%;\"\u003e\n \u003cp\u003e0.194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16%;\"\u003e\n \u003cp\u003e0.218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24%;\"\u003e\n \u003cp\u003eLY%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22%;\"\u003e\n \u003cp\u003e-0.250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16%;\"\u003e\n \u003cp\u003e-0.268\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24%;\"\u003e\n \u003cp\u003eNLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22%;\"\u003e\n \u003cp\u003e0.241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16%;\"\u003e\n \u003cp\u003e0.262\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24%;\"\u003e\n \u003cp\u003eD-\u0026nbsp;dimer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22%;\"\u003e\n \u003cp\u003e0.100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16%;\"\u003e\n \u003cp\u003e0.102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16%;\"\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e4．The factors associated with aortic valve calcification were subjected to binary logistic regression, and the results demonstrated that age, history of diabetes mellitus, DBP, APOB/APOA1, Cys-c, and NLR were independent risk factors for aortic valve calcification. Upon standardization of the data, it was observed that an increase of one standard deviation in age, history of diabetes mellitus, DBP, APOB/APOA1, Cys-c, and NLR resulted in a 0.638, 0.387, -0.410, 0.357, 0.322, and 0.555 standard deviation increase in aortic valve calcification, respectively.\u003c/p\u003e\n\u003cp\u003eTable 5 Binary logistic regression analysis of aortic valve calcification\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 108px;\"\u003e\n \u003cp\u003eItem\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 224px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 222px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-6.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e-0.776\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e0.638\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.835\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e0.387\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eDBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e-0.410\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eAPOB/APOA1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1.827\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e0.357\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eCys-c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1.673\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e0.322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eNLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.464\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e0.555\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e5．The ROC analysis included the variables age, history of diabetes, DBP, APOB/APOA1, Cys-c, and NLR. The areas under the curve for these variables were 0.679, 0.600, 0.641, 0.583, 0.645, and 0.647, the area under the curve for the combined prediction of aortic valve calcification by the above indexes was 0.796, and the maximum Uden index of this prediction model was 0.522, corresponding to a sensitivity of 0.769 and a specificity of 0.754.\u003c/p\u003e\n\u003cp\u003eTable 6 ROC curves for aortic valve calcification and various risk factors\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 111px;\"\u003e\n \u003cp\u003eItem\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 111px;\"\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 221px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 111px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003eLower limits\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003eUpper limits\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003eCombine model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e0.796\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e0.743\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e0.850\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e0.679\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e0.616\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e0.742\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e0.600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e0.532\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e0.668\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003eDBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e0.641\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e0.575\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e0.707\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003eAPOB/APOA1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e0.583\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e0.516\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e0.649\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003eCys-c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e0.645\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e0.578\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e0.712\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003eNLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e0.647\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e0.582\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e0.712\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Discussion","content":"\u003cp\u003eCAVD is a prevalent form of heart valve disease across the globe. It is characterized by the progressive fibrosis and calcification of the aortic valve. In its early stages, the disease presents with leaflet thickening and mild calcification. However, as the calcification progresses, the aortic valve develops stenosis, which impedes the heart's pumping function. If left untreated, the disease progresses rapidly, resulting in a poor prognosis. Surgical intervention remains the only viable treatment option \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. The pathology of this condition is complex and shares numerous similarities with atherosclerosis. It involves several pathological processes, including chronic inflammation, disorders of lipid metabolism, fibrotic remodeling, and calcification \u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. Although lipids are primarily associated with the pathogenesis of CAVD, statins have not shown consistent results in improving aortic stenosis according to the literature \u003csup\u003e[\u003cspan additionalcitationids=\"CR6 CR7\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. Therefore, the search for more sensitive and accurate lipid parameters for the prediction of aortic valve calcification could help in the study of CAVD prevention and its mechanisms. Parameters commonly used clinically to assess lipids are TC, TG, LDL, HDL, APOA1, APOB, and their derivatives, including cumulative LDL exposure \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e, APOB/APOA1, non-HDL, residual cholesterol, AIP \u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn this study, we found that cumulative LDL exposure, APOA1, APOB/APOA1 and non-HDL/HDL in lipid parameters were associated with aortic valve calcification. The APOB/APOA1 ratio is a sensitive indicator of the equilibrium between atherosclerosis-promoting and anti-atherosclerotic factors, with LDL, MDL, VLDL, and Lipoprotein α each containing 1 APOB molecule, and a change in any of these indices leading to an imbalance in the APOB/APOA1 ratio \u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. Prior research has demonstrated that the APOB/APOA1 ratio is a risk factor for cardiovascular disease and is associated with an unfavorable prognosis for cardiovascular disease \u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. APOB and APOA1 levels as predictors of cardiovascular events and all-cause mortality in patients with chronic kidney disease \u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. However, few studies have been reported on the APOB/APOA1 ratio and calcific aortic valve disease. APOB acts as a ligand for the surface receptor of LDL, transports cholesterol from the liver to the periphery and induces platelet activation, degranulation, and adhesion release to promote the inflammatory response; alternatively, natural polymorphic APOB danger-associated signaling 1 has been found to efficiently activate platelets and promote platelet-leukocyte interactions, which plays an important role in the promotion of inflammatory response by APOB \u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. ApoA1 is the main protein component in HDL, which can inhibit platelet activation, reduce clot strength and stability by inhibiting thromboxane A2 release, and bind with HDL receptor, which not only promotes reverse cholesterol transport and prevents cholesterol from being deposited abnormally and damaging the vascular endothelium, but also activates the activity of inducible nitric oxide synthase, thus maintaining endothelial cell integrity and acting as a protective agent \u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. ApoB promotes the inflammatory response whereas ApoA1 suppresses the systemic inflammatory state \u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. While the NLR ratio reflects the level of systemic inflammation, The APOB/APOA1 and NLR were significantly higher in the aortic valve calcification group than in the control group, which suggests that the APOB/APOA1 ratio is an indicator of the balance between lipid and inflammatory responses in patients with aortic valve calcification. A high APOB/APOA1 ratio suggests that the balance between \u0026lsquo;promotion\u0026rsquo; and \u0026lsquo;inhibition\u0026rsquo; is disrupted, which may explain the increased risk of aortic valve calcification with an elevated APOB/APOA1 ratio.\u003c/p\u003e \u003cp\u003eGlobally, there is a clear transition in the incidence of cardiovascular disease from the young to the old, with an exponential increase with age \u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. A previous large-scale survey demonstrated that the prevalence of aortic stenosis was approximately 0.4% in individuals younger than 45 years of age, 1.5% in those aged 65 years and older, and 3.4% in those aged 75 years and older\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e.As in previous studies, the results of the present study showed that the mean age of the aortic valve calcification group was significantly higher than that of the control group, suggesting an increase in the occurrence of calcific aortic valve disease with increasing age.\u003c/p\u003e \u003cp\u003eThe prevalence of diabetes is increasing year by year, and there are now more than 150\u0026nbsp;million people with diabetes globally. Diabetes is associated with the development of several cardiovascular diseases. Wang Xue's study identified coronary heart disease and diabetes mellitus as risk factors for calcific heart valve disease through the analysis of their medical history \u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. Cheng Na\u0026rsquo; study finds diabetes is associated with the development of degenerative heart valve disease \u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. In conjunction with the results of this study it is illustrated that diabetes mellitus is positively associated with calcific aortic valve lesions and is a risk factor for the development of calcific aortic valve lesions.\u003c/p\u003e \u003cp\u003eBlood pressure is strongly associated with the development and prognosis of many cardiovascular diseases \u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e, A Mendelian randomization study \u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e showed that both diastolic and systolic blood pressure were significantly associated with several cardiovascular diseases, including myocardial infarction, increasing the risk of these diseases. Zhang\u0026rsquo;s study found that low diastolic blood pressure is a risk factor for diastolic insufficiency of the heart \u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. Similar results were found in the study by Chen Yanmei \u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. In terms of pathological changes, a decrease in diastolic blood pressure results in a slowing of blood flow at the aortic valve, increasing the likelihood of stagnation. This promotes contact between blood components and the aortic valve, allowing for reactions with the valve. This results in the promotion of aortic valve calcification, which in turn leads to incomplete valve closure and the regurgitation of some ventricular blood during diastole. This further contributes to the reduction in diastolic blood pressure. Elevated diastolic blood pressure has been identified as a protective factor against calcific aortic valve disease (OR\u0026thinsp;=\u0026thinsp;0.932, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) \u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e. In conjunction with the results of this study, diastolic blood pressure was negatively correlated with calcific aortic valve disease, suggesting that lower diastolic blood pressure promotes the development of calcific aortic valve disease.\u003c/p\u003e \u003cp\u003eCys-c is a class of low molecular weight non-glycosylated proteins and a member of the human cysteine protease inhibitor superfamily \u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e. Cys-c is widely distributed in human tissue cells and blood, and the kidney is the only metabolic pathway for Cys-c, which is filtered in the glomerulus and reabsorbed and catabolized in the proximal tubule. Some studies have confirmed that Cys-c is more accurate and sensitive to the early and slight changes in glomerular filtration rate, and can be used to assess the early stage of renal function impairment \u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e. And in recent years, Cys-c has been found to be valuable in the prediction of cardiovascular disease, with one study suggesting that Cys-c is independently associated with coronary artery calcification \u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. Elevated Cys-c associated with coronary atherosclerotic plaque formation in Vakili\u0026rsquo;s study \u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e. Cho\u0026rsquo;s study finds Cys-c to be a valid marker for predicting cardiovascular disease progression or new onset \u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e. Cys-c can inhibit tissue protease activity under physiological conditions, thus preventing the breakdown of cells by proteases. When vascular wall damage occurs, the increased release of inflammatory mediators leads to a disruption of the balance between hydrolyzing proteases and Cys-c in the vascular wall, which results in the disruption of the integrity of the arterial vasculature, thereby contributing to the formation of atherosclerosis \u003csup\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e. In this study, Cys-c was found to be positively associated with CAVD, and is a risk factor for aortic valve calcification.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe lipid parameters APOA1, APOB/APOA1, cumulative LDL exposure, and Non-HDL/HDL have been demonstrated to be associated with the development of aortic valve calcification. Furthermore, age, history of diabetes mellitus, DBP, APOB/APOA1, Cys-c, and NLR have been identified as valid predictors of this condition, and thus may assist in guiding the clinical management of patients.\u003c/p\u003e"},{"header":"Limitations","content":"\u003cp\u003eIt must be acknowledged that the present study is a single-center, small-sample, retrospective study, which may have resulted in the observed results being affected by other vascular calcifications. Consequently, it would be beneficial for future multicenter, large-sample, prospective studies to be conducted in order to confirm these findings.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAIP\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Atherogenic Index of Plasma\u003c/p\u003e\n\u003cp\u003eALP\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Alkaline Phosphatase\u003c/p\u003e\n\u003cp\u003eAPOA1\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Apolipoprotein A1\u003c/p\u003e\n\u003cp\u003eAPOB\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Apolipoprotein B\u003c/p\u003e\n\u003cp\u003eBMI\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Body Mass Index\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBSA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Body Surface Area\u003c/p\u003e\n\u003cp\u003eCAVD\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Calcific Aortic Valve Disease\u003c/p\u003e\n\u003cp\u003eCys-c\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Cystatin c\u003c/p\u003e\n\u003cp\u003eDBP\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Diastolic Blood Pressure\u003c/p\u003e\n\u003cp\u003eHbA1c\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Glycated Hemoglobin A1c\u003c/p\u003e\n\u003cp\u003eHDL\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;High-Density Lipoprotein\u003c/p\u003e\n\u003cp\u003eIDL\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Intermediate-Density Lipoprotein\u003c/p\u003e\n\u003cp\u003eLDH\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Lactate Dehydrogenase\u003c/p\u003e\n\u003cp\u003eLDL\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Low-Density Lipoprotein\u003c/p\u003e\n\u003cp\u003eLY%\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Lymphocyte Percentage\u003c/p\u003e\n\u003cp\u003eNEUT%\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Neutrophil Percentage\u003c/p\u003e\n\u003cp\u003eNLR\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Neutrophil-to-Lymphocyte Ratio\u003c/p\u003e\n\u003cp\u003eSBP\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Systolic Blood Pressure\u003c/p\u003e\n\u003cp\u003eTC\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Total Cholesterol\u003c/p\u003e\n\u003cp\u003eTG\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Triglyceride\u003c/p\u003e\n\u003cp\u003eUA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Uric Acid\u003c/p\u003e\n\u003cp\u003eVLDL \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Very Low-Density Lipoprotein\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions conceptualization\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWang yuxing: methodology; investigation; software; formal analysis; data curation; writing-original draft preparation; writing-review and editing; Yu ming: methodology; software; formal analysis; Yang song: investigation; data curation; Mei jiajie: formal analysis; Liu zhenzhu: formal analysis; Geng zhao hong: formal analysis; Xie wenli: formal analysis; Wang hongyan: supervision; Niu nan: supervision; resources; Qu peng: project administration; supervision\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003eEthics approval has been approved by\u0026nbsp;the Second Hospital of Dalian Medical University.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe confirm that the manuscript has been read and approved for publication by all of the named authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eOsnabrugge RL, Mylotte D, Head SJ, Van Mieghem NM, Nkomo VT, LeReun CM, Bogers AJ, Piazza N, Kappetein AP. 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DOI:10.3969/j.issn.1671-4695.2021.09.010.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"lipid, aortic valve calcification, ROC curve","lastPublishedDoi":"10.21203/rs.3.rs-5364924/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5364924/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground: Calcific Aortic Valve Disease (CAVD) is a prevalent heart valve disease. The ratio of two apolipoproteins with distinct functions (APOB/APOA1) has been proposed as a novel assessment index for the evaluation of cardiovascular diseases. The aim of this article is to discuss the role of lipids parameters such as APOB/APOA1 in CAVD and the risk factors for CAVD, to develop a predictive model for CAVD, and to evaluate the sensitivity and specificity of this model.\u003c/p\u003e\n\u003cp\u003eMethod: Patients who initially presented to the Department of Cardiology of the Second Affiliated Hospital of Dalian Medical University between 1 January 2023 and 31 December 2023 were retrospectively identified and included in the study. Patients were divided into an aortic valve calcification group (111 cases) and a control group (201 cases) based on CT findings. The patients' clinical data, laboratory examination results, and chest CT images were collected and analyzed. A variety of statistical methods were used to analyses risk factors for CAVD in order to construct a CAVD prediction model and to assess its sensitivity and specificity.\u003c/p\u003e\n\u003cp\u003eResults：Lipid parameters APOA1, APOB/APOA1, cumulative LDL exposure and non-HDL/HDL were significantly associated with aortic valve calcification. Age, history of diabetes, DBP, APOB/APOA1, Cys-c and NLR are identified as independent risk factors for CAVD, and the combination of the above indexes in the prediction of aortic valve calcification was 0.796, corresponding to a sensitivity of 0.769 and a specificity of 0.755.\u003c/p\u003e\n\u003cp\u003eConclusion: APOA1, APOB/APOA1, cumulative LDL exposure, and Non-HDL/HDL have been demonstrated to be associated withCAVD. Furthermore, age, history of diabetes mellitus, DBP, APOB/APOA1, Cys-c, and NLR have been identified as valid predictors of CAVD.\u003c/p\u003e","manuscriptTitle":"Value of APOB/APOA1 ratio in prediction of calcific aortic valve disease","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-02 22:27:22","doi":"10.21203/rs.3.rs-5364924/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8cd34f14-de53-4679-a0b0-df7397264f3c","owner":[],"postedDate":"December 2nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-08T17:42:33+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-02 22:27:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5364924","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5364924","identity":"rs-5364924","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}