The Assessment of the Relationship Between Anticholinergic Burden and Short-Term Blood Pressure Variability

The Assessment of the Relationship Between Anticholinergic Burden and Short-Term Blood Pressure Variability | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Assessment of the Relationship Between Anticholinergic Burden and Short-Term Blood Pressure Variability Furkan KÖŞKER, Reşit Emre ALPARĞAN, Muhammed Ali COŞKUNER, Gökhan KÖKER, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6253850/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 May, 2025 Read the published version in BMC Pharmacology and Toxicology → Version 1 posted 11 You are reading this latest preprint version Abstract Background: This study aimed to investigate the relationship between short-term blood pressure variability (BPV) and anticholinergic burden (ACB) in adults with hypertension. Methods: This study included 238 hypertensive patients aged 50 and older who underwent ambulatory blood pressure monitoring. The medications used by the patients were recorded, and the ACB of each medication was calculated using the ACB Scale. The BPV was assessed based on 24-hour ambulatory blood pressure measurements using three methods: standard deviation (SD), coefficient of variation of the standard deviation (SD-CoV), and weighted standard deviation (wSD), with evaluations conducted for both day-time and night-time periods. Results: A total of 139 patients (58.4%) had no ACB score, 64 (26.9%) had an ACB score of 1, and 35 (14.7%) had an ACB score of 2 or higher. ACB scores were significantly higher among patients with heart disease. Both short-term BPV and ACB tended to increase with age. However, no statistically significant relationship was found between ACB and mean blood pressure, nocturnal blood pressure dips, or any parameters of short-term BPV. Conclusion: No significant association was found between ACB and short-term BPV. To the best of our knowledge, this is the first study to investigate this relationship, which may inspire further research. Anticholinergic burden hypertension ambulatory blood pressure measurement blood pressure variability BACKGROUND Mean clinical blood pressure values are traditionally considered the gold standard for the diagnosis and treatment of hypertension in patients with hypertension; however, recent studies conducted with hypertensive individuals have demonstrated that the evaluation and quantification of blood pressure variability (BPV), in addition to standard blood pressure values have both physiopathological and prognostic significance [ 1 , 2 ]. There is strong evidence suggesting that increased BPV is independently associated with a higher risk of target organ damage, cardiovascular events, and death [ 1 , 3 ]. BPV is examined in three groups: very short, short, and long-term BPV [ 4 ]. Very short-term BPV refers to fluctuations in blood pressure between the pulses [ 4 ]. Short-term BPV relates to changes in blood pressure that occur within 24 hours and is characterized by regular circadian changes such as night-time blood pressure drop and morning blood pressure fluctuation [ 4 ]. A 24-hour ambulatory blood pressure measurement evaluates short-term BPV. In this technique, blood pressure is measured at intervals (usually 15–30 minutes) determined by the clinician day and night [ 1 , 5 ]. Long-term BPV refers to changes in blood pressure daily, visit to visit, and season to season [ 4 ]. Anticholinergic drugs are agents that reduce or block the effects of acetylcholine on smooth muscle cells, glands, and parasympathetic nervous system receptors in the central nervous system. The undesirable effects caused by these agents are referred to as anticholinergic side effects [ 6 ]. Anticholinergic side effects can be examined in two groups, which are central and peripheral side effects. Peripheral side effects include decreased secretion, reduced gastrointestinal motility, constipation, urinary retention, vision problems, tachycardia, hypertension, hot intolerance, and hyperthermia. Anticholinergic central side effects occur due to decreased activity of acetylcholine in the brain, such as confusion, lack of focus, sedation, memory impairment, and decreased cognitive functions. The anticholinergic burden is the cumulative effect of taking one or multiple drugs with anticholinergic properties [ 7 ]. The anticholinergic burden scale (ACB) is a four-point scale based on published data and expert opinions published by Boustani in 2008 and updated in 2012. The anticholinergic load of drugs is assessed with a score of 0 to 3, including no anticholinergic load (0 points), probable anticholinergic load (1 point), and definite anticholinergic load (2 or 3 points). The ACB scale includes 88 drugs with known anticholinergic activity [ 8 – 10 ]. In this study, we investigated the relationship between the anticholinergic burden caused by drugs used in individuals over 50 years of age and diagnosed with hypertension and short-term BPV. METHODS Setting, Participant characteristics. and procedures This prospective, single-center, and observational study was carried out with adult hypertensive patients aged 50 years and over receiving antihypertensive treatment. The participants were enrolled from internal medicine and cardiology outpatient settings of a university hospital between 01.07.2021 and 15.09.2022, and they were enrolled following an ambulatory blood pressure measurement that was conducted. Enrollees younger than 50 years old, without an existing diagnosis of hypertension, who have had medication added or removed from their treatment within the last 3 months, with end-stage cancer, end-stage heart failure, cirrhosis, a history of recent trauma or surgical intervention, with advanced dementia, and those who were hesitant to give written informed consent were excluded. Explanation of aims and protocol of the study, taking history, recording of demographic characteristics including age and gender, alcohol use, smoking, recording of comorbid diseases including hypertension, diabetes mellitus, chronic ischemic heart disease, hypercholesterolemia, congestive heart failure, chronic obstructive pulmonary disease, chronic kidney disease, and the drugs used were carried out face-to-face in a private room under the guarantee of confidentiality based on participants statements. The sample size was calculated using the G* Power 3.1 software according to the following data, and the sample size was found to be 250. The local ethics committee approved the study protocol (2021/ E-50687469-799), and written informed consent was obtained from each participant at enrollment. All procedures followed the Turkish Medicine and Medical Devices Agency Good Clinical Practices Guidelines and the Declaration of Helsinki. Calculation of Anticholinergic Burden The ACB of the drugs used by the patients was calculated according to the scoring system of the Anticholinergic Burden Scale [11]. ACB is a scale based on a systematic literature review of drugs with known anticholinergic activity. It was published in the United States by Malaz Boustani in 2008 and was updated in 2012. The anticholinergic burdens of the drugs are evaluated with a score of 0 to 3, with no anticholinergic burden (0 points), possible anticholinergic burden (1 point), and definite anticholinergic burden (2 or above). The ACB scale includes 88 drugs with known anticholinergic activity. Administration of Ambulatory Blood Pressure Measurement and Interpretation of Short-term Blood Pressure Variability Ambulatory blood pressure was measured with the IEM Mobil-O-Graph device, and sleep and wake times were noted. Standard deviation (SD) of 24-hour consecutive blood pressure measurements, coefficient of variation of the SD (SD-CoV), and weighted mean SD (wSD) variables according to night and daytime were used for evaluating the results of ambulatory blood pressure measurements. Blood pressure decreases typically during sleep [4]. This circadian rhythm in blood pressure has led to a new classification. A decrease of 10% or more in the blood pressure measured at night compared to the daytime value is defined as dipper hypertension, and a decrease of less than 10% is defined as non-dipper hypertension. A higher rate of cardiovascular mortality and morbidity has been observed in patients with non-dipper hypertension [12, 13]. The ambulatory blood pressure meter calculated the SD of 24-hour consecutive blood pressure measurements. The SD-CoV was considered the value obtained by dividing the SD of the blood pressure measurement series by the arithmetic mean of the blood pressure measurements (SD/mean) series and multiplying the result by 100. The wSD according to night and day periods was obtained by adding 14 times the mean standard deviation of the day and 6 times the mean standard deviation of the night and dividing the sum by 20. Statistical Analysis The statistical analyses were performed with the ‘Statistical Package for Social Sciences (SPSS) (Version 26.0, Chicago, Illinois). The Shapiro-Wilk test was used to test the normality of the data. The results were expressed as mean and SD for normally distributed continuous variables. Categorical data were presented as absolute numbers and percentages of the total. The differences between continuous variables were compared using the Student's T-Test for two groups and the One-Way ANOVA test for multiple groups. The differences between skewed variables were compared using the Mann-Whitney U test for two groups and the Kruskal Wallis test for multiple groups. The chi-square test was used to compare categorical variables. The relationship between age and ACB was evaluated by calculating the Pearson Correlation (r), and the relationship between age and short-term BPV parameters was evaluated by calculating the Spearman Correlation (r). The p-value was accepted as <0.05 for statistical significance. RESULTS A total of 238 patients with a mean age of 70 (min:50, max:98, IQR:18) years were included in the study, and 35.7% (n=85) of the participants were male. The most common comorbid diseases other than hypertension noted among the participants were diabetes mellitus (42.9%, n=102), chronic ischemic heart disease (26.9%, n=64), and congestive heart failure (14.3%, n=34). The basic demographic and general characteristics of the participants were also given in Table 1. Table 1 . Basic demographics and general characteristics of participants Total n=238 Age (years), median (IQR), [min-max] 70 (18) [50-98] 50-64 years, n (%) 83 (34.9) ≥65 years, n (%) 155 (65.1) Female gender, n (%) 153 (64.3) Comorbid Diseases Diabetes mellitus, n (%) 102 (42.9) Chronic ischemic heart disease, n (%) 64 (26.9) Congestive heart failure, n (%) 34 (14.3) Chronic kidney disease*, n (%) 28 (11.8) Atrial Fibrillation, n (%) 18 (7.6) Chronic obstructive pulmonary disease, n (%) 12 (5.0) Results of Ambulatory Blood Pressure Measurement SBP (mmHg), 24 hours, mean (SD) 126.68 (16.47) SBP (mmHg), Day interval, mean (SD) 127.58 (16.66) SBP (mmHg), Night interval, mean (SD) 123.51 (17.42) DBP (mmHg), 24 hours, mean (SD) 74.59 (10.36) DBP (mmHg), Day interval, mean (SD) 75.36 (10.61) DBP (mmHg), Night İnterval, mean (SD) 72.18 (11.17) MAP (mmHg), 24 hours, mean (SD) 98.21(12.31) MAP (mmHg), Day interval, mean (SD) 99.06 (12.49) MAP (mmHg), Night interval, mean (SD) 95.45 (13.16) Dippers, n (%) 70 (29.4) N: absolute number; IQR: interquartile range; Min: Minimum; Max: Maximum; SBP: systolic blood pressure; SD: Standard deviation; DBP: diastolic blood pressure; MAP: mean arterial pressure. Of the participants, 139 (58.4%) had no ACB score, 64 (26.9%) had an ACB score of 1, and 35 (14.7%) had an ACB score of 2 or above. While the use of drugs leading to higher ACB scores was significantly more frequent among atherosclerotic cardiovascular disease and congestive heart failure patients (p<0.001 for both), no such significance was observed among patients with diabetes mellitus (p=0.069), chronic kidney disease (p=0.212), and chronic obstructive pulmonary disease (p=0.053). In addition, the correlation test revealed that advancing age was strongly correlated with an increase in ACB (r=0.308, p<0.001). The mean systolic blood pressure (SBP) of the participants was 126.68 (16.47) mmHg, the mean diastolic blood pressure (DBP) was 74.59 (10.36) mmHg, and the mean arterial pressure (MAP) was 98.21(12.31) mmHg (Table 1). The mean SBP, DBP, and MAP of the patients were similar among patients with no (ACB score=0), possible (ACB score=1), or definite (ACB score ≥ 2) ACB (p=0.950, p=0.820, p=0.818, respectively). Among the patients, 29.4% (n:70) were evaluated as dipper and 70.6% (n:168) as non-dipper, and no relationship was found between the decrease in blood pressure at night and the ACB (p=0.266). The comparison of short-term BPV parameters, including SD, SD-CoV, and wSD of SBP, DBP, and MAP, revealed no significant relationship between short-term BPV and ACB (p> 0.05 for all). The compared values of each group and parameter were given in Table 2. Table 2 . The comparison of short-term blood pressure variability parameters of participants. Total (n=238) ACB Score=0 (n=139) ACB Score=1 (n=64) ACB Score≥2 (n=35) P SBP-SD, mmHg, median (IQR) 13.50 (6.33) 13.10 (6.40) 14.05 (5.55) 14.40 (6.60) 0.489 SBP SD-CoV, median (IQR) 10.68 (3.80) 10.39 (4.30) 11.05 (3.12) 10.81 (2.99) 0.298 SBP wSD, median (IQR) 12.57 (5.67) 12.01 (5.74) 13.20 (5.06) 12.70 (5.62) 0.126 DBP-SD, mmHg, median (IQR) 9.65 (3.38) 9.70 (3.45) 9.60 (3.40) 9.80 (2.60) 0.904 DBP, SD-CoV, median (IQR) 13.10 (4.59) 12.62 (4.54) 13.13 (5.04) 13.92 (4.41) 0.518 DBP, wSD, median (IQR) 9.10 (3.03) 9.00 (2.88) 9.06 (3.32) 9.29 (2.72) 0.788 MAP-SD, mmHg, median (IQR) 10.00 (4.07) 9.90 (4.30) 10.05 (3.82) 10.30 (4.00) 0.946 MAP, SD-CoV, median (IQR) 10.23 (3.62) 10.22 (3.89) 10.23 (2.91) 10.68 (3.10) 0.992 MAP, wSD, median (IQR) 11.33 (4.62) 10.97 (4.92) 11.77 (4.28) 11.91 (4.37) 0.177 Dippers, n (%) 70 (29.4) 46 (19.3) 17 (7.1) 7 (2.9) 0.266 N: absolute number; ACB: anticholinergic burden; SBP: systolic blood pressure; SD: Standard deviation; IQR: Interquartile range; SD-CoV: Standard deviation-coefficient of variation; wSD: weighted standard deviation; DBP: diastolic blood pressure, MAP: mean arterial pressure; ACB: anticholinergic burden scale; p<0.05 considered significant. In addition, when the patients were divided into two groups: patients without ACB and those with ACB scores of 1 or above, and SD, SD-CoV, and wSD of SBP, DBP, and MAP were compared, the results similarly showed no significant relationship between any short-term BPV parameter and ACB score (Table-3) (p> 0.05 for all). Also, being either a dipper or a non-dipper hypertension patient was not associated with ACB (p>0.05) (Table 2 and Table 3). Table 3 . The comparison of short-term blood pressure variability parameters of participants. Total (n=238) ACB Score=0 (n=139) ACB Score≥1 (n=99) P SBP-SD, mmHg, median (IQR) 13.50 (6.33) 12.90 (6.40) 14.20 (5.70) 0.240 SBP SD-CoV, median (IQR) 10.68 (3.80) 10.39 (4.30) 10.96 (3.04) 0.120 SBP wSD, median (IQR) 12.57 (5.67) 12.01 (5.74) 14.08 (5.26) 0.051 DBP-SD, mmHg, median (IQR) 9.66 (3.38) 9.70 (3.45) 9.60 (3.10) 0.708 DBP, SD-CoV, median (IQR) 13.10 (4.59) 12.62 (4.54) 13.80 (4.79) 0.499 DBP, wSD, median (IQR) 9.10 (3.03) 9.00 (2.88) 9.20 (3.00) 0.562 MAP-SD, mmHg, median (IQR) 10.00 (4.07) 9.90 (4.30) 10.10 (3.90) 0.854 MAP, SD-CoV, median (IQR) 10.23 (3.62) 10.22 (3.89) 10.24 (2.84) 0.906 MAP, wSD, median (IQR) 11.33 (4.62) 10.97 (4.92) 11.78 (4.43) 0.065 Dippers, n (%) 70 (29.4) 46 (19.3) 24 (10.1) 0.140 N: absolute number; ACB: anticholinergic burden; SBP: systolic blood pressure; SD: Standard deviation; IQR: Interquartile range; SD-CoV: Standard deviation-coefficient of variation; wSD: weighted standard deviation; DBP: diastolic blood pressure, MAP: mean arterial pressure; ACB: anticholinergic burden scale; p<0.05 considered significant. The correlation between age and short-term BPV parameters is reported in Table 4. While all short-term BPV parameters of SBP were strongly correlated with advancing age, the only significant correlations were observed in SD-CoV, wSD of MAP, and SD-CoV of DBP. Table 4 . The correlation of age with short-term blood pressure variability parameters of participants. r P SBP-SD, mmHg, median (IQR) 0.179 0.006 SBP SD-CoV, median (IQR) 0.233 <0.001 SBP wSD, median (IQR) 0.221 <0.001 DBP-SD, mmHg, median (IQR) 0.035 0.593 DBP, SD-CoV, median (IQR) 0.232 <0.001 DBP, wSD, median (IQR) 0.101 0.121 MAP-SD, mmHg, median (IQR) 0.063 0.334 MAP, SD-CoV, median (IQR) 0.170 0.009 MAP, wSD, median (IQR) 0.198 0.002 SBP: systolic blood pressure; SD: Standard deviation; IQR: Interquartile range; SD-CoV: Standard deviation-coefficient of variation; wSD: weighted standard deviation; DBP: diastolic blood pressure, MAP: mean arterial pressure; ACB: anticholinergic burden scale; p<0.05 considered significant. DISCUSSION In this study, we assessed the relationship between ACB and short-term BPV in hypertensive patients over 50 years of age. Results revealed no statistically significant relationship between ACB and mean blood pressure, nocturnal blood pressure dips, or any parameters of short-term BPV. In the literature search, we found no studies indicating a relationship between anticholinergic drug use and blood pressure variability or examining the relationship between anticholinergic drug load and short-term blood pressure variability. Recent studies on BPV have highlighted its adverse effects on cardiovascular outcomes, including increased mortality due to cerebrovascular disease, coronary artery disease, end-stage renal disease, cardiovascular diseases, and overall mortality [ 14 ]. These findings suggest that managing BPV and lowering absolute blood pressure levels may help achieve better cardiovascular protection in patients with hypertension [ 1 ]. As people age, several underlying factors contribute to increased BPV, including hemodynamic instability, atherosclerosis, arterial stiffness, baroreflex dysfunction, endothelial impairment, and subclinical inflammation [ 15 , 16 ]. Similarly, our study found a positive correlation between patients' age and BPV across multiple variables. A study by Reinold et al. using data from approximately 16 million patients in the German Pharmacoepidemiological Research Database found a significant relationship between anticholinergic burden, as measured by the ACB scale, and age [ 17 ]. Similarly, Valladales et al., in a study of 3,760 patients, reported that individuals aged 75 and older were more likely to be prescribed anticholinergic drugs, with one-third of them using at least one such drug at this age [ 18 ]. Our study also found a strong correlation between patients' age and ACB scores, which indicate the ACB of the drugs they used. The use of drugs leading to higher ACB scores was significantly more frequent among the patients with congestive heart failure and atherosclerotic cardiovascular disease [ 19 ]. Similarly, in our study, the ACB score was significantly higher among the patients with congestive heart failure and atherosclerotic cardiovascular disease. This association may be linked to the ACB of certain medications used to manage atherosclerotic cardiovascular disease, including atenolol, metoprolol, digoxin, warfarin, captopril, nifedipine, and isosorbide mononitrate. A study by L. Vetrano in Italy involving 3,761 elderly participants living in nursing homes examined the relationship between anticholinergic drug burden, hospitalization, and mortality. The study found a higher anticholinergic burden associated with increased hospitalization and all-cause mortality in this population [ 20 ]. Similarly, a study by Landi et al. investigating the use of anticholinergic drugs and their adverse effects in a frail elderly population reported a significantly higher frequency of heart failure in patients with a high anticholinergic drug burden [ 19 ]. Ayer et al. investigated the hemodynamic effects of parasympathetic blockade using a peripheral muscarinic antagonist, scopolamine methyl bromide, in rats. The study demonstrated that muscarinic receptor inhibition significantly increased blood pressure and BPV [ 21 ]. However, unlike the findings in rats, our study found no relationship between patients' short-term BPV and the anticholinergic burden of the drugs they used. Our study has several limitations. The findings may not fully represent the general population since it was conducted at a single tertiary healthcare center. Additionally, the relatively small sample size is another limitation. The ACB scale categorizes drugs based on their anticholinergic burden: no burden (0 points), possible burden (1 point), and definite burden (2 or 3 points). In our study, 139 patients had no anticholinergic burden, 64 had a possible burden, and 35 had a definite burden. The relatively small number of patients in each category is a limitation. Furthermore, ambulatory blood pressure measurements were performed on 109 patients hospitalized in the Internal Medicine Clinic for various reasons. Since these patients were not isolated from environmental factors that may influence blood pressure variability, and their underlying conditions requiring hospitalization could also affect blood pressure variability, this represents another study limitation. Our study used the ACB scale published in 2008 and updated in 2012. A systematic review highlighted that the ACB scale was the most frequently cited and validated for adverse outcomes [ 22 ]. However, the ACB scale does not account for certain factors, including the varying effects of drugs on different muscarinic receptor subtypes, potential synergistic or antagonistic drug interactions, the development of tolerance to anticholinergic effects over time, drug duration, and dosage. While the ACB scale is widely cited and associated with adverse outcomes, these limitations should be considered when interpreting the anticholinergic burden [ 22 ]. Additionally, anticholinergic side effects are dose-dependent, and the scale’s categorization (0:1:2:3) may not accurately reflect the proportional anticholinergic activity of different drugs [ 23 ]. CONCLUSION The elderly population is steadily increasing due to rising life expectancy. Both short-term BPV and ACB also tend to rise with age. Increased BPV is independently associated with a higher risk of target organ damage, cardiovascular events, and mortality [ 14 ]. Therefore, BPV should be considered when evaluating hypertension patients' blood pressure. Anticholinergic drugs are linked to various adverse outcomes in older adults, including increased falls, fractures due to falls, cognitive decline, the development of dementia, malnutrition, as well as higher rates of emergency department visits and hospitalizations [ 24 , 25 ]. To minimize such risks associated with ACB, the use of drugs with anticholinergic side effects should be reduced, especially in elderly patients. Medications without anticholinergic properties should be prescribed, and the anticholinergic burden should be carefully considered when selecting medications. This study did not find a statistically significant relationship between ACB and short-term BPV. Our literature review did not identify any studies that have investigated the relationship between anticholinergic drug use and short-term BPV. To the best of our knowledge, this is the first study to explore this potential connection in humans and may pave the way for more comprehensive research in this area. Declarations Ethics approval and consent to participate: The Clinical Research Ethics Committee of Health Sciences University Gülhane Research and Training Hospital approved the study protocol (approval date and number: 2021/ E-50687469-799), and a written informed consent was obtained from each participant before enrolled to the study. Consent for publication: Not applicable Availability of data and materials: The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interests : The authors declare no competing interests. Funding : The authors declare no funding interests. Authors' contributions : All authors took responsibility and took part in the design, data collection, statistical analysis, writing and critical review of the study. Acknowledgements : Nothing to declare. Clinical Trial Number : Not applicable Conflicts of interest : There are no conflict of interest Support : We declare that we did not receive any support other than authors of this manuscript. References Chadachan VM, Ye MT, Tay JC, Subramaniam K, Setia S (2018) Understanding short-term blood-pressure-variability phenotypes: from concept to clinical practice. Int J Gen Med Volume 11:241–254. https://doi.org/10.2147/IJGM.S164903 Sega R, Corrao G, Bombelli M, Beltrame L, Facchetti R, Grassi G, Ferrario M, Mancia G (2002) Blood Pressure Variability and Organ Damage in a General Population. Hypertension 39:710–714. https://doi.org/10.1161/hy0202.104376 Leoncini G, Viazzi F, Storace G, Deferrari G, Pontremoli R (2013) Blood pressure variability and multiple organ damage in primary hypertension. J Hum Hypertens 27:663–670. https://doi.org/10.1038/jhh.2013.45 Höcht C (2013) Blood Pressure Variability: Prognostic Value and Therapeutic Implications. 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Eur Heart J 39:3021–3104. https://doi.org/10.1093/eurheartj/ehy339 Mancia G, Verdecchia P (2015) Clinical Value of Ambulatory Blood Pressure. Circ Res 116:1034–1045. https://doi.org/10.1161/CIRCRESAHA.116.303755 Malik EZ, Abdulhadi B, Mezue KN, Lerma E V., Rangaswami J (2018) Clinical hypertension: Blood pressure variability. Disease-a-Month 64:5–13. https://doi.org/10.1016/j.disamonth.2017.08.003 Bencivenga L, De Souto Barreto P, Rolland Y, Hanon O, Vidal J-S, Cestac P, Vellas B, Rouch L (2022) Blood pressure variability: A potential marker of aging. Ageing Res Rev 80:101677. https://doi.org/10.1016/j.arr.2022.101677 PIERDOMENICO S, LAPENNA D, DITOMMASO R, DICARLO S, ESPOSITO A, DIMASCIO R, BALLONE E, CUCCURULLO F, MEZZETTI A (2006) Blood Pressure Variability and Cardiovascular Risk in Treated Hypertensive Patients. Am J Hypertens 19:991–997. https://doi.org/10.1016/j.amjhyper.2006.03.009 Reinold J, Braitmaier M, Riedel O, Haug U (2021) Anticholinergic burden: First comprehensive analysis using claims data shows large variation by age and sex. PLoS One 16:e0253336. https://doi.org/10.1371/journal.pone.0253336 Valladales-Restrepo LF, Machado-Alba JE (2020) Potentially inappropriate prescriptions of anticholinergic drugs in patients with benign prostatic hyperplasia. The Aging Male 23:785–792. https://doi.org/10.1080/13685538.2019.1595572 Landi F, Dell’Aquila G, Collamati A, Martone AM, Zuliani G, Gasperini B, Eusebi P, Lattanzio F, Cherubini A (2014) Anticholinergic Drug Use and Negative Outcomes Among the Frail Elderly Population Living in a Nursing Home. J Am Med Dir Assoc 15:825–829. https://doi.org/10.1016/j.jamda.2014.08.002 Vetrano DL, La Carpia D, Grande G, Casucci P, Bacelli T, Bernabei R, Onder G, Agabiti N, Bartolini C, Bernabei R, Bettiol A, Bonassi S, Caputi AP, Cascini S, Chinellato A, Cipriani F, Corrao G, Davoli M, Fini M, Gini R, Giorgianni F, Kirchmayer U, Lapi F, Lombardi N, Lucenteforte E, Mugelli A, Onder G, Rea F, Roberto G, Sorge C, Tari M, Trifirò G, Vannacci A, Vetrano DL, Vitale C (2016) Anticholinergic Medication Burden and 5-Year Risk of Hospitalization and Death in Nursing Home Elderly Residents With Coronary Artery Disease. J Am Med Dir Assoc 17:1056–1059. https://doi.org/10.1016/j.jamda.2016.07.012 Ayer A, Antic V, Dulloo AG, Van Vliet BN, Montani J-P (2007) Hemodynamic consequences of chronic parasympathetic blockade with a peripheral muscarinic antagonist. American Journal of Physiology-Heart and Circulatory Physiology 293:H1265–H1272. https://doi.org/10.1152/ajpheart.00326.2007 Salahudeen MS, Duffull SB, Nishtala PS (2015) Anticholinergic burden quantified by anticholinergic risk scales and adverse outcomes in older people: a systematic review. BMC Geriatr 15:31. https://doi.org/10.1186/s12877-015-0029-9 Zheng Y-B, Shi L, Zhu X-M, Bao Y-P, Bai L-J, Li J-Q, Liu J-J, Han Y, Shi J, Lu L (2021) Anticholinergic drugs and the risk of dementia: A systematic review and meta-analysis. Neurosci Biobehav Rev 127:296–306. https://doi.org/10.1016/j.neubiorev.2021.04.031 Naharci MI, Katipoglu B, Tasci I (2022) Association of anticholinergic burden with undernutrition in older adults: A cross‐sectional study. Nutrition in Clinical Practice 37:1215–1224. https://doi.org/10.1002/ncp.10821 Hsu W, Huang S, Lu W, Wen Y, Chen L, Hsiao F (2021) Impact of Multiple Prescriptions With Anticholinergic Properties on Adverse Clinical Outcomes in the Elderly: A Longitudinal Cohort Study in Taiwan. Clin Pharmacol Ther 110:966–974. https://doi.org/10.1002/cpt.2217 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 28 May, 2025 Read the published version in BMC Pharmacology and Toxicology → Version 1 posted Editorial decision: Revision requested 23 Apr, 2025 Reviews received at journal 18 Apr, 2025 Reviews received at journal 07 Apr, 2025 Reviewers agreed at journal 04 Apr, 2025 Reviewers agreed at journal 02 Apr, 2025 Reviewers agreed at journal 28 Mar, 2025 Reviewers invited by journal 27 Mar, 2025 Editor invited by journal 26 Mar, 2025 Editor assigned by journal 19 Mar, 2025 Submission checks completed at journal 19 Mar, 2025 First submitted to journal 18 Mar, 2025 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-6253850","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":442656435,"identity":"2dd9492f-4524-4dab-801f-753b5462323c","order_by":0,"name":"Furkan KÖŞKER","email":"","orcid":"","institution":"University of Health Sciences, Gülhane School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Furkan","middleName":"","lastName":"KÖŞKER","suffix":""},{"id":442656438,"identity":"50c0229d-295f-4a86-9b57-c549e2cd6cff","order_by":1,"name":"Reşit Emre ALPARĞAN","email":"","orcid":"","institution":"University of Health Sciences, Gülhane School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Reşit","middleName":"Emre","lastName":"ALPARĞAN","suffix":""},{"id":442656439,"identity":"e0d87c38-dac5-4092-8405-87b286909e72","order_by":2,"name":"Muhammed Ali COŞKUNER","email":"","orcid":"","institution":"University of Health Sciences, Antalya State Hospital","correspondingAuthor":false,"prefix":"","firstName":"Muhammed","middleName":"Ali","lastName":"COŞKUNER","suffix":""},{"id":442656440,"identity":"b3d33921-9ee7-4201-b01d-14473643155e","order_by":3,"name":"Gökhan KÖKER","email":"","orcid":"","institution":"University of Health Sciences, Antalya Training and Research Hospital","correspondingAuthor":false,"prefix":"","firstName":"Gökhan","middleName":"","lastName":"KÖKER","suffix":""},{"id":442656443,"identity":"7c2b1a8b-d094-4147-8331-af83ce116e00","order_by":4,"name":"Bilgin Bahadır BAŞGÖZ","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzklEQVRIiWNgGAWjYBACAwkeCIMfRCQUkKJFsgGkxYAULQYHwCQRWsylew9+/PHLxt74/OrEDw8MGOT5xQ7g12I551yyNG9fGrPZjbebJYAOM5w5O4GAw27kGEgz9hxmM7txdgNIS4LBbcJajH/+7DnMYzzj7OYfxGoxk+D5cVjCgL93G3G2AP2SZs3bkGYgcYN3m0WCgQRhvwBD7PDNH39s7Pn7z26++aPCRp5fmoAWMGBsAxISYJUSRCgHgz9AzH+AWNWjYBSMglEw0gAAAvJFh9q6cBUAAAAASUVORK5CYII=","orcid":"","institution":"University of Health Sciences, Antalya State Hospital","correspondingAuthor":true,"prefix":"","firstName":"Bilgin","middleName":"Bahadır","lastName":"BAŞGÖZ","suffix":""}],"badges":[],"createdAt":"2025-03-18 13:53:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6253850/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6253850/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s40360-025-00952-w","type":"published","date":"2025-05-28T15:57:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":83782776,"identity":"5cb1e2d3-3d07-4016-9da3-6731a564e5af","added_by":"auto","created_at":"2025-06-02 16:04:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":715911,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6253850/v1/5aa6d866-d9db-4d35-9bcf-7bb66cb29dde.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Assessment of the Relationship Between Anticholinergic Burden and Short-Term Blood Pressure Variability","fulltext":[{"header":"BACKGROUND","content":"\u003cp\u003eMean clinical blood pressure values are traditionally considered the gold standard for the diagnosis and treatment of hypertension in patients with hypertension; however, recent studies conducted with hypertensive individuals have demonstrated that the evaluation and quantification of blood pressure variability (BPV), in addition to standard blood pressure values have both physiopathological and prognostic significance [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. There is strong evidence suggesting that increased BPV is independently associated with a higher risk of target organ damage, cardiovascular events, and death [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. BPV is examined in three groups: very short, short, and long-term BPV [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Very short-term BPV refers to fluctuations in blood pressure between the pulses [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Short-term BPV relates to changes in blood pressure that occur within 24 hours and is characterized by regular circadian changes such as night-time blood pressure drop and morning blood pressure fluctuation [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. A 24-hour ambulatory blood pressure measurement evaluates short-term BPV. In this technique, blood pressure is measured at intervals (usually 15\u0026ndash;30 minutes) determined by the clinician day and night [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Long-term BPV refers to changes in blood pressure daily, visit to visit, and season to season [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAnticholinergic drugs are agents that reduce or block the effects of acetylcholine on smooth muscle cells, glands, and parasympathetic nervous system receptors in the central nervous system. The undesirable effects caused by these agents are referred to as anticholinergic side effects [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Anticholinergic side effects can be examined in two groups, which are central and peripheral side effects. Peripheral side effects include decreased secretion, reduced gastrointestinal motility, constipation, urinary retention, vision problems, tachycardia, hypertension, hot intolerance, and hyperthermia. Anticholinergic central side effects occur due to decreased activity of acetylcholine in the brain, such as confusion, lack of focus, sedation, memory impairment, and decreased cognitive functions. The anticholinergic burden is the cumulative effect of taking one or multiple drugs with anticholinergic properties [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The anticholinergic burden scale (ACB) is a four-point scale based on published data and expert opinions published by Boustani in 2008 and updated in 2012. The anticholinergic load of drugs is assessed with a score of 0 to 3, including no anticholinergic load (0 points), probable anticholinergic load (1 point), and definite anticholinergic load (2 or 3 points). The ACB scale includes 88 drugs with known anticholinergic activity [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this study, we investigated the relationship between the anticholinergic burden caused by drugs used in individuals over 50 years of age and diagnosed with hypertension and short-term BPV.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eSetting, Participant characteristics. and procedures\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis prospective, single-center, and observational study was carried out with adult hypertensive patients aged 50 years and over receiving antihypertensive treatment. The participants were enrolled from internal medicine and cardiology outpatient settings of a university hospital between 01.07.2021 and 15.09.2022, and they were enrolled following an ambulatory blood pressure measurement that was conducted. Enrollees younger than 50 years old, without an existing diagnosis of hypertension, who have had medication added or removed from their treatment within the last 3 months, with end-stage cancer, end-stage heart failure, cirrhosis, a history of recent trauma or surgical intervention, with advanced dementia, and those who were hesitant to give written informed consent were excluded.\u003c/p\u003e\n\u003cp\u003eExplanation of aims and protocol of the study, taking history, recording of demographic characteristics including age and gender, alcohol use, smoking, recording of comorbid diseases including hypertension, diabetes mellitus, chronic ischemic heart disease, hypercholesterolemia, congestive heart failure, chronic obstructive pulmonary disease, chronic kidney disease, and the drugs used were carried out face-to-face in a private room under the guarantee of confidentiality based on participants statements. The sample size was calculated using the G* Power 3.1 software according to the following data, and the sample size was found to be 250. The local ethics committee approved the study protocol (2021/ E-50687469-799), and written informed consent was obtained from each participant at enrollment. All procedures followed the Turkish Medicine and Medical Devices Agency Good Clinical Practices Guidelines and the Declaration of Helsinki.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eCalculation of Anticholinergic Burden\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe ACB of the drugs used by the patients was calculated according to the scoring system of the Anticholinergic Burden Scale [11]. ACB is a scale based on a systematic literature review of drugs with known anticholinergic activity. It was published in the United States by Malaz Boustani in 2008 and was updated in 2012. The anticholinergic burdens of the drugs are evaluated with a score of 0 to 3, with no anticholinergic burden (0 points), possible anticholinergic burden (1 point), and definite anticholinergic burden (2 or above). The ACB scale includes 88 drugs with known anticholinergic activity.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eAdministration of Ambulatory Blood Pressure Measurement and Interpretation of Short-term Blood Pressure Variability\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAmbulatory blood pressure was measured with the IEM Mobil-O-Graph device, and sleep and wake times were noted. Standard deviation (SD) of 24-hour consecutive blood pressure measurements, coefficient of variation of the SD (SD-CoV), and weighted mean SD (wSD) variables according to night and daytime were used for evaluating the results of ambulatory blood pressure measurements.\u0026nbsp;Blood pressure decreases typically during sleep [4]. This circadian rhythm in blood pressure has led to a new classification. A decrease of 10% or more in the blood pressure measured at night compared to the daytime value is defined as dipper hypertension, and a decrease of less than 10% is defined as non-dipper hypertension. A higher rate of cardiovascular mortality and morbidity has been observed in patients with non-dipper hypertension [12, 13].\u003c/p\u003e\n\u003cp\u003eThe ambulatory blood pressure meter calculated the SD of 24-hour consecutive blood pressure measurements. The SD-CoV was considered the value obtained by dividing the SD of the blood pressure measurement series by the arithmetic mean of the blood pressure measurements (SD/mean) series and multiplying the result by 100. The wSD according to night and day periods was obtained by adding 14 times the mean standard deviation of the day and 6 times the mean standard deviation of the night and dividing the sum by 20.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe statistical analyses were performed with the \u0026lsquo;Statistical Package for Social Sciences (SPSS) (Version 26.0, Chicago, Illinois). The Shapiro-Wilk test was used to test the normality of the data. The results were expressed as mean and SD for normally distributed continuous variables. Categorical data were presented as absolute numbers and percentages of the total. The differences between continuous variables were compared using the Student\u0026apos;s T-Test for two groups and the One-Way ANOVA test for multiple groups. The differences between skewed variables were compared using the Mann-Whitney U test for two groups and the Kruskal Wallis test for multiple groups. The chi-square test was used to compare categorical variables. The relationship between age and ACB was evaluated by calculating the Pearson Correlation (r), and the relationship between age and short-term BPV parameters was evaluated by calculating the Spearman Correlation (r). The p-value was accepted as \u0026lt;0.05 for statistical significance.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eA total of 238 patients with a mean age of 70 (min:50, max:98, IQR:18) years were included in the study, and 35.7% (n=85) of the participants were male. The most common comorbid diseases other than hypertension noted among the participants were diabetes mellitus (42.9%, n=102), chronic ischemic heart disease (26.9%, n=64), and congestive heart failure (14.3%, n=34). The basic demographic and general characteristics of the participants were also given in Table 1. \u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"652\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 652px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e. Basic demographics and general characteristics of participants\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 453px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en=238\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 453px;\"\u003e\n \u003cp\u003eAge (years), median (IQR), [min-max]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e70 (18) [50-98]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 453px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 50-64 years, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e83 (34.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 453px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026ge;65 years, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e155 (65.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 453px;\"\u003e\n \u003cp\u003eFemale gender, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e153 (64.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 652px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eComorbid Diseases\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 453px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Diabetes mellitus, n (%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e102 (42.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 453px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Chronic ischemic heart disease, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e64 (26.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 453px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Congestive heart failure, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e34 (14.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 453px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Chronic kidney disease*, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e28 (11.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 453px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Atrial Fibrillation, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e18 (7.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 453px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Chronic obstructive pulmonary disease, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e12 (5.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 652px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eResults of Ambulatory Blood Pressure Measurement\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 453px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;SBP (mmHg), 24 hours, mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e126.68 (16.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 453px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;SBP (mmHg), Day interval, mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e127.58 (16.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 453px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;SBP (mmHg), Night interval, mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e123.51 (17.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 453px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;DBP (mmHg), 24 hours, mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e74.59 (10.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 453px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;DBP (mmHg), Day interval, mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e75.36 (10.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 453px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;DBP (mmHg), Night İnterval, mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e72.18 (11.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 453px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;MAP (mmHg), 24 hours, mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e98.21(12.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 453px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;MAP (mmHg), Day interval, mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e99.06 (12.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 453px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;MAP (mmHg), Night interval, mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e95.45 (13.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 453px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Dippers, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e70 (29.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eN: absolute number; IQR: interquartile range; Min: Minimum; Max: Maximum; SBP: systolic blood pressure; SD: Standard deviation; DBP: diastolic blood pressure; MAP: mean arterial pressure.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOf the participants, 139 (58.4%) had no ACB score, 64 (26.9%) had an ACB score of 1, and 35 (14.7%) had an ACB score of 2 or above. While the use of drugs leading to higher ACB scores was significantly more frequent among\u0026nbsp;atherosclerotic cardiovascular disease and congestive heart failure patients (p\u0026lt;0.001 for both), no such significance was observed among patients with diabetes mellitus (p=0.069), chronic kidney disease (p=0.212), and chronic obstructive pulmonary disease (p=0.053). In addition, the correlation test revealed that advancing age was strongly correlated with an increase in ACB (r=0.308, p\u0026lt;0.001).\u003c/p\u003e\n\u003cp\u003eThe mean systolic blood pressure (SBP) of the participants was 126.68 (16.47) mmHg, the mean diastolic blood pressure (DBP) was 74.59 (10.36) mmHg, and the mean arterial pressure (MAP) was 98.21(12.31) mmHg (Table 1). The mean SBP, DBP, and MAP of the patients were similar among patients with no (ACB score=0), possible (ACB score=1), or definite (ACB score \u0026ge; 2) ACB (p=0.950, p=0.820, p=0.818, respectively).\u0026nbsp;Among the patients, 29.4% (n:70) were evaluated as dipper and 70.6% (n:168) as non-dipper, and no relationship was found between the decrease in blood pressure at night and the ACB (p=0.266).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe comparison of short-term BPV parameters, including SD, SD-CoV, and wSD of SBP, DBP, and MAP, revealed no significant relationship between short-term BPV and ACB (p\u0026gt; 0.05 for all). The compared values of each group and parameter were given in Table 2.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"680\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 680px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e. The comparison of short-term blood pressure variability parameters of participants.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 227px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003cp\u003e(n=238)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eACB Score=0 (n=139)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eACB Score=1 (n=64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eACB Score\u0026ge;2 (n=35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003eSBP-SD, mmHg, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e13.50 (6.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e13.10 (6.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e14.05 (5.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e14.40 (6.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.489\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003eSBP SD-CoV, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e10.68 (3.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e10.39 (4.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e11.05 (3.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e10.81 (2.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.298\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003eSBP wSD, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e12.57 (5.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e12.01 (5.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e13.20 (5.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e12.70 (5.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.126\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003eDBP-SD, mmHg, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e9.65 (3.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e9.70 (3.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e9.60 (3.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e9.80 (2.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.904\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003eDBP, SD-CoV, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e13.10 (4.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e12.62 (4.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e13.13 (5.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e13.92 (4.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.518\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003eDBP, wSD, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e9.10 (3.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e9.00 (2.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e9.06 (3.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e9.29 (2.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.788\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003eMAP-SD, mmHg, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e10.00 (4.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e9.90 (4.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e10.05 (3.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e10.30 (4.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.946\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003eMAP, SD-CoV, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e10.23 (3.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e10.22 (3.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e10.23 (2.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e10.68 (3.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.992\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 227px;\"\u003e\n \u003cp\u003eMAP, wSD, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e11.33 (4.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e10.97 (4.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e11.77 (4.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e11.91 (4.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.177\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 227px;\"\u003e\n \u003cp\u003eDippers, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e70 (29.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e46 (19.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e17 (7.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e7 (2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.266\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eN: absolute number; ACB: anticholinergic burden; SBP: systolic blood pressure; SD: Standard deviation; IQR: Interquartile range; SD-CoV: Standard deviation-coefficient of variation; wSD: weighted standard deviation; DBP: diastolic blood pressure, MAP: mean arterial pressure; ACB: anticholinergic burden scale; p\u0026lt;0.05 considered significant.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn addition, when the patients were divided into two groups: patients without ACB and those with ACB scores of 1 or above, and SD, SD-CoV, and wSD of SBP, DBP, and MAP were compared, the results similarly showed no significant relationship between any short-term BPV parameter and ACB score (Table-3) (p\u0026gt; 0.05 for all). Also, being either a dipper or a non-dipper hypertension patient was not associated with ACB (p\u0026gt;0.05) (Table 2 and Table 3). \u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"680\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 680px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e. The comparison of short-term blood pressure variability parameters of participants.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 227px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003cp\u003e(n=238)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eACB Score=0 (n=139)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eACB Score\u0026ge;1 (n=99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003eSBP-SD, mmHg, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e13.50 (6.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e12.90 (6.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e14.20 (5.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e0.240\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003eSBP SD-CoV, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e10.68 (3.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e10.39 (4.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e10.96 (3.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e0.120\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003eSBP wSD, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e12.57 (5.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e12.01 (5.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e14.08 (5.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003eDBP-SD, mmHg, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e9.66 (3.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e9.70 (3.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e9.60 (3.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e0.708\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003eDBP, SD-CoV, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e13.10 (4.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e12.62 (4.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e13.80 (4.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e0.499\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003eDBP, wSD, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e9.10 (3.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e9.00 (2.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e9.20 (3.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e0.562\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003eMAP-SD, mmHg, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e10.00 (4.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e9.90 (4.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e10.10 (3.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e0.854\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003eMAP, SD-CoV, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e10.23 (3.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e10.22 (3.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e10.24 (2.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e0.906\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 227px;\"\u003e\n \u003cp\u003eMAP, wSD, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e11.33 (4.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e10.97 (4.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e11.78 (4.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e\u0026nbsp;0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 227px;\"\u003e\n \u003cp\u003eDippers, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e70 (29.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e46 (19.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e24 (10.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e0.140\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eN: absolute number; ACB: anticholinergic burden; SBP: systolic blood pressure; SD: Standard deviation; IQR: Interquartile range; SD-CoV: Standard deviation-coefficient of variation; wSD: weighted standard deviation; DBP: diastolic blood pressure, MAP: mean arterial pressure; ACB: anticholinergic burden scale; p\u0026lt;0.05 considered significant.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe correlation between age and short-term BPV parameters is reported in Table 4. While all short-term BPV parameters of SBP were strongly correlated with advancing age, the only significant correlations were observed in\u0026nbsp;SD-CoV, wSD of MAP, and SD-CoV of DBP.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"680\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 680px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 4\u003c/strong\u003e. The correlation of age with short-term blood pressure variability parameters of participants.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 227px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003er\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003eSBP-SD, mmHg, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e0.179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.006\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003eSBP SD-CoV, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e0.233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003eSBP wSD, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e0.221\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003eDBP-SD, mmHg, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003e0.593\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003eDBP, SD-CoV, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e0.232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003eDBP, wSD, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e0.101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003e0.121\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003eMAP-SD, mmHg, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003e0.334\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003eMAP, SD-CoV, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e0.170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.009\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 227px;\"\u003e\n \u003cp\u003eMAP, wSD, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e0.198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSBP: systolic blood pressure; SD: Standard deviation; IQR: Interquartile range; SD-CoV: Standard deviation-coefficient of variation; wSD: weighted standard deviation; DBP: diastolic blood pressure, MAP: mean arterial pressure; ACB: anticholinergic burden scale; p\u0026lt;0.05 considered significant.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eIn this study, we assessed the relationship between ACB and short-term BPV in hypertensive patients over 50 years of age. Results revealed no statistically significant relationship between ACB and mean blood pressure, nocturnal blood pressure dips, or any parameters of short-term BPV.\u003c/p\u003e \u003cp\u003eIn the literature search, we found no studies indicating a relationship between anticholinergic drug use and blood pressure variability or examining the relationship between anticholinergic drug load and short-term blood pressure variability. Recent studies on BPV have highlighted its adverse effects on cardiovascular outcomes, including increased mortality due to cerebrovascular disease, coronary artery disease, end-stage renal disease, cardiovascular diseases, and overall mortality [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. These findings suggest that managing BPV and lowering absolute blood pressure levels may help achieve better cardiovascular protection in patients with hypertension [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAs people age, several underlying factors contribute to increased BPV, including hemodynamic instability, atherosclerosis, arterial stiffness, baroreflex dysfunction, endothelial impairment, and subclinical inflammation [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Similarly, our study found a positive correlation between patients' age and BPV across multiple variables.\u003c/p\u003e \u003cp\u003eA study by Reinold et al. using data from approximately 16\u0026nbsp;million patients in the German Pharmacoepidemiological Research Database found a significant relationship between anticholinergic burden, as measured by the ACB scale, and age [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Similarly, Valladales et al., in a study of 3,760 patients, reported that individuals aged 75 and older were more likely to be prescribed anticholinergic drugs, with one-third of them using at least one such drug at this age [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Our study also found a strong correlation between patients' age and ACB scores, which indicate the ACB of the drugs they used.\u003c/p\u003e \u003cp\u003eThe use of drugs leading to higher ACB scores was significantly more frequent among the patients with congestive heart failure and atherosclerotic cardiovascular disease [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Similarly, in our study, the ACB score was significantly higher among the patients with congestive heart failure and atherosclerotic cardiovascular disease. This association may be linked to the ACB of certain medications used to manage atherosclerotic cardiovascular disease, including atenolol, metoprolol, digoxin, warfarin, captopril, nifedipine, and isosorbide mononitrate.\u003c/p\u003e \u003cp\u003eA study by L. Vetrano in Italy involving 3,761 elderly participants living in nursing homes examined the relationship between anticholinergic drug burden, hospitalization, and mortality. The study found a higher anticholinergic burden associated with increased hospitalization and all-cause mortality in this population [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Similarly, a study by Landi et al. investigating the use of anticholinergic drugs and their adverse effects in a frail elderly population reported a significantly higher frequency of heart failure in patients with a high anticholinergic drug burden [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAyer et al. investigated the hemodynamic effects of parasympathetic blockade using a peripheral muscarinic antagonist, scopolamine methyl bromide, in rats. The study demonstrated that muscarinic receptor inhibition significantly increased blood pressure and BPV [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. However, unlike the findings in rats, our study found no relationship between patients' short-term BPV and the anticholinergic burden of the drugs they used.\u003c/p\u003e \u003cp\u003eOur study has several limitations. The findings may not fully represent the general population since it was conducted at a single tertiary healthcare center. Additionally, the relatively small sample size is another limitation. The ACB scale categorizes drugs based on their anticholinergic burden: no burden (0 points), possible burden (1 point), and definite burden (2 or 3 points). In our study, 139 patients had no anticholinergic burden, 64 had a possible burden, and 35 had a definite burden. The relatively small number of patients in each category is a limitation. Furthermore, ambulatory blood pressure measurements were performed on 109 patients hospitalized in the Internal Medicine Clinic for various reasons. Since these patients were not isolated from environmental factors that may influence blood pressure variability, and their underlying conditions requiring hospitalization could also affect blood pressure variability, this represents another study limitation.\u003c/p\u003e \u003cp\u003eOur study used the ACB scale published in 2008 and updated in 2012. A systematic review highlighted that the ACB scale was the most frequently cited and validated for adverse outcomes [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. However, the ACB scale does not account for certain factors, including the varying effects of drugs on different muscarinic receptor subtypes, potential synergistic or antagonistic drug interactions, the development of tolerance to anticholinergic effects over time, drug duration, and dosage. While the ACB scale is widely cited and associated with adverse outcomes, these limitations should be considered when interpreting the anticholinergic burden [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Additionally, anticholinergic side effects are dose-dependent, and the scale\u0026rsquo;s categorization (0:1:2:3) may not accurately reflect the proportional anticholinergic activity of different drugs [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThe elderly population is steadily increasing due to rising life expectancy. Both short-term BPV and ACB also tend to rise with age. Increased BPV is independently associated with a higher risk of target organ damage, cardiovascular events, and mortality [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Therefore, BPV should be considered when evaluating hypertension patients' blood pressure.\u003c/p\u003e \u003cp\u003eAnticholinergic drugs are linked to various adverse outcomes in older adults, including increased falls, fractures due to falls, cognitive decline, the development of dementia, malnutrition, as well as higher rates of emergency department visits and hospitalizations [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. To minimize such risks associated with ACB, the use of drugs with anticholinergic side effects should be reduced, especially in elderly patients. Medications without anticholinergic properties should be prescribed, and the anticholinergic burden should be carefully considered when selecting medications.\u003c/p\u003e \u003cp\u003eThis study did not find a statistically significant relationship between ACB and short-term BPV. Our literature review did not identify any studies that have investigated the relationship between anticholinergic drug use and short-term BPV. To the best of our knowledge, this is the first study to explore this potential connection in humans and may pave the way for more comprehensive research in this area.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e The Clinical Research Ethics Committee of Health Sciences University G\u0026uuml;lhane Research and Training Hospital approved the study protocol (approval date and number: 2021/ E-50687469-799), and a written informed consent was obtained from each participant before enrolled to the study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e The authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e: The authors declare no funding interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e All authors took responsibility and took part in the design, data collection, statistical analysis, writing and critical review of the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e Nothing to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Number\u003c/strong\u003e\u003cstrong\u003e: \u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest\u003c/strong\u003e: There are no conflict of interest\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupport\u003c/strong\u003e: We declare that we did not receive any support other than authors of this manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eChadachan VM, Ye MT, Tay JC, Subramaniam K, Setia S (2018) Understanding short-term blood-pressure-variability phenotypes: from concept to clinical practice. Int J Gen Med Volume 11:241\u0026ndash;254. https://doi.org/10.2147/IJGM.S164903\u003c/li\u003e\n\u003cli\u003eSega R, Corrao G, Bombelli M, Beltrame L, Facchetti R, Grassi G, Ferrario M, Mancia G (2002) Blood Pressure Variability and Organ Damage in a General Population. Hypertension 39:710\u0026ndash;714. https://doi.org/10.1161/hy0202.104376\u003c/li\u003e\n\u003cli\u003eLeoncini G, Viazzi F, Storace G, Deferrari G, Pontremoli R (2013) Blood pressure variability and multiple organ damage in primary hypertension. J Hum Hypertens 27:663\u0026ndash;670. https://doi.org/10.1038/jhh.2013.45\u003c/li\u003e\n\u003cli\u003eH\u0026ouml;cht C (2013) Blood Pressure Variability: Prognostic Value and Therapeutic Implications. ISRN Hypertension 2013:1\u0026ndash;16. https://doi.org/10.5402/2013/398485\u003c/li\u003e\n\u003cli\u003eParati G, Ochoa JE, Lombardi C, Bilo G (2013) Assessment and management of blood-pressure variability. 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Eur Heart J 39:3021\u0026ndash;3104. https://doi.org/10.1093/eurheartj/ehy339\u003c/li\u003e\n\u003cli\u003eMancia G, Verdecchia P (2015) Clinical Value of Ambulatory Blood Pressure. Circ Res 116:1034\u0026ndash;1045. https://doi.org/10.1161/CIRCRESAHA.116.303755\u003c/li\u003e\n\u003cli\u003eMalik EZ, Abdulhadi B, Mezue KN, Lerma E V., Rangaswami J (2018) Clinical hypertension: Blood pressure variability. Disease-a-Month 64:5\u0026ndash;13. https://doi.org/10.1016/j.disamonth.2017.08.003\u003c/li\u003e\n\u003cli\u003eBencivenga L, De Souto Barreto P, Rolland Y, Hanon O, Vidal J-S, Cestac P, Vellas B, Rouch L (2022) Blood pressure variability: A potential marker of aging. Ageing Res Rev 80:101677. https://doi.org/10.1016/j.arr.2022.101677\u003c/li\u003e\n\u003cli\u003ePIERDOMENICO S, LAPENNA D, DITOMMASO R, DICARLO S, ESPOSITO A, DIMASCIO R, BALLONE E, CUCCURULLO F, MEZZETTI A (2006) Blood Pressure Variability and Cardiovascular Risk in Treated Hypertensive Patients. Am J Hypertens 19:991\u0026ndash;997. https://doi.org/10.1016/j.amjhyper.2006.03.009\u003c/li\u003e\n\u003cli\u003eReinold J, Braitmaier M, Riedel O, Haug U (2021) Anticholinergic burden: First comprehensive analysis using claims data shows large variation by age and sex. PLoS One 16:e0253336. https://doi.org/10.1371/journal.pone.0253336\u003c/li\u003e\n\u003cli\u003eValladales-Restrepo LF, Machado-Alba JE (2020) Potentially inappropriate prescriptions of anticholinergic drugs in patients with benign prostatic hyperplasia. The Aging Male 23:785\u0026ndash;792. https://doi.org/10.1080/13685538.2019.1595572\u003c/li\u003e\n\u003cli\u003eLandi F, Dell\u0026rsquo;Aquila G, Collamati A, Martone AM, Zuliani G, Gasperini B, Eusebi P, Lattanzio F, Cherubini A (2014) Anticholinergic Drug Use and Negative Outcomes Among the Frail Elderly Population Living in a Nursing Home. J Am Med Dir Assoc 15:825\u0026ndash;829. https://doi.org/10.1016/j.jamda.2014.08.002\u003c/li\u003e\n\u003cli\u003eVetrano DL, La Carpia D, Grande G, Casucci P, Bacelli T, Bernabei R, Onder G, Agabiti N, Bartolini C, Bernabei R, Bettiol A, Bonassi S, Caputi AP, Cascini S, Chinellato A, Cipriani F, Corrao G, Davoli M, Fini M, Gini R, Giorgianni F, Kirchmayer U, Lapi F, Lombardi N, Lucenteforte E, Mugelli A, Onder G, Rea F, Roberto G, Sorge C, Tari M, Trifir\u0026ograve; G, Vannacci A, Vetrano DL, Vitale C (2016) Anticholinergic Medication Burden and 5-Year Risk of Hospitalization and Death in Nursing Home Elderly Residents With Coronary Artery Disease. J Am Med Dir Assoc 17:1056\u0026ndash;1059. https://doi.org/10.1016/j.jamda.2016.07.012\u003c/li\u003e\n\u003cli\u003eAyer A, Antic V, Dulloo AG, Van Vliet BN, Montani J-P (2007) Hemodynamic consequences of chronic parasympathetic blockade with a peripheral muscarinic antagonist. American Journal of Physiology-Heart and Circulatory Physiology 293:H1265\u0026ndash;H1272. https://doi.org/10.1152/ajpheart.00326.2007\u003c/li\u003e\n\u003cli\u003eSalahudeen MS, Duffull SB, Nishtala PS (2015) Anticholinergic burden quantified by anticholinergic risk scales and adverse outcomes in older people: a systematic review. BMC Geriatr 15:31. https://doi.org/10.1186/s12877-015-0029-9\u003c/li\u003e\n\u003cli\u003eZheng Y-B, Shi L, Zhu X-M, Bao Y-P, Bai L-J, Li J-Q, Liu J-J, Han Y, Shi J, Lu L (2021) Anticholinergic drugs and the risk of dementia: A systematic review and meta-analysis. Neurosci Biobehav Rev 127:296\u0026ndash;306. https://doi.org/10.1016/j.neubiorev.2021.04.031\u003c/li\u003e\n\u003cli\u003eNaharci MI, Katipoglu B, Tasci I (2022) Association of anticholinergic burden with undernutrition in older adults: A cross‐sectional study. Nutrition in Clinical Practice 37:1215\u0026ndash;1224. https://doi.org/10.1002/ncp.10821\u003c/li\u003e\n\u003cli\u003eHsu W, Huang S, Lu W, Wen Y, Chen L, Hsiao F (2021) Impact of Multiple Prescriptions With Anticholinergic Properties on Adverse Clinical Outcomes in the Elderly: A Longitudinal Cohort Study in Taiwan. Clin Pharmacol Ther 110:966\u0026ndash;974. https://doi.org/10.1002/cpt.2217\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-pharmacology-and-toxicology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"phat","sideBox":"Learn more about [BMC Pharmacology and Toxicology](http://bmcpharmacoltoxicol.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/phat/Default.aspx","title":"BMC Pharmacology and Toxicology","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Anticholinergic burden, hypertension, ambulatory blood pressure measurement, blood pressure variability","lastPublishedDoi":"10.21203/rs.3.rs-6253850/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6253850/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e This study aimed to investigate the relationship between short-term blood pressure variability (BPV) and anticholinergic burden (ACB) in adults with hypertension.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003eThis study included 238 hypertensive patients aged 50 and older who underwent ambulatory blood pressure monitoring. The medications used by the patients were recorded, and the ACB of each medication was calculated using the ACB Scale. The BPV was assessed based on 24-hour ambulatory blood pressure measurements using three methods: standard deviation (SD), coefficient of variation of the standard deviation (SD-CoV), and weighted standard deviation (wSD), with evaluations conducted for both day-time and night-time periods.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eA total of 139 patients (58.4%) had no ACB score, 64 (26.9%) had an ACB score of 1, and 35 (14.7%) had an ACB score of 2 or higher. ACB scores were significantly higher among patients with heart disease. Both short-term BPV and ACB tended to increase with age. However, no statistically significant relationship was found between ACB and mean blood pressure, nocturnal blood pressure dips, or any parameters of short-term BPV.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eNo significant association was found between ACB and short-term BPV. To the best of our knowledge, this is the first study to investigate this relationship, which may inspire further research.\u003c/p\u003e","manuscriptTitle":"The Assessment of the Relationship Between Anticholinergic Burden and Short-Term Blood Pressure Variability","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-15 10:49:30","doi":"10.21203/rs.3.rs-6253850/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-04-23T13:38:01+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-18T07:52:27+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-07T14:26:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"272336390770479373444999043602974077400","date":"2025-04-04T17:30:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"48261561913617467015109036646256758602","date":"2025-04-02T17:43:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"7125697996281792266360613880856732187","date":"2025-03-28T13:51:24+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-27T22:11:18+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-03-26T18:28:54+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-19T08:54:57+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-19T08:49:48+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pharmacology and Toxicology","date":"2025-03-18T13:46:27+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-pharmacology-and-toxicology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"phat","sideBox":"Learn more about [BMC Pharmacology and Toxicology](http://bmcpharmacoltoxicol.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/phat/Default.aspx","title":"BMC Pharmacology and Toxicology","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"04fb2089-9843-4f54-a276-2df6fd5a5d22","owner":[],"postedDate":"April 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-06-02T15:58:48+00:00","versionOfRecord":{"articleIdentity":"rs-6253850","link":"https://doi.org/10.1186/s40360-025-00952-w","journal":{"identity":"bmc-pharmacology-and-toxicology","isVorOnly":false,"title":"BMC Pharmacology and Toxicology"},"publishedOn":"2025-05-28 15:57:00","publishedOnDateReadable":"May 28th, 2025"},"versionCreatedAt":"2025-04-15 10:49:30","video":"","vorDoi":"10.1186/s40360-025-00952-w","vorDoiUrl":"https://doi.org/10.1186/s40360-025-00952-w","workflowStages":[]},"version":"v1","identity":"rs-6253850","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6253850","identity":"rs-6253850","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}