Epicardial Fat Thickness and Nighttime Systolic Blood Pressure Variability in Healthy Young Adults

Epicardial Fat Thickness and Nighttime Systolic Blood Pressure Variability in Healthy Young Adults | 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 Epicardial Fat Thickness and Nighttime Systolic Blood Pressure Variability in Healthy Young Adults Güney Sarıoğlu, Yakup Yiğit This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8595870/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Background: We recognise epicardial fat thickness (EFT) as a biomarker for cardiometabolic risk and blood pressure variability (BPV) as a predictor of cardiovascular morbidity. While studies link them in hypertensive patients, data for healthy young adults are lacking. This study examines the relationship between EFT-BPV in healthy young adults. Demonstrating this relationship in healthy young adults is important for the early detection of individuals at risk and for reducing future cardiovascular mortality. Methods: This study included 172 healthy young adults (18–41 years old). EFT was measured via transthoracic echocardiography, and a 24-hour Ambulatory blood pressure monitoring (ABPM) was used to assess BPV. Laboratory tests, body mass index, and fatty liver index were evaluated. Correlations and regression analyses were used to explore associations; significance was set at p < 0.05. Power analysis confirmed an adequate sample size (N=170, f²=0.15, α=0.05, power=0.80). Results: EFT was significantly correlated with 24-hour systolic BPV (r = 0.171, p = 0.028), 24-hour diastolic BPV (r = 0.173, p = 0.025), nighttime systolic BPV (r = 0.193, p = 0.012), and nighttime diastolic BPV (r = 0.183, p = 0.018). Regression analyses identified basophil count, body mass index (BMI), platelet/basophil ratio, and nighttime systolic BPV as independent predictors of EFT (R² = 0.287, p < 0.001). EFT, smoking, and platelet/basophil ratio predicted nighttime systolic BPV (R² = 0.110, p = 0.002). A history of Coronavirus Disease 2019 (COVID-19) showed a positive but non-significant trend with EFT (p = 0.108). Conclusions: This study reveals a novel link between EFT and nighttime systolic BPV in healthy young adults. blood pressure variability cardiovascular risk epicardial fat thickness healthy young adults Figures Figure 1 Figure 2 1. Introduction Epicardial adipose tissue (EAT) is a metabolically active visceral fat tissue surrounding the heart and exhibits both protective and pathogenic properties. Generally, it provides protection and support to the heart; however, an increase in its quantity or impairment in its function elevates the risk of cardiovascular disease [ 1 – 4 ]. EAT contributes to both local and systemic inflammation by releasing proinflammatory cytokines and mediators. EFT correlates positively with local inflammatory markers, such as PCSK9 and CA125, as well as proinflammatory cytokines [ 5 , 6 ]. The EFT involves nerves around the heart that connect to the sympathetic and parasympathetic systems [ 7 ]. It's a biomarker for cardiometabolic risk, with high EFT linked to localised conditions like coronary artery and myocardial diseases, as well as systemic disorders such as metabolic syndrome, diabetes, and liver diseases. Reducing EFT may help lower cardiovascular risk [ 8 – 10 ]. BPV refers to fluctuations in blood pressure (BP) over time; it can be measured in the short-, medium-, and long-term. Increased BPV is strongly associated with numerous serious health outcomes, including cardiovascular disease, stroke, kidney disease, dementia, and death. [ 11 – 13 ]. BPV is significantly associated with various inflammatory markers in both healthy individuals and hypertensive patients. Studies conducted in children and adults have found significant correlations between blood count-based inflammation markers, such as the lymphocyte/monocyte ratio and neutrophil/lymphocyte ratio, and BPV [ 14 , 15 ]. Autonomic imbalance (particularly sympathetic/parasympathetic imbalance) increases BPV in both hypertensive and normotensive individuals [ 16 ]. Increased EFT is associated with BPV and an impaired circadian BP profile. An increase in EFT may increase BPV through autonomic dysfunction and increased sympathetic activity [ 17 , 18 ]. It has been demonstrated that hypertension and high BP increase EFT, but there is no direct study showing that BPV specifically increases EFT [ 19 ]. According to the current literature, no study has specifically evaluated the relationship between EFT and BPV in a healthy young adult population. Our study investigated the relationship between EFT and BPV, both independent predictors of cardiovascular morbidity and mortality, which have not been previously studied in healthy young adults. We hypothesised that, in healthy, normotensive adults aged 18–41, EFT would correlate positively with BPV via inflammation and sympathetic activation. Understanding this link could aid early detection of cardiovascular risk. 2. Methods 2.1. Study population We conducted a prospective, cross-sectional study involving 172 healthy young adults aged 18–41 years who presented to our hospital's cardiology outpatient clinic with atypical chest pain between October 2025 and November 2025 and agreed to participate, with normal cardiac examination findings. We excluded those with chronic diseases, medication use, active infections, obesity, cachexia, or laboratory evidence of acute infection. The study followed the Declaration of Helsinki and received ethics committee approval (decision 2025/8392). All participants signed a consent form. Generative AI was used solely to structure the discussion and clarify statistics, with human oversight verifying all content. 2.2. Laboratory measurements We assessed the EFT values of the participants using transthoracic echocardiography, performed with a 3.2 MHz transducer on a Philips Affinity 50 ultrasound system (Philips, Andover, MA, USA). We measured EFT in the two-dimensional parasternal long-axis view by positioning the M-mode cursor perpendicular to the aortic annulus along the free wall of the right ventricle at end-diastole. We averaged three consecutive cardiac cycle measurements [20]. We assessed participants' arterial blood pressure parameters using a 24-hour ABPM device (Borsam Echo, ABP-06B, manufactured in China) for a continuous 24-hour period. We calculated the FLI using BMI, waist circumference, serum gamma-glutamyl transferase, and triglyceride levels, following the method of Bedogni et al. [21]. We expressed BMI as weight (kg) divided by the square of height (m2). After exhaling, we measured waist circumference at the navel and above the hip bone using a flexible, non-metallic tape measure. We obtained blood samples from the right or left antecubital vein after an 8- to 10-hour fasting period for analysis. We also reviewed medical records to determine the patient's COVID-19 history. 2.3 Statistical analysis We analysed the data using SPSS software (IBM Corp., IBM SPSS Statistics for Mac, Version 27.0. Armonk, NY: IBM Corp; 2020). By analysing the data in their recorded state, we have avoided any potential errors that could have been introduced by imputing missing values. We performed descriptive analysis to characterise the study population. We tested the normality of the variables by the Kolmogorov-Smirnov test. We used an independent-samples t-test for normally distributed data and a Mann-Whitney U test for non-normally distributed data. We established statistical significance at p < 0.05. Additionally, we considered a 95% confidence interval (CI) for differences not encompassing zero statistically significant. We analysed categorical variables using the appropriate chi-square test and reported results as percentages (%) and absolute numbers. We reported the means and standard deviations for normally distributed continuous variables, and the medians (with interquartile ranges) for non-normally distributed continuous variables. We used Pearson correlation for normally distributed variables and Spearman correlation for non-normally distributed variables to evaluate correlations between continuous variables. A power analysis determined the minimum sample size required for multiple linear regression with the predictive variables. Before commencing the study, we determined that the minimum sample size required for the analysis was 170, assuming a moderate effect size for multiple regression analysis (Cohen's f² = 0.15), α = 0.05, and desired power = 0.80. Additionally, we conducted a multiple linear regression analysis using the variables: age, smoking, gender, BMI, basophil count, Platelet/Basophil ratio, nighttime systolic BP dipping, COVID-19 history, nighttime systolic BPV, and TSH levels to forecast the EFT and nighttime systolic BPV. We select variables based on prior studies and clinical importance. We evaluated the effect size using Cohen's f2. 3. Results 3.1. Participant Characteristics A total of 172 young adults were included in the study. The mean age was 29.08 ± 7.16 years, with 67 (39%) males. Smoking was reported in 64 (37.2%) participants, and 55 (32%) had a history of COVID-19. Mean BMI was 23.36 ± 3 kg/m², and the FLI was 23.88 ± 22.47. Laboratory parameters included hemoglobin 14.11 ± 1.75 g/dl, platelet count 266.12 ± 63.73 × 10³/ml, white blood cell count 7.22 ± 1.76 × 10³/ml, and C-reactive protein 2.0 ± 0.19 mg/l. EFT was 4.1 ± 1.26 mm (Table 1). ABPM revealed a nighttime systolic BP dipping percentage of 10.53 ± 6.17%, a diastolic dipper of 14.99 ± 8.21%, an Ambulatory arterial stiffness index (AASI) of 0.35 ± 0.15, and an Ambulatory blood pressure smoothing index (ABPSI) of 0.78 ± 0.16. Mean 24-hour systolic BPV was 17.62 ± 4.61 mmHg, diastolic BPV 15.22 ± 4.43 mmHg, with higher daytime and lower nighttime variability. An independent-samples t-test revealed that EFT was significantly thicker in males than in females (mean difference: -3.707 mm, 95% CI [-7.350, -0.064], t(170) = -2.009, p = 0.046). 3.2. Correlations Between ABPM Parameters and EFT Pearson correlation analysis showed significant positive associations between EFT and 24-hour systolic BPV (r = 0.171, 95% CI [0.019, 0.314], p = 0.028), 24-hour diastolic BPV (r = 0.173, 95% CI [0.022, 0.317], p = 0.025), nighttime systolic BPV (r = 0.193, 95% CI [0.043, 0.335], p = 0.012), and nighttime diastolic BPV (r = 0.183, 95% CI [0.032, 0.326], p = 0.018). No significant correlations were observed with AASI, ABPSI, or dipping patterns (Table 2). 3.3. Predictors of EFT Multiple linear regression using the enter method was performed to identify independent predictors of EFT. The final model was statistically significant, F(7, 163) = 9.395, p < 0.001, explaining 28.7% of the variance in EFT (R² = 0.287, Adjusted R² = 0.257). Basophil count and BMI were the strongest independent predictors. Platelet/basophil ratio, age, nighttime systolic dipper percentage, and nighttime systolic BPV contributed significantly to the model (Figure 1). Figure 1 . Standardised Coefficients and 95% Confidence Intervals for Independent Predictors of Epicardial Fat Thickness The regression coefficient for COVID-19 infection history on EFT was positive (B = 0.110), but the p-value did not reach statistical significance, and the confidence interval included zero (p = 0.108, 95% CI [-0.030, 0.310] respectively). However, when the COVID-19 History variable was removed from the model, the Adjusted R² value, representing the model's overall explanatory power, decreased (0.257 versus 0.250) (Table 3). 3.4. Predictors of Nighttime Systolic Blood Pressure Variability A separate multiple linear regression using the enter method was performed to identify independent predictors of BPV. The model was significant, F(5, 165) = 4.098, p = 0.002, accounting for 11.0% of the variance (R² = 0.110, Adjusted R² = 0.084) (Table 4). Smoking, EFT, and platelet/basophil ratio were independent predictors. Age and TSH were not significant, but removing Age and thyroid-stimulating hormone (TSH) decreased the Adjusted R² from 0.084 to 0.081 and from 0.084 to 0.078, respectively (Figure 2). Figure 2 . Standardised Coefficients and 95% Confidence Intervals for Independent Predictors of Nighttime Systolic Blood Pressure Variability EFT was associated with several BPV parameters and was predicted by basophil count, BMI, and hematologic ratios. EFT and smoking were key factors for nighttime systolic BPV in this young group. Cohen's f² was calculated to assess the effect size of the regression models. For EFT prediction, Cohen's f² = 0.402 indicated a large effect size. For nighttime systolic BPV prediction, Cohen's f² = 0.124 represented a small-to-medium effect size. Standardised regression coefficients (β) further supported moderate effects for basophil count (β = 0.35) and BMI (β = 0.26) on EFT. 4. Discussion This study is the first to assess the link between EFT and BPV in healthy young adults. Our results align with previous research showing a connection between EFT and BPV in hypertensive individuals. However, they also reveal that this relationship exists in a young, healthy population, thus adding new insights to the literature. BPV describes the variability in BP over time, measured across short, medium, and long durations. High BPV is closely associated with serious health risks such as cardiovascular disease, stroke, kidney disease, dementia, and death, even in normotensive individuals. [11-13]. Early detection of increased blood pressure variability in normotensive healthy young adults is important for reducing future mortality and morbidity due to BPV. Our study found that nighttime systolic BPV was most strongly correlated with EFT. In regression models using variables previously identified as predictors of EFT, we determined that the BPV parameter best predicting EFT was nighttime systolic BPV. In our models predicting EFT with nighttime systolic BPV, although the model is meaningful, EFT's predictive power for nighttime BPV is higher than that of nighttime systolic BPV for EFT. The correlation between nighttime systolic BPV and EFT, especially in young, healthy adults, is mediated by interactions among autonomic nervous system activity, vascular tone, and cardiometabolic risk factors. EAT influences cardiac autonomic functions and vascular reactivity by secreting pro-inflammatory and pro-atherogenic mediators, potentially increasing systolic BPV at night when sympathovagal balance shifts [17,18,22]. Systolic BPV is more sensitive than diastolic BPV to changes in arterial wall elasticity, sympathetic activity, and cardiac output. An increase in EFT may trigger systolic pressure fluctuations, mainly by increasing sympathetic activity and inflammation. Diastolic pressure relates to peripheral vascular resistance and arterial relaxation; in healthy young individuals, these are more stable. Thus, the EFT-systolic BPV relationship may be stronger than with diastolic BPV [23,24]. The literature shows that EFT is particularly elevated in non-dippers (those with minimal nighttime BP reduction) and in individuals with high BPV [25]. Significant correlations have been reported between 24-hour average systolic BP, BP variability, and EFT [26]. BP typically drops during the night (dipping). However, increased EFT prevents this drop by increasing sympathetic activity and inflammation, leading to a "non-dipper" pattern. Therefore, EFT is more strongly associated with night-time systolic BPV in particular [19]. Increased EFT enhances nocturnal sympathetic activity and suppresses parasympathetic tone. This particularly affects nighttime systolic BPV; during the day, the correlation weakens as environmental and behavioural factors become dominant [22]. Systolic BP is more affected by arterial stiffness and sympathetic activity. Diastolic BP, in contrast, relies more on peripheral resistance and volume status; thus, the impact of EFT might be less noticeable in this measure. During the day, environmental factors such as physical activity, stress, and nutrition affect BPV and may mask the effect of EFT. At night, autonomic and hormonal effects are more pronounced [18]. This information explains the pathophysiological mechanisms underlying the strong association between EFT thickness and nocturnal systolic BPV in our study, in light of the literature. As we mentioned, epicardial adipose tissue may elevate nighttime systolic BPV by increasing sympathetic activity and inflammation [18,22] (Graphical abstract). In our study, we determined that age and BMI predict EFT. The positive correlations between EFT, age, and BMI have been replicated in numerous studies. Ageing and increased body mass increase visceral fat, thereby increasing EFT [27]. We also found that basophil levels and the platelet/basophil ratio predict EFT. The association of these systemic inflammation indicators with EFT is consistent with the proinflammatory properties of epicardial adipose tissue [22]. In our multiple linear regression analysis, although a history of COVID-19 infection showed a positive trend with EFT, this association was statistically insignificant. However, removing this variable from the model decreased the Adjusted R². Our finding should be re-evaluated as a potential risk factor in larger studies, reflecting the model's variance explanation. The increased EFT observed in COVID-19 survivors may result from myocardial damage and inflammation caused by the infection, as increased EFT has been linked to these conditions [28]. We found that a low nighttime systolic BP dipping significantly explained the variance in EFT in the regression model. Sengul et al. demonstrated that EFT is higher in non-dippers. The non-dipper profile is related to autonomic dysfunction and increased cardiovascular risk [25]. We found that the platelet/basophil ratio, EFT, and smoking positively predict night-time systolic BP. Age and TSH didn't significantly predict nighttime BPV, but removing them weakened the model's explanation, so we kept them. Inflammatory markers, such as the neutrophil-to-lymphocyte ratio, are associated with BP variability. The platelet/basophil ratio may also indicate systemic inflammation and BPV. Inflammation disrupts vascular reactivity and autonomic balance, increasing BPV [22]. EFT is an indicator of visceral adiposity and cardiac inflammation. EFT may increase night-time BPV by enhancing sympathetic activity and inflammation [18]. Smoking increases BPV through endothelial dysfunction, arterial stiffness, and autonomic imbalance. BPV is more common in smokers [29]. Elevated TSH (subclinical hypothyroidism) links to arterial stiffness and autonomic dysfunction. Hypothyroidism may raise BPV by impairing BP regulation. While less studied, thyroid issues are associated with BPV and cardiovascular risk [30]. Although TSH isn't a strong predictor, its positive correlation with nighttime systolic BPV and reduced model power without it hints at potential significance in larger future studies. The main limitation is our inability to establish causality. A cross-sectional design can't determine whether EFT causes BPV or vice versa. Our study is limited to healthy young adults (aged 18–41) with relatively low cardiovascular risk. This situation limits the direct generalisation of the findings to populations with chronic diseases such as hypertension, metabolic syndrome, or old age. Although COVID-19 history helped predict EFT, and age and TSH helped predict nighttime systolic BPV, the lack of significance likely reflects an insufficient sample size. EFT was measured using transthoracic echocardiography, which is easy and cost-effective but more operator-dependent and less sensitive than 3D methods such as cardiac MRI or CT. Future research could improve accuracy using these advanced techniques. To address our study's cross-sectional limitation and establish causality, future research should include mechanistic/intervention studies to determine whether changes in EFT track with changes in BPV over time. The model's modest explanatory power may stem from limited variability in BPV and EFT among healthy young adults. The absence of potential determinants like genetic factors, diet, and lifestyle variables may have reduced the model's explanatory power. Recent studies show BPV interacts with genetics, diet, physical activity, and environment. Future research should include genetic risk scores, detailed dietary data, activity levels, sleep, and stress to improve the model. Modelling gene-diet interactions and lifestyle factors will deepen understanding of BPV [31-33]. The lack of statistical significance in the relationship between COVID-19 history and EFT limits its impact. Still, its contribution to the model suggests it shouldn't be ignored and merits investigation in larger samples. While some studies link increased EFT in COVID-19 patients to myocardial damage and poor outcomes, others find no significance or that results vary with sample size. These findings are preliminary and support further research with larger, multicenter cohorts that consider confounding variables [34,35]. Our study demonstrates that the relationship between EFT and BPV persists even in healthy young adults. The demonstration that the correlation between EFT and nighttime systolic BPV is observed not only in hypertensive individuals but also in young, healthy normotensive individuals suggests that the relationship between EFT and BPV may persist in the subclinical period, opening up a new avenue for the early diagnosis of subclinical cardiovascular risk. For young people with elevated EFT, 24-hour ABPM is helpful to detect subclinical BPV abnormalities, especially at night. Most research targets hypertensive groups, but public health and physician training should highlight visceral fat's role in cardiovascular risk, even among youth. If future longitudinal cohort studies confirm our findings, EFT measurement and BPV monitoring could be added to risk screening for young adults. 5. Conclusion Our study has demonstrated for the first time a significant and independent relationship between EFT and nighttime systolic BPV in healthy young adults. Consequently, our study's findings are broadly consistent with the existing literature and offer a new perspective by demonstrating the relationship between EFT and nighttime systolic BP variability, particularly in healthy young adults. Abbreviations EFT (Epicardial fat thickness) BPV (Blood pressure variability) BP (Blood pressure) ABPM (Ambulatory blood pressure monitoring) BMI (Body mass index) FLI (Fatty liver index) COVID-19 (Coronavirus Disease 2019) EAT (Epicardial adipose tissue) CI (Confidence interval) TSH (Thyroid-stimulating hormone) ABPSI (Ambulatory blood pressure smoothing index) AASI (Ambulatory arterial stiffness index) Declarations Conflicts of Interest: The authors declare no conflicts of interest and have not used any financial resources. Ethical Approval and Consent to Participate : The study was conducted in accordance with the Declaration of Helsinki and approved by the T.C. Inonu University Scientific Research and Publication Ethics Committee Health Sciences Scientific Research Ethics Committee (Protocol No: 2025/8392; Date: 16-09-2025). Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Data Availability Statement: Data will be available upon formal request to the corresponding author. Conflicts of Interest: The authors declare no conflicts of interest. Funding: This research received no external funding. Author Contributions: G Sarıoğlu conceptualized and designed the study. G Sarıoğlu and Y Yiğit provided the study materials or patients. G Sarıoğlu and Y Yiğit contributed to the collection and assembly of data. G Sarıoğlu contributed to the data analyses and interpretation of the study. All authors wrote the manuscript and approved the final version of the manuscript. References Mukherjee AG, Renu K, Gopalakrishnan AV, Jayaraj R, Dey A, Vellingiri B, Ganesan R. 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Int J Obes (Lond). 2022;46(4):866–73. Tables Tables 1 to 4 are available in the supplementary files section Additional Declarations No competing interests reported. Supplementary Files Graphicalabstract.docx Tables.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 29 Jan, 2026 Reviewers agreed at journal 26 Jan, 2026 Reviewers invited by journal 25 Jan, 2026 Editor assigned by journal 19 Jan, 2026 Submission checks completed at journal 19 Jan, 2026 First submitted to journal 13 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8595870","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":577606449,"identity":"7d667963-b09c-4188-aaa8-9be154a8418b","order_by":0,"name":"Güney Sarıoğlu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABB0lEQVRIiWNgGAWjYDADNgYGxgMMFUAWO5DVQKQWhgMMZxgYeJiJ1QICBxjbiNAiH5F78DHPH7t8Pvb2C4d559kk7mdmPvhwBoOdnC4OfYY38pKNeduSLdt4zhQc5t2WltjDzJZsuIEh2djsAA4tM3LMpHkbmA3YJHISgFoOA7XwmEk+YDiQuA23FvPfPH/qoVrmEKFFXiLHjJmH7TBQS/qBw7wNUC0b8Ggx4HljLDm37bgBG88ZhoNzjqUZ9xwG+mWGAW6/yLfnGH5486faQL69/eGDNzU2su3tzQcf9lTYyeHSYgAUZ+IBM3kMkMWxKwfb0gCMuB9gJvsD3MpGwSgYBaNgRAMAZgtahIMYoGsAAAAASUVORK5CYII=","orcid":"","institution":"Ministry of Health, Battalgazi State Hospital.","correspondingAuthor":true,"prefix":"","firstName":"Güney","middleName":"","lastName":"Sarıoğlu","suffix":""},{"id":577606450,"identity":"408afff5-d420-42f7-bf45-a1d1a750bfb3","order_by":1,"name":"Yakup Yiğit","email":"","orcid":"","institution":"Malatya Turgut Özal University Training and Research 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11:35:03","extension":"xml","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":93136,"visible":true,"origin":"","legend":"","description":"","filename":"45d6bc52f6784e13bfbfc66487b53e841structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8595870/v1/d984c79afbe75c88829dc3e7.xml"},{"id":100779367,"identity":"5c368be6-1897-471a-9d88-e1f2bfaf8fa6","added_by":"auto","created_at":"2026-01-21 11:35:48","extension":"html","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":103437,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8595870/v1/ef65bdb068f015695c5b3a01.html"},{"id":100779135,"identity":"083272c6-f5bd-4b16-b33a-f8ceaa956f9e","added_by":"auto","created_at":"2026-01-21 11:33:50","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":38179,"visible":true,"origin":"","legend":"\u003cp\u003eStandardised Coefficients and 95% Confidence Intervals for Independent Predictors of Epicardial Fat Thickness\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCaption:\u003c/strong\u003e The bars show the standardised β coefficient. Square brackets indicate 95% confidence intervals. COVID-19 is coded as a dummy variable (0=No history of COVID-19, 1=COVID-19 history). Significant positive predictors of EFT (where CI excludes zero) include Basophil, BMI, Platelet/Basophil Ratio, Age, Nighttime systolic BP dipping, and Nighttime Systolic BPV. COVID-19 History was not a significant predictor, as its CI includes zero. *= p \u0026lt; 0.05, **= p \u0026lt;0.001. Abbreviations: BP: Blood pressure, EFT: Epicardial fat thickness, COVID-19: Coronavirus Disease 2019, BPV: Blood pressure variability. Confidence intervals were manually calculated using standardised coefficients to ensure accurate visualisation, unlike default unstandardized coefficients.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8595870/v1/73a4a15441f9565da6ea9bf0.png"},{"id":100779107,"identity":"9ec3fdb2-041d-4791-a185-aeff25176f42","added_by":"auto","created_at":"2026-01-21 11:33:05","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":26117,"visible":true,"origin":"","legend":"\u003cp\u003eStandardised Coefficients and 95% Confidence Intervals for Independent Predictors of Nighttime Systolic Blood Pressure Variability\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCaption: \u003c/strong\u003eThe bars show the standardised β coefficient. Square brackets indicate 95% confidence intervals. Smoking is coded as a dummy variable (0 = Non-smoker, 1 = Smoker). Significant positive predictors of BPV (where CI excludes zero) include Platelet/Basophil ratio, EFT, and Smoking. Age and TSH level were not significant predictors, as their CIS values were 0. *= p \u0026lt; 0.05, **= p \u0026lt; 0.001. Abbreviations: EFT: Epicardial fat thickness, TSH: Thyroid-stimulating hormone. \u0026nbsp;Confidence intervals were manually calculated using standardised coefficients to ensure accurate visualisation, unlike default unstandardized coefficients.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8595870/v1/00614177863271a095790db1.png"},{"id":100798042,"identity":"32dd2482-2e72-41f4-b189-9af77a70b49d","added_by":"auto","created_at":"2026-01-21 13:52:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":595345,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8595870/v1/09355a19-6365-443f-a473-880ae2f42125.pdf"},{"id":100779275,"identity":"d305ecd3-ec59-4112-81c3-751a6f3202e5","added_by":"auto","created_at":"2026-01-21 11:35:10","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":270555,"visible":true,"origin":"","legend":"","description":"","filename":"Graphicalabstract.docx","url":"https://assets-eu.researchsquare.com/files/rs-8595870/v1/21df6ced88f6cf94df2a427b.docx"},{"id":100779295,"identity":"ef28268c-29a7-4550-9b25-9b8050d8d149","added_by":"auto","created_at":"2026-01-21 11:35:23","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":935345,"visible":true,"origin":"","legend":"","description":"","filename":"Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-8595870/v1/818b6a61b2966d8025146b0a.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Epicardial Fat Thickness and Nighttime Systolic Blood Pressure Variability in Healthy Young Adults","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eEpicardial adipose tissue (EAT) is a metabolically active visceral fat tissue surrounding the heart and exhibits both protective and pathogenic properties. Generally, it provides protection and support to the heart; however, an increase in its quantity or impairment in its function elevates the risk of cardiovascular disease [\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. EAT contributes to both local and systemic inflammation by releasing proinflammatory cytokines and mediators. EFT correlates positively with local inflammatory markers, such as PCSK9 and CA125, as well as proinflammatory cytokines [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The EFT involves nerves around the heart that connect to the sympathetic and parasympathetic systems [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. It's a biomarker for cardiometabolic risk, with high EFT linked to localised conditions like coronary artery and myocardial diseases, as well as systemic disorders such as metabolic syndrome, diabetes, and liver diseases. Reducing EFT may help lower cardiovascular risk [\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\u003eBPV refers to fluctuations in blood pressure (BP) over time; it can be measured in the short-, medium-, and long-term. Increased BPV is strongly associated with numerous serious health outcomes, including cardiovascular disease, stroke, kidney disease, dementia, and death. [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. BPV is significantly associated with various inflammatory markers in both healthy individuals and hypertensive patients. Studies conducted in children and adults have found significant correlations between blood count-based inflammation markers, such as the lymphocyte/monocyte ratio and neutrophil/lymphocyte ratio, and BPV [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAutonomic imbalance (particularly sympathetic/parasympathetic imbalance) increases BPV in both hypertensive and normotensive individuals [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eIncreased EFT is associated with BPV and an impaired circadian BP profile. An increase in EFT may increase BPV through autonomic dysfunction and increased sympathetic activity [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIt has been demonstrated that hypertension and high BP increase EFT, but there is no direct study showing that BPV specifically increases EFT [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. According to the current literature, no study has specifically evaluated the relationship between EFT and BPV in a healthy young adult population.\u003c/p\u003e\u003cp\u003eOur study investigated the relationship between EFT and BPV, both independent predictors of cardiovascular morbidity and mortality, which have not been previously studied in healthy young adults. We hypothesised that, in healthy, normotensive adults aged 18\u0026ndash;41, EFT would correlate positively with BPV via inflammation and sympathetic activation. Understanding this link could aid early detection of cardiovascular risk.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cp\u003e\u003cstrong\u003e2.1. Study population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;We conducted a prospective, cross-sectional study involving 172 healthy young adults aged 18–41 years who presented to our hospital's cardiology outpatient clinic with atypical chest pain between October 2025 and November 2025 and agreed to participate, with normal cardiac examination findings.\u003c/p\u003e\n\u003cp\u003eWe excluded those with chronic diseases, medication use, active infections, obesity, cachexia, or laboratory evidence of acute infection.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The study followed the Declaration of Helsinki and received ethics committee approval (decision 2025/8392). All participants signed a consent form. Generative AI was used solely to structure the discussion and clarify statistics, with human oversight verifying all content.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2. Laboratory measurements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe assessed\u0026nbsp;the EFT values of the participants using transthoracic echocardiography, performed with\u0026nbsp;a 3.2 MHz transducer on a Philips Affinity 50 ultrasound system (Philips, Andover, MA, USA). We measured EFT in the two-dimensional parasternal long-axis view by positioning the M-mode cursor perpendicular to the aortic annulus along the free wall of the right ventricle at end-diastole. We averaged three consecutive cardiac cycle measurements [20]. We assessed participants' arterial blood pressure parameters using a 24-hour ABPM device (Borsam Echo, ABP-06B, manufactured in China) for a continuous 24-hour period.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;We calculated the FLI using BMI, waist circumference, serum gamma-glutamyl transferase, and triglyceride levels, following the method of Bedogni et al. [21]. We expressed BMI as weight (kg) divided by the square of height (m2). After exhaling, we measured waist circumference at the navel and above the hip bone using a flexible, non-metallic tape measure. We obtained blood samples from the right or left antecubital vein after an 8- to 10-hour fasting period for analysis. We also reviewed medical records to determine the patient's COVID-19 history. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Statistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe analysed the\u0026nbsp;data using SPSS software (IBM Corp., IBM SPSS Statistics for Mac, Version 27.0. Armonk, NY: IBM Corp; 2020).\u0026nbsp;By analysing the data in their recorded state, we have avoided any potential errors that could have been introduced by imputing missing values.\u0026nbsp;We performed\u0026nbsp;descriptive analysis to characterise the study population.\u0026nbsp;We tested the normality of the variables by the Kolmogorov-Smirnov test. We used an independent-samples t-test for normally distributed data and a Mann-Whitney U test for non-normally distributed data. We established statistical significance at p \u0026lt; 0.05.\u003cbr\u003e\u0026nbsp; Additionally, we considered a 95% confidence interval (CI) for differences not encompassing zero statistically significant. We analysed categorical variables using the appropriate chi-square test and reported results\u0026nbsp;as percentages (%) and absolute numbers.\u0026nbsp;We reported the means and standard deviations for normally distributed continuous variables, and the medians (with interquartile ranges) for non-normally distributed continuous variables.\u0026nbsp;We used Pearson correlation for normally distributed variables and Spearman correlation for non-normally distributed variables to evaluate correlations between continuous variables. A power analysis determined the minimum sample size required for multiple linear regression with the predictive variables. Before commencing the study, we determined that the minimum sample size required for the analysis was 170, assuming a moderate effect size for multiple regression analysis (Cohen's f² = 0.15), α = 0.05, and desired power = 0.80.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Additionally, we conducted a multiple linear regression analysis using the variables: age, smoking, gender, BMI, basophil count, Platelet/Basophil ratio, nighttime systolic BP dipping, COVID-19 history, nighttime systolic BPV, and\u0026nbsp;TSH levels\u0026nbsp;to forecast the EFT and nighttime systolic BPV. We select variables based on prior studies and clinical importance.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;We evaluated the effect size using Cohen's f2.\u0026nbsp;\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e3.1. Participant Characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 172 young adults were included in the study. The mean age was 29.08 \u0026plusmn; 7.16 years, with 67 (39%) males. Smoking was reported in 64 (37.2%) participants, and 55 (32%) had a history of COVID-19. Mean BMI was 23.36 \u0026plusmn; 3 kg/m\u0026sup2;, and the FLI was 23.88 \u0026plusmn; 22.47. Laboratory parameters included hemoglobin 14.11 \u0026plusmn; 1.75 g/dl, platelet count 266.12 \u0026plusmn; 63.73 \u0026times; 10\u0026sup3;/ml, white blood cell count 7.22 \u0026plusmn; 1.76 \u0026times; 10\u0026sup3;/ml, and C-reactive protein 2.0 \u0026plusmn; 0.19 mg/l. EFT was 4.1 \u0026plusmn; 1.26 mm (Table 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eABPM revealed a nighttime systolic BP dipping percentage of 10.53 \u0026plusmn; 6.17%, a diastolic dipper of 14.99 \u0026plusmn; 8.21%, an Ambulatory arterial stiffness index (AASI) of 0.35 \u0026plusmn; 0.15, and an Ambulatory blood pressure smoothing index (ABPSI) of 0.78 \u0026plusmn; 0.16. Mean 24-hour systolic BPV was 17.62 \u0026plusmn; 4.61 mmHg, diastolic BPV 15.22 \u0026plusmn; 4.43 mmHg, with higher daytime and lower nighttime variability.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;An independent-samples t-test revealed that EFT was significantly thicker in males than in females (mean difference: -3.707 mm, 95% CI [-7.350, -0.064], t(170) = -2.009, p = 0.046).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2. Correlations Between ABPM Parameters and EFT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003ePearson correlation analysis showed significant positive associations between EFT and 24-hour systolic BPV (r = 0.171, 95% CI [0.019, 0.314], p = 0.028), 24-hour diastolic BPV (r = 0.173, 95% CI [0.022, 0.317], p = 0.025), nighttime systolic BPV (r = 0.193, 95% CI [0.043, 0.335], p = 0.012), and nighttime diastolic BPV (r = 0.183, 95% CI [0.032, 0.326], p = 0.018). No significant correlations were observed with AASI, ABPSI, or dipping patterns (Table 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3. Predictors of EFT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Multiple linear regression using the enter method was performed to identify independent predictors of EFT. The final model was statistically significant, F(7, 163) = 9.395, p \u0026lt; 0.001, explaining 28.7% of the variance in EFT (R\u0026sup2; = 0.287, Adjusted R\u0026sup2; = 0.257).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Basophil count and BMI were the strongest independent predictors. Platelet/basophil ratio, age, nighttime systolic dipper percentage, and nighttime systolic BPV contributed significantly to the model (Figure 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 1\u003c/strong\u003e. Standardised Coefficients and 95% Confidence Intervals for Independent Predictors of Epicardial Fat Thickness\u003c/p\u003e\n\u003cp\u003eThe regression coefficient for COVID-19 infection history on EFT was positive (B = 0.110), but the p-value did not reach statistical significance, and the confidence interval included zero (p = 0.108, 95% CI [-0.030, 0.310] respectively). However, when the COVID-19 History variable was removed from the model, the Adjusted R\u0026sup2; value, representing the model\u0026apos;s overall explanatory power, decreased (0.257 versus 0.250) (Table 3).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4. Predictors of Nighttime Systolic Blood Pressure Variability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eA separate multiple linear regression using the enter method was performed to identify independent predictors of BPV. The model was significant, F(5, 165) = 4.098, p = 0.002, accounting for 11.0% of the variance (R\u0026sup2; = 0.110, Adjusted R\u0026sup2; = 0.084) (Table 4).\u003c/p\u003e\n\u003cp\u003eSmoking, EFT, and platelet/basophil ratio were independent predictors. Age and TSH were not significant, but removing Age and thyroid-stimulating hormone (TSH) decreased the Adjusted R\u0026sup2; from 0.084 to 0.081 and from 0.084 to 0.078, respectively (Figure 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 2\u003c/strong\u003e. Standardised Coefficients and 95% Confidence Intervals for Independent Predictors of Nighttime Systolic Blood Pressure Variability\u003c/p\u003e\n\u003cp\u003eEFT was associated with several BPV parameters and was predicted by basophil count, BMI, and hematologic ratios. EFT and smoking were key factors for nighttime systolic BPV in this young group.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; Cohen\u0026apos;s f\u0026sup2; was calculated to assess the effect size of the regression models. For EFT prediction, Cohen\u0026apos;s f\u0026sup2; = 0.402 indicated a large effect size. For nighttime systolic BPV prediction, Cohen\u0026apos;s f\u0026sup2; = 0.124 represented a small-to-medium effect size. Standardised regression coefficients (\u0026beta;) further supported moderate effects for basophil count (\u0026beta; = 0.35) and BMI (\u0026beta; = 0.26) on EFT.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study is the first to assess the link between EFT and BPV in healthy young adults. Our results align with previous research showing a connection between EFT and BPV in hypertensive individuals. However, they also reveal that this relationship exists in a young, healthy population, thus adding new insights to the literature.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;BPV describes the variability in BP over time, measured across short, medium, and long durations. High BPV is closely associated with serious health risks such as cardiovascular disease, stroke, kidney disease, dementia, and death, even in normotensive individuals.\u003c/p\u003e\n\u003cp\u003e[11-13]. Early detection of increased blood pressure variability in normotensive healthy young adults is important for reducing future mortality and morbidity due to BPV.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Our study found that nighttime systolic BPV was most strongly correlated with EFT. In regression models using variables previously identified as predictors of EFT, we determined that the BPV parameter best predicting EFT was nighttime systolic BPV. In our models predicting EFT with nighttime systolic BPV, although the model is meaningful, EFT\u0026apos;s predictive power for nighttime BPV is higher than that of nighttime systolic BPV for EFT.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The correlation between nighttime systolic BPV and EFT, especially in young, healthy adults, is mediated by interactions among autonomic nervous system activity, vascular tone, and cardiometabolic risk factors. EAT influences cardiac autonomic functions and vascular reactivity by secreting pro-inflammatory and pro-atherogenic mediators, potentially increasing systolic BPV at night when sympathovagal balance shifts [17,18,22].\u003c/p\u003e\n\u003cp\u003eSystolic BPV is more sensitive than diastolic BPV to changes in arterial wall elasticity, sympathetic activity, and cardiac output. An increase in EFT may trigger systolic pressure fluctuations, mainly by increasing sympathetic activity and inflammation. Diastolic pressure relates to peripheral vascular resistance and arterial relaxation; in healthy young individuals, these are more stable. Thus, the EFT-systolic BPV relationship may be stronger than with diastolic BPV [23,24].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The literature shows that EFT is particularly elevated in non-dippers (those with minimal nighttime BP reduction) and in individuals with high BPV [25].\u0026nbsp;Significant correlations have been reported between 24-hour average systolic BP, BP variability, and EFT [26].\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;BP typically drops during the night (dipping). However, increased EFT prevents this drop by increasing sympathetic activity and inflammation, leading to a \u0026quot;non-dipper\u0026quot; pattern. Therefore, EFT is more strongly associated with night-time systolic BPV in particular [19].\u0026nbsp;Increased EFT enhances nocturnal sympathetic activity and suppresses parasympathetic tone. This particularly affects nighttime systolic BPV; during the day, the correlation weakens as environmental and behavioural factors become dominant [22]. Systolic BP is more affected by arterial stiffness and sympathetic activity. Diastolic BP, in contrast, relies more on peripheral resistance and volume status; thus, the impact of EFT might be less noticeable in this measure. During the day, environmental factors such as physical activity, stress, and nutrition affect BPV and may mask the effect of EFT. At night, autonomic and hormonal effects are more pronounced [18]. This information explains the pathophysiological mechanisms underlying the strong association between EFT thickness and nocturnal systolic BPV in our study, in light of the literature.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;As we mentioned, epicardial adipose tissue may elevate nighttime systolic BPV by increasing sympathetic activity and inflammation [18,22] (Graphical abstract).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;In our study, we determined that age and BMI predict EFT. The positive correlations between EFT, age, and BMI have been replicated in numerous studies. Ageing and increased body mass increase visceral fat, thereby increasing EFT [27].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;We also found that basophil levels and the platelet/basophil ratio predict EFT. The association of these systemic inflammation indicators with EFT is consistent with the proinflammatory properties of epicardial adipose tissue [22].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;In our multiple linear regression analysis, although a history of COVID-19 infection showed a positive trend with EFT, this association was statistically insignificant. However, removing this variable from the model decreased the Adjusted R\u0026sup2;.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Our finding should be re-evaluated as a potential risk factor in larger studies, reflecting the model\u0026apos;s variance explanation. The increased EFT observed in COVID-19 survivors may result from myocardial damage and inflammation caused by the infection, as increased EFT has been linked to these conditions [28].\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;We found that a low nighttime systolic BP dipping significantly explained the variance in EFT in the regression model. Sengul et al. demonstrated that EFT is higher in non-dippers. The non-dipper profile is related to autonomic dysfunction and increased cardiovascular risk [25].\u003c/p\u003e\n\u003cp\u003eWe found that the platelet/basophil ratio, EFT, and smoking positively predict night-time systolic BP. Age and TSH didn\u0026apos;t significantly predict nighttime BPV, but removing them weakened the model\u0026apos;s explanation, so we kept them.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Inflammatory markers, such as the neutrophil-to-lymphocyte ratio, are associated with BP variability. The platelet/basophil ratio may also indicate systemic inflammation and BPV. Inflammation disrupts vascular reactivity and autonomic balance, increasing BPV [22].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;EFT is an indicator of visceral adiposity and cardiac inflammation. EFT may increase night-time BPV by enhancing sympathetic activity and inflammation [18].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Smoking increases BPV through endothelial dysfunction, arterial stiffness, and autonomic imbalance. BPV is more common in smokers [29].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Elevated TSH (subclinical hypothyroidism) links to arterial stiffness and autonomic dysfunction. Hypothyroidism may raise BPV by impairing BP regulation. While less studied, thyroid issues are associated with BPV and cardiovascular risk [30]. Although TSH isn\u0026apos;t a strong predictor, its positive correlation with nighttime systolic BPV and reduced model power without it hints at potential significance in larger future studies.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The main limitation is our inability to establish causality. A cross-sectional design can\u0026apos;t determine whether EFT causes BPV or vice versa. Our study is limited to healthy young adults (aged 18\u0026ndash;41) with relatively low cardiovascular risk. This situation limits the direct generalisation of the findings to populations with chronic diseases such as hypertension, metabolic syndrome, or old age. Although COVID-19 history helped predict EFT, and age and TSH helped predict nighttime systolic BPV, the lack of significance likely reflects an insufficient sample size. EFT was measured using transthoracic echocardiography, which is easy and cost-effective but more operator-dependent and less sensitive than 3D methods such as cardiac MRI or CT. Future research could improve accuracy using these advanced techniques. To address our study\u0026apos;s cross-sectional limitation and establish causality, future research should include mechanistic/intervention studies to determine whether changes in EFT track with changes in BPV over time.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The model\u0026apos;s modest explanatory power may stem from limited variability in BPV and EFT among healthy young adults.\u0026nbsp;The absence of potential determinants like genetic factors, diet, and lifestyle variables may have reduced the model\u0026apos;s explanatory power. Recent studies show BPV interacts with genetics, diet, physical activity, and environment. Future research should include genetic risk scores, detailed dietary data, activity levels, sleep, and stress to improve the model. Modelling gene-diet interactions and lifestyle factors will deepen understanding of BPV [31-33]. The lack of statistical significance in the relationship between COVID-19 history and EFT limits its impact. Still, its contribution to the model suggests it shouldn\u0026apos;t be ignored and merits investigation in larger samples. While some studies link increased EFT in COVID-19 patients to myocardial damage and poor outcomes, others find no significance or that results vary with sample size. These findings are preliminary and support further research with larger, multicenter cohorts that consider confounding variables [34,35].\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur study demonstrates that the relationship between EFT and BPV persists even in healthy young adults. The demonstration that the correlation between EFT and nighttime systolic BPV is observed not only in hypertensive individuals but also in young, healthy normotensive individuals suggests that the relationship between EFT and BPV may persist in the subclinical period, opening up a new avenue for the early diagnosis of subclinical cardiovascular risk.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;For young people with elevated EFT, 24-hour ABPM is helpful to detect subclinical BPV abnormalities, especially at night. Most research targets hypertensive groups, but public health and physician training should highlight visceral fat\u0026apos;s role in cardiovascular risk, even among youth. If future longitudinal cohort studies confirm our findings, EFT measurement and BPV monitoring could be added to risk screening for young adults.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eOur study has demonstrated for the first time a significant and independent relationship between EFT and nighttime systolic BPV in healthy young adults. Consequently, our study's findings are broadly consistent with the existing literature and offer a new perspective by demonstrating the relationship between EFT and nighttime systolic BP variability, particularly in healthy young adults.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eEFT (Epicardial fat thickness)\u003c/p\u003e\n\u003cp\u003eBPV (Blood pressure variability)\u003c/p\u003e\n\u003cp\u003eBP (Blood pressure)\u003c/p\u003e\n\u003cp\u003eABPM (Ambulatory blood pressure monitoring)\u003c/p\u003e\n\u003cp\u003eBMI (Body mass index)\u003c/p\u003e\n\u003cp\u003eFLI (Fatty liver index)\u003c/p\u003e\n\u003cp\u003eCOVID-19 (Coronavirus\u0026nbsp;Disease 2019)\u003c/p\u003e\n\u003cp\u003eEAT (Epicardial adipose tissue)\u003c/p\u003e\n\u003cp\u003eCI (Confidence interval)\u003c/p\u003e\n\u003cp\u003eTSH (Thyroid-stimulating hormone)\u003c/p\u003e\n\u003cp\u003eABPSI (Ambulatory blood pressure smoothing index)\u003c/p\u003e\n\u003cp\u003eAASI (Ambulatory arterial stiffness index)\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflicts of Interest:\u0026nbsp;\u003c/strong\u003eThe authors declare no conflicts of interest and have not used any financial resources.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval and Consent to Participate\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003eThe study was conducted in accordance with the Declaration of Helsinki and approved by the T.C. Inonu University Scientific Research and Publication Ethics Committee Health Sciences Scientific Research Ethics Committee (Protocol No: 2025/8392; Date: 16-09-2025).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent Statement:\u0026nbsp;\u003c/strong\u003eInformed consent was obtained from all subjects involved in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement:\u0026nbsp;\u003c/strong\u003eData will be available upon formal request to the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest:\u0026nbsp;\u003c/strong\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis research received no external funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e G Sarıoğlu conceptualized and designed the study. G Sarıoğlu and Y Yiğit provided the study materials or patients. G Sarıoğlu and Y Yiğit contributed to the collection and assembly of data. G Sarıoğlu contributed to the data analyses and interpretation of the study. All authors wrote the manuscript and approved the final version of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eMukherjee AG, Renu K, Gopalakrishnan AV, Jayaraj R, Dey A, Vellingiri B, Ganesan R. Epicardial adipose tissue and cardiac lipotoxicity: A review. Life Sci. 2023;328:121913. Retraction in: Life Sci. 2025;372:123630.\u003c/li\u003e\n \u003cli\u003eAntonopoulos AS, Antoniades C. The role of epicardial adipose tissue in cardiac biology: classic concepts and emerging roles. J Physiol. 2017;595(12):3907\u0026ndash;17.\u003c/li\u003e\n \u003cli\u003eIacobellis G. Epicardial adipose tissue in contemporary cardiology. Nat Rev Cardiol. 2022;19(9):593\u0026ndash;606.\u003c/li\u003e\n \u003cli\u003eLi C, Liu X, Adhikari BK, Chen L, Liu W, Wang Y, Zhang H. 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J Cardiovasc Ultrasound. 2016;24(4):272\u0026ndash;3.\u003c/li\u003e\n \u003cli\u003eMohamed W, Ishak KN, Baharum N, Zainudin N, Lim HY, Noh M, Ahmad W, Zaman Huri H, Zuhdi A, Sukahri S, Govindaraju K, Abd Jamil AH. Ethnic disparities and its association between epicardial adipose tissue thickness and cardiometabolic parameters. Adipocyte. 2024;13(1):2314032.\u003c/li\u003e\n \u003cli\u003eAydin H, Toprak A, Deyneli O, Yazici D, Tar\u0026ccedil;in O, Sancak S, Yavuz D, Akalin S. Epicardial fat tissue thickness correlates with endothelial dysfunction and other cardiovascular risk factors in patients with metabolic syndrome. Metab Syndr Relat Disord. 2010;8(3):229\u0026ndash;34.\u003c/li\u003e\n \u003cli\u003eSengul C, Cevik C, Ozveren O, Duman D, Eroglu E, Oduncu V, Tanboga HI, CanMM, Akgun T, Dindar I. Epicardial fat thickness is associated with non-dipper blood pressure pattern in patients with essential hypertension. Clin Exp Hypertens. 2012;34(3):165\u0026ndash;70.\u003c/li\u003e\n \u003cli\u003eTurak O, \u0026Ouml;zcan F, Canpolat U, Mendi MA, \u0026Ouml;ks\u0026uuml;z F, \u0026Ouml;zeke \u0026Ouml;, Tok D, \u0026Ccedil;ağlı K, Aras D, Aydoğdu S. Prehipertansiyonda epikardiyal yağ dokusu kalınlığı ile kan basıncı d\u0026uuml;zeyleri arasındakı ilişki [Relation between epicardial adipose tissue thickness and blood pressure levels in prehypertension]. Turk Kardiyol Dern Ars. 2014;42(4):358\u0026ndash;64.\u003c/li\u003e\n \u003cli\u003eAitken-Buck HM, Moharram M, Babakr AA, Reijers R, Van Hout I, Fomison-Nurse IC, Sugunesegran R, Bhagwat K,Davis PJ, Bunton RW, et al. Relationship between epicardial adipose tissue thickness and epicardial adipocyte size with increasing body mass index. Adipocyte. 2019;8(1):412\u0026ndash;20.\u003c/li\u003e\n \u003cli\u003e\u0026Ouml;zer S, Bulut E, \u0026Ouml;zyıldız AG, Peker M, Turan OE. Myocardial injury in COVID-19 patients is associated with the thickness of epicardial adipose tissue. Kardiologiia. 2021;61(6):48\u0026ndash;53.\u003c/li\u003e\n \u003cli\u003eChi X, Li M, Zhan X, Man H, Xu S, Zheng D, Bi J, Wang Y, Liu C. Relationship between carotid artery sclerosis and blood pressure variability in essential hypertension patients. Comput Biol Med. 2018;92:73\u0026ndash;77.\u003c/li\u003e\n \u003cli\u003eAsvold BO, Bj\u0026oslash;ro T, Vatten LJ. Associations of TSH levels within the reference range with future blood pressure and lipid concentrations: 11-year follow-up of the HUNT study. Eur J Endocrinol. 2013;169(1):73\u0026ndash;82.\u003c/li\u003e\n \u003cli\u003ePazoki R, Dehghan A, Evangelou E, Warren H, Gao H, Caulfield M, Elliott P, Tzoulaki I. 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Int J Infect Dis. 2022;120:33\u0026ndash;40.\u003c/li\u003e\n \u003cli\u003eMehta R, Bello-Chavolla OY, Mancillas-Adame L, Rodriguez-Flores M, PedrazaNR, Encinas BR, Carri\u0026oacute;n CIP, \u0026Aacute;vila MIJ, Valladares-Garc\u0026iacute;a JC, Vanegas-Cedillo PE, et al. Epicardial adipose tissue thickness is associated with increased COVID-19 severity and mortality. Int J Obes (Lond). 2022;46(4):866\u0026ndash;73.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 4 are available in the supplementary files section\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"artery-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Artery Research](https://arteryresearch.biomedcentral.com/)","snPcode":"44200","submissionUrl":"https://submission.springernature.com/new-submission/44200/3","title":"Artery Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"blood pressure variability, cardiovascular risk, epicardial fat thickness, healthy young adults","lastPublishedDoi":"10.21203/rs.3.rs-8595870/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8595870/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e We recognise epicardial fat thickness (EFT) as a biomarker for cardiometabolic risk and blood pressure variability (BPV) as a predictor of cardiovascular morbidity. While studies link them in hypertensive patients, data for healthy young adults are lacking. This study examines the relationship between EFT-BPV in healthy young adults. Demonstrating this relationship in healthy young adults is important for the early detection of individuals at risk and for reducing future cardiovascular mortality.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e This study included 172 healthy young adults (18–41 years old). EFT was measured via transthoracic echocardiography, and a 24-hour Ambulatory blood pressure monitoring (ABPM) was used to assess BPV. Laboratory tests, body mass index, and fatty liver index were evaluated. Correlations and regression analyses were used to explore associations; significance was set at p \u0026lt; 0.05. Power analysis confirmed an adequate sample size (N=170, f²=0.15, α=0.05, power=0.80).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e EFT was significantly correlated with 24-hour systolic BPV (r = 0.171, p = 0.028), 24-hour diastolic BPV (r = 0.173, p = 0.025), nighttime systolic BPV (r = 0.193, p = 0.012), and nighttime diastolic BPV (r = 0.183, p = 0.018). Regression analyses identified basophil count, body mass index (BMI), platelet/basophil ratio, and nighttime systolic BPV as independent predictors of EFT (R² = 0.287, p \u0026lt; 0.001). EFT, smoking, and platelet/basophil ratio predicted nighttime systolic BPV (R² = 0.110, p = 0.002). A history of Coronavirus Disease 2019 (COVID-19) showed a positive but non-significant trend with EFT (p = 0.108).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eThis study reveals a novel link between EFT and nighttime systolic BPV in healthy young adults.\u003c/p\u003e","manuscriptTitle":"Epicardial Fat Thickness and Nighttime Systolic Blood Pressure Variability in Healthy Young Adults","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-21 10:02:22","doi":"10.21203/rs.3.rs-8595870/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-01-29T14:29:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"134965640167991837670924370017537724357","date":"2026-01-26T13:22:15+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-25T16:30:53+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-19T11:22:08+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-19T11:18:18+00:00","index":"","fulltext":""},{"type":"submitted","content":"Artery Research","date":"2026-01-13T22:17:29+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"artery-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Artery Research](https://arteryresearch.biomedcentral.com/)","snPcode":"44200","submissionUrl":"https://submission.springernature.com/new-submission/44200/3","title":"Artery Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"c05c0b1d-2893-4421-b7d8-edeb5bc48d02","owner":[],"postedDate":"January 21st, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-01-25T16:38:13+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-21 10:02:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8595870","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8595870","identity":"rs-8595870","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}