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Article

Systemic Immune Inflammatory Index as Predictor of Blood Pressure Variability in Newly Diagnosed Hypertensive Adults Aged 18–75

by
Yücel Karaca
1,
Mehdi Karasu
1,*,
Mehmet Ali Gelen
1,
Şeyda Şahin
1,
Özkan Yavçin
1,
İrfan Yaman
2 and
Şıho Hidayet
3
1
Department of Cardiology, Fethi Sekin Sehir Hastanesi, 23280 Elazıg, Turkey
2
Department of Cardiology, Gaziantep Şehir Hastanesi, 27470 Gaziantep, Turkey
3
Department of Cardiology, Malatya İnönü Üniversitesi Tıp Fakültesi, 44280 Malatya, Turkey
*
Author to whom correspondence should be addressed.
J. Clin. Med. 2024, 13(22), 6647; https://doi.org/10.3390/jcm13226647
Submission received: 7 October 2024 / Revised: 26 October 2024 / Accepted: 31 October 2024 / Published: 6 November 2024
(This article belongs to the Section Cardiology)
Browse Figures
Versions Notes

Abstract

:
Background: Accumulating evidence from clinical trials, large registries, and meta-analyses of population studies shows that increased Blood Pressure Variability (BPV) is predictive of Cardiovascular (CV) outcomes, independently of the average Blood Pressure (BP) values. One of the mechanisms explaining the relationship between BPV and target organ damage is the inflammatory response. The Systemic Immune Inflammation Index (SII), which relies on peripheral blood cell counts, including platelets, neutrophils, and lymphocytes, has emerged as a predictor of prognosis and outcomes in various diseases. The aim of this study was to investigate the association of the SII with Ambulatory Blood Pressure Variability (ABPV) in newly diagnosed hypertensive patients. Methods: This study was designed as a cross-sectional observational study. A total of 1606 consecutive newly diagnosed Hypertension (HT) patients were included in the study. The population was evaluated across 3 different categories according to HT grades (5 groups), eligibility for antihypertensive therapy (2 groups) and ABPV levels (2 groups). Results: Significant differences were observed between ABPV groups in terms of Neutrophil to Lymphocyte ratio, Platelet to Lymphocyte ratio, glucose, SII, high-sensitive CRP, HT grade, Inter-Ventricular Septum, Posterior Wall thickness, and Left Ventricular Mass (p < 0.005). There was a significant relationship between SII and ABPV (r: 0.619, p < 0.05). At the cutoff value of 580.49, SII had 77% sensitivity and 71% specificity for ABPV > 14 (AUC: 0.788). Conclusions: SII may assist in developing an early treatment approach to minimize complications in patients with high ABPV who are at a higher risk of CV events.

1. Introduction

Hypertension (HT) stands as one of the foremost preventable cardiovascular risk factors [1]. It is well established that hypertensive individuals face a higher risk of cardiovascular (CV) mortality, cerebrovascular diseases, renal failure, and retinopathy compared to their normotensive counterparts [2,3,4].
In daily life, blood pressure (BP) values fluctuate due to various activities, and these fluctuations can have significant implications for health. To provide a more representative estimate of BP levels than traditional clinical measurements, 24 h Ambulatory Blood Pressure Monitoring (ABPM) has emerged as a valuable tool [5]. ABPM not only offers a more accurate reflection of BP but is also closely associated with target organ damage, surpassing the predictive power of office BP measurements [5].
Extending Ambulatory Blood Pressure Monitoring (ABPM) to 48 h or beyond could indeed provide a more comprehensive assessment of Blood Pressure Variability (BPV), potentially capturing a fuller range of daily activities and sleep patterns. Studies indicate that prolonged ABPM may offer better prognostic accuracy for cardiovascular outcomes, particularly in patients with suspected high variability [6,7]. The additional data could also improve the reliability of BP variability indices, further clarifying the relationship between BP variability and inflammatory markers like SII.
However, there are some feasibility challenges. Longer monitoring periods could reduce patient compliance due to discomfort, interruptions to daily routines, or issues with the device, which could lower data quality if patients alter their behaviors. Additionally, ABPM devices are calibrated for 24 h intervals, so extending to 48 h might require adjustments in calibration and data interpretation.
Individuals with greater BP variability may experience more pronounced target organ damage than those with a stable 24 h mean BP [8]. Metrics like the standard deviation (SD) of 24 h BP, which quantifies BP variability, have been shown to correlate with the progression of organ damage over time [9].
Furthermore, despite the diagnostic challenges posed by BP variability, accumulating evidence from clinical trials, large registries, and population-based meta-analyses underscores the prognostic significance of Blood Pressure Variability (BPV) in predicting CV outcomes, independently of average BP values [10,11,12,13,14]. Moreover, BPV appears to extend its implications beyond CV outcomes, affecting a broader spectrum of disease outcomes and complications [15,16,17].
Inflammation has been well recognized as a contributor to the pathophysiology of HT [18]. Among the mechanisms connecting BPV to target organ damage, the inflammatory response has garnered attention. Experimental evidence suggests that elevated BP and BPV may stimulate endothelial expression of cytokines and promote inflammation [19]. However, the precise relationship between BPV and inflammation in individuals with HT remains unexplored.
The Systemic Immune Inflammation Index (SII), which relies on peripheral blood cell counts, including platelets, neutrophils, and lymphocytes, has emerged as a predictor of prognosis and outcomes in cancer and cardiac patients [20,21,22]. A previous study demonstrated SII as an independent predictor of major adverse CV events in coronary artery disease (CAD) patients [21]. Although an association between non-dipper HT, which was characterized by a drop of less than 10% in systolic and diastolic BP, and SII has been noted in a prior study [23], its relationship with variability indices and HT grades has yet to be established.
Hypertension (HT) is one of the foremost preventable cardiovascular risk factors, particularly affecting specific demographics with age- and health-based susceptibilities. In our study, we focused on newly diagnosed hypertensive patients within a defined age range and without significant comorbidities to understand the role of ambulatory blood pressure variability (ABPV) in predicting outcomes for this demographic.
With these considerations in mind, we designed this study to investigate the association of SII with Ambulatory Blood Pressure Variability (ABPV) in newly diagnosed hypertensive patients. This investigation aims to shed light on the interplay between SII, BPV, and HT severity, potentially informing early interventions to mitigate complications in hypertensive individuals at higher CV risk.

2. Methods

This cross-sectional observational study included newly diagnosed hypertensive patients aged 18–75, who were otherwise free from severe comorbid conditions such as coronary artery disease, renal impairment, or peripheral artery disease. Patients meeting these criteria were selected to specifically address the predictive value of the Systemic Immune Inflammation Index (SII) in an age- and health-defined population, ensuring the study’s relevance to this subset of the hypertensive population. It was conducted in accordance with the International Code of Ethics and the Declaration of Helsinki, and it received approval from the local ethics committee (approval number: 2022/06, date: 26 January 2022). Informed written consent was obtained from all of the participants prior to the study.
The study included a total of 1606 consecutive newly diagnosed HT patients who presented to the cardiology outpatient clinic of İnönü University between 1 June 2022 and 1 April 2023, either with complaints of high blood pressure (BP) or who were found to have high BP without any complaints. To ensure that the study included patients with pure hypertension, the exclusion criteria were broad. Individuals under 18 years of age or over 75 years of age, those with secondary HT, known coronary artery disease (CAD), peripheral artery disease, heart failure, arrhythmias, significant valvular diseases, shift workers, estimated glomerular filtration rate (eGFR) < 90 mL/Minute/1.73 m2 (indicating renal failure), and those using antihypertensive drugs, catecholamines, α- or β-blockers, or tranquilizers were excluded. A total of 1220 patients were excluded from the study based on these criteria, leaving data from the study group, consisting of 386 patients, for analysis.
At the time of their initial visit, patient data, including age, gender, echocardiographic information, and laboratory results, were collected. Blood samples were drawn between 8 and 10 am following a 12 h fasting period to assess hemogram, biochemistry, and high-sensitivity C-Reactive Protein (hs-CRP) levels. The Systemic Immune Inflammation Index (SII) was calculated using the formula Platelet × Neutrophil/Lymphocyte (P × N)/L, where P, N, and L denote platelet, neutrophil, and lymphocyte counts in peripheral blood, respectively [23]. Body mass index (BMI) was calculated.
Office BP measurements were taken with an aneroid sphygmomanometer using a cuff that measured between 36 and 42 cm in width (ERKA Perfect-Aneroid; ERKA, BadTölz, Germany). Two measurements were obtained after a 5 min rest, and the average was recorded. Patients with a BP reading of 140/90 mmHg or higher were referred for Ambulatory Blood Pressure Monitoring (ABPM).
A fully automatic BP monitoring device (BR-102 Plus, Schiller, Baar, Switzerland) was used for 24 h ABPM. The non-dominant arm was selected for cuff placement. Measurements were taken every 10 min during the day and every 20 min at night. The device was reinserted in patients with less than a 70% measurement completion rate. Awake and sleep times were determined based on patient-provided information. Nighttime BP reduction (percent) was calculated using the following formula: 100 × [1 − (sleep systolic BP/awake systolic BP)] [23]. Dipping status was defined as a decline in systolic and diastolic BP of greater than 10%, while non-dipper HT was characterized by a drop of less than 10%. BP variability was quantified using the standard deviation (SD) of systolic blood pressure (SBP) as an index [2,24].
Based on the ABPM results, patients were categorized according to BP measurements following the American Heart Association (AHA) 2017 guideline [25] (Table 1).
Normal: SBP less than 120 and DBP less than 80 mm Hg. High–normal: SBP 120 to 129 and DBP less than 80 mm Hg. Stage 1 hypertension: SBP 130 to 139 or DBP 80 to 89 mm Hg. Stage 2 hypertension: SBP greater than or equal to 140 mm Hg or DBP greater than or equal to 90 mm Hg. Severe hypertension defined as SBP ≥ 180 mmHg or DBP ≥ 120 mmHg.
To provide a comprehensive overview of the patient population’s clinical, laboratory, and demographic characteristics, the population was analyzed across three different categories:
  • Category 1: The population was divided into 5 groups (Group 1: normal BP, Group 2: high-normal BP, Group 3: grade 1 HT, Group 4: grade 2 HT, Group 5: severe HT) based on ABP values.
  • Category 2: The population was divided into two groups (Group 1: normal BP, high-normal BP, and grade 1 HT; Group 2: grade 2 and severe HT) based on eligibility for starting antihypertensive therapy.
  • Category 3: After calculating ABPV, the fuzzy c-means (FCM) algorithm [26] was applied to divide patients into low–intermediate (Group 1: ABPV ≤ 14) and high (Group 2: ABPV > 14) variability groups. Employing a clustering algorithm instead of dividing patients according to percentiles was preferred, because theoretically this approach is able to establish optimal boundaries, which ensure that individuals placed in the same class are the most similar and separated ones are the most dissimilar.
Echocardiography was performed by an experienced cardiologist who was blinded to the patient clinical and laboratory data using the Vivid 9 (General Electric Healthcare, Chicago, IL, USA) system with a 2.5–3.5 MHz transducer. The examination was conducted with patients in the left decubitus position, measuring the interventricular septum (IVS) and posterior wall (PW) thicknesses. The left ventricular mass index (LVMI) was calculated by dividing left ventricular mass (LVM) by body surface area. All measurements were repeated at least three times and averaged.

3. Statistical Analysis

Statistical analyses were conducted using the Statistical Package for the Social Sciences (SPSS Inc., Chicago, IL, USA) for Windows version 21.0. The normality of continuous variables was assessed using the Kolmogorov–Smirnov test. Normally distributed variables are presented as mean ± standard deviation, while non-normally distributed variables are presented as median (minimum–maximum) values. Descriptive statistics include percentages and absolute values.
To compare baseline clinical characteristics among the five groups, we employed an analysis of variance (ANOVA) test. Due to the inability to achieve a normal distribution in the ANOVA test conducted for the hypertension groups and a notable difference in group sizes, the Games–Howell test was employed for post hoc analysis. Given the presence of five distinct groups, we applied the Bonferroni correction to mitigate the margin of error.
Sociodemographic and clinical characteristics of two groups were compared using independent sample t-tests. Logistic regression models were utilized to determine whether SII was independently associated with grade 2 and severe hypertension.
Receiver operating characteristic (ROC) analysis was performed to identify the most sensitive SII cutoff level for identifying grade 2 and severe hypertensive patients.
Correlation analysis was employed to investigate the relationship between Ambulatory Blood Pressure Variability (ABPV) and SII.
Additionally, linear regression analysis was conducted to determine the effects of independent variables on ABPV.
Finally, ROC analysis was used to determine the most sensitive SII cutoff level for identifying patients with ABPV > 14.

4. Results

Table 1 outlines the general demographic and clinical characteristics of the hypertension groups, categorized by HT grades. Multiple comparisons were conducted, and detailed differences between the groups are presented in both Table 1 and Figure 1.
The ABPV value was significantly lower in the normal blood pressure group compared to the severe hypertension group (p < 0.001). The ABPV value was significantly lower in the high–normal blood pressure group compared to the grade 1, grade 2, and severe hypertension groups (p < 0.005). In the grade 2 hypertension group, the ABPV value was significantly higher than the high–normal blood pressure group but significantly lower than the severe hypertension group (p = 0.002). In the severe hypertension group, the ABPV value was significantly higher compared to the normal blood pressure, high-normal blood pressure, and grade 2 hypertension groups (p < 0.005).
Table 2 demonstrates a significant difference between the two BP groups categorized by eligibility for antihypertensive therapy in terms of several variables, including SII, neutrophil to lymphocyte ratio (N/L), platelet to lymphocyte ratio (P/L), White Blood Cell count (WBC), neutrophil count (N), lymphocyte count (L), platelet count (Plt), Ambulatory Blood Pressure Variability (ABPV), and the presence of a dipping pattern (p < 0.05). Notably, gender showed a significant difference when comparing five different groups based on HT grades, but no significant difference was observed in the two-group comparison based on eligibility for antihypertensive therapy.
Table 3 reveals significant differences between ABPV groups in terms of N/L, P/L, glucose, Left Ventricular Mass (LVM), SII, high-sensitivity C-Reactive Protein (hs-CRP), HT grade, Posterior Wall thickness (PW), and Interventricular Septum thickness (IVS) (p < 0.005). Specifically, N/L, P/L, glucose, LVM, SII, hs-CRP, HT grade, PW, and IVS were found to be higher in the ABPV > 14 group, while no significant difference was observed between the groups concerning gender. Correlation analysis indicated a significant relationship between SII and ABPV (Figure 2) (Pearson Correlation: 0.619, p < 0.05).
After conducting simple linear and multiple linear regression analyses to assess the impact of independent variables on ABPV, only SII was identified as an independent predictor of ABPV (Table 4). Due to multicollinearity, WBC, N, L, and Plt were not included in the regression analysis. According to the ROC analysis results, SII exhibited a sensitivity of 77% and specificity of 71% for identifying ABPV > 14 at a cutoff value of 580.49 (AUC: 0.788) (Figure 3).
Furthermore, there was a statistically significant difference between the dipper and non-dipper groups in terms of SII and ABPV (p < 0.05). However, there was no significant difference between the ABPV groups concerning dipper status.
In examining SII’s predictive value for ABPV, the results are most applicable to patients fitting the study’s demographic and health profile, as our inclusion criteria excluded patients with significant health conditions or those outside the defined age range. This ensures a more targeted application of the findings within a specific hypertensive patient population.

5. Discussion

The primary finding of our study underscores a notable correlation between the Systemic Immune Inflammatory Index (SII) and Ambulatory Blood Pressure Variability (ABPV), further identifying SII as an independent predictor for ABPV > 14.
Previous reports have also demonstrated significant associations between inflammatory markers and elevated blood pressure (BP) in seemingly healthy individuals [19]. Moreover, the link between high-sensitivity C-reactive protein (hs-CRP) levels and the development of hypertension (HT) indicates the inflammatory component of HT [27]. Kawada et al. have established an independent relationship between neutrophil levels and HT [28]. Platelet aggregation tendencies are heightened in hypertensive patients [29], as reported in our study, which showed a significant increase in SII levels among patients with grade 2 and severe HT.
Individuals with non-dipper HT exhibit a more pronounced inflammatory response [30]. A previous study by Akyüz et al. found that SII was elevated in the non-dipper HT group and served as an independent indicator of non-dipper HT [23]. In our study, although SII was significantly higher in the ABPV > 14 group, there was no difference between the two groups in terms of dipper status.
In our study, we showed that increased HT grades have increased ABPV values. Although there were no statistically significant differences between groups, the normal HT group had higher ABPV levels compared to the high–normal HT group and grade 1 patients had higher ABPV levels than grade 2 patients. This observation could be explained by several factors:
Physiological Compensation [31]: Individuals in the higher grades may have early regulatory adaptations, such as enhanced vascular responsiveness or autonomic adjustments, that buffer BP fluctuations more effectively. This could lead to relatively stable BP readings despite a trend toward higher baseline BP.
Early Hypertensive Changes Without Variability Increase [32]: In some cases, as BP levels gradually increase toward hypertensive ranges, variability may initially remain low. This could indicate that in early stages of BP elevation, compensatory mechanisms prevent large fluctuations, with variability potentially rising only in more established hypertensive states.
Measurement Artifacts and Sample Characteristics [33]: Differences in ABPV between the groups could also result from population characteristics or inherent measurement variability within ABPM itself. Factors such as differing stress responses, daily routines, or sample size variations might have impacted the results, leading to an unexpectedly lower ABPV in the high–normal and grade 2 groups.
Increased BPV reflects alterations in functional and structural cardiovascular mechanisms, subclinical or established cardiovascular damage, or underlying pathological conditions, all of which are associated with a poorer prognosis [34]. While there are more data available on office BPV for predicting cardiovascular outcomes compared to home BPV or ABPV, the prognostic value of office BPV depends on the measurement methodology [35]. It is important to note that office BP levels do not capture dynamic changes in BPV induced by daily activities. Home blood pressure monitoring (BPM) requires user training and medical supervision [36].
The independent association of short-term BPV from 24 h Ambulatory Blood Pressure Monitoring (ABPM) recordings with preclinical organ damage is supported by a meta-analysis that found a link between the standard deviation (SD) of 24 h systolic BP (SBP) and daytime SBP with greater Left Ventricular Mass (LVM) [37]. Moreover, meta-analyses have shown that increased short-term BPV from ABPM is associated with a higher risk of cardiovascular events [2]. Our study aligns with these findings, as we observed that 24 h ambulatory SBP variability >14 levels were associated with greater LVM and Left Ventricular Mass Index (LVMI), which is consistent with previous research.
Abramson et al. demonstrated positive associations between markers of inflammation and BPV in healthy, normotensive adults [38]. In newly diagnosed hypertensive individuals, independent of SBP, hs-CRP, and E-selectin levels are related to awake SBPV [39]. Kim et al. observed an association between BPV and inflammatory markers such as TNF-α and IL-6 in hypertensive patients [40]. In a post hoc analysis of a Multi-Ethnic Study of Atherosclerosis (MESA) study, Wong, K. H. et al. found a significant association of IL-6 with higher BPV levels [41]. Ciabano et al. showed that E-selectin, which is an endothelial cell adhesion molecule involved in vascular inflammation, is associated with increased ambulatory diastolic blood pressure variability in patients with type 2 diabetes [42]. Xu, C. et al. suggested that patients with a higher level of hsCRP tended to have larger blood pressure fluctuations [43]. Our study similarly uncovered a significant relationship between inflammation and ABPV, but with the distinction of evaluating 24 h ambulatory SBPV instead of office BPV, and the utilization of SII as a novel marker of inflammation.
To our knowledge, this is the first study to report statistically significant positive associations between SII and 24 h SBPV in newly diagnosed hypertensive patients. Our findings suggest that SII may serve as an independent predictor of increased cardiovascular risk related to ABPV specifically within a relatively healthy, newly diagnosed hypertensive population. Given our selection criteria, these results are likely most relevant for clinicians managing similar patient profiles. The study population’s age and health factors should be considered when extrapolating these findings to other hypertensive populations with broader age ranges or coexisting diseases.

6. Limitations

The present study is subject to several limitations. Firstly, due to its cross-sectional design, we are unable to establish causality between Blood Pressure Variability (BPV) and inflammation. To elucidate the causal relationship between BPV and inflammation, future research may require long-term follow-up studies or well-designed intervention studies.
Secondly, the timing of various measurements, including awakening and sleep, relied on self-reported patient information. Utilizing actigraphy or objective measures might offer more precise definitions of these time periods.
These limitations underscore the need for further investigations to comprehensively understand the relationship between BPV and inflammation and to overcome the constraints inherent in the current study design.

7. Conclusions

This study demonstrates a significant association between the Systemic Immune Inflammation Index (SII) and Ambulatory Blood Pressure Variability (ABPV) in newly diagnosed hypertensive patients. SII emerged as an independent predictor of ABPV > 14, indicating that elevated inflammation levels correlate with higher BP variability. Moreover, ABPV was associated with increased Left Ventricular Mass (LVM) and Left Ventricular Mass Index (LVMI), underscoring the potential utility of ABPM in identifying patients at heightened cardiovascular risk. SII may serve as a practical inflammatory marker in managing hypertension-related complications, particularly in patients with high ABPV.
Clinicians should interpret these findings as most applicable to newly diagnosed hypertensive patients who meet similar demographic and health criteria as those in our study. Further research could explore whether these findings extend to hypertensive populations with more varied age and health profiles.

Author Contributions

Conceptualization, Ş.Ş., Ö.Y. and Ş.H.; Methodology, Y.K.; Software, M.K.; Validation, Y.K. and Ş.Ş.; Formal analysis, Ö.Y.; Investigation, M.A.G., Ş.Ş. and İ.Y.; Resources, M.A.G. and İ.Y.; Data curation, M.A.G. and İ.Y.; Writing—original draft, M.K.; Writing—review & editing, M.K.; Visualization, Ş.H.; Supervision, Y.K., Ö.Y. and Ş.H. All authors have read and agreed to the published version of the manuscript.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Institutional Review Board Statement

The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Ethics Committee of İnönü University Faculty of Medicine (protocol code:2022/06, date of approval: 26 January 2022).

Informed Consent Statement

Written informed consent has been obtained from the patients to publish this paper.

Data Availability Statement

The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding author(s).

Acknowledgments

I would like to thank all my colleagues mentioned in this article who contributed to the design of the study, data collection, data analysis, and writing.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Quintal, J.; Candjondjo, A.P.; Rato, Q.; Ferreira, E.M.; Sousa, J.; Silva, M.J.; Casas, J.D.; Coelho, R.A.; Farinha, J.M.; Esteves, A.F.; et al. Differences in 10-Year Cardiovascular Risk Estimation Using SCORE and SCORE2 Risk Prediction Tools: A Moderate Risk Country Population Analysis. Eur. J. Prev. Cardiol. 2023, 30, zwad125.330. [Google Scholar] [CrossRef]
  2. Stevens, S.L.; Wood, S.; Koshiaris, C.; Law, K.; Glasziou, P.; Stevens, R.J.; McManus, R.J. Blood Pressure Variability and Cardiovascular Disease: Systematic Review and Meta-Analysis. BMJ 2016, 354, i4098. [Google Scholar] [CrossRef] [PubMed]
  3. Smith, T.O.; Sillito, J.A.; Goh, C.H.; Abdel-Fattah, A.R.; Einarsson, A.; Soiza, R.L.; Mamas, M.A.; Tan, M.P.; Potter, J.F.; Loke, Y.K.; et al. Association Between Different Methods of Assessing Blood Pressure Variability and Incident Cardiovascular Disease, Cardiovascular Mortality and All-Cause Mortality: A Systematic Review. Age Ageing 2020, 49, 184–192. [Google Scholar] [CrossRef] [PubMed]
  4. Meissner, A. Hypertension and the Brain: A Risk Factor for More Than Heart Disease. Cerebrovasc. Dis. 2016, 42, 255–262. [Google Scholar] [CrossRef] [PubMed]
  5. Mancia, G.; Verdecchia, P. Clinical Value of Ambulatory Blood Pressure: Evidence and Limits. Circ. Res. 2015, 116, 1034–1045. [Google Scholar] [CrossRef] [PubMed]
  6. Bovha Hus, K.; Kersnik Levart, T. Does the Duration of Ambulatory Blood Pressure Measurement Matter in Diagnosing Arterial Hypertension in Children? Blood Press. Monit. 2019, 24, 199–202. [Google Scholar] [CrossRef]
  7. Hermida, R.C.; Ayala, D.E.; Fontao, M.J.; Mojón, A.; Fernández, J.R. Ambulatory Blood Pressure Monitoring: Importance of Sampling Rate and Duration—48 Versus 24 Hours—On the Accurate Assessment of Cardiovascular Risk. Chronobiol. Int. 2013, 30, 55–67. [Google Scholar] [CrossRef]
  8. Heshmatollah, A.; Ma, Y.; Fani, L.; Koudstaal, P.J.; Ikram, M.A.; Ikram, M.K. Visit-to-Visit Blood Pressure Variability and the Risk of Stroke in the Netherlands: A Population-Based Cohort Study. PLoS Med. 2022, 19, e1003942. [Google Scholar] [CrossRef]
  9. Chen, Z.; Jiang, X.; Wu, J.; Lin, L.; Zhou, Z.; Li, M.; Wang, C. Association Between Short-Term Blood Pressure Variability and Target Organ Damage in Non-Dialysis Patients with Chronic Kidney Disease. BMC Nephrol. 2024, 25, 111. [Google Scholar] [CrossRef]
  10. Hansen, T.W.; Thijs, L.; Li, Y.; Boggia, J.; Kikuya, M.; Björklund-Bodegård, K.; Richart, T.; Ohkubo, T.; Jeppesen, J.; Torp-Pedersen, C.; et al. Prognostic Value of Reading-to-Reading Blood Pressure Variability over 24 Hours in 8938 Subjects from 11 Populations. Hypertension 2010, 55, 1049–1057. [Google Scholar] [CrossRef]
  11. Gosmanova, E.O.; Mikkelsen, M.K.; Molnar, M.Z.; Lu, J.L.; Yessayan, L.T.; Kalantar-Zadeh, K.; Kovesdy, C.P. Association of Systolic Blood Pressure Variability with Mortality, Coronary Heart Disease, Stroke, and Renal Disease. J. Am. Coll. Cardiol. 2016, 68, 1375–1386. [Google Scholar] [CrossRef] [PubMed]
  12. Dasa, O.; Smith, S.M.; Howard, G.; Cooper-DeHoff, R.M.; Gong, Y.; Handberg, E.; Pepine, C.J. Association of 1-Year Blood Pressure Variability with Long-Term Mortality Among Adults with Coronary Artery Disease: A Post Hoc Analysis of a Randomized Clinical Trial. JAMA Netw. Open 2021, 4, e218418. [Google Scholar] [CrossRef] [PubMed]
  13. Mehlum, M.H.; Liestøl, K.; Kjeldsen, S.E.; Julius, S.; Hua, T.A.; Rothwell, P.M.; Mancia, G.; Parati, G.; Weber, M.A.; Berge, E. Blood Pressure Variability and Risk of Cardiovascular Events and Death in Patients with Hypertension and Different Baseline Risks. Eur. Heart J. 2018, 39, 2243–2251. [Google Scholar] [CrossRef]
  14. Diaz, K.M.; Tanner, R.M.; Falzon, L.; Levitan, E.B.; Reynolds, K.; Shimbo, D.; Muntner, P. Visit-to-Visit Variability of Blood Pressure and Cardiovascular Disease and All-Cause Mortality: A Systematic Review and Meta-Analysis. Hypertension 2014, 64, 965–982. [Google Scholar] [CrossRef] [PubMed]
  15. Manning, L.; Hirakawa, Y.; Arima, H.; Wang, X.; Chalmers, J.; Wang, J.; Lindley, R.; Heeley, E.; Delcourt, C.; Neal, B.; et al. Blood Pressure Variability and Outcome After Acute Intracerebral Haemorrhage: A Post-Hoc Analysis of INTERACT2, a Randomised Controlled Trial. Lancet Neurol. 2014, 13, 364–373. [Google Scholar] [CrossRef]
  16. Li, F.K.; An, D.W.; Guo, Q.H.; Zhang, Y.Q.; Qian, J.Y.; Hu, W.G.; Li, Y.; Wang, J.G. Day-by-Day Blood Pressure Variability in Hospitalized Patients with COVID-19. J. Clin. Hypertens. 2021, 23, 1675–1680. [Google Scholar] [CrossRef]
  17. Melgarejo, J.D.; Maestre, G.E.; Mena, L.J.; Lee, J.H.; Petitto, M.; Chávez, C.A.; Calmon, G.; Silva, E.; Thijs, L.; Al-Aswad, L.A.; et al. Normal-Tension Glaucomatous Optic Neuropathy Is Related to Blood Pressure Variability in the Maracaibo Aging Study. Hypertens. Res. 2021, 44, 1105–1112. [Google Scholar] [CrossRef]
  18. Virdis, A.; Dell’Agnello, U.; Taddei, S. Impact of Inflammation on Vascular Disease in Hypertension. Maturitas 2014, 78, 179–183. [Google Scholar] [CrossRef]
  19. Gutteridge, D.S.; Tully, P.J.; Smith, A.E.; Loetscher, T.; Keage, H.A. Cross-Sectional Associations Between Short and Mid-Term Blood Pressure Variability, Cognition, and Vascular Stiffness in Older Adults. Cereb. Circ. Cogn. Behav. 2023, 5, 100181. [Google Scholar] [CrossRef]
  20. Hu, B.; Yang, X.R.; Xu, Y.; Sun, Y.F.; Sun, C.; Guo, W.; Zhang, X.; Wang, W.M.; Qiu, S.J.; Zhou, J.; et al. Systemic Immune-Inflammation Index Predicts Prognosis of Patients After Curative Resection for Hepatocellular Carcinoma. Clin. Cancer Res. 2014, 20, 6212–6222. [Google Scholar] [CrossRef]
  21. Yang, Y.L.; Wu, C.H.; Hsu, P.F.; Chen, S.C.; Huang, S.S.; Chan, W.L.; Lin, S.J.; Chou, C.Y.; Chen, J.W.; Pan, J.P.; et al. Systemic Immune-Inflammation Index (SII) Predicted Clinical Outcome in Patients with Coronary Artery Disease. Eur. J. Clin. Invest. 2020, 50, e13230. [Google Scholar] [CrossRef] [PubMed]
  22. Gok, M.; Kurtul, A. A Novel Marker for Predicting Severity of Acute Pulmonary Embolism: Systemic Immune-Inflammation Index. Scand. Cardiovasc. J. 2021, 55, 91–96. [Google Scholar] [CrossRef] [PubMed]
  23. Akyüz, A.; Işık, F. Systemic Immune-Inflammation Index: A Novel Predictor for Non-dipper Hypertension. Cureus 2022, 14, e28176. [Google Scholar] [CrossRef] [PubMed]
  24. Stergiou, G.S.; Parati, G. How to Best Assess Blood Pressure? The Ongoing Debate on the Clinical Value of Blood Pressure Average and Variability. Hypertension 2011, 57, 1041–1042. [Google Scholar] [CrossRef] [PubMed]
  25. Whelton, P.; Carey, R.; Aronow, W.; Casey, D.E.; Collins, K.J.; Dennison Himmelfarb, C.; DePalma, S.M.; Gidding, S.; Jamerson, K.A.; Jones, D.W.; et al. 2017 ACC/AHA/AAPA/ABC/ACPM/AGS/APhA/ASH/ASPC/NMA/PCNA Guideline for the Prevention, Detection, Evaluation, and Management of High Blood Pressure in Adults. J. Am. Coll. Cardiol. 2018, 71, e127–e248. [Google Scholar] [CrossRef]
  26. Bezdek, J.C. Pattern Recognition with Fuzzy Objective Function Algorithms; Plenum Press: New York, NY, USA, 1981. [Google Scholar]
  27. Sesso, H.D.; Buring, J.E.; Rifai, N.; Blake, G.J.; Gaziano, J.M.; Ridker, P.M. C-Reactive Protein and the Risk of Developing Hypertension. JAMA 2003, 290, 2945–2951. [Google Scholar] [CrossRef]
  28. Kawada, T.; Morihashi, M.; Ueda, H.; Sirato, T. Neutrophil Cell Count is Related to Hypertension in Workers: A Cross-Sectional Study. Vasc. Dis. Prev. 2007, 4, 225–228. [Google Scholar] [CrossRef]
  29. Nityanand, S.; Pande, I.; Bajpai, V.K.; Singh, L.; Chandra, M.; Singh, B.N. Platelets in Essential Hypertension. Thromb. Res. 1993, 72, 447–454. [Google Scholar] [CrossRef]
  30. Sunbul, M.; Gerin, F.; Durmus, E.; Kivrak, T.; Sari, I.; Tigen, K.; Cincin, A. Neutrophil to Lymphocyte and Platelet to Lymphocyte Ratio in Patients with Dipper Versus Non-Dipper Hypertension. Clin. Exp. Hypertens. 2014, 36, 217–221. [Google Scholar] [CrossRef]
  31. Mancia, G.; Grassi, G. The Autonomic Nervous System and Hypertension. Circ. Res. 2014, 114, 1804–1814. [Google Scholar] [CrossRef]
  32. Parati, G.; Faini, A.; Valentini, M. Blood Pressure Variability: Its Measurement and Significance in Hypertension. Curr. Hypertens. Rep. 2006, 8, 199–204. [Google Scholar] [CrossRef] [PubMed]
  33. O’Brien, E.; Atkins, N.; Staessen, J. Factors Influencing Validation of Ambulatory Blood Pressure Measuring Devices. J. Hypertens. 1995, 13, 1235–1240. [Google Scholar] [PubMed]
  34. Parati, G.; Ochoa, J.E.; Lombardi, C.; Bilo, G. Assessment and Management of Blood-Pressure Variability. Nat. Rev. Cardiol. 2013, 10, 143–155, Erratum in Nat. Rev. Cardiol. 2014, 11, 314. [Google Scholar] [CrossRef] [PubMed]
  35. Levitan, E.B.; Kaciroti, N.; Oparil, S.; Julius, S.; Muntner, P. Blood Pressure Measurement Device, Number and Timing of Visits, and Intra-Individual Visit-to-Visit Variability of Blood Pressure. J. Clin. Hypertens. 2012, 14, 744–750. [Google Scholar] [CrossRef] [PubMed]
  36. Stergiou, G.S.; Palatini, P.; Parati, G.; O’Brien, E.; Januszewicz, A.; Lurbe, E.; Persu, A.; Mancia, G.; Kreutz, R. 2021 European Society of Hypertension Practice Guidelines for Office and Out-of-Office Blood Pressure Measurement. J. Hypertens. 2021, 39, 1293–1302. [Google Scholar] [CrossRef]
  37. Madden, J.M.; O’Flynn, A.M.; Fitzgerald, A.P.; Kearney, P.M. Correlation Between Short-Term Blood Pressure Variability and Left-Ventricular Mass Index: A Meta-Analysis. Hypertens. Res. 2016, 39, 171–177. [Google Scholar] [CrossRef]
  38. Abramson, J.L.; Lewis, C.; Murrah, N.V.; Anderson, G.T.; Vaccarino, V. Relation of C-Reactive Protein and Tumor Necrosis Factor-Alpha to Ambulatory Blood Pressure Variability in Healthy Adults. Am. J. Cardiol. 2006, 98, 649–652. [Google Scholar] [CrossRef]
  39. Tatasciore, A.; Zimarino, M.; Renda, G.; Zurro, M.; Soccio, M.; Prontera, C.; Emdin, M.; Flacco, M.; Schillaci, G.; De Caterina, R. Awake Blood Pressure Variability, Inflammatory Markers and Target Organ Damage in Newly Diagnosed Hypertension. Hypertens. Res. 2008, 31, 2137–2146. [Google Scholar] [CrossRef]
  40. Kim, K.I.; Lee, J.H.; Chang, H.J.; Cho, Y.S.; Youn, T.J.; Chung, W.Y.; Chae, I.H.; Choi, D.J.; Park, K.U.; Kim, C.H. Association Between Blood Pressure Variability and Inflammatory Marker in Hypertensive Patients. Circ. J. 2008, 72, 293–298. [Google Scholar] [CrossRef]
  41. Wong, K.H.; Muddasani, V.; Peterson, C.; Sheibani, N.; Arkin, C.; Cheong, I.; Majersik, J.J.; Biffi, A.; Petersen, N.; Falcone, G.J. Baseline Serum Biomarkers of Inflammation and Subsequent Visit-to-Visit Blood Pressure Variability: A Post Hoc Analysis of MESA. Am. J. Hypertens. 2023, 36, 144–147. [Google Scholar] [CrossRef]
  42. Ciobanu, D.M.; Bala, C.; Rusu, A.; Cismaru, G.; Roman, G. E-Selectin Is Associated with Daytime and 24-Hour Diastolic Blood Pressure Variability in Type 2 Diabetes. Biomedicines 2022, 10, 279. [Google Scholar] [CrossRef] [PubMed]
  43. Xu, C.; Fu, Z.; Wu, W.; Zhang, J.; Liu, M.; Gao, L. Association between High-Sensitivity C-Reactive Protein and Blood Pressure Variability in Subacute Stage of Ischemic Stroke. Brain Sci. 2023, 13, 998. [Google Scholar] [CrossRef] [PubMed]
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Figure 1. Mean levels of ABPV according to hypertension grades.
Figure 1. Mean levels of ABPV according to hypertension grades.
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Figure 2. Correlation between SII and ABPV. SII: mean ± Std: 635.45 ± 265.17; ABPV: mean ± Std: 14.75 ± 4.71; Pearson Correlation: 0.619; p: = 0.000; * correlation is significant at the 0.01 level.
Figure 2. Correlation between SII and ABPV. SII: mean ± Std: 635.45 ± 265.17; ABPV: mean ± Std: 14.75 ± 4.71; Pearson Correlation: 0.619; p: = 0.000; * correlation is significant at the 0.01 level.
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Figure 3. ROC analysis of SII performed to predict ABPV > 14.
Figure 3. ROC analysis of SII performed to predict ABPV > 14.
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Table 1. Demographic, echocardiographic, and clinical variables of the study population sample.
Table 1. Demographic, echocardiographic, and clinical variables of the study population sample.
Normal (58)High–Normal (60)Grade 1 (53) Grade 2 (102)Severe (113)
Mean ± SdMean ± SdMean ± SdMean ± SdMean ± Sdp Value (Between the Groups)
SII412.90 ± 123.6 d,e425.21 ± 142.5 d,e442.78 ± 149.9 d,e746.49 ± 202.6 a,b,c,e851.44 ± 224.94 a,b,c,d<0.001
Age (years)59.71 ± 10.2254.87 ± 8.9256.51 ± 11.6055.52 ± 10.9755.78 ± 10.410.068
Height (cm)169.03 ± 8.43171.13 ± 7.30170.64 ± 8.12169.18 ± 7.62167.90 ± 6.320.090
Weight (kg)73.98 ± 11.1174.53 ± 12.1375.25 ± 9.3973.30 ± 10.2171.64 ± 11.110.656
BMİ, kg/m225.81 ± 2.6525.42 ± 3.4325.91 ± 3.2225.64 ± 3.2425.39 ± 3.490.391
Creatine, mg/dL0.70 ± 0.140.77 ± 0.150.75 ± 0.190.76 ± 0.160.75 ± 0.170.122
Gender (female), N (%)41 (70.7%)29 (48.3%)33 (62.3%)55 (53.9%)68 (60.2%)0.115
Glucose, mg/dL100.09 ± 14.1797.90 ± 11.00100.02 ± 11.7798.18 ± 12.7795.22 ± 12.440.289
Uric acid, mg/dL4.08 ± 1.374.25 ± 1.204.48 ± 1.684.27 ± 1.254.45 ± 1.420.415
Cholesterol, mg/dL201.22 ± 41.77208.17 ± 40.28209.79 ± 42.76211.47 ± 45.37204.43 ± 44.130.990
HDL, mg/dL59.34 ± 66.5848.67 ± 10.6645.32 ± 11.1845.61 ± 10.7945.69 ± 10.240.019
LDL, mg/dL120.28 ± 32.44129.03 ± 38.52130.81 ± 31.35132.09 ± 35.59124.34 ± 35.020.163
Triglyceride, mg/dL158.43 ± 90.57160.28 ± 89.43192.02 ± 252.7176.77 ± 147.1171.66 ± 79.810.133
hs-CRP, mg/L0.40 ± 0.200.36 ± 0.170.49 ± 0.400.43 ± 0.250.49 ± 0.310.026
Hb, g/dL14.00 ± 1.5914.83 ± 1.2214.31 ± 1.3514.37 ± 1.6314.28 ± 1.620.175
WBC count, ×10/μL7.39 ± 2.23 e7.86 ± 2.977.36 ± 1.77 e8.18 ± 2.618.68 ± 2.04 a,c0.002
PLT count, ×10/μL271.12 ± 52.58 e254.85 ± 63.52 e245.09 ± 49.81 d,e288.13 ± 73.27 c,e331.55 ± 67.63 a,b,c,d<0.001
Neutrophil count, ×10/μL3.92 ± 1.334.27 ± 1.854.14 ± 1.325.17 ± 1.935.53 ± 1.380.099
Lymphocyte count, ×10/μL2.66 ± 0.952.63 ± 1.032.39 ± 0.702.03 ± 0.752.28 ± 0.900.062
Monocytes count, ×10/μL0.57 ± 0.220.67 ± 0.270.57 ± 0.220.66 ± 0.230.59 ± 0.190.006
ABPV 12.79 ± 2.98 e12.32 ± 2.54 c,d,e15.08 ± 4.53 b14.52 ± 4.78 b,e17.10 ± 5.22 a,b,d<0.001
DİPPER, N (%)41 (70.7%) d,e46 (76.7%) d,e35 (66.0%) e43 (42.2%) a,b35 (31.0%) a,b,c<0.001
LVDD47.41 ± 3.2948.67 ± 3.2746.96 ± 3.2446.93 ± 3.2247.33 ± 3.530.693
LVSD31.06 ± 3.8131.18 ± 4.1130.83 ± 3.5730.87 ± 4.3830.93 ± 3.680.189
İVS10.21 ± 1.3110.78 ± 1.1110.32 ± 1.4210.46 ± 1.3911.80 ± 1.300.404
PW9.58 ± 1.0110.05 ± 1.029.95 ± 1.269.90 ± 1.2210.97 ± 1.120.217
LVM166.95 ± 37.50185.69 ± 34.09170.19 ± 41.61170.64 ± 39.42200.67 ± 38.370.464
LVMİ90.05 ± 20.1199.98 ± 22.4390.71 ± 22.7492.81 ± 23.02110.86 ± 23.440.677
N/L1.55 ± 0.47 d,e1.70 ± 0.50 d,e1.86 ± 0.70 d,e2.71 ± 0.86 a,b,c2.64 ± 0.74 a,b,c<0.001
P/L110.74 ± 34.06 d,e107.08 ± 40.68 d,e110.01 ± 33.22 d,e153.53 ± 44.98 a,b,c159.89 ± 46.20 a,b,c<0.001
a: significantly different from the normal blood pressure group (p < 0.005); b: significantly different from the high–normal blood pressure group (p < 0.005); c: significantly different from the grade 1 group (p < 0.005); d: significantly different from the grade 2 group (p < 0.005); e: significantly different from the severe HT group (p < 0.005). (Bonferroni correction was used to counteract the multiple comparisons problem.) SII: Systemic Immune Inflammatory Index; BMI: Body Mass Index; HDL: High-Density Lipoprotein; LDL: Low-Density Lipoprotein; hs-CRP: high sensitive CRP; Hb: hemoglobin; WBC: white blood cell; PLT: platelet; ABPV: Ambulatuary Blood Pressure Variability; LVDD: Left Ventricle Diastolic Diameter; LVSD: Left Ventricle Systolic Diameter; IVS: Inter-Ventricular Septum; PW: Posterior Wall; LVM: Left Ventricle Mass; LVMI: Left Ventricle Mass Index; N/L: neutrophil to lymphocyte ratio; P/L: platelet to lymphocyte ratio.
Table 2. Demographic, echocardiographic, and clinical variables of the hypertension groups.
Table 2. Demographic, echocardiographic, and clinical variables of the hypertension groups.
Group 1 (Non-Candidate for Antihypertensive Therapy) (171)Group 2 (Candidate for Antihypertensive Therapy) (215)
Mean ± SdMean ± Sdp
SII426.48 ± 138.54801.65 ± 220.47<0.001
Gender (female), N (%)103 (60.2%)123 (57.2%)0.550
Age (years)57.02 ± 10.3955.66 ± 10.660.208
BMİ, kg/m225.70 ± 3.1125.51 ± 3.370.563
Glucose, mg/dL99.30 ± 12.3696.62 ± 12.660.038
Creatine, mg/dL0.74 ± 0.170.75 ± 0.160.466
Uric acid, mg/dL4.26 ± 1.424.36 ± 1.340.475
Cholesterol, mg/dL206.32 ± 41.49207.77 ± 44.760.743
HDL, mg/dL51.25 ± 40.0045.65 ± 10.480.050
LDL, mg/dL126.61 ± 34.49128.01 ± 35.420.697
Triglyceride, mg/dL169.49 ± 159.05174.09 ± 116.380.743
hs-CRP, mg/L0.41 ± 0.270.46 ± 0.290.082
Hb, g/dL14.39 ± 1.4314.32 ± 1.620.693
WBC count, ×10/μL7.54 ± 2.398.44 ± 2.34<0.001
PLT count, ×10/μL257.35 ± 56.57310.95 ± 73.48<0.001
Neutrophil count, ×10/μL4.11 ± 1.535.36 ± 1.67<0.001
Lymphocyte count, ×10/μL2.57 ± 0.912.16 ± 0.84<0.001
Monocytes count, ×10/μL0.60 ± 0.240.62 ± 0.220.323
N/L1.70 ± 0.572.67 ± 0.80<0.001
P/L0.02 ± 0.010.02 ± 0.010.037
ABPV13.34 ± 3.5915.87 ± 5.17<0.001
Dipper (N/%)122 (71.3%)78 (36.3%)<0.001
LVDD, mm47.71 ± 3.3347.14 ± 3.380.096
LVSD, mm31.03 ± 3.8330.90 ± 4.020.746
İVS, mm10.44 ± 1.3011.16 ± 1.50<0.001
PW, mm9.86 ± 1.1110.46 ± 1.28<0.001
LV MASS174.53 ± 38.39186.43 ± 41.590.004
LVMİ93.74 ± 22.13102.30 ± 24.89<0.001
SII: Systemic Immune Inflammatory Index; BMI: Body Mass Index; HDL: High-Density Lipoprotein; LDL: Low-Density Lipoprotein; hs-CRP: high sensitive CRP; Hb: hemoglobin; WBC: white blood cell; PLT: platelet; ABPV: Ambulatuary Blood Pressure Variability; LVDD: Left Ventricle Diastolic Diameter; LVSD: Left Ventricle Systolic Diameter; IVS: Inter-Ventricular Septum; PW: Posterior Wall; LVM: Left Ventricle Mass; LVMI: Left Ventricle Mass Index; N/L: neutrophil to lymphocyte ratio; P/L: platelet to lymphocyte ratio.
Table 3. Comparison of demographic, echocardiographic, and clinical variables according to ABPV.
Table 3. Comparison of demographic, echocardiographic, and clinical variables according to ABPV.
ABPV < 14 (203)ABPV > 14 (183)p
Mean ± Sd.Mean ± Sd.
N/L1.89 ± 0.702.61 ± 0.85<0.001
P/L122.49 ± 41.53150.48 ± 50.25<0.001
LVMİ96.31 ± 23.35 100.93 ± 24.640.060
LV MASS177.18 ± 38.89185.56 ± 42.050.043
PW, mm9.99 ± 1.1710.42 ± 1.280.001
IVS, mm10.64 ± 1.3411.06 ± 1.540.004
LVSD, mm30.85 ± 3.9831.07 ± 3.870.576
LVDD, mm47.52 ± 3.2447.25 ± 3.500.431
DİPPER, N (%)110 (54.2%)90 (49.2%)0.327
ABP Grade2.00 ± 1.422.83 ± 1.30<0.001
SII510.95 ± 210.65773.54 ± 251.05<0.001
Gender (female), N (%)112 (55.2%)114 (62.3%)0.156
Age (years)56.14 ± 10.2756.38 ± 10.870.828
BMİ, kg/m225.40 ± 3.2025.80 ± 3.300.232
Glucose, mg/dL96.49 ± 12.5099.26 ± 12.520.030
Creatine, mg/dL0.74 ± 0.160.74 ± 0.160.917
HDL, mg/dL50.76 ± 36.7945.20 ± 10.990.050
LDL, mg/dL130.12 ± 34.95124.36 ± 34.840.106
hs-CRP, mg/L0.40 ± 0.210.48 ± 0.330.007
Hb, g/dL14.44 ± 1.4214.24 ± 1.640.211
Triglyceride, mg/dL161.13 ± 78.23184.16 ± 180.250.111
Cholesterol, mg/dL209.71 ± 41.66204.25 ± 44.960.218
SII: Systemic Immune Inflammatory Index; BMI: Body Mass Index; HDL: High-Density Lipoprotein; LDL: Low-Density Lipoprotein; hs-CRP: high sensitive CRP; Hb: hemoglobin; WBC: white blood cell; PLT: platelet; ABPV: Ambulatuary Blood Pressure Variability; LVDD: Left Ventricle Diastolic Diameter; LVSD: Left Ventricle Systolic Diameter; IVS: Inter-Ventricular Septum; PW: Posterior Wall; LVM: Left Ventricle Mass; LVMI: Left Ventricle Mass Index; N/L: neutrophil to lymphocyte ratio; P/L: platelet to lymphocyte ratio.
Table 4. Linear and multiple linear regression analysis for independent predictors of ABPV.
Table 4. Linear and multiple linear regression analysis for independent predictors of ABPV.
Univariate AnalysisMultivariate Analysis
rpBeta%95 CIp
Age 0.0110.8280.001−0.003 −0.0050.672
N/L0.4200.0000.074−0.007 −0.1550.072
P/L0.2920.000−0.001−0.002 −0.0010.198
Gender−0.0730.157−0.059−0.164 −0.0450.264
SII0.4950.0000.0010.001 −0.001<0.001
hs-CRP0.1410.0050.098−0.060 −0.2550.225
BMİ0.0610.2320.008−0.006 −0.0210.272
Creatine0.0050.917−0.014−0.326 −0.2990.932
LDL0.0820.106−0.001−0.002 −0.0000.142
ABP Grade0.2910.000−0.022−0.064 −0.0200.295
SII: Systemic Immune Inflammatory Index; BMI: Body Mass Index; hs-CRP: high sensitive CRP; N/L: neutrophil to lymphocyte ratio; P/L: platelet to lymphocyte ratio.
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Karaca, Y.; Karasu, M.; Gelen, M.A.; Şahin, Ş.; Yavçin, Ö.; Yaman, İ.; Hidayet, Ş. Systemic Immune Inflammatory Index as Predictor of Blood Pressure Variability in Newly Diagnosed Hypertensive Adults Aged 18–75. J. Clin. Med. 2024, 13, 6647. https://doi.org/10.3390/jcm13226647

AMA Style

Karaca Y, Karasu M, Gelen MA, Şahin Ş, Yavçin Ö, Yaman İ, Hidayet Ş. Systemic Immune Inflammatory Index as Predictor of Blood Pressure Variability in Newly Diagnosed Hypertensive Adults Aged 18–75. Journal of Clinical Medicine. 2024; 13(22):6647. https://doi.org/10.3390/jcm13226647

Chicago/Turabian Style

Karaca, Yücel, Mehdi Karasu, Mehmet Ali Gelen, Şeyda Şahin, Özkan Yavçin, İrfan Yaman, and Şıho Hidayet. 2024. "Systemic Immune Inflammatory Index as Predictor of Blood Pressure Variability in Newly Diagnosed Hypertensive Adults Aged 18–75" Journal of Clinical Medicine 13, no. 22: 6647. https://doi.org/10.3390/jcm13226647

APA Style

Karaca, Y., Karasu, M., Gelen, M. A., Şahin, Ş., Yavçin, Ö., Yaman, İ., & Hidayet, Ş. (2024). Systemic Immune Inflammatory Index as Predictor of Blood Pressure Variability in Newly Diagnosed Hypertensive Adults Aged 18–75. Journal of Clinical Medicine, 13(22), 6647. https://doi.org/10.3390/jcm13226647

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