Relationship between visit-to-visit blood pressure variability and depressive mood in Korean primary care patients

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Methods The Family Cohort Study in Primary Care (FACTS) used a prospective cohort that was established to investigate the relationship between the familial environment and health in Korean primary care patients. Depressive mood was assessed as a score of 21 points or more on a Korean-type Center for Epidemiologic Studies Depression scale. BP was measured at the initial visit and first and second follow-up visits. BPV was calculated using the average of the differences between the measurements at the initial visit and first follow-up visit and at the first and second follow-up visits. High visit-to-visit BPV was defined when the average difference fell within the fourth quartile. Logistic regression analysis was used to estimate the association of high BPV with depressive mood and a range of variables. Results Of the 371 participants, 43 (11.6%) had depressive mood according to the depression score. In multivariate analysis, the odds ratio (OR) (OR: 2.26, 95% confidence interval (CI): 1.11–4.60) for high systolic BP (SBP) variability in participants with depressive mood was more than twice that in participants without depressive mood. Additionally, older age (OR: 31.91, 95% CI: 3.74–272.33 among participants aged ≥ 70 years) and use of antihypertensive medication (OR: 1.77, 95% CI: 1.02–3.05) were associated with high SBP variability. Conclusions Depressive mood was associated with high visit-to-visit SBP variability in primary care patients. Older age and use of antihypertensive medication were also associated with high SBP variability. depressive mood blood pressure blood pressure variability primary care visit-to-visit blood pressure variability. Figures Figure 1 Introduction Depression is a common disease that often has a chronic-recurrent course [ 1 ] and affects approximately 2–4% of the community population and 10% of primary care patients [ 2 , 3 ]. Major depressive disorder is one of the leading causes of disease burden worldwide [ 1 , 3 , 4 ], and depressive symptoms are linked to major chronic and cardiovascular diseases [ 5 – 8 ]. Blood pressure (BP) variability (BPV) refers to the oscillations in BP that occur within a range of time, such as minutes, over a period of 24 h or longer. This phenomenon is thought to be due to intricate interactions among extrinsic behavioral factors and intrinsic cardiovascular regulatory mechanisms [ 9 ]. Recent research indicates that BPV is independently associated with cardiovascular events and target organ damage [ 10 – 12 ]. The 24 h ambulatory BP monitoring method is commonly used to evaluate short-term BPV [ 13 , 14 ], while long-term BPV is typically evaluated based on BP measurements obtained during periodic visits to clinics, commonly conducted monthly or yearly [ 14 ]. Previous research of the relationship between depression and BP has produced conflicting findings [ 15 – 17 ]. Several studies reported that depression is associated with decreased BP [ 15 , 16 , 18 ], while other studies found that depression is related to an increased risk of hypertension (HTN) [ 19 , 20 ]. Collectively, these findings indicate that there is considerable inconsistency regarding the association between depression and BP. The associations between BPV and emotional status, such as depressive symptoms or anxiety, are relatively consistent in prior research [ 21 – 24 ], although fewer studies evaluated BPV as a factor. Elderly-onset depression affects diurnal variations in BP and is associated with cerebral infarction [ 22 ]. Furthermore, a study reported a significant association between late-onset depression and higher systolic BPV [ 23 ]. Despite the established link between depression and BPV, there is a paucity of research about the association between long-term visit-to-visit BPV and depression [ 21 ]. Therefore, this study aimed to evaluate the effect of depressive mood on long-term visit-to-visit BPV among primary care patients in Korea. Methods Study subjects The Family Cohort Study in Primary Care (FACTS) was established to evaluate the effects of the familial environment on health of primary care patients. The study cohort was based on couples and included married, cohabitating, separated, and divorced individuals. Participants (both partners of the couples) were recruited from people aged between 40 and 75 years who visited the department of family medicine at one of 22 university hospitals nationwide from April 2009 to June 2011 for periodic health checkups or treatment of chronic diseases such as HTN, diabetes, and dyslipidemia. Data of individual participants were used in this study. All participants provided written informed consent, and this survey was approved by the Institutional Review Board of Asan Medical Center (2016 − 1183). Study characteristics used as variables Demographic characteristics were prospectively collected by interviewers or primary care physicians using questions regarding educational status, monthly income, and previous medical history, including HTN, diabetes, and hyperlipidemia. Educational level was categorized into three groups: 12 years. Monthly income was evaluated by total household income using a single question and was divided into four categories: < 2.00 million Won ( $ 1715), 2.00–3.99 million Won ( $ 1715–3430), 4.00–5.99 million Won ( $ 3430–5145), and ≥ 6.00 million Won ( $ 5145). The presence of HTN, diabetes, or dyslipidemia was determined from the study participants’ medical records, which defined when the participants were reported to have any of these diseases and when they started taking antihypertensive medications, oral hypoglycemic agents, insulin, or lipid-lowering agents. Height and body weight were measured to the nearest 0.1 cm and 0.1 kg by trained interviewers. Body mass index (BMI) was calculated as [weight (kg)]/[height (m)] 2 and was categorized into three groups: < 23.0 kg/m 2 , 23.0–24.9 kg/m 2 , and ≥ 25.0 kg/m 2 . BP was measured using a mercury manometer after 10 min of rest in the sitting position [ 18 , 25 ]. Definition of depressive mood and high visit-to-visit BPV Depressive mood was assessed using a Korean-type Center for Epidemiologic Studies Depression (CES-D) scale. Depressive mood was defined when subjects had a score of 21 points or more [ 26 ]. BP was measured at the initial visit and first and second follow-up visits. The visit intervals were between 6 and 24 months. Visit-to-visit systolic BP (SBP) variability was defined as the average SBP difference between the initial visit and first follow-up visit and between the first and second follow-up visits. Visit-to-visit diastolic BP (DBP) variability was calculated using the same method. High BPV was defined as high when it fell within the fourth quartile (higher than the 75 th percentile) of average SBP variation and DBP variation, respectively. Thus, the standard for high BPV was a between-interval difference higher than 15 mmHg for SBP and 12 mmHg for DBP. Statistical analysis Variables are presented as numbers with percentages or means with standard deviations (SDs). To compare the characteristics between participants with and without depressive mood, the chi-square test was performed for categorical variables and the t -test was performed for continuous variables. Binary logistic regression analysis was performed to estimate the odds ratios (ORs) and 95% confidence intervals (CIs) for associations between high BPV and each variable, including depressive mood. Multivariate logistic regression analysis was performed to determine associations of high BPV with age, sex, BMI, use of antihypertensive medication, and depressive mood. All statistical analyses were performed using SPSS ver. 21.0 (IBM Co., Armonk, NY, USA). A two-tailed P-value < 0.05 was considered statistically significant. Results Characteristics of the participants A total of 1040 participants were initially enrolled, but 88 of them were excluded because they did not undergo an initial BP measurement. Among the remaining 952 participants, 485 were lost to first or second follow-up, 44 were excluded due to a lack of follow-up BP check, and 52 were excluded because CES-D scores or previous medical history were missing (Fig. 1 ). Of the remaining 371 participants, 43 (11.6%) had depressive mood according to their CES-D scores. The baseline characteristics of the participants are shown in Table 1 . Mean age was 60.08 ± 8.06 years overall and did not significantly differ between participants with and without depressive mood (58.98 ± 7.61 vs. 60.22 ± 8.12 years, P = 0.343). A higher percentage of women than men had depressive mood (16.1% vs. 7.0%, P = 0.009). More than half of participants (55.8%) were taking antihypertensive medication, 19.4% were taking an oral hypoglycemic agent or insulin, and 41.2% were taking lipid-lowering agents. Histories of medications for HTN, diabetes, and dyslipidemia did not significantly differ between participants with and without depressive mood. Table 1 Baseline characteristics of study participants Characteristics Total (n = 371) Participants without depressive mood (n = 328) Participants with depressive mood (n = 43) P-value N (%) or mean (SD) Age (years) Mean (SD) 60.08 (8.06) 60.22 (8.12) 58.98 (7.61) 0.343 < 50 34 (9.2) 31 (91.2) 3 (8.8) 0.503 50–59 122 (32.9) 103 (84.4) 19 (15.6) 60–69 178 (48.0) 161 (90.4) 17 (9.6) ≥ 70 37 (10.0) 33 (89.2) 4 (10.8) Sex Men 185 (49.9) 172 (93.0) 13 (7.0) 0.009 Women 186 (50.1) 156 (83.9) 30 (16.1) BMI (kg/m 2 ) Mean (SD) 25.05 (3.19) 25.14 (3.25) 24.33 (2.68) 0.124 12 183 (49.3) 166 (90.7) 17 (9.3) 0.328 12 108 (29.1) 94 (87.0) 14 (13.0) < 12 78 (21.0) 66 (84.6) 12 (15.4) Unknown 2 (0.5) Monthly income (10,000 Won/month) ≥ 600 114 (30.7) 106 (93.0) 8 (7.0) 0.152 400–599 78 (21.0) 67 (85.9) 11 (14.1) 200–399 113 (30.5) 100 (88.5) 13 (11.5) < 200 54 (14.6) 44 (81.5) 10 (18.5) Unknown 12 (3.2) Medication Hypertension 207 (55.8) 184 (88.9) 23 (11.3) 0.747 Diabetes mellitus 72 (19.4) 67 (93.1) 5 (6.9) 0.219 Hyperlipidemia 153 (41.2) 139 (90.8) 14 (9.2) 0.251 Initial and follow-up BP measurements Table 2 shows the initial and follow-up BP measurements of the participants according to depressive mood. Mean SBP and DBP at the initial, first follow-up, and second follow-up visits did not significantly differ between participants with and without depressive mood. Average SBP and DBP variation was 11.72 and 8.38 mmHg, respectively, and neither SBP nor DBP variation significantly differed between participants with and without depressive mood. When SBP variation was divided into quartiles, the percentages of patients with depressive mood were 8.6%, 9.6%, 8.0%, and 19.0% in the first, second, third, and fourth quartiles, respectively. The percentage of patients with depressive mood was significantly higher in the fourth quartile of SBP variation, but did not differ significantly between the other quartiles. Table 2 Initial and follow-up BP of participants according to depressive mood Characteristics Total Participants without depressive mood Participants with depressive mood (n = 371) (n = 328) (n = 43) Mean (SD) P-value SBP (mmHg) Initial 126.18 (13.20) 126.49 (13.02) 123.77 (14.44) 0.203 First follow-up 125.01 (13.55) 125.22 (13.71) 123.44 (12.23) 0.312 Second follow-up 124.04 (13.68) 124.22 (13.47) 122.67 (15.27) 0.414 DBP (mmHg) Initial 77.46 (10.06) 77.43(10.05) 77.65 (10.25) 0.894 First follow-up 76.04 (9.37) 76.08 (9.30) 75.70 (9.98) 0.802 Second follow-up 74.04 (9.46) 73.96 (9.39) 74.67 (10.01) 0.641 Average SBP variation (mmHg) 11.72 (7.42) 11.50 (7.45) 13.36 (7.11) 0.124 Average DBP variation (mmHg) 8.38 (6.35) 8.49 (6.33) 7.51 (6.52) 0.344 N (%) P-value SBP variation quartile First 81 (21.8) 74 (91.4) 7 (8.6) 0.034 Second 115 (31.0) 104 (90.4) 11 (9.6) Third 75 (20.2) 69 (92.0) 6 (8.0) Fourth 100 (27.0) 81 (81.0) 19 (19.0) DBP variation quartile First 102 (27.5) 87(85.3) 15 (14.7) 0.306 Second 97 (26.1) 85 (87.6) 12 (12.4) Third 86 (23.2) 80 (93.0) 6 (7.0) Fourth 86 (23.2) 76 (88.4) 10 (11.6) Logistic regression analysis of associations of high BPV with participant characteristics and depressive mood Table 3 presents the individual ORs for factors associated with high BP variability. We estimated univariate and multivariate ORs for age, sex, BMI, use of antihypertensive medication, and depressive mood. In multivariate analysis, the OR for high SBP variability in participants with depressive mood was more than twice that in participants without depressive mood (OR: 2.26, 95% CI: 1.11–4.60, P = 0.024). By contrast, depressive mood was not associated with high DBP variability (OR: 0.86, 95% CI: 0.38–1.91, P = 0.702). Table 3 Logistic regression analysis of factors associated with high SBP variability and DBP variability High SBP variability High DBP variability Characteristics N (%) Crude OR Multivariate OR N (%) Crude OR Multivariate OR OR (95% CI) P-value OR (95% CI) P-value OR (95% CI) P-value* OR (95% CI) P-value* Age (years) < 50 1 (2.9) 1 (Reference) 1 (Reference) 3 (8.8) 1 (Reference) 1 (Reference) 50–59 36 (29.5) 13.81 (1.82–104.88) 0.011 9.9 (1.28–78.12) 0.028 34 (27.9) 3.99 (1.15–13.93) 0.030 3.39 (0.94–12.21) 0.062 60–69 45 (25.3) 11.17 (1.48–84.00) 0.019 10.26 (1.32–79.81) 0.026 41 (23.0) 3.09 (0.90–10.64) 0.073 2.59 (0.72–9.24) 0.144 ≥ 70 18 (48.6) 31.26 (3.86–253.08) 0.001 31.91 (3.74–272.33) 0.002 8 (21.6) 2.85 (0.69–11.80) 0.148 2.67 (0.1–11.69) 0.193 Sex Men 45 (24.3) 1 (Reference) 1 (Reference) 40 (21.6) 1 (Reference) 1 (Reference) Women 55 (29.6) 1.31 (0.82–2.07) 0.255 1.63 (0.96–2.78) 0.072 46 (24.7) 1.19 (0.74–1.93) 0.478 1.25 (0.74–2.11) 0.414 BMI (kg/m 2 ) < 23.0 22 (26.5) 1 (Reference) 1 (Reference) 15 (18.1) 1 (Reference) 1 (Reference) 23.0–24.9 28 (27.7) 1.06 (0.55–2.05) 0.854 0.96 (0.47–1.95) 0.909 25 (24.8) 1.49 (0.73–3.06) 0.276 1.41 (0.67–2.94) 0.366 ≥ 25.0 45 (26.3) 0.99 (0.55–1.79) 0.974 0.80 (0.42–1.56) 0.518 41 (24.0) 1.43 (0.74–2.77) 0.289 1.28 (0.63–2.57) 0.493 Antihypertensive medication No 33 (20.1) 1 (Reference) 1 32 (19.5) 1 (Reference) 1 (Reference) Yes 67 (32.4) 1.96 (1.21–3.18) 0.006 1.77 (1.02–3.05) 0.042 54 (26.1) 1.46 (0.89–2.39) 0.137 1.29 (0.74–2.23) 0.371 Depressive mood No 81 (24.7) 1 (Reference) 1 76 (23.2) 1 (Reference) 1 (Reference) Yes 19 (44.2) 2.41 (1.26–4.63) 0.008 2.26 (1.11–4.60) 0.024 10 (23.3) 1.01 (0.47–2.13) 0.990 0.86 (0.38–1.91) 0.702 Discussion In this study, we found a significant association between depressive mood and visit-to-visit SBP variability in primary care patients. Furthermore, advanced age and use of antihypertensive medication were also linked with high SBP variability. However, we did not observe an association between high DBP variability and depression. We can apply our results to clinical settings for primary care practice because all participants were patients visiting primary care clinics for routine health checkups or management of chronic diseases including HTN. Based on our results, careful BP monitoring might be necessary for patients with depressive mood visiting primary care regardless of the presence of HTN. Previous studies reported autonomic dysfunction in individuals with depression [ 27 – 29 ], which is characterized by elevated plasma or urinary levels of catecholamine compared with controls [ 27 ]. Additionally, depressed patients may exhibit exaggerated heart rate responses to physical or psychological stressors, even those without other medical conditions [ 27 , 29 ]. Building upon these results, the present study showed that depressive mood can affect BPV, possibly due to autonomic dysfunction in patients with depressive mood. Our findings revealed that advanced age was related to higher SBP variability, particularly among participants aged 70 years or older, who exhibited a more than 30-fold increase in the OR for high SBP variability compared with participants in their 40s. These results are consistent with prior research that reported an association between BPV and advanced age [ 30 – 33 ]. This could be an effect of increased arterial stiffness in older age, with associated alterations in the arterial vessel wall and increased BPV [ 14 ]. Our study population consisted of primary care patients with and without HTN, and we found that participants taking antihypertensive medication were more likely to exhibit SBP variability than participants not taking antihypertensive medication. However, DBP variability was not associated with depressive mood, advanced age, or use of antihypertensive medication. This lack of association could be due to an age-related decrease in DBP. Moreover, the quartile range of DBP variability was lower than that of SBP variability. Furthermore, SBP variability is linked to mortality [ 31 , 34 , 35 ] and cardiovascular diseases [ 34 , 36 – 38 ]. Hence, SBP variability may reflect variations in morbidity and mortality better than DBP variability. This study has several limitations. First, the study only measured BP during clinic visits and did not include at-home BP measurements or daily BPV. Second, our study evaluated the depressive mood of participants only once (at study recruitment) and therefore we could not consider changes in mood over time as the study progressed, including at the follow-up BP checks. Thus, we could not evaluate whether changed mood status was associated with changes in BPV. Finally, although we considered several potential confounding factors that could affect BPV, such as age, sex, BMI, and use of antihypertensive medication, unmeasured residual confounding factors may have existed. In addition, patients visiting primary physicians were recruited for this study and therefore several participants with chronic diseases were included, and these diseases may have affected their BPV. Despite these limitations, our study is meaningful because it examined BPV in primary care patients and used a standardized questionnaire (CES-D scale) concerning depressive mood [ 26 ]. Based on our study, BPV should be closely monitored in patients with depressive mood, with old age, and taking antihypertensive drugs. In particular, when patients with depressive mood visit primary care clinics, monitoring of visit-to-visit SBP might be important for their healthcare. Conclusion Based on our findings, it is crucial to closely monitor BPV in patients with depression, elderly patients, and patients taking antihypertensive medication. This is important because patients presenting with symptoms of depression are commonly encountered in clinical practice and require not only management of their depression but also close monitoring of their BP. abbreviations BMI, body mass index; BP, blood pressure; BPV, blood pressure variability; CES-D, Center for Epidemiologic Studies Depression; CI, confidence interval; DBP, diastolic blood pressure; FACTS, Family Cohort Study in Primary Care; HTN, hypertension; OR, odds ratio; SBP, systolic blood pressure; SD, standard deviation. Declarations Ethics approval and consent to participate This study adheres to the ethical guidelines of the 1975 Declaration of Helsinki as reflected in a priori approval by the Institutional Review Board (IRB) of Asan Medical Center (2016-1183). All participants provided written informed consent for participation of the Family Cohort Study in Primary Care. Consent for publication Not applicable Availability of data and materials The data are not publicly shared because we do not have permission from the Institutional Review Board to distribute the data. The analytic methods are available from the corresponding authors upon reasonable request. Competing interests The authors declare that they have no conflicts of interest. Funding This research was part of the FACTS in Korea supported by the Korea Centers for Disease Control and Prevention (2011E7400300), Seoul, Korea. Authors’ contributions JA Lee, S Sunwoo, and YS Kim conceptualized and designed this work. JA Lee conducted all analyses, provided clinical expertise, and helped to interpret the results. GH Lee and JA Lee drafted the manuscript, figure, and tables. All authors reviewed and edited the final manuscript. 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Cite Share Download PDF Status: Published Journal Publication published 07 May, 2024 Read the published version in BMC Primary Care → Version 1 posted Editorial decision: Revision requested 10 Jan, 2024 Reviews received at journal 09 Jan, 2024 Reviews received at journal 29 Sep, 2023 Reviewers agreed at journal 14 Sep, 2023 Reviewers agreed at journal 09 Sep, 2023 Reviewers invited by journal 06 Sep, 2023 Editor assigned by journal 04 Jul, 2023 Editor invited by journal 16 Apr, 2023 Submission checks completed at journal 16 Apr, 2023 First submitted to journal 06 Apr, 2023 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2783850","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":192230609,"identity":"6f24199c-b439-46cd-9463-5d7892c21e1a","order_by":0,"name":"Ga Hee Lee","email":"","orcid":"","institution":"Asan Medical Center","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ga","middleName":"Hee","lastName":"Lee","suffix":""},{"id":192230610,"identity":"4f3f78e8-8d05-4748-9e8b-f77f1ef6a550","order_by":1,"name":"Jung Ah Lee","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyUlEQVRIiWNgGAWjYBACPoYDIEqCh59oLWwQLTZykg3EawGDNGODA0RrYTxj9vBLzeHEzbebn0l83MMgzy9GQDMbwxlzY5ljhxO33TlmJjnjGYPhzNkJBLWYSUuwAbXcSDA25jnAkGBwmygt/4AOm5H+2fgPsVokP7YBvS+RY/iYgTgtx8qkGfts5CRu5BQ+7DkgQdgv/BKHt0n++AaMyhnpGw78OGAjzy9NQAuDxAEGZh4kLgHlYGsaGBh/EKFuFIyCUTAKRjAAALGeQ87WxsQPAAAAAElFTkSuQmCC","orcid":"","institution":"Asan Medical Center","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Jung","middleName":"Ah","lastName":"Lee","suffix":""},{"id":192230611,"identity":"6ba79861-dda6-4180-a3f0-cbc516343e45","order_by":2,"name":"Sung Sunwoo","email":"","orcid":"","institution":"Asan Medical Center","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Sung","middleName":"","lastName":"Sunwoo","suffix":""},{"id":192230612,"identity":"4447b689-447a-470a-8de0-d7e338769ee1","order_by":3,"name":"Young Sik Kim","email":"","orcid":"","institution":"Asan Medical Center","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Young","middleName":"Sik","lastName":"Kim","suffix":""}],"badges":[],"createdAt":"2023-04-06 05:44:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2783850/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2783850/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12875-024-02404-6","type":"published","date":"2024-05-07T04:01:07+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":35944947,"identity":"ffd9e98a-6d69-48ed-874a-4da53dce336c","added_by":"auto","created_at":"2023-04-18 14:58:32","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":80567,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of study participants\u003c/p\u003e","description":"","filename":"figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2783850/v1/6eb2ba61de6e65985755c01e.jpg"},{"id":56140571,"identity":"5973a5f5-a22a-4b13-a57b-4852a02e0c90","added_by":"auto","created_at":"2024-05-09 04:35:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1730296,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2783850/v1/c85f327c-ee6a-4bc4-a0c2-7665e877ad5e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Relationship between visit-to-visit blood pressure variability and depressive mood in Korean primary care patients","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDepression is a common disease that often has a chronic-recurrent course [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] and affects approximately 2\u0026ndash;4% of the community population and 10% of primary care patients [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Major depressive disorder is one of the leading causes of disease burden worldwide [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], and depressive symptoms are linked to major chronic and cardiovascular diseases [\u003cspan additionalcitationids=\"CR6 CR7\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBlood pressure (BP) variability (BPV) refers to the oscillations in BP that occur within a range of time, such as minutes, over a period of 24 h or longer. This phenomenon is thought to be due to intricate interactions among extrinsic behavioral factors and intrinsic cardiovascular regulatory mechanisms [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Recent research indicates that BPV is independently associated with cardiovascular events and target organ damage [\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The 24 h ambulatory BP monitoring method is commonly used to evaluate short-term BPV [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], while long-term BPV is typically evaluated based on BP measurements obtained during periodic visits to clinics, commonly conducted monthly or yearly [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePrevious research of the relationship between depression and BP has produced conflicting findings [\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Several studies reported that depression is associated with decreased BP [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], while other studies found that depression is related to an increased risk of hypertension (HTN) [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Collectively, these findings indicate that there is considerable inconsistency regarding the association between depression and BP.\u003c/p\u003e \u003cp\u003eThe associations between BPV and emotional status, such as depressive symptoms or anxiety, are relatively consistent in prior research [\u003cspan additionalcitationids=\"CR22 CR23\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], although fewer studies evaluated BPV as a factor. Elderly-onset depression affects diurnal variations in BP and is associated with cerebral infarction [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Furthermore, a study reported a significant association between late-onset depression and higher systolic BPV [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Despite the established link between depression and BPV, there is a paucity of research about the association between long-term visit-to-visit BPV and depression [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Therefore, this study aimed to evaluate the effect of depressive mood on long-term visit-to-visit BPV among primary care patients in Korea.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy subjects\u003c/h2\u003e \u003cp\u003eThe Family Cohort Study in Primary Care (FACTS) was established to evaluate the effects of the familial environment on health of primary care patients. The study cohort was based on couples and included married, cohabitating, separated, and divorced individuals. Participants (both partners of the couples) were recruited from people aged between 40 and 75 years who visited the department of family medicine at one of 22 university hospitals nationwide from April 2009 to June 2011 for periodic health checkups or treatment of chronic diseases such as HTN, diabetes, and dyslipidemia. Data of individual participants were used in this study. All participants provided written informed consent, and this survey was approved by the Institutional Review Board of Asan Medical Center (2016\u0026thinsp;\u0026minus;\u0026thinsp;1183).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStudy characteristics used as variables\u003c/h2\u003e \u003cp\u003eDemographic characteristics were prospectively collected by interviewers or primary care physicians using questions regarding educational status, monthly income, and previous medical history, including HTN, diabetes, and hyperlipidemia. Educational level was categorized into three groups: \u0026lt; 12 years, 12 years, and \u0026gt;\u0026thinsp;12 years. Monthly income was evaluated by total household income using a single question and was divided into four categories: \u0026lt; 2.00\u0026nbsp;million Won (\u003cspan\u003e$\u003c/span\u003e1715), 2.00\u0026ndash;3.99\u0026nbsp;million Won (\u003cspan\u003e$\u003c/span\u003e1715\u0026ndash;3430), 4.00\u0026ndash;5.99\u0026nbsp;million Won (\u003cspan\u003e$\u003c/span\u003e3430\u0026ndash;5145), and \u0026ge;\u0026thinsp;6.00\u0026nbsp;million Won (\u003cspan\u003e$\u003c/span\u003e5145).\u003c/p\u003e \u003cp\u003eThe presence of HTN, diabetes, or dyslipidemia was determined from the study participants\u0026rsquo; medical records, which defined when the participants were reported to have any of these diseases and when they started taking antihypertensive medications, oral hypoglycemic agents, insulin, or lipid-lowering agents. Height and body weight were measured to the nearest 0.1 cm and 0.1 kg by trained interviewers. Body mass index (BMI) was calculated as [weight (kg)]/[height (m)]\u003csup\u003e2\u003c/sup\u003e and was categorized into three groups: \u0026lt; 23.0 kg/m\u003csup\u003e2\u003c/sup\u003e, 23.0\u0026ndash;24.9 kg/m\u003csup\u003e2\u003c/sup\u003e, and \u0026ge;\u0026thinsp;25.0 kg/m\u003csup\u003e2\u003c/sup\u003e. BP was measured using a mercury manometer after 10 min of rest in the sitting position [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eDefinition of depressive mood and high visit-to-visit BPV\u003c/h2\u003e \u003cp\u003eDepressive mood was assessed using a Korean-type Center for Epidemiologic Studies Depression (CES-D) scale. Depressive mood was defined when subjects had a score of 21 points or more [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. BP was measured at the initial visit and first and second follow-up visits. The visit intervals were between 6 and 24 months. Visit-to-visit systolic BP (SBP) variability was defined as the average SBP difference between the initial visit and first follow-up visit and between the first and second follow-up visits. Visit-to-visit diastolic BP (DBP) variability was calculated using the same method. High BPV was defined as high when it fell within the fourth quartile (higher than the 75\u003csup\u003eth\u003c/sup\u003e percentile) of average SBP variation and DBP variation, respectively. Thus, the standard for high BPV was a between-interval difference higher than 15 mmHg for SBP and 12 mmHg for DBP.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eVariables are presented as numbers with percentages or means with standard deviations (SDs). To compare the characteristics between participants with and without depressive mood, the chi-square test was performed for categorical variables and the \u003cem\u003et\u003c/em\u003e-test was performed for continuous variables. Binary logistic regression analysis was performed to estimate the odds ratios (ORs) and 95% confidence intervals (CIs) for associations between high BPV and each variable, including depressive mood. Multivariate logistic regression analysis was performed to determine associations of high BPV with age, sex, BMI, use of antihypertensive medication, and depressive mood. All statistical analyses were performed using SPSS ver. 21.0 (IBM Co., Armonk, NY, USA). A two-tailed P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristics of the participants\u003c/h2\u003e \u003cp\u003eA total of 1040 participants were initially enrolled, but 88 of them were excluded because they did not undergo an initial BP measurement. Among the remaining 952 participants, 485 were lost to first or second follow-up, 44 were excluded due to a lack of follow-up BP check, and 52 were excluded because CES-D scores or previous medical history were missing (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Of the remaining 371 participants, 43 (11.6%) had depressive mood according to their CES-D scores. The baseline characteristics of the participants are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Mean age was 60.08\u0026thinsp;\u0026plusmn;\u0026thinsp;8.06 years overall and did not significantly differ between participants with and without depressive mood (58.98\u0026thinsp;\u0026plusmn;\u0026thinsp;7.61 vs. 60.22\u0026thinsp;\u0026plusmn;\u0026thinsp;8.12 years, P\u0026thinsp;=\u0026thinsp;0.343). A higher percentage of women than men had depressive mood (16.1% vs. 7.0%, P\u0026thinsp;=\u0026thinsp;0.009). More than half of participants (55.8%) were taking antihypertensive medication, 19.4% were taking an oral hypoglycemic agent or insulin, and 41.2% were taking lipid-lowering agents. Histories of medications for HTN, diabetes, and dyslipidemia did not significantly differ between participants with and without depressive mood.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of study participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;371)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eParticipants without depressive mood (n\u0026thinsp;=\u0026thinsp;328)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eParticipants with depressive mood\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;43)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eN (%) or mean (SD)\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 (years)\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\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60.08 (8.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60.22 (8.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58.98 (7.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.343\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34 (9.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31 (91.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (8.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.503\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50\u0026ndash;59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e122 (32.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e103 (84.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19 (15.6)\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\u003e60\u0026ndash;69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e178 (48.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e161 (90.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17 (9.6)\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\u003e\u0026ge;\u0026thinsp;70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37 (10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33 (89.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (10.8)\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\u003eSex\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\u003eMen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e185 (49.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e172 (93.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13 (7.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWomen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e186 (50.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e156 (83.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30 (16.1)\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\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.05 (3.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.14 (3.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.33 (2.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.124\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;23.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e83 (22.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72 (86.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11 (13.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.139\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e23.0\u0026ndash;24.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e101 (27.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85 (84.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (15.8)\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\u003e\u0026ge;\u0026thinsp;25.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e171 (46.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e157 (91.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14 (8.2)\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\u003eEducation (years)\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\u003e\u0026gt;\u0026thinsp;12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e183 (49.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e166 (90.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17 (9.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.328\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e108 (29.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e94 (87.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14 (13.0)\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\u003e\u0026lt;\u0026thinsp;12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78 (21.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66 (84.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (15.4)\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\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (0.5)\u003c/p\u003e \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\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMonthly income (10,000 Won/month)\u003c/p\u003e \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\u003e\u0026ge;\u0026thinsp;600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e114 (30.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e106 (93.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (7.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.152\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e400\u0026ndash;599\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78 (21.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67 (85.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11 (14.1)\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\u003e200\u0026ndash;399\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e113 (30.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100 (88.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13 (11.5)\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\u003e\u0026lt;\u0026thinsp;200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54 (14.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44 (81.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (18.5)\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\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (3.2)\u003c/p\u003e \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\u003eMedication\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\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e207 (55.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e184 (88.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23 (11.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.747\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes mellitus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72 (19.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67 (93.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (6.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.219\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHyperlipidemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e153 (41.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e139 (90.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14 (9.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.251\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eInitial and follow-up BP measurements\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the initial and follow-up BP measurements of the participants according to depressive mood. Mean SBP and DBP at the initial, first follow-up, and second follow-up visits did not significantly differ between participants with and without depressive mood. Average SBP and DBP variation was 11.72 and 8.38 mmHg, respectively, and neither SBP nor DBP variation significantly differed between participants with and without depressive mood. When SBP variation was divided into quartiles, the percentages of patients with depressive mood were 8.6%, 9.6%, 8.0%, and 19.0% in the first, second, third, and fourth quartiles, respectively. The percentage of patients with depressive mood was significantly higher in the fourth quartile of SBP variation, but did not differ significantly between the other quartiles.\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\u003eInitial and follow-up BP of participants according to depressive mood\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eParticipants without depressive mood\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eParticipants with depressive mood\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\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 \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;371)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;328)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;43)\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP (mmHg)\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\u003eInitial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e126.18 (13.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e126.49 (13.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e123.77 (14.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.203\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirst follow-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e125.01 (13.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e125.22 (13.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e123.44 (12.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.312\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecond follow-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e124.04 (13.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e124.22 (13.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e122.67 (15.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.414\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBP (mmHg)\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\u003eInitial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e77.46 (10.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77.43(10.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e77.65 (10.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.894\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirst follow-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e76.04 (9.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76.08 (9.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75.70 (9.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.802\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecond follow-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74.04 (9.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73.96 (9.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74.67 (10.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.641\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage SBP variation (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.72 (7.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.50 (7.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.36 (7.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.124\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage DBP variation (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.38 (6.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.49 (6.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.51 (6.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.344\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eN (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP variation quartile\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\u003eFirst\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e81 (21.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74 (91.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (8.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecond\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e115 (31.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e104 (90.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11 (9.6)\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\u003eThird\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75 (20.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69 (92.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (8.0)\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\u003eFourth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100 (27.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81 (81.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19 (19.0)\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\u003eDBP variation quartile\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\u003eFirst\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e102 (27.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e87(85.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15 (14.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.306\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecond\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e97 (26.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85 (87.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (12.4)\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\u003eThird\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e86 (23.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80 (93.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (7.0)\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\u003eFourth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e86 (23.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76 (88.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (11.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eLogistic regression analysis of associations of high BPV with participant characteristics and depressive mood\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the individual ORs for factors associated with high BP variability. We estimated univariate and multivariate ORs for age, sex, BMI, use of antihypertensive medication, and depressive mood. In multivariate analysis, the OR for high SBP variability in participants with depressive mood was more than twice that in participants without depressive mood (OR: 2.26, 95% CI: 1.11\u0026ndash;4.60, P\u0026thinsp;=\u0026thinsp;0.024). By contrast, depressive mood was not associated with high DBP variability (OR: 0.86, 95% CI: 0.38\u0026ndash;1.91, P\u0026thinsp;=\u0026thinsp;0.702).\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\u003eLogistic regression analysis of factors associated with high SBP variability and DBP variability\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eHigh SBP variability\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c11\" namest=\"c7\"\u003e \u003cp\u003eHigh DBP variability\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eN (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eCrude OR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eMultivariate OR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eN (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eCrude OR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003eMultivariate OR\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eP-value*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eP-value*\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1 (2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3 (8.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50\u0026ndash;59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e36 (29.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.81 (1.82\u0026ndash;104.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.9 (1.28\u0026ndash;78.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e34 (27.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.99 (1.15\u0026ndash;13.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.39 (0.94\u0026ndash;12.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e60\u0026ndash;69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45 (25.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.17 (1.48\u0026ndash;84.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.26 (1.32\u0026ndash;79.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e41 (23.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.09 (0.90\u0026ndash;10.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.59 (0.72\u0026ndash;9.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.144\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18 (48.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.26 (3.86\u0026ndash;253.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31.91 (3.74\u0026ndash;272.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8 (21.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.85 (0.69\u0026ndash;11.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.67 (0.1\u0026ndash;11.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.193\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45 (24.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e40 (21.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWomen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e55 (29.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.31 (0.82\u0026ndash;2.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.63 (0.96\u0026ndash;2.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e46 (24.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.19 (0.74\u0026ndash;1.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.478\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.25 (0.74\u0026ndash;2.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.414\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt; 23.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22 (26.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e15 (18.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e23.0\u0026ndash;24.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28 (27.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.06 (0.55\u0026ndash;2.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.854\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.96 (0.47\u0026ndash;1.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e25 (24.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.49 (0.73\u0026ndash;3.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.41 (0.67\u0026ndash;2.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.366\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge; 25.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45 (26.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.99 (0.55\u0026ndash;1.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.974\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.80 (0.42\u0026ndash;1.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.518\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e41 (24.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.43 (0.74\u0026ndash;2.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.28 (0.63\u0026ndash;2.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.493\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntihypertensive medication\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33 (20.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e32 (19.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e67 (32.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.96 (1.21\u0026ndash;3.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.77 (1.02\u0026ndash;3.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e54 (26.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.46 (0.89\u0026ndash;2.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.29 (0.74\u0026ndash;2.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.371\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepressive mood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e81 (24.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e76 (23.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19 (44.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.41 (1.26\u0026ndash;4.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.26 (1.11\u0026ndash;4.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e10 (23.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.01 (0.47\u0026ndash;2.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.86 (0.38\u0026ndash;1.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.702\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we found a significant association between depressive mood and visit-to-visit SBP variability in primary care patients. Furthermore, advanced age and use of antihypertensive medication were also linked with high SBP variability. However, we did not observe an association between high DBP variability and depression. We can apply our results to clinical settings for primary care practice because all participants were patients visiting primary care clinics for routine health checkups or management of chronic diseases including HTN. Based on our results, careful BP monitoring might be necessary for patients with depressive mood visiting primary care regardless of the presence of HTN.\u003c/p\u003e \u003cp\u003ePrevious studies reported autonomic dysfunction in individuals with depression [\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], which is characterized by elevated plasma or urinary levels of catecholamine compared with controls [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Additionally, depressed patients may exhibit exaggerated heart rate responses to physical or psychological stressors, even those without other medical conditions [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Building upon these results, the present study showed that depressive mood can affect BPV, possibly due to autonomic dysfunction in patients with depressive mood.\u003c/p\u003e \u003cp\u003eOur findings revealed that advanced age was related to higher SBP variability, particularly among participants aged 70 years or older, who exhibited a more than 30-fold increase in the OR for high SBP variability compared with participants in their 40s. These results are consistent with prior research that reported an association between BPV and advanced age [\u003cspan additionalcitationids=\"CR31 CR32\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. This could be an effect of increased arterial stiffness in older age, with associated alterations in the arterial vessel wall and increased BPV [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Our study population consisted of primary care patients with and without HTN, and we found that participants taking antihypertensive medication were more likely to exhibit SBP variability than participants not taking antihypertensive medication. However, DBP variability was not associated with depressive mood, advanced age, or use of antihypertensive medication. This lack of association could be due to an age-related decrease in DBP. Moreover, the quartile range of DBP variability was lower than that of SBP variability. Furthermore, SBP variability is linked to mortality [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] and cardiovascular diseases [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan additionalcitationids=\"CR37\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Hence, SBP variability may reflect variations in morbidity and mortality better than DBP variability.\u003c/p\u003e \u003cp\u003eThis study has several limitations. First, the study only measured BP during clinic visits and did not include at-home BP measurements or daily BPV. Second, our study evaluated the depressive mood of participants only once (at study recruitment) and therefore we could not consider changes in mood over time as the study progressed, including at the follow-up BP checks. Thus, we could not evaluate whether changed mood status was associated with changes in BPV. Finally, although we considered several potential confounding factors that could affect BPV, such as age, sex, BMI, and use of antihypertensive medication, unmeasured residual confounding factors may have existed. In addition, patients visiting primary physicians were recruited for this study and therefore several participants with chronic diseases were included, and these diseases may have affected their BPV.\u003c/p\u003e \u003cp\u003eDespite these limitations, our study is meaningful because it examined BPV in primary care patients and used a standardized questionnaire (CES-D scale) concerning depressive mood [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Based on our study, BPV should be closely monitored in patients with depressive mood, with old age, and taking antihypertensive drugs. In particular, when patients with depressive mood visit primary care clinics, monitoring of visit-to-visit SBP might be important for their healthcare.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eBased on our findings, it is crucial to closely monitor BPV in patients with depression, elderly patients, and patients taking antihypertensive medication. This is important because patients presenting with symptoms of depression are commonly encountered in clinical practice and require not only management of their depression but also close monitoring of their BP.\u003c/p\u003e"},{"header":"abbreviations ","content":"\u003cp\u003eBMI, body mass index; BP, blood pressure; BPV, blood pressure variability; CES-D, Center for Epidemiologic Studies Depression; CI, confidence interval; DBP, diastolic blood pressure; FACTS, Family Cohort Study in Primary Care; HTN, hypertension; OR, odds ratio; SBP, systolic blood pressure; SD, standard deviation.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis study adheres to the ethical guidelines of the 1975 Declaration of Helsinki as reflected in a priori approval by the Institutional Review Board (IRB) of Asan Medical Center (2016-1183). All participants provided written informed consent for participation of the Family Cohort Study in Primary Care.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConsent for publication\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAvailability of data and materials\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe data are not publicly shared because we do not have permission from the Institutional Review Board to distribute the data. The analytic methods are available from the corresponding authors upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCompeting interests\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis research was part of the FACTS in Korea supported by the Korea Centers for Disease Control and Prevention (2011E7400300), Seoul, Korea.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAuthors\u0026rsquo; contributions\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eJA Lee, S Sunwoo, and YS Kim conceptualized and designed this work. JA Lee conducted all analyses, provided clinical expertise, and helped to interpret the results. GH Lee and JA Lee drafted the manuscript, figure, and tables. All authors reviewed and edited the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAcknowledgments\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eKaton WJ: \u003cstrong\u003eEpidemiology and treatment of depression in patients with chronic medical illness\u003c/strong\u003e. \u003cem\u003eDialogues Clin Neurosci \u003c/em\u003e2011, \u003cstrong\u003e13\u003c/strong\u003e(1):7-23.\u003c/li\u003e\n\u003cli\u003eKaton W, Schulberg H: \u003cstrong\u003eEpidemiology of depression in primary care\u003c/strong\u003e. \u003cem\u003eGen Hosp Psychiatry \u003c/em\u003e1992, \u003cstrong\u003e14\u003c/strong\u003e(4):237-247.\u003c/li\u003e\n\u003cli\u003eWaraich P, Goldner EM, Somers JM, Hsu L: \u003cstrong\u003ePrevalence and incidence studies of mood disorders: a systematic review of the literature\u003c/strong\u003e. \u003cem\u003eCan J Psychiatry \u003c/em\u003e2004, \u003cstrong\u003e49\u003c/strong\u003e(2):124-138.\u003c/li\u003e\n\u003cli\u003eCollaborators GMD: \u003cstrong\u003eGlobal, regional, and national burden of 12 mental disorders in 204 countries and territories, 1990\u0026ndash;2019: a systematic analysis for the Global Burden of Disease Study 2019\u003c/strong\u003e. \u003cem\u003eThe Lancet Psychiatry \u003c/em\u003e2022, \u003cstrong\u003e9\u003c/strong\u003e(2):137-150.\u003c/li\u003e\n\u003cli\u003eElderon L, Whooley MA: \u003cstrong\u003eDepression and cardiovascular disease\u003c/strong\u003e. \u003cem\u003eProgress in cardiovascular diseases \u003c/em\u003e2013, \u003cstrong\u003e55\u003c/strong\u003e(6):511-523.\u003c/li\u003e\n\u003cli\u003eLi H, Zheng D, Li Z, Wu Z, Feng W, Cao X, Wang J, Gao Q, Li X, Wang W: \u003cstrong\u003eAssociation of depressive symptoms with incident cardiovascular diseases in middle-aged and older Chinese adults\u003c/strong\u003e. \u003cem\u003eJAMA network open \u003c/em\u003e2019, \u003cstrong\u003e2\u003c/strong\u003e(12):e1916591-e1916591.\u003c/li\u003e\n\u003cli\u003eCarney RM, Freedland KE: \u003cstrong\u003eDepression and coronary heart disease\u003c/strong\u003e. \u003cem\u003eNat Rev Cardiol \u003c/em\u003e2017, \u003cstrong\u003e14\u003c/strong\u003e(3):145-155.\u003c/li\u003e\n\u003cli\u003eLi H, Zheng D, Li Z, Wu Z, Feng W, Cao X, Wang J, Gao Q, Li X, Wang W\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eAssociation of Depressive Symptoms With Incident Cardiovascular Diseases in Middle-Aged and Older Chinese Adults\u003c/strong\u003e. \u003cem\u003eJAMA Netw Open \u003c/em\u003e2019, \u003cstrong\u003e2\u003c/strong\u003e(12):e1916591.\u003c/li\u003e\n\u003cli\u003eParati G, Ochoa JE, Lombardi C, Bilo G: \u003cstrong\u003eAssessment and management of blood-pressure variability\u003c/strong\u003e. \u003cem\u003eNat Rev Cardiol \u003c/em\u003e2013, \u003cstrong\u003e10\u003c/strong\u003e(3):143-155.\u003c/li\u003e\n\u003cli\u003eChia YC, Lim HM, Ching SM: \u003cstrong\u003eLong-Term Visit-to-Visit Blood Pressure Variability and Renal Function Decline in Patients With Hypertension Over 15 Years\u003c/strong\u003e. \u003cem\u003eJ Am Heart Assoc \u003c/em\u003e2016, \u003cstrong\u003e5\u003c/strong\u003e(11).\u003c/li\u003e\n\u003cli\u003eKawai T, Ohishi M, Kamide K, Onishi M, Takeya Y, Tatara Y, Oguro R, Yamamoto K, Sugimoto K, Rakugi H: \u003cstrong\u003eThe impact of visit-to-visit variability in blood pressure on renal function\u003c/strong\u003e. \u003cem\u003eHypertens Res \u003c/em\u003e2012, \u003cstrong\u003e35\u003c/strong\u003e(2):239-243.\u003c/li\u003e\n\u003cli\u003eStevens SL, Wood S, Koshiaris C, Law K, Glasziou P, Stevens RJ, McManus RJ: \u003cstrong\u003eBlood pressure variability and cardiovascular disease: systematic review and meta-analysis\u003c/strong\u003e. \u003cem\u003eBMJ \u003c/em\u003e2016, \u003cstrong\u003e354\u003c/strong\u003e:i4098.\u003c/li\u003e\n\u003cli\u003eMancia G: \u003cstrong\u003eShort- and long-term blood pressure variability: present and future\u003c/strong\u003e. \u003cem\u003eHypertension \u003c/em\u003e2012, \u003cstrong\u003e60\u003c/strong\u003e(2):512-517.\u003c/li\u003e\n\u003cli\u003eParati G, Ochoa JE, Lombardi C, Bilo G: \u003cstrong\u003eBlood pressure variability: assessment, predictive value, and potential as a therapeutic target\u003c/strong\u003e. \u003cem\u003eCurr Hypertens Rep \u003c/em\u003e2015, \u003cstrong\u003e17\u003c/strong\u003e(4):537.\u003c/li\u003e\n\u003cli\u003eLenoir H, Lacombe JM, Dufouil C, Ducimetiere P, Hanon O, Ritchie K, Dartigues JF, Alperovitch A, Tzourio C: \u003cstrong\u003eRelationship between blood pressure and depression in the elderly. 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mortality and cardiovascular disease: a systematic review and meta-analysis\u003c/strong\u003e. \u003cem\u003eJournal of Hypertension \u003c/em\u003e2017, \u003cstrong\u003e35\u003c/strong\u003e(1):10-17.\u003c/li\u003e\n\u003cli\u003eChowdhury EK, Owen A, Krum H, Wing LM, Nelson MR, Reid CM, Second Australian National Blood Pressure Study Management C: \u003cstrong\u003eSystolic blood pressure variability is an important predictor of cardiovascular outcomes in elderly hypertensive patients\u003c/strong\u003e. \u003cem\u003eJ Hypertens \u003c/em\u003e2014, \u003cstrong\u003e32\u003c/strong\u003e(3):525-533.\u003c/li\u003e\n\u003cli\u003eLiu M, Chen X, Zhang S, Lin J, Wang L, Liao X, Zhuang X: \u003cstrong\u003eAssessment of Visit-to-Visit Blood Pressure Variability in Adults With Optimal Blood Pressure: A New Player in the Evaluation of Residual Cardiovascular Risk?\u003c/strong\u003e \u003cem\u003eJ Am Heart Assoc \u003c/em\u003e2022, \u003cstrong\u003e11\u003c/strong\u003e(9):e022716.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-primary-care","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"famp","sideBox":"Learn more about [BMC Primary Care](https://bmcprimcare.biomedcentral.com/)","snPcode":"","submissionUrl":"https://author-welcome.nature.com/12875","title":"BMC Primary Care","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"depressive mood, blood pressure, blood pressure variability, primary care, visit-to-visit blood pressure variability.","lastPublishedDoi":"10.21203/rs.3.rs-2783850/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2783850/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWe evaluated the effect of depressive mood on long-term visit-to-visit blood pressure (BP) variability (BPV) in primary care patients in Korea.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe Family Cohort Study in Primary Care (FACTS) used a prospective cohort that was established to investigate the relationship between the familial environment and health in Korean primary care patients. Depressive mood was assessed as a score of 21 points or more on a Korean-type Center for Epidemiologic Studies Depression scale. BP was measured at the initial visit and first and second follow-up visits. BPV was calculated using the average of the differences between the measurements at the initial visit and first follow-up visit and at the first and second follow-up visits. High visit-to-visit BPV was defined when the average difference fell within the fourth quartile. Logistic regression analysis was used to estimate the association of high BPV with depressive mood and a range of variables.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eOf the 371 participants, 43 (11.6%) had depressive mood according to the depression score. In multivariate analysis, the odds ratio (OR) (OR: 2.26, 95% confidence interval (CI): 1.11\u0026ndash;4.60) for high systolic BP (SBP) variability in participants with depressive mood was more than twice that in participants without depressive mood. Additionally, older age (OR: 31.91, 95% CI: 3.74\u0026ndash;272.33 among participants aged\u0026thinsp;\u0026ge;\u0026thinsp;70 years) and use of antihypertensive medication (OR: 1.77, 95% CI: 1.02\u0026ndash;3.05) were associated with high SBP variability.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions\u003c/b\u003e\u003c/p\u003e \u003cp\u003eDepressive mood was associated with high visit-to-visit SBP variability in primary care patients. Older age and use of antihypertensive medication were also associated with high SBP variability.\u003c/p\u003e","manuscriptTitle":"Relationship between visit-to-visit blood pressure variability and depressive mood in Korean primary care patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-04-18 14:58:27","doi":"10.21203/rs.3.rs-2783850/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-01-10T09:42:23+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-01-10T02:45:27+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2023-09-29T20:45:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"da14693c-ea38-4543-99e2-e74f9de4edc2","date":"2023-09-14T13:16:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"e2a5027c-7acb-4325-8045-1418d0406812","date":"2023-09-09T11:27:16+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2023-09-06T11:05:50+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-07-04T11:28:28+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2023-04-16T07:44:14+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2023-04-16T07:43:09+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Primary Care","date":"2023-04-06T05:42:28+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-primary-care","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"famp","sideBox":"Learn more about [BMC Primary Care](https://bmcprimcare.biomedcentral.com/)","snPcode":"","submissionUrl":"https://author-welcome.nature.com/12875","title":"BMC Primary Care","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9ecf40cc-543e-4bf2-a0e3-aee2db303b48","owner":[],"postedDate":"April 18th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-05-09T04:01:08+00:00","versionOfRecord":{"articleIdentity":"rs-2783850","link":"https://doi.org/10.1186/s12875-024-02404-6","journal":{"identity":"bmc-primary-care","isVorOnly":false,"title":"BMC Primary Care"},"publishedOn":"2024-05-07 04:01:07","publishedOnDateReadable":"May 7th, 2024"},"versionCreatedAt":"2023-04-18 14:58:27","video":"","vorDoi":"10.1186/s12875-024-02404-6","vorDoiUrl":"https://doi.org/10.1186/s12875-024-02404-6","workflowStages":[]},"version":"v1","identity":"rs-2783850","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2783850","identity":"rs-2783850","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}