AUCTORES
Globalize your Research
Research Article | DOI: https://doi.org/10.31579/2767-7370/042
1 Department of Cardiology, Fatsa state hospital, Ordu, Turkey.
2 Department of Cardiology, Fethi Sekin Sehir Hastanesi, Elazıg, Turkey.
3 Department of Cardiology, Malatya Turgut Ozal Universitesi Kardiyoloji ABD, Malatya, Turkey.
*Corresponding Author: Mehdi Karasu, MD, Department of Cardiology Fethi Sekin Sehir Hastanesi, Elazıg, Turkey.
Citation: Abdulmelik Deniz, Mehdi Karasu, Yusuf Akin, Hasan Ata Bolayir (2023), The Relationship Between the Systemic Immune-Inflammation Index and Non-Dipper Hypertension in Patients with Newly Diagnosed Hypertension, J New Medical Innovations and Research, 4(3);10.31579/2767-7370/042
Copyright: © 2023, Mehdi Karasu. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Received: 01 June 2023 | Accepted: 12 June 2023 | Published: 23 June 2023
Keywords: systemic ımmune-inflammation ındex; non-dipper hypertension; high sensitive c reactive protein
Background: It has been determined that there is an increase in mortality and morbidity rates due to cardiovascular diseases, which are observed more frequently in individuals with non-dipper hypertension (NDPHT). In this study, it was aimed to investigate whether there is a relationship between the systemic immune-inflammation index (SII), and NDPHT.
Materials and Methods: Patients who applied to the cardiology outpatient clinic between January 2022 to April 2022 with newly diagnosed HT were included in the study consecutively. Twenty-four hour ambulatory blood pressure (BP) measurement was performed for each patient, and the study population was divided into three groups as dipper hypertension (DPHT), NDPHT and normotensive. The patients were evaluated with their demographic characteristics, comorbidities and laboratory values. SII was calculated for each patient.
Results: There was no significant difference between the groups in demographic parameters. Left ventricular wall was thicker in the hypertensive group than the control group, and the left ventricular mass index (LVMİ) was higher in the hypertensive group. (p=0.017). Hs-CRP levels were significantly higher in the non-dipper hypertensive group than in the other groups (p=0.028). SII >347 had 69% sensitivity and 77% specificity in predicting NDPHT (area under the curve 0.789, p<0.001).
Conclusion: İt is feasible to develop a auxiliary diagnostic method with 69% sensitivity and 77% specificity (SII>347) to predict non-dipping status in newly diagnosed treatment-naive hypertensive patients.
Hypertension (HT) is one of the leading preventable cardiovascular risk factors. (1) It is known that cardiovascular mortality, cerebrovascular diseases, renal failure, and retinopathy are more common in hypertensive individuals compared to healthy people. (2-4) Although arterial blood pressure (BP) values are constantly changing during the normal day, they show a circadian rhythm. (5)
Due to the lifestyle and daily living activities of people in their normal daily lives, changes occur in BP values and blood pressure can exceed the target values. For this reason, it is reported that evaluation should not be made with only one measurement before diagnosing HT and it is more valid to take 24-hour BP averages. (6)
When BP values are examined, it has been determined that BP values changes with the circadian rhythm during the day. İn studies conducted in the literature, nighttime BP values are lower than daytime BP values and the decrease in BP differs between individuals. In a study conducted with healthy individuals, it was reported that ambulatory BP values reached the highest values in the morning, decreased during the day, and remained at the lowest values after sleeping. (7)
In classification created by ambulatory BP monitoring, it is stated that a decrease of 10% or more in BP values at sleep time compared to the daytime BP values is defined as Dipper HT (DHT) and a decrease of less than 10% is defined as Non-Dipper HT (NDPHT). (8,9)
DPHT and NDPHT are important in cardiovascular and cerebrovascular diseases in patients with HT and normotensive individuals. (10) Many studies have showed that target organ damage and cardiovascular events have been associated with circadian rhythm abnormalities such as increased morning BP (MBP), increased MBP surge (MBPS) and nondipping status. (5,11)
Different hypotheses have been proposed about the causes of the development of a Non-Dipper (NDP) pattern, including a renal mechanism due to high salt intake (12), alteration of the sleep-wake cycle (13), higher activity during night rest (14) and the relationship of the NDP pattern with a decrease in the parasympathetic nervous function and an increase in the sympathetic nervous function. (15) However, the causes of the development of the NDP pattern are still unclear.
When analyzed in terms of the pathophysiology of NDPHT, it was determined that the number of lymphocytes circulating in the peripheral blood in the inflammation region decreased. (16) It is stated that leukocytes, one of the blood components, perform a physiological response to stress in humans and the increase in the amount of this response is manifested by a decrease in the number of lymphocytes. (17) It is stated that one of the factors in the etiology of these events that occur in the body due to inflammation, ischemia, thrombosis and embolism in the development of DPHT is due to an increase in the number of platelets or an enlargement in volume. (18)
It is stated that SII is a new generation inflammation biomarker created with whole blood parameters. (17) This biomarker is a value that obtained by multiplying the neutrophil count by the platelet count and dividing the result by the lymphocyte count..
SII has been emerged to predict the prognosis and outcomes in cancer and cardiac patients based on peripheral blood cells such as platelet, neutrophil, and lymphocyte. (19-21) It has been reported that SII is an independent predictor of major adverse cardiovascular events in coronary artery disease patients (CAD). (20)
In this study, we aimed to evaluate the relation of SII with NDPHT in newly diagnosed HT patients with the homogenous cardiovascular risk profile.
Seventy patients with newly diagnosed HT admitted to the cardiology outpatient clinic between January 2022 and April 2022 were included. Thirty-five volunteers with similar demographic characteristics who applied to the cardiology outpatient clinic were included in the study as control group.
The diagnosis of HT was based on repeated measurements of systolic BP ≥140 and/or diastolic BP ≥90 mmHg at at least three separate visits. Twenty-four hour Ambulatory BP measurement (ABPM) was performed for each patient, and the study population was divided into three groups as DPHT, NDPHT and normotensive control groups according to whether there was a ≥10
Statistical analyzes were performed using the Statistical Package for the Social Sciences (SPSS Inc., Chicago, IL, USA) for Windows 21.0. The Kolmogorov-Smirnov test was used to determine whether the continuous variables were normally distributed. Normally distributed variables were given as mean±standard deviation, and non-normally distributed variables were given as median (minimum-maximum) values. Descriptive statistics are given as percentages and absolute values. Baseline features were compared with the chi-square test. When appropriate, one-way Analysis of Variance (ANOVA) or Kruskal-Wallis test was used to compare 3 groups for continuous variables. Differences between subgroups were revealed using the Dunn procedure (for data not normally distributed). Data were analyzed using univariate logistic and multivariate logistic regression models to determine whether SII was independently associated with the risk of NDPHT. Univariate analyzes considered the following variables: Hemoglobin, BMİ, hs-CRP, age, gender, LVMİ, smoking, fasting blood glucose, and creatinine. Covariates with p<0>
Demographic, laboratory and echocardiographic characteristics of the study population are shown below. (Table 1) There was no significant difference between the groups in demographic parameters. In the echocardiographic evaluation, it was determined that the left ventricular wall was thicker in the HT group than the control group, and the LVMİ was higher in the HT group. (p=0.017)
Daytime and nighttime BP averages of DPHT and NDPHT groups are given in Table 2. In the NDPHT group, a statistically significant decrease in BP was observed at night, and the nighttime mean values were higher compared to the DPH group.
Levels of SII were found to be significantly different between the three groups. (p<0>0.05). (figure 1). Serum hs-CRP levels were significantly higher in the NDPHT group than in the other groups (p=0.028). However, there was no significant difference between the DPHT group and the control group (p>0.05). (Figure 2.) In HT patients, SII levels were positively correlated with hs-CRP levels (r=0.213, p=0.027). Subsequent multivariate regression analysis showed that high SII and hs-CRP levels could independently predict NDPHT (Table 3). In ROC analysis, SII >347 threshold; it had 69% sensitivity and 77% specificity in predicting disease (area under the curve 0.789, p<0>
NS: nonsignificant
Figure 1: Comparison of the groups in terms of SII values
NS: nonsignificant, hs-CRP: high-sensitivity C-reactive protein
Figure 2: Comparison of the groups in terms of hs-CRP levels
NS: nonsignificant, hs-CRP: high-sensitivity C-reactive protein
Figure 2: Comparison of the groups in terms of hs-CRP levels
| Control (n=35) | Dipper (n=35) | Non dipper (n=35) | P | |
| Age | 51±6.4 | 47,2±9,3 | 47,4±5,2 | 0.125 |
| Men, n (%) | 15 (42) | 21 (60) | 16 (46) | 0.307 |
| Body mass index (kg/m2) | 22,8 (19,3–25,9) | 22,7 (19,5–26) | 20,6(19,3-25,7) | 0.105 |
| Smokers | 8 (22.9) | 4 (11.4) | 4 (11.4) | 0.307 |
| Total cholesterol (mg/dL) | 184,9±39,4 | 197.4±48.6 | 192,3±39,4 | 0.469 |
| Low-density lipoprotein cholesterol (mg/dL) | 130.2±39.4 | 132.2±40,8 | 135.8±35,7 | 0.826 |
| High-density lipoprotein cholesterol (mg/dL) | 46 (30–76) | 44 (30–72) | 44 (30–95) | 0.863 |
| Triglycerides (mg/dL) | 108 (48–294) | 132 (46–223) | 129 (55–300) | 0.120 |
| Creatinine (mg/dL) | 0,8 (0,5–1,4) | 0,8 (0,5–1,3) | 0,7 (0,4–1,1) | 0.421 |
| Fasting glucose (mg/dL) | 92 (77–116) | 89 (69-109) | 85 (51–112) | 0.183 |
| Hemoglobin g/dL | 14,2±1,5 | 14,8±1,1 | 14,2±1,7 | 0.135 |
| White blood cell count(103/mm3) | 8.2 (4.8–11.4) | 8,8 (5,8–12,6) | 9,8 (6,0–15,4) | 0.078 |
| Platelet count(103/mm3) | 157 (121–198) | 168 (148–200) | 180 (161–209) | 0.086 |
| hs-CRP (mg/L) | 2,9 (0,1–15,0) | 2.2 (0.1–11.0) | 4.0 (1.0–13.84) | 0.013 |
| NLR | 1.53 (1.33-1.81) | 1.67 (1.49-1.89) | 1.90 (1.77-2.09) | 0.066 |
| SII | 267 (201-327) | 311 (279-351) | 577 (328-888) | <0> |
| Echocardiographic parameters | ||||
| IVS (mm) | 9,9 (7.3–11.8) | 10.9 (8.9–14.2) | 12,0 (10,9-13,9) | 0.019 |
| Posterior wall (mm) | 9,5 (7.1–11.1) | 10,4 (8,3–12,4) | 10,9 (7,9–13,2) | 0.024 |
| LVEDD (mm) | 46 (39–56) | 45 (39–51) | 46 (39-60) | 0.667 |
| LVMI (g/m2)2) | 83,3 (56,9–156,2) | 91,6 (47,8–137,1) | 97,5 (63.8–151) | 0.017 |
All values are presented as mean value, SD, median value (min - maximum), or n (%). hs-CRP: high sensitivity C-reactive protein, NLR: neutrophil lymphocyte ratio, SII: systemic inflammation index, IVS: inter ventricular septum, LVEDD: left ventricular end-diastolic diameter, LVMI: left ventricular mass index
Figure 3: Determination of the threshold SII parameter to predict non-dipper hypertensives, receiver operating characteristic curve
| Dipper | Non-dipper | p | |
| Daytime SBP | 151±6 | 152±8 | 0,874 |
| Daytime DBP | 92±4 | 94±5 | 0,678 |
| Night SBP | 132±7 | 143±5 | <0> |
| Night DBP | 80±6 | 89±3 | <0> |
| 24-hour SBP | 146±7 | 149±6 | 0,234 |
| 24-hour SBP | 93±4 | 94±5 | 0,884 |
| Nocturnal base | %7 | <0> |
SBP: Systolic Blood Pressure DBP: Diastolic blood pressure
Table 1” Main characteristics of the study population
| Variables | Univariate regression analysis | P | Multivariate regression analysis | p |
| Age | 1.034 (0.975–1.097) | 0.260 | - | - |
| Female sex | 0,561 (0,217–1.449) | 0.233 | - | - |
| Smoking | 1.000 (0,229-4,361) | 1.000 | - | - |
| Hemoglobin | 0,736 (0,522–1,038) | 0.081 | 0,777 (0,532–1,137) | 0.194 |
| NLR | 0,975 (0.940–1.011) | 0.177 | - | - |
| creatinine | 0,209 (0,016–2.718) | 0.231 | - | - |
| LVMI | 1.015 (0.991–1.040) | 0.225 | - | - |
| BMI | 0.787 (0.608–1.018) | 0.068 | 0,789 (0,589–1,056) | 0.111 |
| hs-CRP | 1.221 (1.045–1.426) | 0.012 | 1.201 (1.005–1.435) | 0.044 |
| SII | 1.008 (1.003–1.014) | 0.004 | 1.007 (1.001–1.013) | 0.021 |
BMI: body mass index, LVMI: left ventricular mass index, hs-CRP: high-sensitivity Reactive Protein
Table 2: Daytime and 24-hour mean systolic and diastolic blood pressure readings between groups with dipper hypertension and non-dipper hypertension
| Variables | Univariate regression analysis | P | Multivariate regression analysis | p |
| Age | 1.034 (0.975–1.097) | 0.260 | - | - |
| Female sex | 0,561 (0,217–1.449) | 0.233 | - | - |
| Smoking | 1.000 (0,229-4,361) | 1.000 | - | - |
| Hemoglobin | 0,736 (0,522–1,038) | 0.081 | 0,777 (0,532–1,137) | 0.194 |
| NLR | 0,975 (0.940–1.011) | 0.177 | - | - |
| creatinine | 0,209 (0,016–2.718) | 0.231 | - | - |
| LVMI | 1.015 (0.991–1.040) | 0.225 | - | - |
| BMI | 0.787 (0.608–1.018) | 0.068 | 0,789 (0,589–1,056) | 0.111 |
| hs-CRP | 1.221 (1.045–1.426) | 0.012 | 1.201 (1.005–1.435) | 0.044 |
| SII | 1.008 (1.003–1.014) | 0.004 | 1.007 (1.001–1.013) | 0.021 |
BMI: body mass index, LVMI: left ventricular mass index, hs-CRP: high-sensitivity Reactive Protein
Table 3: Univariate and multivariate logistic regression analysis
The current study shows that: SII is superior to platelet, neutrophil, lymphocyte, NLR, and PLR for identifying the presence of NDPHT. In association with increased serum hs-CRP levels and increased LVMI, increased SII levels may be suitable candidate to predict NDPHT. This study also shows that it is feasible to develop a auxiliary diagnostic method with 69% sensitivity and 77% specificity (SII>347) in predicting non-dipping status in newly diagnosed treatment-naive HT patients.
Inflammation plays a crucial role in the pathogenesis of HT and is associated with BP variability. (22) Inflammatory markers have been widely investigated for their usefulness in predicting high-risk patients with HT for adverse outcomes. Kim et al. demonstrated that inflammatory mediators including Tumor Necrosis Factor alfa (TNF-α), Interleukine-6 (IL-6), and hs-CRP were related to BP variability. (23) Kaya et al. reported that NDPHT patients had increased platelet activation than DPHT patients. (24) Neutrophil counts were found to be elevated before the development of HT in experimental models on mice. (25) Tatsukawa et al. observed that increased neutrophil count was significantly related to HT incidence in Japanese women. (26) Angeli et al. reported that neutrophil count was an independent predictor of HT and was a predictor of cardiovascular events. (27) Barhoumi et al. demonstrated that regulatory T lymphocytes, which is a subset of T lymphocytes, suppress the BP elevation and vascular injury mediated by angiotensin-II in an animal study. (28) Sunbul et al. found that hypertensive patients with non-dipper patterns had low lymphocyte counts as compared to dipper hypertensive patients. (29) Both NLR and PLR have been investigated in HT previously. Elevated NLR was related to the prevalence of HT and was associated with resistant HT. (30,31) Sun et al. reported that higher NLR was associated with all cause mortality in hypertensive patients. (32) NLR and PLR were found higher in NDPHT than DPHT. (29) Song et al. showed that PLR was associated with concurrent HT in obstructive sleep apnea. (33) NLR was found to be significantly correlated with BP variability in a previous study reported by Kilicaslan et al. (34) In addition to this finding, Yildirim et al. showed that both NLR and PLR were associated with BP variability. (35) As mentioned above, all markers including neutrophil, lymphocyte, and platelet and combinations such as NLR and PLR were associated with the development of HT, BP variability, and adverse outcomes. Bolayır et al. found that hs-CRP levels were significantly higher in non-dippers and there was a positive and graded relationship between hs-CRP and average night-time BP. (36) İn Yucel et al.’s study non-dipper patients had significantly higher hs-CRP levels than dipper and normotensive patients. (37) In consistent with previous studies we found that serum hs-CRP levels were significantly higher in non-dipper group than the other groups, hs-CRP levels were also significantly higher in NDPHT than DPHT but there were no significant difference between dippers and the control group.
Previous studies have reported that patients with circadian abnormalities have significantly greater left ventricular hypertrophy (LVH). (38,39) İn Kılıc et al.’s study evaluating target organ damage in normotensive patients, non-dipping pattern was associated with an increase in LVMI. (40) İn another study it was shown that although there was no difference in systolic BP, dipping pattern was independently associated with an increase in LVMI. (41) Shibuya Y et al. showed increased myocardial thickness in normotensive individuals with NDPHT. (42) Howewer Cuspidi C et al. showed that left ventricular wall thickness, absolute LVM and LVM indexed by body surface area were slightly, but not signicantly higher in non-dippers. (43) It has already been commented that the greater prevalence of LVH reported by some studies in non-dippers may not be due to the non-dipping pattern, but probably to a higher BP level over 24 h. (44) In another study comparing dipping and non-dipping patterns in individuals with high-normal BP, LVMI was not different between groups. (45) İn our study although the left ventricle wall was thicker in the hypertensive group than the control group and higher LVMI was found in hypertensives there were no significant difference between NDPHT and DPHT .
İn the literature we across very few studies that have investigated the role of SII in hypertensive patients. Cirakcioglu et al. showed the significant association of SII with carotid intima-media thickness in HT patients. (46) Saylik, F., & Sarıkaya, R. showed the association between SII and exaggerated MBPS in newly diagnosed treatment-naive hypertensive patients. (47)
In our study, although there was no significant difference between NDPHT and DPHT in terms of platelet count, WBC count and NLR, there was a significant difference between the two groups in terms of SII. SII was better for identifying NDPHT than platelet, neutrophil, lymphocyte, NLR, and PLR. In this context, SII, which consists of all those markers, might better and widely reflects the immune status and its association with HT and also with non-dipper pattern.
The major limitation was that we included newly diagnosed and treatment-naive hypertensive patients. So, we could not evaluate the effect of treatment on NDPHT, and our study cannot be generalized to all hypertensive patients, especially those on antihypertensive medication. Because the blood counts were measured on the admission day and were not measured on the following days, we could not test the effect of changes in peripheral blood cell counts on BP. The decision of times, including awaking and sleeping, was based on patients’ selfreported times. Thus, the use of an objective method might be better to define those periods.
SII is superior to platelet, neutrophil, lymphocyte, NLR, and PLR for identifying the presence of NDPHT. In association with increased serum hs-CRP levels and increased LVMI, increased SII levels may be suitable candidate to predict NDPHT. This study also shows that it is feasible to develop a auxiliary diagnostic method with 69% sensitivity and 77% specificity (SII>347) in predicting non-dipping status in newly diagnosed treatment-naive hypertensive patients.
Ethics approval and consent to participate: All patients were informed about the aims of the study and their Written/verbal informed consent for participation was obtained. This study was approved by Fırat University Ethics Committee in accordance with the International Code of Ethics and the Declaration of Helsinki
Consent for publication: Written/verbal informed consent was obtained from patients for anonymized information to be published.
Availability of data and materials: All needed data can be obtained from corresponding author.
Competing interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding: The author(s) received no financial support for the research, authorship, and/or publication of this article.
Authors' contributions: All authors contributed to: (1) substantial contributions to conception and design, or acquisition of data, or analysis and interpretation of data, (2) drafting the article or revising it critically for important intellectual content, and, (3) final approval of the version to be published.
Acknowledgements: I would like to thank all my colleagues mentioned in the article who contributed to the design of the study, data collection, data analysis and writing. The current study was created from the thesis study conducted by Hasan Ata Bolayır.
Clearly Auctoresonline and particularly Psychology and Mental Health Care Journal is dedicated to improving health care services for individuals and populations. The editorial boards' ability to efficiently recognize and share the global importance of health literacy with a variety of stakeholders. Auctoresonline publishing platform can be used to facilitate of optimal client-based services and should be added to health care professionals' repertoire of evidence-based health care resources.
