Association Between Serum Uric Acid Levels and Progression of Coronary Artery Calcification in Asian Population

Association Between Serum Uric Acid Levels and Progression of Coronary Artery Calcification in Asian Population | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Association Between Serum Uric Acid Levels and Progression of Coronary Artery Calcification in Asian Population Yuki Ohmoto-Sekine, Makiko Ishihara, Kazuhisa Amakawa, Yumiko Ohike, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-139520/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 10 You are reading this latest preprint version Abstract Background : Coronary artery calcium (CAC) is associated coronary heart disease risk. In addition, CAC progression is associated with worsening coronary atherosclerosis and predicts future cardiac events. We aimed to investigate risk factors for the progression of CAC in an asymptomatic Japanese population using low dose computed tomography (CT) lung cancer screening performed during routine health checkup. Methods: The risk factors for CAC progression were analyzed in 771 asymptomatic subjects who underwent repeated CAC measurement. Results : Of the 771 subjects, 632 (82.0%) were males with a mean age of 56 years old, 208 (27.0%) had a history of hypertension, 299 (38.8%) had dyslipidemia, 81 (10.5%) had diabetes mellitus, and 180 (23.3%) had hyperuricemia. During observation, 103 (13.4%) subjects had CAC progression, while 515 subjects (66.8%) showed no calcification. On multivariate analysis, increasing uric acid level was significantly associated with the progression of CAC (odds ratio 1.203, 95% confidence interval 1.045-1.387, P = 0.010). Conclusions : The results of this study show that hyperuricemia is associated with CAC progression in asymptomatic Japanese subjects. Cardiac & Cardiovascular Systems Cardiothoracic Surgery Atherosclerosis Coronary artery calcium progression Hyperuricemia Risk factor Background Arteriosclerosis is a cause of ischemic heart disease in many patients. It is important, therefore, to identify factors associated with the progression of arteriosclerosis to prevent ischemic heart disease. Coronary artery calcium (CAC) measured by computed tomography (CT) correlates with the amount of coronary atherosclerotic plaque, is a noninvasive technique to evaluate the severity of coronary atherosclerosis, and has been used to predict future coronary events.[ 1 – 5 ] Furthermore, progression of coronary atherosclerosis has been studied using CAC progression in longitudinal studies.[ 6 – 8 ] A previous study showed age, sex, diabetes, and dyslipidemia to be risk factors for atherosclerotic diseases, in addition to the CAC score.[ 5 ] However, it is still unknown whether these risk factors assessed in cross-sectional studies have the same influence on progression of coronary atherosclerosis as factors assessed in longitudinal studies. Thus, we aimed to investigate risk factors of coronary atherosclerosis progression, measured as CAC progression, in an asymptomatic Japanese population using low dose CT lung cancer screening performed during a routine health checkup. Methods Study population The study included 2124 asymptomatic subjects who voluntarily underwent CT scan for lung cancer screening at a general health checkup in Toranomon Hospital Health Management Center between April 2010 and December 2012 at baseline. Of these subjects, 836 had at least two visits at our institution. Of these 836 subjects, 65 were excluded from the study for the following reasons: 56 subjects had a history of coronary artery disease, 7 had a pacemaker or valve implantation that could cause artifacts, and 2 had no records of a calcium score. A total of 771 subjects were included in the final analysis. The average time between CT scans was 3.1 ± 0.9 years. Medical histories and current medications were derived from medical questionnaires. Hypertension was defined as systolic blood pressure (SBP) ≥ 140 mmHg or diastolic blood pressure (DBP) ≥ 90 mmHg without medication in the outpatient clinic on at least two separate measurements or antihypertensive medication use. Diabetes mellitus was defined as fasting blood glucose ≥ 126 mg/dL, HbA1c ≥ 6.5%, or use of medication for diabetes. Dyslipidemia was defined as total cholesterol ≥ 240 mg/dL, low-density lipoprotein (LDL) cholesterol ≥ 140 mg/dL, triglyceride (TG) ≥ 150 mg/dL, high-density lipoprotein (HDL) cholesterol < 40 mg/dL, or previous use of lipid-lowering medication. Hyperuricemia was defined as uric acid ≥ 7.1 mg/dL. Obesity was defined as a body mass index (BMI) ≥ 25 kg/m 2 , according to the criteria in the World Health Organization (WHO) Asia-Pacific guidelines.[ 9 ] Metabolic syndrome was classified using the NCEP/ATP-III recommendations.[ 10 ] Smoking habits (never, former, and current) were obtained from a self-completed questionnaire. The institutional review boards (IRB) at our hospital (Toranomon Hospital IRB [IRB 1175]) approved the study, informed consent was obtained from all subjects, and the study was performed in accordance with the declaration of Helsinki. Cac Scoring Assessment And Definition Of Progression Of Cac All subjects underwent lung cancer screening with a multidetector CT system (Acquilion 64; Toshiba Medical Systems, Tochigi, Japan). CT scans were acquired during one deep inspiratory breath hold, without use of contrast medium or ECG-gating. Low dose lung CT scan was performed using a tube voltage of 120 kV, a tube current of 60–70 mA and 30–35 mAs based on the body size of the subject, and a 300–400 mm field of view. The technical parameters for lung cancer screening CT and reconstruction of images for CAC scoring have been documented previously.[ 11 ] In brief, the image was reconstructed using non-overlapping 3.0 mm slices with a 260-mm field of view, which is the standard method used in clinical practice based on electron beam CT.[ 12 ] Use of slice thickness and overlapping reconstruction have a major influence on CAC scoring;[ 13 ] thus we used the same reconstruction protocol as that used for cardiac CT for CAC scoring. CAC scores were calculated quantitatively according to the methods by Agatston et al. [ 14 ] We defined the progression of CAC into three groups. No calcification observed during follow up was defined as 0 calcification (group A). Using the percentage methods, CAC progression was defined as an annual percentage change ≥ 15% at follow up in some studies.[ 15 , 16 ] Considering of definite CAC progression, we defined annual percentage change ≥ 20% at follow up as severe progression (group C), and < 20% change at follow up was defined as no or mild progression (group B). Statistical analysis Data are expressed as mean ± standard deviation for continuous variables, and as frequencies and percentages for categorical variables. The significance of differences in quantitative data was determined by the Mann-Whitney U -test and Kruskal Wallis test. Ordered logistic regression analysis was applied to determine which factors were associated with the progression of CAC (0 CAC, no or mild CAC progression, and severe CAC progression). Statistical analyses were performed using SPSS for Windows, ver. 13.0 (Chicago, IL, USA), with P < 0.05 considered significant. Results Clinical characteristics of the subjects The characteristics of subjects are shown in Table 1 . (Please insert Table 1 here.) Of the 771subjects, 632 (82.0%) were males with a mean age of 56 years old, 208 (27.0%) had a history of hypertension, 299 (38.8%) had dyslipidemia, 81 (10.5%) had diabetes mellitus, and 180 (23.3%) had hyperuricemia. There were 515 subjects (66.8%) in group A, 153 (19.8%) in group B, and 103 (13.4%) in group C. Compared to group A, subjects in group C were older and more likely to be male. Risk factors including renal function disorder (creatinine, estimate glomerular filtration rate (eGFR)) and uric acid level were significantly higher in group C than groups A and B. Total cholesterol, LDL cholesterol, HDL cholesterol, smoking habit, and alcohol intake were not significantly different among the three groups. Table 1 Characteristics of the study participants Group A ( n = 515) Group B ( n = 153) Group C ( n = 103) P value Age (years) 53.2 ± 9.4 61.4 ± 9.5 61.9 ± 9.1 < 0.001 Sex (male, %) 404 (78.4) 134 (87.6) 94 (91.3) 0.001 Body mass index (kg/m 2 ) 23.4 ± 3.1 23.6 ± 2.9 24.3 ± 3.0 0.013 Abdominal circumference (cm) 83.7 ± 9.0 85.2 ± 8.2 87.0 ± 8.6 0.001 Systolic blood pressure (mmHg) 120.1 ± 13.3 123.5 ± 14.2 124.7 ± 12.2 < 0.001 Diastolic blood pressure (mmHg) 77.1 ± 9.0 78.4 ± 8.9 79.2 ± 8.6 0.067 Fasting blood glucose (mg/dL) 102.3 ± 15.3 103.7 ± 13.6 112.1 ± 32.1 < 0.001 HbA1c (%) 5.4 ± 0.5 5.5 ± 0.6 5.6 ± 0.8 < 0.001 Total cholesterol (mg/dL) 204.8 ± 32.2 210.7 ± 33.2 206.2 ± 33.9 0.158 LDL cholesterol (mg/dL) 118.7 ± 29.1 121.2 ± 28.5 117.9 ± 32.8 0.458 HDL cholesterol (mg/dL) 56.6 ± 14.9 54.2 ± 14.9 54.2 ± 13.7 0.103 Triglycerides (mg/dL) 116.6 ± 69.1 135.7 ± 95.3 133.1 ± 85.5 0.046 L/H ratio 2.24 ± 0.80 2.36 ± 0.70 2.30 ± 0.80 0.096 Creatinine (mg/dL) 0.79 ± 0.19 0.80 ± 0.15 0.93 ± 1.06 0.045 eGFR (ml/min/1.73 m 2 ) 78.8 ± 14.3 76.0 ± 12.5 73.4 ± 15.3 < 0.001 Uric acid (mg/dL) 5.85 ± 1.32 6.04 ± 1.34 6.21 ± 1.24 0.007 Hypertension (n, %) 115 (22.3) 46 (30.1) 47 (45.6) < 0.001 Diabetes (n, %) 45 (8.7) 14 (9.2) 22 (21.4) < 0.001 Dyslipidemia (n, %) 175 (34.0) 73 (47.7) 51 (49.5) < 0.001 Smoking history (n, %) current 179 (35.1) 45 (30.2) 38 (37.3) 0.441 including former 363 (70.5) 112 (73.2) 81 (76.6) 0.229 CKD (eGFR < 60 ml/min/1.73 m 2 ) (n, %) 31 (6.0) 12 (7.8) 16 (15.5) 0.004 Hyperuricemia (n, %) 102 (19.8) 40 (26.1) 38 (36.9) < 0.001 Metabolic syndrome 86 (16.7) 33 (21.6) 27 (26.2) 0.052 Alcohol intake (≥ 20 g/day) (n, %) 157 (30.5) 40 (26.1) 38 (36.9) 0.308 Mean number of constituent factors for metabolic syndrome (n, %) 1.1 ± 1.2 1.5 ± 1.3 1.8 ± 1.2 < 0.001 Data are shown as the mean and (SD) or percentages. HbA1c, glycated hemoglobin; LDL, low-density lipoprotein; HDL, high-density lipoprotein; L/H ratio, LDL/HDL ratio; LDL, eGFR, estimated glomerular filtration rate; CKD, chronic kidney disease; SD, standard deviation Risk Factors Associated With Severe Progression Of Cac Univariate regression analyses were performed to determine factors affecting progression of CAC. (Please insert Table 2 here.) The progression of CAC was associated with conventional risk factors with the exception of total cholesterol, LDL cholesterol, smoking habit, and alcohol intake. Table 2 Results of the univariate analysis of risk factors for CAC progression Variable Risk ratio 95% CI P value lower limit upper limit Age (/1 year) 1.090 1.452 3.552 < 0.001 Sex (male/female) 2.262 1.452 1.109 < 0.001 Body mass index (/1 kg/m 2 ) 1.051 1.002 1.102 0.040 Abdominal circumference (/1 cm) 1.032 1.015 1.050 < 0.001 Systolic blood pressure (/1 mmHg) 1.021 1.010 1.033 < 0.001 Diastolic blood pressure (/1 mmHg) 1.020 1.003 1.037 0.018 Fasting blood glucose (/1 mg/dL) 1.018 1.009 1.027 < 0.001 HbA1c (/1%) 1.864 1.436 2.421 < 0.001 Total cholesterol (/1 mg/dL) 1.003 0.999 1.008 0.159 LDL cholesterol (/1 mg/dL) 1.001 0.996 1.006 0.730 HDL cholesterol (/1 mg/dL) 0.989 0.979 0.999 0.037 Triglyceride (/1 mg/dL) 1.003 1.001 1.004 0.006 eGFR (ml/min/1.73 m 2 ) 0.979 0.969 0.990 < 0.001 Uric acid (/1 mg/dL) 1.167 1.042 1.306 0.007 Hypertension (y/n)* 2.116 1.540 2.907 < 0.001 Diabetes mellitus (y/n)* 1.970 1.263 3.070 0.003 Dyslipidemia (y/n)* 1.794 1.331 2.418 < 0.001 Hyperuricemia (y/n)* 1.856 1.332 2.586 < 0.001 CKD (≥ 60/<60 ml/min/1.73 m 2 ) 2.125 1.281 3.526 0.004 Metabolic syndrome (ATP III) (y/n) 1.551 1.083 2.221 0.017 The number of risk factors for METS(/1) 1.346 1.119 1.512 < 0.001 Smoking history (current, ex / no) 1.311 0.936 1.837 0.115 Alcohol (≥ 20/<20 g/day) 0.480 0.206 1.121 0.090 *CI, confidence interval; CAC, coronary artery calcium; HbA1C, glycated hemoglobin; LDL, low-density lipoprotein; HDL, high-density lipoprotein; eGFR, estimate glomerular filtration rate; CKD, chronic kidney disease; ATP III, Adult Treatment Panel III; METS, metabolic syndrome The results of multivariate analysis of clinical parameters associated with progression of CAC score are shown in Table 3 . The multivariate model was adjusted for age, sex, abdominal circumference, systolic blood pressure, triglyceride level, hemoglobin A1c level, eGFR, and uric acid level. On multivariate analysis, high uric acid level was significantly associated with progression of CAC (odds ratio 1.203, 95% confidence interval 1.045–1.387, P = 0.010). (Please insert Table 3 here.) Table 3 Results of the ordered logistic regression analysis of risk factors for CAC progression Variable Risk ratio 95% CI P value lower limit upper limit Age (/1 year) 1.111 1.088 1.134 < 0.001 Sex (male/female) 2.719 1.618 4.570 < 0.001 Uric acid (/1 mg/dL) 1.203 1.045 1.387 0.010 Triglyceride (/1 mg/dL) 1.002 1.000 1.004 0.030 *CI, confidence interval; CAC, coronary artery calcium Discussion This is the first study showing that hyperuricemia is a risk factor for CAC progression in asymptomatic Japanese subjects. There are some published studies evaluating the risk factors for CAC progression. From the MESA study, standard coronary risk factors including age, sex, race, smoking, BMI, blood pressure, and diabetes mellitus correlated with both high CAC score and progression.[ 8 ] Another studies showed that fatty liver [ 17 ] and metabolic syndrome [ 18 ] were associated with CAC progression. And recent study from China showed the association between uric acid and CAC progression in young adults with originally zero CAC [ 19 ]. Considering about the relationship between uric acid and coronary artery disease, Sun et al studied about the relationship between uric acid and coronary atherosclerosis [ 20 ], and Lv et al showed it among young adults less than 35 years of age [ 21 ]. But there are still limited number of studies which are evaluated the relationship between uric acid and CAC. Mostly many studies had not been analyzed the association with uric acid and CAC. In this study, we investigated risk factors including not only traditional coronary risk factors but also uric acid level. We found hyperuricemia to be associated with CAC progression. Although it has been hypothesized that uric acid provides an antioxidant defense in humans, previous clinical and epidemiological studies suggested that elevated uric acid levels are associated with cardiovascular diseases. Several publications demonstrated the association between serum uric acid levels and cardiovascular disorders, including hypertension, [ 22 – 24 ] coronary artery disease, [ 25 – 27 ] and carotid artery atherosclerosis.[ 28 ] The potential mechanisms of the association between hyperuricemia and cardiovascular diseases are related to xanthine oxidase and urate transporters. Higher concentration of uric acid may reflect high levels of xanthine oxidase activity and oxidative stress. The action of xanthine oxidase leads to generation of superoxide anions and is one of the principle sources of reactive oxygen species (ROS) in the human vasculature. [ 29 , 30 ] Allopurinol is rapidly metabolized to ozypurinol, an analogue of xanthine that preferentially binds to xanthine oxidase, thereby inhibiting its activity.[ 31 ] A recent study revealed that urate transporters, which have a major role in renal regulation of urate excretion, are expressed in smooth muscle cells. In case of hyperuricemia, urate transporters may allow uric acid to enter human vascular smooth muscle cells. [ 32 , 33 ] As a result, the renin angiotensin system pathway is activated and NO synthesis is inhibited, causing endothelial dysfunction and cardiovascular diseases. This study has several limitations. First, the study was retrospective and subjects were self-referred, which may have caused a selection bias compared to population-based participants. In addition, subjects were enrolled based on repeated participation in health screening examinations, and subjects who did not undergo a second CT scan were excluded. Second, use of lung cancer screening CT may also be a limitation, because the CAC score using this method tends to be lower than that that using ECG-gated CT. [ 11 , 34 , 35 ] The main reason for the lower score may be the radiation dose used in lung cancer screening CT, which is lower than that that used in ECG-gated CT. However, a recent meta-analysis by Xie et al. [ 34 ] showed that CAC score categories correlated well between ECG-gated CT and lung cancer screening CT, although the values were still slightly lower than in ECG gated CT. Third, the follow up duration may not be sufficient to evaluate CAC progression. More long-term follow up studies are warranted. Conclusions Our study showed that CAC progressed in one-third of subjects and hyperuricemia was a risk factor for CAC progression in this asymptomatic Japanese population. Abbreviations BMI body mass index; CAC:coronary artery calcium; CT:computed tomography; DBP:diastolic blood pressure; ECG:electrocardiogram; eGFR:estimated glomerular filtration rate; HbA1c:glycated hemoglobin; HDL:high-density lipoprotein; IRB:institutional review board; LDL:low-density lipoprotein; MESA Multi-Ethnic Study of Atherosclerosis; NCEP/ATP-III:National Cholesterol Education Program Adult Treatment Panel III; NO:nitric oxide; ROS:reactive oxygen species; SBP:systolic blood pressure; TG:triglyceride; WHO:World Health Organization Declarations Ethics approval and consent to participate The institutional review boards (IRB) at our hospital (Toranomon Hospital IRB [IRB 1175]) approved the study, and informed consent was obtained from all subjects. Consent for publication Not applicable. Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding This research received no grant from any funding agency in the public, commercial, or not-for-profit sectors. Authors' contributions YOS conceived of the study, set the study design, collected data, analyzed and interpreted the data, and was a major contributor in writing the manuscript. MI, KA and YO collected data, analyzed and interpreted the data. YA oversaw the study assessments. All authors oversaw, contributed and approved the final manuscripts. Acknowledgement We would like to thank all study participants. Authors' information (optional) References Detrano R, Guerci AD, Carr JJ, Bild DE, Burke G, Folsom AR, et al. Coronary calcium as a predictor of coronary events in four racial or ethnic groups. N Engl J Med. 2008;358:1336–45. 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Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Major revision 24 Jan, 2021 Reviews received at journal 15 Jan, 2021 Reviews received at journal 13 Jan, 2021 Reviewers agreed at journal 11 Jan, 2021 Reviewers agreed at journal 11 Jan, 2021 Reviewers invited by journal 11 Jan, 2021 Editor assigned by journal 11 Jan, 2021 Editor invited by journal 11 Jan, 2021 Submission checks completed at journal 11 Jan, 2021 First submitted to journal 02 Jan, 2021 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. We do this by developing innovative software and high quality services for the global research community. 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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-139520","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":7925305,"identity":"b27993e5-c2d9-4616-8c9f-55a8d09e29ba","order_by":0,"name":"Yuki Ohmoto-Sekine","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA30lEQVRIiWNgGAWjYBADOTQ+DyENCQzGaGqI0JLYQ7SDDI43H93w8YdN+n723oefCyq2yZkzsF/7wCBzB7eWM8fSbs5ISMvt4TluLD3jzG1jywae4hkMPM9wa7mRY3abJ+Fwbo9EGoM0b9vtxA0HeJKBfjlMUEs6j0Qa82+StCQAtbBBbWE/jFeLJNgvaWmGPWeOsVnzAP1icJiHmSEBj1/4jjcfu/HBxkaevb2N+TZPxW05g+Ptjxk+9uAOMYUDGELMPAbAaMIUhwH5Bkwx9gcMDD9waxkFo2AUjIIRBwA3RVZsfd0QxQAAAABJRU5ErkJggg==","orcid":"","institution":"Toranomon Hospital Health Management Center","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Yuki","middleName":"","lastName":"Ohmoto-Sekine","suffix":""},{"id":7925306,"identity":"372a66de-8c4c-4df8-8b6d-14a20017d16b","order_by":1,"name":"Makiko Ishihara","email":"","orcid":"","institution":"Toranomon Hospital Imaging Center","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Makiko","middleName":"","lastName":"Ishihara","suffix":""},{"id":7925307,"identity":"2645c1f2-790a-4440-a4f2-c44ad75977a9","order_by":2,"name":"Kazuhisa Amakawa","email":"","orcid":"","institution":"Toranomon Hospital Health Management Center","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Kazuhisa","middleName":"","lastName":"Amakawa","suffix":""},{"id":7925308,"identity":"c8d2fe6c-868f-4c01-af64-0e15585be2a1","order_by":3,"name":"Yumiko Ohike","email":"","orcid":"","institution":"Toranomon Hospital Health Management Center","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yumiko","middleName":"","lastName":"Ohike","suffix":""},{"id":7925309,"identity":"9abfe9bc-4185-484e-8032-2dff64fe7760","order_by":4,"name":"Yasuji Arase","email":"","orcid":"","institution":"Toranomon Hospital Health Management Center","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yasuji","middleName":"","lastName":"Arase","suffix":""}],"badges":[],"createdAt":"2021-01-02 09:44:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-139520/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-139520/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":13645101,"identity":"0017486a-3c60-48d8-8aef-a10e04fe5bd6","added_by":"auto","created_at":"2021-09-17 09:18:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":330083,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-139520/v1/158a23b8-e3e1-4b2f-8c6e-e365028c8af3.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eAssociation Between Serum Uric Acid Levels and Progression of Coronary Artery Calcification in Asian Population\u003c/p\u003e","fulltext":[{"header":"Background","content":" \u003cp\u003eArteriosclerosis is a cause of ischemic heart disease in many patients. It is important, therefore, to identify factors associated with the progression of arteriosclerosis to prevent ischemic heart disease. Coronary artery calcium (CAC) measured by computed tomography (CT) correlates with the amount of coronary atherosclerotic plaque, is a noninvasive technique to evaluate the severity of coronary atherosclerosis, and has been used to predict future coronary events.[\u003cspan additionalcitationids=\"CR2 CR3 CR4\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] Furthermore, progression of coronary atherosclerosis has been studied using CAC progression in longitudinal studies.[\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eA previous study showed age, sex, diabetes, and dyslipidemia to be risk factors for atherosclerotic diseases, in addition to the CAC score.[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] However, it is still unknown whether these risk factors assessed in cross-sectional studies have the same influence on progression of coronary atherosclerosis as factors assessed in longitudinal studies. Thus, we aimed to investigate risk factors of coronary atherosclerosis progression, measured as CAC progression, in an asymptomatic Japanese population using low dose CT lung cancer screening performed during a routine health checkup.\u003c/p\u003e "},{"header":"Methods","content":" \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eThe study included 2124 asymptomatic subjects who voluntarily underwent CT scan for lung cancer screening at a general health checkup in Toranomon Hospital Health Management Center between April 2010 and December 2012\u0026nbsp;at baseline. Of these subjects, 836 had at least two visits at our institution. Of these 836 subjects, 65 were excluded from the study for the following reasons: 56 subjects had a history of coronary artery disease, 7 had a pacemaker or valve implantation that could cause artifacts, and 2 had no records of a calcium score. A total of 771 subjects were included in the final analysis. The average time between CT scans was 3.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u0026nbsp;years. Medical histories and current medications were derived from medical questionnaires. Hypertension was defined as systolic blood pressure (SBP)\u0026thinsp;\u0026ge;\u0026thinsp;140\u0026nbsp;mmHg or diastolic blood pressure (DBP)\u0026thinsp;\u0026ge;\u0026thinsp;90\u0026nbsp;mmHg without medication in the outpatient clinic on at least two separate measurements or antihypertensive medication use. Diabetes mellitus was defined as fasting blood glucose\u0026thinsp;\u0026ge;\u0026thinsp;126\u0026nbsp;mg/dL, HbA1c\u0026thinsp;\u0026ge;\u0026thinsp;6.5%, or use of medication for diabetes. Dyslipidemia was defined as total cholesterol\u0026thinsp;\u0026ge;\u0026thinsp;240\u0026nbsp;mg/dL, low-density lipoprotein (LDL) cholesterol\u0026thinsp;\u0026ge;\u0026thinsp;140\u0026nbsp;mg/dL, triglyceride (TG)\u0026thinsp;\u0026ge;\u0026thinsp;150\u0026nbsp;mg/dL, high-density lipoprotein (HDL) cholesterol\u0026thinsp;\u0026lt;\u0026thinsp;40\u0026nbsp;mg/dL, or previous use of lipid-lowering medication. Hyperuricemia was defined as uric acid\u0026thinsp;\u0026ge;\u0026thinsp;7.1\u0026nbsp;mg/dL. Obesity was defined as a body mass index (BMI)\u0026thinsp;\u0026ge;\u0026thinsp;25\u0026nbsp;kg/m\u003csup\u003e2\u003c/sup\u003e, according to the criteria in the World Health Organization (WHO) Asia-Pacific guidelines.[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] Metabolic syndrome was classified using the NCEP/ATP-III recommendations.[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] Smoking habits (never, former, and current) were obtained from a self-completed questionnaire. The institutional review boards (IRB) at our hospital (Toranomon Hospital IRB [IRB 1175]) approved the study, informed consent was obtained from all subjects, and the study was performed in accordance with the declaration of Helsinki.\u003c/p\u003e \u003c/div\u003e \n\u003ch2\u003eCac Scoring Assessment And Definition Of Progression Of Cac\u003c/h2\u003e\n \u003cp\u003eAll subjects underwent lung cancer screening with a multidetector CT system (Acquilion 64; Toshiba Medical Systems, Tochigi, Japan). CT scans were acquired during one deep inspiratory breath hold, without use of contrast medium or ECG-gating. Low dose lung CT scan was performed using a tube voltage of 120\u0026nbsp;kV, a tube current of 60\u0026ndash;70\u0026nbsp;mA and 30\u0026ndash;35 mAs based on the body size of the subject, and a 300\u0026ndash;400\u0026nbsp;mm field of view. The technical parameters for lung cancer screening CT and reconstruction of images for CAC scoring have been documented previously.[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] In brief, the image was reconstructed using non-overlapping 3.0\u0026nbsp;mm slices with a 260-mm field of view, which is the standard method used in clinical practice based on electron beam CT.[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] Use of slice thickness and overlapping reconstruction have a major influence on CAC scoring;[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] thus we used the same reconstruction protocol as that used for cardiac CT for CAC scoring. CAC scores were calculated quantitatively according to the methods by Agatston et al. [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eWe defined the progression of CAC into three groups. No calcification observed during follow up was defined as 0 calcification (group A). Using the percentage methods, CAC progression was defined as an annual percentage change\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;15% at follow up in some studies.[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] Considering of definite CAC progression, we defined annual percentage change\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;20% at follow up as severe progression (group C), and \u0026lt;\u0026thinsp;20% change at follow up was defined as no or mild progression (group B).\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eData are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation for continuous variables, and as frequencies and percentages for categorical variables. The significance of differences in quantitative data was determined by the Mann-Whitney \u003cem\u003eU\u003c/em\u003e-test and Kruskal Wallis test. Ordered logistic regression analysis was applied to determine which factors were associated with the progression of CAC (0 CAC, no or mild CAC progression, and severe CAC progression). Statistical analyses were performed using SPSS for Windows, ver. 13.0 (Chicago, IL, USA), with \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered significant.\u003c/p\u003e \u003c/div\u003e "},{"header":"Results","content":" \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eClinical characteristics of the subjects\u003c/h2\u003e \u003cp\u003eThe characteristics of subjects are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. (Please insert Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e here.) Of the 771subjects, 632 (82.0%) were males with a mean age of 56\u0026nbsp;years old, 208 (27.0%) had a history of hypertension, 299 (38.8%) had dyslipidemia, 81 (10.5%) had diabetes mellitus, and 180 (23.3%) had hyperuricemia. There were 515 subjects (66.8%) in group A, 153 (19.8%) in group B, and 103 (13.4%) in group C. Compared to group A, subjects in group C were older and more likely to be male. Risk factors including renal function disorder (creatinine, estimate glomerular filtration rate (eGFR)) and uric acid level were significantly higher in group C than groups A and B. Total cholesterol, LDL cholesterol, HDL cholesterol, smoking habit, and alcohol intake were not significantly different among the three groups.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of the study participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGroup A\u003c/p\u003e \u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;515)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGroup B\u003c/p\u003e \u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;153)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGroup C\u003c/p\u003e \u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;103)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53.2\u0026thinsp;\u0026plusmn;\u0026thinsp;9.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61.4\u0026thinsp;\u0026plusmn;\u0026thinsp;9.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61.9\u0026thinsp;\u0026plusmn;\u0026thinsp;9.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (male, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e404 (78.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e134 (87.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e94 (91.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody mass index (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.4\u0026thinsp;\u0026plusmn;\u0026thinsp;3.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.3\u0026thinsp;\u0026plusmn;\u0026thinsp;3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbdominal circumference (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e83.7\u0026thinsp;\u0026plusmn;\u0026thinsp;9.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85.2\u0026thinsp;\u0026plusmn;\u0026thinsp;8.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e87.0\u0026thinsp;\u0026plusmn;\u0026thinsp;8.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystolic blood pressure (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e120.1\u0026thinsp;\u0026plusmn;\u0026thinsp;13.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e123.5\u0026thinsp;\u0026plusmn;\u0026thinsp;14.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e124.7\u0026thinsp;\u0026plusmn;\u0026thinsp;12.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiastolic blood pressure (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e77.1\u0026thinsp;\u0026plusmn;\u0026thinsp;9.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78.4\u0026thinsp;\u0026plusmn;\u0026thinsp;8.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e79.2\u0026thinsp;\u0026plusmn;\u0026thinsp;8.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFasting blood glucose (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e102.3\u0026thinsp;\u0026plusmn;\u0026thinsp;15.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e103.7\u0026thinsp;\u0026plusmn;\u0026thinsp;13.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e112.1\u0026thinsp;\u0026plusmn;\u0026thinsp;32.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbA1c (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal cholesterol (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e204.8\u0026thinsp;\u0026plusmn;\u0026thinsp;32.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e210.7\u0026thinsp;\u0026plusmn;\u0026thinsp;33.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e206.2\u0026thinsp;\u0026plusmn;\u0026thinsp;33.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.158\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL cholesterol (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e118.7\u0026thinsp;\u0026plusmn;\u0026thinsp;29.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e121.2\u0026thinsp;\u0026plusmn;\u0026thinsp;28.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e117.9\u0026thinsp;\u0026plusmn;\u0026thinsp;32.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.458\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL cholesterol (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56.6\u0026thinsp;\u0026plusmn;\u0026thinsp;14.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54.2\u0026thinsp;\u0026plusmn;\u0026thinsp;14.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54.2\u0026thinsp;\u0026plusmn;\u0026thinsp;13.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.103\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglycerides (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e116.6\u0026thinsp;\u0026plusmn;\u0026thinsp;69.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e135.7\u0026thinsp;\u0026plusmn;\u0026thinsp;95.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e133.1\u0026thinsp;\u0026plusmn;\u0026thinsp;85.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL/H ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.096\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.79\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.80\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.93\u0026thinsp;\u0026plusmn;\u0026thinsp;1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR (ml/min/1.73\u0026nbsp;m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78.8\u0026thinsp;\u0026plusmn;\u0026thinsp;14.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76.0\u0026thinsp;\u0026plusmn;\u0026thinsp;12.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e73.4\u0026thinsp;\u0026plusmn;\u0026thinsp;15.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUric acid (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.85\u0026thinsp;\u0026plusmn;\u0026thinsp;1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.04\u0026thinsp;\u0026plusmn;\u0026thinsp;1.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.21\u0026thinsp;\u0026plusmn;\u0026thinsp;1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension (n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e115 (22.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46 (30.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47 (45.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes (n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45 (8.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (9.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22 (21.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDyslipidemia (n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e175 (34.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73 (47.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51 (49.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking history (n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e179 (35.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45 (30.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38 (37.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.441\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eincluding former\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e363 (70.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e112 (73.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81 (76.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.229\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCKD (eGFR\u0026thinsp;\u0026lt;\u0026thinsp;60\u0026nbsp;ml/min/1.73\u0026nbsp;m\u003csup\u003e2\u003c/sup\u003e) (n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31 (6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (7.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (15.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHyperuricemia (n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e102 (19.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40 (26.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38 (36.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetabolic syndrome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e86 (16.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33 (21.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27 (26.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol intake (\u0026ge;\u0026thinsp;20\u0026nbsp;g/day) (n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e157 (30.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40 (26.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38 (36.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.308\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean number of constituent factors for metabolic syndrome (n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eData are shown as the mean and (SD) or percentages. HbA1c, glycated hemoglobin; LDL, low-density lipoprotein; HDL, high-density lipoprotein; L/H ratio, LDL/HDL ratio; LDL, eGFR, estimated glomerular filtration rate; CKD, chronic kidney disease; SD, standard deviation\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \n\u003ch2\u003eRisk Factors Associated With Severe Progression Of Cac\u003c/h2\u003e\n \u003cp\u003eUnivariate regression analyses were performed to determine factors affecting progression of CAC. (Please insert Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e here.) The progression of CAC was associated with conventional risk factors with the exception of total cholesterol, LDL cholesterol, smoking habit, and alcohol intake.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of the univariate analysis of risk factors for CAC progression\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRisk ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003elower limit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eupper limit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (/1\u0026nbsp;year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.452\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.552\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (male/female)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.452\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody mass index (/1\u0026nbsp;kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbdominal circumference (/1\u0026nbsp;cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystolic blood pressure (/1\u0026nbsp;mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiastolic blood pressure (/1\u0026nbsp;mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFasting blood glucose (/1\u0026nbsp;mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbA1c (/1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.864\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.436\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal cholesterol (/1\u0026nbsp;mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.159\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL cholesterol (/1\u0026nbsp;mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.730\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL cholesterol (/1\u0026nbsp;mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.989\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.979\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglyceride (/1\u0026nbsp;mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR (ml/min/1.73\u0026nbsp;m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.979\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.969\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUric acid (/1\u0026nbsp;mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension (y/n)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.907\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes mellitus (y/n)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.970\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDyslipidemia (y/n)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.794\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHyperuricemia (y/n)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.856\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.586\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCKD (\u0026ge;\u0026thinsp;60/\u0026lt;60\u0026nbsp;ml/min/1.73\u0026nbsp;m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.526\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetabolic syndrome (ATP III) (y/n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.551\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThe number of risk factors for METS(/1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.346\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.512\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking history (current, ex / no)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.936\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.837\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.115\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol (\u0026ge;\u0026thinsp;20/\u0026lt;20\u0026nbsp;g/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.480\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.090\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e*CI, confidence interval; CAC, coronary artery calcium; HbA1C, glycated hemoglobin; LDL, low-density lipoprotein; HDL, high-density lipoprotein; eGFR, estimate glomerular filtration rate; CKD, chronic kidney disease; ATP III, Adult Treatment Panel III; METS, metabolic syndrome\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe results of multivariate analysis of clinical parameters associated with progression of CAC score are shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The multivariate model was adjusted for age, sex, abdominal circumference, systolic blood pressure, triglyceride level, hemoglobin A1c level, eGFR, and uric acid level. On multivariate analysis, high uric acid level was significantly associated with progression of CAC (odds ratio 1.203, 95% confidence interval 1.045\u0026ndash;1.387, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.010). (Please insert Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e here.)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of the ordered logistic regression analysis of risk factors for CAC progression\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRisk ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003elower limit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eupper limit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (/1\u0026nbsp;year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (male/female)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.719\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.618\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.570\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUric acid (/1\u0026nbsp;mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglyceride (/1\u0026nbsp;mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e*CI, confidence interval; CAC, coronary artery calcium\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e "},{"header":"Discussion","content":" \u003cp\u003eThis is the first study showing that hyperuricemia is a risk factor for CAC progression in asymptomatic Japanese subjects. There are some published studies evaluating the risk factors for CAC progression. From the MESA study, standard coronary risk factors including age, sex, race, smoking, BMI, blood pressure, and diabetes mellitus correlated with both high CAC score and progression.[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] Another studies showed that fatty liver [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] and metabolic syndrome [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] were associated with CAC progression. And recent study from China showed the association between uric acid and CAC progression in young adults with originally zero CAC [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Considering about the relationship between uric acid and coronary artery disease, Sun et al studied about the relationship between uric acid and coronary atherosclerosis [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], and Lv et al showed it among young adults less than 35\u0026nbsp;years of age [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. But there are still limited number of studies which are evaluated the relationship between uric acid and CAC. Mostly many studies had not been analyzed the association with uric acid and CAC. In this study, we investigated risk factors including not only traditional coronary risk factors but also uric acid level. We found hyperuricemia to be associated with CAC progression.\u003c/p\u003e \u003cp\u003eAlthough it has been hypothesized that uric acid provides an antioxidant defense in humans, previous clinical and epidemiological studies suggested that elevated uric acid levels are associated with cardiovascular diseases. Several publications demonstrated the association between serum uric acid levels and cardiovascular disorders, including hypertension, [\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] coronary artery disease, [\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] and carotid artery atherosclerosis.[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] The potential mechanisms of the association between hyperuricemia and cardiovascular diseases are related to xanthine oxidase and urate transporters. Higher concentration of uric acid may reflect high levels of xanthine oxidase activity and oxidative stress. The action of xanthine oxidase leads to generation of superoxide anions and is one of the principle sources of reactive oxygen species (ROS) in the human vasculature. [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] Allopurinol is rapidly metabolized to ozypurinol, an analogue of xanthine that preferentially binds to xanthine oxidase, thereby inhibiting its activity.[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] A recent study revealed that urate transporters, which have a major role in renal regulation of urate excretion, are expressed in smooth muscle cells. In case of hyperuricemia, urate transporters may allow uric acid to enter human vascular smooth muscle cells. [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] As a result, the renin angiotensin system pathway is activated and NO synthesis is inhibited, causing endothelial dysfunction and cardiovascular diseases.\u003c/p\u003e \u003cp\u003eThis study has several limitations. First, the study was retrospective and subjects were self-referred, which may have caused a selection bias compared to population-based participants. In addition, subjects were enrolled based on repeated participation in health screening examinations, and subjects who did not undergo a second CT scan were excluded. Second, use of lung cancer screening CT may also be a limitation, because the CAC score using this method tends to be lower than that that using ECG-gated CT. [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] The main reason for the lower score may be the radiation dose used in lung cancer screening CT, which is lower than that that used in ECG-gated CT. However, a recent meta-analysis by Xie et al. [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] showed that CAC score categories correlated well between ECG-gated CT and lung cancer screening CT, although the values were still slightly lower than in ECG gated CT. Third, the follow up duration may not be sufficient to evaluate CAC progression. More long-term follow up studies are warranted.\u003c/p\u003e "},{"header":"Conclusions","content":" \u003cp\u003eOur study showed that CAC progressed in one-third of subjects and hyperuricemia was a risk factor for CAC progression in this asymptomatic Japanese population.\u003c/p\u003e "},{"header":"Abbreviations","content":" \u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ebody mass index; CAC:coronary artery calcium; CT:computed tomography; DBP:diastolic blood pressure; ECG:electrocardiogram; eGFR:estimated glomerular filtration rate; HbA1c:glycated hemoglobin; HDL:high-density lipoprotein; IRB:institutional review board; LDL:low-density lipoprotein; MESA Multi-Ethnic Study of Atherosclerosis; NCEP/ATP-III:National Cholesterol Education Program Adult Treatment Panel III; NO:nitric oxide; ROS:reactive oxygen species; SBP:systolic blood pressure; TG:triglyceride; WHO:World Health Organization\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe institutional review boards (IRB) at our hospital (Toranomon Hospital IRB [IRB 1175]) approved the study, and informed consent was obtained from all subjects.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no grant from any funding agency in the public, commercial, or\u003c/p\u003e\n\u003cp\u003enot-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYOS conceived of the study, set the study design, collected data, analyzed and interpreted the data, and was a major contributor in writing the manuscript. MI, KA and YO collected data, analyzed and interpreted the data.\u003c/p\u003e\n\u003cp\u003eYA oversaw the study assessments. All authors oversaw, contributed and approved the final manuscripts.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank all study participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' information\u003c/strong\u003e (optional)\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eDetrano R, Guerci AD, Carr JJ, Bild DE, Burke G, Folsom AR, et al. Coronary calcium as a predictor of coronary events in four racial or ethnic groups. N Engl J Med. 2008;358:1336\u0026ndash;45.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBudoff MJ, Shaw LJ, Liu ST, Weinstein SR, Mosler TP, Tseng PH, et al. Long-term prognosis associated with coronary calcification: observations from a registry of 25,253 patients. 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Circulation. 2007; 116:894\u0026ndash;900.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMitsuhashi T, Hibi K, Kosuge M, Morita S, Komura N, Kusama I, et al. Relation between hyperinsulinemia and nonculprit plaque characteristics in nondiabetic patients with acute coronary syndromes. JACC Cardiovasc Imaging. 2011; 4:392\u0026ndash;401.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNeogi T, Ellison RC, Hunt S, Terkeltaub R, Felson DT, Zhang Y. Serum uric acid is associated with carotid plaques: The National Heart, Lung, and Blood Institute Family Heart Study. J Rheumatol. 2009; 36:378\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBerry C, Hamilton CA, Brosnan MJ, Magill FG, Berg GA, McMurray JJ, et al. Investigation into the sources of superoxide in human blood vessels: angiotensin II increases superoxide production in human internal mammary arteries. 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Nephrol Dial Transplant. 2013; 28:603\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXie X, Zhao Y, de Bock GH, de Jong PA, Mali WP, Oudkerk M, et al. Validation and prognosis of coronary artery calcium scoring in nontriggered thoracic computed tomography: systematic review and meta-analysis. Circ Cardiovasc Imaging. 2013; 6:514\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBudoff MJ, Nasir K, Kinney GL, Hokanson JE, Barr RG, Steiner R, et al. Coronary artery and thoracic calcium on noncontrast thoracic CT scans: comparison of ungated and gated examinations in patients from the COPD Gene cohort. J Cardiovasc Comput Tomogr. 2011; 5:113\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Atherosclerosis, Coronary artery calcium progression, Hyperuricemia, Risk factor","lastPublishedDoi":"10.21203/rs.3.rs-139520/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-139520/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: Coronary artery calcium (CAC) is associated coronary heart disease risk. In addition, CAC progression is associated with worsening coronary atherosclerosis and predicts future cardiac events. We aimed to investigate risk factors for the progression of CAC in an asymptomatic Japanese population using low dose computed tomography (CT) lung cancer screening performed during routine health checkup.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e The risk factors for CAC progression were analyzed in 771 asymptomatic subjects who underwent repeated CAC measurement. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: Of the 771 subjects, 632 (82.0%) were males with a mean age of 56 years old, 208 (27.0%) had a history of hypertension, 299 (38.8%) had dyslipidemia, 81 (10.5%) had diabetes mellitus, and 180 (23.3%) had hyperuricemia. During observation, 103 (13.4%) subjects had CAC progression, while 515 subjects (66.8%) showed no calcification. On multivariate analysis, increasing uric acid level was significantly associated with the progression of CAC (odds ratio 1.203, 95% confidence interval 1.045-1.387, \u003cem\u003eP\u003c/em\u003e = 0.010).\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e: The results of this study show that hyperuricemia is associated with CAC progression in asymptomatic Japanese subjects.\u003c/p\u003e","manuscriptTitle":"Association Between Serum Uric Acid Levels and Progression of Coronary Artery Calcification in Asian Population","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-01-13 16:19:39","doi":"10.21203/rs.3.rs-139520/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2021-01-25T01:03:21+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2021-01-15T13:18:56+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2021-01-13T14:25:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"f08a87ae-1aeb-4176-a5b5-d4b726759bd9","date":"2021-01-11T12:53:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"0e22d24e-adfa-419a-90f7-615fce60e737","date":"2021-01-11T12:47:46+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2021-01-11T12:34:31+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2021-01-11T08:28:47+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2021-01-11T07:50:23+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2021-01-11T07:46:28+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cardiovascular Disorders","date":"2021-01-02T09:33:35+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bc88f633-71f6-425c-948f-d7f1cb5d12df","owner":[],"postedDate":"January 13th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[{"id":1862172,"name":"Cardiac \u0026 Cardiovascular Systems"},{"id":1862173,"name":"Cardiothoracic Surgery"}],"tags":[],"updatedAt":"2021-01-25T20:53:01+00:00","versionOfRecord":[],"versionCreatedAt":"2021-01-13 16:19:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-139520","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-139520","identity":"rs-139520","version":["v1"]},"buildId":"FbvkV6FR0MCFSLy54lSbu","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}