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Objectives: There is evidence that pulse wave velocity (PWV ) can predict the occurrence of abdominal aortic calci- fication (AAC), while the association between estimated PWV (ePWV ) and AAC has not been reported, so our study aimed to analyze the association between ePWV and AAC. Methods: The study enrolled 3140 adults between the ages of 40 and 80 who participated in the 2013–2014 National Health and Nutrition Examination Survey. Using multivariate logistic regression analysis, multivariate linear regression and receiver operating characteristic (ROC) curve to evaluate the association between ePWV and AAC. Results: The ePWV was significantly higher in participants with AAC compared with those without AAC. And ePWV had a high correlation with age and AAC (correlation coefficient = 0.906 and 0.332, both P < 0.001). Individuals in high ePWV group had significantly higher percentage of AAC compared to low ePWV group (OR = 2.971, 95% CI 2.529–3.490, P < 0.001) in the crude model. After adjusting for all confounding variables, ePWV was still significantly higher (Model 3, OR = 1.962, 95% CI 1.612–2.389, P < 0.001). While after adjusting for all confounding variables plus age (Model 4), ePWV, when as a categorical variable, was no longer significantly positively associated with AAC. Addi- tionally, the ROC curve indicated that both ePWV and age had some diagnostic value for AAC (AUC = 0.690, P < 0.001; AUC = 0.708, P < 0.001). Conclusions: In the age range of 40–80 years, ePWV did have an association with AAC but did not have predictive power beyond age. Keywords: Pulse wave velocity, Estimated pulse wave velocity, Abdominal aortic calcification, NHANES, Arterial stiffness 1 Introduction the development of biomarkers and the improvement of In recent years, growing studies have focused on the calcification assessment tools, the studies on abdominal pathological mechanism of vascular calcification. How - aortic calcification (AAC) have gradually increased . ever, most previous studies on vascular calcification In the studies of the connection between cardiovascu- mainly focused on coronary artery calcification, but with lar diseases, chronic kidney disease, diabetes and osteo- porosis, arterial calcification, particularly AAC, has a a pivotal position. AAC can be evaluated by several imag- Xiaoxu Guo and Chenzhao Xu have contributed equally to this work. ing tools, including dual-energy X-ray absorptiometry *Correspondence: firstname.lastname@example.org (DXA) and lateral abdominal X-ray [2, 3]. Each tool has 1 its own disadvantages and advantages, among which Department of Digestive Diseases, Beijing Luhe Hospital, Capital Medical University, Beijing 101100, China DXA is a relatively fast, safe, easy to obtain and inex- Full list of author information is available at the end of the article pensive way to evaluate AAC . At present, the 8-point © The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. Guo et al. Artery Research semiquantitative score (AAC-8) and 24-point semi- map and receiver operating characteristic (ROC) . quantitative score (AAC-24) from DXA are mainly used After excluding participants with missing age, blood to evaluate the severity of AAC in clinical studies [4, 5]. pressure and AAC score parameters, we finally enrolled AAC is an actively regulated pathological process, which 3140 adults between the ages of 40 and 80 who partici- is usually related to smoking, age, inflammation and met - pated in the 2013–2014 NHANES, the specific details of abolic dysregulation [2, 6]. Previous studies have shown which were available elsewhere . The protocol was that AAC is associated with coronary artery disease [7, approved by the NCHS Research Ethics Review Board 8], stroke , fracture , cardiovascular diseases mor- (Protocol #2011-17) and in accord with the Declaration tality , all-cause mortality . Therefore, it is essential of Helsinki, all participants signed written informed con- to explore and intervene the controllable risk factors of sent. The data cleaning algorithm was shown in Fig. 1. AAC to prevent premature cardiovascular diseases and death. 2.2 Data Collection and Definitions The pulse wave velocity (PWV), as a noninvasive and Using standardized interview questionnaire to collect the easily available indicator, has been widely used to screen demographic information of each participant, including for arterial stiffness [12–14]. There is evidence that age, sex, race, smoking, hypertension and diabetes. We PWV is related to subclinical and clinical cardiovascu- divided race into five groups: Mexican American, non- lar diseases, including AAC, and could independently Hispanic White, non-Hispanic Black, other Hispanic predict future adverse cardiovascular events [15–19]. and other races. We divided smoking into two groups Therefore, PWV can be used as an effective measur - according to whether smoked at least 100 cigarettes in a ing tool to screen participants with high risk factors of lifetime: absent and present. According to ADAs diabetes cardiovascular metabolism in general population. How- diagnostic criteria, diabetes was defined as self-reported ever, in a large-scale population health survey, it is still diagnosis, or taking hypoglycemic drugs, or fasting a time-consuming and labor-intensive work to measure plasma glucose (FPG) ≥ 7.0 mmol/L, or hemoglobin PWV by a relatively expensive measuring tool. Accord- A1c (HbA1c) ≥ 6.5% . Hypertension was defined ingly, the method of estimating PWV based on the com- as the systolic blood pressure (SBP) ≥ 140 mmHg and/ bination formula of age and mean blood pressure (MBP) or diastolic blood pressure (DBP) ≥ 90 mmHg or taking was developed, namely ePWV . And previous study antihypertensive drugs . Using standardized proce- showed that ePWV was highly correlated with PWV in dures to measure height, weight, SBP and DBP of each the internal model building queue and the external ver- participant, and the body mass index (BMI, kg/m ) was ification queue, and could be used as a reliable alterna - determined by a method, that is, weight (kg)/height (m) . tive marker of PWV . Since then, increasing scholars Using the difference between SBP and DBP to calculate paid attention to the association between ePWV and pulse pressure (PP). Using standardized operational pro- cardiovascular diseases, and found that ePWV was inde- cedures to collect blood sample of each participant for pendently associated with cardiovascular and cerebro- measurement of blood markers, including triglycerides vascular diseases and all-cause mortality [21–26]. And (TGs), total cholesterol (TC), high-density lipoprotein some studies also showed that compared with measured cholesterol (HDL-C), blood urea nitrogen (BUN), cre- PWV, ePWV had similar or higher predictive value for atinine (CR), uric acid (UA), alkaline phosphatase (ALP), cardiovascular events [27, 28]. FPG, HbA1c, total calcium (Tca) and adjusted calcium However, as far as we know, although there is evidence (Adj-Ca). The Adj-Ca (mg/dL) = measured calcium (mg/ that PWV is related to AAC , it has certain limita- dL) + 0.8 × albumin (g/dL) + 3.2 . tions because it is impossible to obtain PWV in epide- The ePWV was determined by using the formula miological studies post hoc. Therefore, we used ePWV, described by Greve et al. , which was derived from an alternative index highly related to PWV, among adults the reference value of Collaboration Cohort . The from the National Health and Nutrition Examination ePWV was estimated on the basis of age and MBP, that −3 2 Survey (NHANES) to explore the correlation with AAC, is, ePWV = 9.587 − 0.402 × age + 4.560 × 10 × age − 5 2 − 3 and to evaluate whether ePWV is an independent predic- − 2.621 × 10 × age × MBP + 3.176 × 10 × age × −2 tor of AAC. MBP − 1.832 × 10 × MBP. MBP was calculated as DBP + 0.4(SBP − DBP) . In our seemingly healthy 2 Methods population, all participants were divided into two 2.1 Study Population groups according to the median of ePWV: high ePWV In this cross-sectional observational study, we followed (≥ 8.98 m/s) and low ePWV (< 8.98 m/s). the methods of Wang et al. in 2022, that is, multivariate NHANES professionals firstly detected calcium depos - logistic regression analysis, correlation analysis, forest its in the abdominal aorta by DXA, and then scored the Guo et al. Artery Research Fig. 1 Flow chart of selected participants. NHANES National Health and Nutrition Examination Survey, AAC abdominal aortic calcification degree of calcification by using the semi-quantified Kaup - and Adj-Ca; Model 3: adjusted for race, smoking, hyper- pila scoring system, which included 8-point and 24-point tension, diabetes, BMI, TGs, TC, HDL-C, BUN, CR, UA, systems, detailed scoring criteria could be found in other FPG, HbA1c, Adj-Ca, SBP and DBP. Model 4: adjusted literatures [4, 5]. In this study, we chosed a 24-point scale for variables included in Model 3 plus age. We also per- to determine the severity of AAC and divided all partici- formed ROC analysis to evaluate the diagnostic per- pants into two groups: non-AAC group (AAC score = 0) formance of ePWV and other parameters for AAC. All and AAC group (AAC score > 0). Statistical tests were conducted by using MedCalc 19.1 and SPSS 26.0. A two-tailed P value < 0.05 was defined as 2.3 Statistical Analysis statistically significant. Continuous variables were showed as mean ± standard deviation or median (quartiles: Q1, Q3), categorical vari-3 Results ables were showed as numbers (percentages). Using the 3.1 Characteristics of Study Participants independent-sample t-test or Mann–Whitney U test and The 3140 individuals (median age: 58.0 years; 48.3% men) Pearson chi-square test or fisher’s exact test to assess included in this study were classified into two groups on the differences between groups. Using the Pearson cor - the basis of the presence or absence of AAC: non-AAC relation or spearman’s rank to assess the correlations (AAC score = 0) and AAC group (AAC score > 0). There between ePWV and other covariates. Using the multi- were significant differences in races between groups variate logistic regression and multivariate linear regres- (P < 0.001). The ePWV was higher in individuals with sion with four models to explore the association between AAC than those without AAC (P < 0.001). Individuals ePWV and AAC. Crude model: unadjusted; Model 1: with AAC were older, had higher percentages of smok- adjusted for race, smoking, hypertension and diabetes; ing, diabetes and hypertension, higher levels of SBP and Model 2: adjusted for race, smoking, hypertension, diabe- PP, but lower levels of BMI and DBP than those without tes, BMI, TGs, TC, HDL-C, BUN, CR, UA, FPG, HbA1c AAC (P < 0.001). In terms of blood markers, TGs, BUN, Guo et al. Artery Research CR, UA, FPG, HbA1c, Tca and Adj-Ca were higher, while as categorical variable, Individuals in high ePWV group TC and HDL-C were lower among individuals with AAC had significantly higher percentage of AAC compared than those free from AAC (P < 0.05) (Table 1). to low ePWV group (OR = 2.971, 95% CI 2.529–3.490, P < 0.001) in the crude model. After gradually adjusting 3.2 A ssociations Between ePWV and Covariates for the confounding variables, the risk of participants In addition, we used the pearson correlation or spear- who suffered from AAC declined step by step in high man’s rank analyses to test the associations between ePWV group, but it was still significantly higher than ePWV (as a continuous variable) and other covariates. that of participants with low ePWV (Model 1, 2 and 3: The results showed that ePWV was positively related to OR = 2.503, 95% CI 2.109–2.970, P < 0.001; OR = 2.271, age, smoking, diabetes, hypertension, SBP, DBP, PP, MBP, 95% CI 1.903–2.711, P < 0.001; OR = 1.962, 95% CI TGs, HDL-C, BUN, CR, UA, FPG, HbA1c, ALP, Tca, Adj- 1.612–2.389, P < 0.001; respectively). Additionally, mul- Ca and AAC, but negatively related to TC and LDL-C tivariate linear regression analysis showed that after (P < 0.05) (Table 2). adjusting for the covariables contained in Model 3, the AAC score increased by 0.606 points for each additional 3.3 A ssociations Between ePWV and AAC unit of ePWV (β = 0.606, 95% CI 0.529–0.684, P < 0.001) As shown in Table 3 and Fig. 2, multivariate logistic (Table 4). While after adjusting for all confounding vari- regression analyses showed that when ePWV was viewed ables plus age (Model 4), ePWV, when as a categorical Table 1 Baseline characteristics of participants with and without AAC Variables Total population Non-AAC (n = 2193) AAC (n = 947) P value Age (years) 58.0 (48.0, 68.0) 55.0 (46.0, 64.0) 66.0 (56.0, 75.0) < 0.001 Male 1518 (48.3%) 1045 (47.7%) 473 (49.9%) 0.237 Race < 0.001 Non-Hispanic white 1375 (43.8%) 869 (39.6%) 506 (53.4%) Non-Hispanic black 620 (19.7%) 465 (21.2%) 155 (16.4%) Mexican–American 412 (13.1%) 316 (14.4%) 96 (10.1%) Other Hispanic 298 (9.5%) 225 (10.3%) 73(7.7%) Other races 435 (13.9%) 318 (14.5%) 117 (12.4%) Smoking history 1452(46.2%) 940 (42.9%) 512 (54.1%) < 0.001 Diabetes 648 (20.6%) 403 (18.4%) 245 (25.9%) < 0.001 Hypertension 1486 (47.3%) 910 (41.5%) 576 (60.8%) < 0.001 BMI (kg/m ) 28.4 ± 5.6 28.8 ± 5.8 27.7 ± 4.8 < 0.001 SBP (mm Hg) 127.2 ± 18.3 125.4 ± 17.5 131.6 ± 19.2 < 0.001 DBP (mm Hg) 71.3 ± 10.8 72.3 ± 10.6 68.9 ± 10.8 < 0.001 PP (mm Hg) 56.0 ± 17.5 53.1 ± 15.9 62.6 ± 19.2 < 0.001 MBP (mm Hg) 93.7 ± 11.4 93.5 ± 11.4 94.0 ± 11.4 0.324 EPWV (m/s) 9.3 ± 2.0 8.7 ± 1.8 10.3 ± 2.1 < 0.001 TGs (mg/dL) 132.0 (86.0, 192.8) 125.0 (82.0, 185.0) 134.0 (93.5, 195.5) 0.003 TC (mg/dL) 196.0 ± 42.7 197.5 ± 42.0 192.6 ± 44.2 0.003 LDL-C (mg/dL) 114.8 ± 36.0 115.7 ± 35.2 112.9 ± 37.7 0.173 HDL-C (mg/dL) 54.1 ± 16.5 54.5 ± 16.8 53.0 ± 15.6 0.016 BUN (mg/dL) 14.3 ± 6.2 13.7 ± 5.4 15.7 ± 7.5 < 0.001 CR (mg/dL) 0.9 (0.7, 1.0) 0.9 (0.7, 1.0) 0.9 (0.8, 1.1) < 0.001 UA (mg/dL) 5.5 ± 1.4 5.4 ± 1.3 5.6 ± 1.4 < 0.001 FPG (mg/dL) 98.0 (90.0, 110.0) 97.0 (89.0, 110.0) 101.0 (92.0, 118.0) < 0.001 HbA1c (%) 5.7 (5.4, 6.0) 5.6 (5.3, 5.9) 5.8 (5.4, 6.2) < 0.001 ALP (IU/L) 65.0 (53.0, 77.0) 65.0 (53.0, 77.0) 66.0 (54.0, 77.0) 0.301 Tca (mg/dL) 9.5 ± 0.4 9.4 ± 0.4 9.5 ± 0.4 0.008 Adj-Ca (mg/dL) 9.3 ± 0.3 9.2 ± 0.3 9.3 ± 0.3 < 0.001 AAC abdominal aortic calcification, BMI body mass index, SBP systolic blood pressure, DBP diastolic blood pressure, PP pulse pressure, MBP mean blood pressure, ePWV estimated pulse wave velocity, TGs triglycerides, TC total cholesterol, LDL-C low-density lipoprotein cholesterol, HDL-C high-density lipoprotein cholesterol, BUN blood urea nitrogen, CR creatinine, UA uric acid, FPG fasting plasma glucose, HbA1c hemoglobin A1c, ALP alkaline phosphatase, Tca total calcium, Adj-Ca adjusted calcium Guo et al. Artery Research Table 2 Associations between ePWV (as a continuous variable) that ePWV, age, SBP, PP and MBP had certain diagnostic and covariates performance for AAC, among which ePWV and age had better performance (AUC = 0.690 and 0.708) (Fig. 3). Correlation coefficient P value Age .906 < 0.001 4 Discussion Smoking history .033 0.023 For all we know, we found that in the age range of Diabetes .126 < 0.001 40–80 years, ePWV did have an association with AAC Hypertension .302 < 0.001 but did not have predictive power beyond age for the first SBP .632 < 0.001 time. This study disclosed that there were statistical dif - DBP .104 < 0.001 ferences in several clinical features between non-AAC PP .602 < 0.001 and AAC groups, and individuals with traditional cardio- MBP .457 < 0.001 vascular metabolic risk factors were more likely to suffer TGs .044 0.013 from AAC. Furthermore, we also found that after adjust- TC − .070 < 0.001 ing for confounding variables, the higher ePWV was LDL-C − .127 < 0.001 independently related to higher risk of AAC. While after HDL-C .046 0.009 adjusting for all confounding variables plus age, ePWV, BUN .305 < 0.001 when as a categorical variable, was no longer significantly CR .217 < 0.001 positively associated with AAC, but when used as a con- UA .158 < 0.001 tinuous variable, ePWV still had a positive correlation FPG .171 < 0.001 with AAC. And we also found that ePWV had a moder- HbA1c .269 < 0.001 ate diagnostic performance for AAC. ALP .103 < 0.001 The Study of Osteoporotic Fractures demonstrated Tca .074 < 0.001 that individuals who suffered from AAC were older, had Adj-Ca .157 < 0.001 higher percentages of smoking and diabetes and higher AAC .332 < 0.001 SBP than those without AAC . In recent years, two articles published in the Nephrol Dial Transplant have ePWV estimated pulse wave velocity, SBP systolic blood pressure, DBP diastolic blood pressure, PP pulse pressure, MBP mean blood pressure, TGs triglycerides, also shown that participants with AAC were older and TC total cholesterol, LDL-C low-density lipoprotein cholesterol, HDL-C high- had higher percentage of diabetes than those without density lipoprotein cholesterol, BUN blood urea nitrogen, CR creatinine, UA uric acid, FPG fasting plasma glucose, HbA1c hemoglobin A1c, ALP alkaline AAC, and among them, Chen et al. have shown that phosphatase, Tca total calcium, Adj-Ca adjusted calcium, AAC abdominal aortic participants with AAC had higher levels of HbA1c and calcification estimated glomerular filtration rate (eGFR), higher per - centages of smoking and hypertension [34, 35], which was principally in accordance with our study. In addition, Table 3 Assocaition between ePWV and AAC (categorical our study also found that participants with AAC had models) higher levels of ePWV and other metabolic parameters. OR 95% CI P value In a word, the above studies showed that participants with traditional cardiovascular metabolic risk factors Crude model 2.971 2.529–3.490 < 0.001 were more likely to suffer from AAC. Model 1 2.503 2.109–2.970 < 0.001 Moreover, previous study have shown that measured Model 2 2.271 1.903–2.711 < 0.001 PWV, as a worthy marker of arterial stiffness, was posi - Model 3 1.962 1.612–2.389 < 0.001 tively correlated with age, SBP and length of AAC, and Model 4 0.716 0.529–0.969 0.031 PWV was considered to be a useful predictor of AAC The OR was tested by viewing low ePWV as reference . Similarly, Lioufas et al. also reported that higher Crude model: unadjusted; Model 1: adjusted for race, smoking, hypertension and diabetes; Model 2: adjusted for race, smoking, hypertension, diabetes, body PWV was related to elder, diabetes, SBP, ALP and pres- mass index, triglycerides, total cholesterol, high-density lipoprotein cholesterol, ence of AAC . The same was true of our study with blood urea nitrogen, creatinine, uric acid, fasting plasma glucose, hemoglobin ePWV. A1c and adjusted calcium; Model 3: adjusted for variables included in Model 2 and systolic blood pressure and diastolic blood pressure; Model 4: adjusted for And since the advent of ePWV, many studies showed variables included in Model 3 plus age that ePWV, similar to the predictive performance of PWV, could also independently predict subclinical and variable, was no longer significantly positively associ - clinical cardiovascular diseases. For instance, HSU et al. ated with AAC (Tables 3), but when used as a continuous found that ePWV was an independent risk factor for variable, ePWV still had a positive correlation with AAC long-term cardiogenic and all-cause death of patients (Tables 4). In addition, the ROC curve analysis indicated with cardiovascular diseases whether in univariable or Guo et al. Artery Research Fig. 2 Forest map of model 3. After adjusting for race, smoking, hypertension, diabetes, SBP, DBP, BMI, TGs, TC, HDL-C, BUN, CR, UA, FPG, HbA1c and Adj-Ca in Model 3, higher ePWV was still associated with a higher risk of AAC. The OR was tested by viewing low ePWV as reference. ePWV estimated pulse wave velocity, AAC abdominal aortic calcification, SBP systolic blood pressure, DBP diastolic blood pressure, BMI body mass index, TGs triglycerides, TC total cholesterol; HDL-C high-density lipoprotein cholesterol, BUN blood urea nitrogen, CR creatinine, UA uric acid, FPG fasting plasma glucose, HbA1c hemoglobin A1c, Adj-Ca adjusted calcium, OR odds ratio, CI confidence interval multivariable analysis, and they also found ePWV had death . Moreover, the Kuopio Ischemic Heart Dis- higher predictive value for cardiac death than measured ease Cohort Study showed that higher ePWV was inde- PWV . Besides, Vishram-Nielsen et al. discovered pendently associated with the increased risk of stroke in that high ePWV, independent of Framingham Risk Score middle-aged men . And we also found that ePWV and systematic coronary risk evaluation, was associated had some diagnostic value for AAC. Therefore, ePWV with main end-point events including mortality and was worthy of being estimated for prediction of AAC and cardiovascular morbidity in multivariable cox regres- other cardiovascular diseases. sion analysis , which was basically consistent with a Although our study had achieved encouraging secondary analysis by Vlachopoulos et al., that is, in the results, there were still several shortcomings. First of Sprint population, ePWV is independent of Framing- all, our study failed to determine the causal association ham risk score to predict the main outcome and all-cause between ePWV and AAC. In addition, due to the lack Guo et al. Artery Research Table 4 Association between ePWV and AAC (continuous 5 Conclusion models) IN conclusion, our study showed that there was a signifi - cant correlation between ePWV and AAC among adults β 95% CI P value aged 40–80, which not only expanded the research field Crude model 0.630 0.574–0.686 < 0.001 of ePWV, but also filled in the knowledge gap of the cor - Model 1 0.570 0.510–0.630 < 0.001 relation study between ePWV and AAC, and provided Model 2 0.505 0.442–0.568 < 0.001 new ideas for preventing and intervening premature car- Model 3 0.606 0.529–0.684 < 0.001 diovascular diseases. Model 4 2.009 1.625–2.392 < 0.001 Crude model: unadjusted; Model 1: adjusted for race, smoking, hypertension Abbreviations and diabetes; Model 2: adjusted for race, smoking, hypertension, diabetes, body PWV: Pulse wave velocity; AAC : Abdominal aortic calcification; ePWV: mass index, triglycerides, total cholesterol, high-density lipoprotein cholesterol, Estimated pulse wave velocity; ROC: Receiver operating characteristic; DXA: blood urea nitrogen, creatinine, uric acid, fasting plasma glucose, hemoglobin Dual-energy X-ray absorptiometry; MBP: Mean blood pressure; NHANES: A1c and adjusted calcium; Model 3: adjusted for variables included in Model 2 National Health and Nutrition Examination Survey; FPG: Fasting plasma and systolic blood pressure and diastolic blood pressure; Model 4: adjusted for variables included in Model 3 plus age glucose; HbA1c: Hemoglobin A1c; SBP: Systolic blood pressure; DBP: Diastolic blood pressure; BMI: Body mass index; PP: Pulse pressure; TGs: Triglycerides; TC: Total cholesterol; HDL-C: High-density lipoprotein cholesterol; BUN: Blood urea nitrogen; CR: Creatinine; UA: Uric acid; ALP: Alkaline phosphatase; Tca: Total calcium; Adj-Ca: Adjusted calcium; eGFR: Estimated glomerular filtration rate. Acknowledgements We thanked investigators and participants of NHANES for their contributions. Author Contributions XXG and CZX designed the study, collected and analyzed the statistics, and wrote the manuscript. YQL made contribution to the writing. XXG coordinated and supervised data collection, and reviewed the manuscript. All authors read and approved the final manuscript. Funding None. Availability of Data and Materials The data and materials used in this study are available on NHANES website. Declarations Ethical Approval and Informed Consent The protocol was approved by the National Center for Health Statistics of the Center for Disease Control and Prevention Institutional Review Board (Protocol #2011-17), all participants provided written informed consent. Consent for Publication Not applicable. Fig. 3 Receiver operating characteristic (ROC) curve evaluating predictive effects of ePWV, age, SBP, PP and MBP on AAC Competing interest The authors have no conflicts of interest to disclose. Author details Department of Digestive Diseases, Beijing Luhe Hospital, Capital Medical University, Beijing 101100, China. Dongfang Hospital, Beijing University of PWV data, we could not compare the difference of of Chinese Medicine, Beijing 100078, China. predictive efficacy of ePWV and PWV for AAC, so it Received: 13 September 2022 Accepted: 12 December 2022 was impossible to know whether ePWV could replace PWV as a predictive biomarker for AAC. Moreo- ver, we only considered some common confounding factors and might miss other potential risk factors, such as nutrition, diet, drugs and genetic susceptibil- References ity. Additionally, this study only included adults aged 1. Golledge J. 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Association of estimated pulse wave velocity
Artery Research – Springer Journals
Published: Mar 1, 2023
Keywords: Pulse wave velocity; Estimated pulse wave velocity; Abdominal aortic calcification; NHANES; Arterial stiffness
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