The association between the aggregate index of systemic inflammation and cardiovascular risk in peritoneal dialysis patients

Yin X., Zou J., Yang J.

ObjectiveThe investigation purpose was to examine the correlation between the aggregate index of systemic inflammation (AISI) and rheumatoid arthritis (RA) by utilizing the NHANES database from the years 1999 to 2018.MethodsThe NHANES database was utilized to extract data spanning from 1999 to 2018. AISI, comprising neutrophils (NEU), monocytes (MONO), platelets (PLT), and lymphocytes (LYM), was computed based on counts. The identification of RA patients was accomplished through questionnaire data. To investigate the connection between AISI and RA, a weighted multivariate regression and subgroup analysis were conducted. In addition, restricted cubic splines (RCS) were employed for examining non-linear associations.ResultsThe study encompassed a total of 41,986 patients, among whom 2,642 (6.29%) were diagnosed with RA. Upon controlling for all covariates, the outcomes of the multivariate logistic regression assay demonstrated a statistically significant association between higher Ln(AISI) levels and elevated odds of RA (odds ratio [OR]: 1.097; 95% confidence interval [CI]: 1.096–1.099, p < 0.001). The interaction test findings indicate that there is no statistically significant impact within this particular association. The results of the RCS regression model revealed a non-linear pattern in the correlation between Ln(AISI) and RA. The threshold level of AISI for RA was determined as 298.9. The risk of RA rises steeply when AISI surpasses the threshold value.ConclusionOverall, a positive association has been observed between AISI and RA. This study highlights the potential of AISI as an innovative, vital, and appropriate inflammatory biomarker for predicting the risk of developing rheumatoid arthritis in older individuals residing in the United States.

Zhao X., Huang L., Hu J., Jin N., Hong J., Chen X.

Abstract Background Systemic inflammation markers have recently been identified as being associated with cardiac disorders. However, limited research has been conducted to estimate the pre-diagnostic associations between these markers and paroxysmal atrial fibrillation (PAF). Our aim is to identify potential biomarkers for early detection of PAF. Methods 91 participants in the PAF group and 97 participants in the non-PAF group were included in this study. We investigated the correlations between three systemic inflammation markers, namely the systemic immune inflammation index (SII), system inflammation response index (SIRI), and aggregate index of systemic inflammation (AISI), and PAF. Results The proportion of patients with PAF gradually increased with increasing logSII, logSIRI, and logAISI tertiles. Compared to those in the lowest tertiles, the PAF risks in the highest logSII and logSIRI tertiles were 3.2-fold and 2.9-fold, respectively. Conversely, there was no significant correlation observed between logAISI and PAF risk within the highest tertile of logAISI. The restricted cubic splines (RCS) analysis revealed a non-linear relationship between the elevation of systemic inflammation markers and PAF risk. Specifically, the incidence of PAF is respectively increased by 56%, 95%, and 150% for each standard deviation increase in these variables. The ROC curve analysis of logSII, logSIRI and logAISI showed that they had AUC of 0.6, 0.7 and 0.6, respectively. It also demonstrated favorable sensitivity and specificity of these systemic inflammation markers in detecting the presence of PAF. Conclusions In conclusion, our study reveals significant positive correlations between SII, SIRI, and AISI with the incidence of PAF.

Liao M., Liu L., Bai L., Wang R., Liu Y., Zhang L., Han J., Li Y., Qi B.

Objective Carotid atherosclerosis is a chronic inflammatory disease, which is a major cause of ischemic stroke. The purpose of this study was to analyze the relationship between carotid atherosclerosis and novel inflammatory markers, including platelet to lymphocyte ratio (PLR), neutrophil to lymphocyte ratio (NLR), lymphocyte to monocyte ratio (LMR), platelet to neutrophil ratio (PNR), neutrophil to lymphocyte platelet ratio (NLPR), systemic immune-inflammation index (SII), systemic inflammation response index (SIRI), and aggregate index of systemic inflammation (AISI), in order to find the best inflammatory predictor of carotid atherosclerosis. Method We included 10015 patients who underwent routine physical examinations at the physical examination center of our hospital from January 2016 to December 2019, among whom 1910 were diagnosed with carotid atherosclerosis. The relationship between novel inflammatory markers and carotid atherosclerosis was analyzed by logistic regression, and the effectiveness of each factor in predicting carotid atherosclerosis was evaluated by receiver operating characteristic (ROC) curve and area under the curve (AUC). Result The level of PLR, LMR and PNR in the carotid atherosclerosis group were lower than those in the non-carotid atherosclerosis group, while NLR, NLPR, SII, SIRI and AISI in the carotid atherosclerosis group were significantly higher than those in the non-carotid atherosclerosis group. Logistic regression analysis showed that PLR, NLR, LMR, PNR, NLPR, SII, SIRI, AISI were all correlated with carotid atherosclerosis. The AUC value of NLPR was the highest, which was 0.67, the cut-off value was 0.78, the sensitivity was 65.8%, and the specificity was 57.3%. The prevalence rate of carotid atherosclerosis was 12.4% below the cut-off, 26.6% higher than the cut-off, and the prevalence rate increased by 114.5%. Conclusion New inflammatory markers were significantly correlated with carotid atherosclerosis, among which NLPR was the optimum inflammatory marker to predict the risk of carotid atherosclerosis.

Yang Y., Xu Y., Liu S., Lu P., Zhou H., Yang M.

Shvartz V., Sokolskaya M., Ispiryan A., Basieva M., Kazanova P., Shvartz E., Talibova S., Petrosyan A., Kanametov T., Donakanyan S., Bockeria L., Golukhova E.

Introduction. The pathogenesis of aortic stenosis includes the processes of chronic inflammation, calcification, lipid metabolism disorders, and congenital structural changes. The goal of our study was to determine the predictive value of novel biomarkers of systemic inflammation and some hematological indices based on the numbers of leukocytes and their subtypes in the development of early hospital medical conditions after mechanical aortic valve replacement in patients with aortic stenosis. Materials and methods. This was a cohort study involving 363 patients who underwent surgical intervention for aortic valve pathology between 2014 and 2020. The following markers of systemic inflammation and hematological indices were studied: SIRI (Systemic Inflammation Response Index), SII (Systemic Inflammation Index), AISI (Aggregate Index of Systemic Inflammation), NLR (Neutrophil/Lymphocyte Ratio), PLR (Platelet/Lymphocyte Ratio), and MLR (Monocyte/Lymphocyte Ratio). Associations of the levels of these biomarkers and indices with the development of in-hospital death, acute kidney injury, postoperative atrial fibrillation, stroke/acute cerebrovascular accident, and bleeding were calculated. Results. According to an ROC analysis, an SIRI > 1.5 (p < 0.001), an SII > 718 (p = 0.002), an AISI > 593 (p < 0.001), an NLR > 2.48 (p < 0.001), a PLR > 132 (p = 0.004), and an MLR > 0.332 (p < 0.001) were statistically significantly associated with in-hospital death. Additionally, an SIRI > 1.5 (p < 0.001), an NLR > 2.8 (p < 0.001), and an MLR > 0.392 (p < 0.001) were associated with bleeding in the postoperative period. In a univariate logistic regression, SIRI, SII, AISI, and NLR were statistically significant independent factors associated with in-hospital death. In a multivariate logistic regression model, SIRI was the most powerful marker of systemic inflammation. Conclusion. SIRI, SII, AISI, and NLR as novel biomarkers of systemic inflammation were associated with in-hospital mortality. Of all markers and indices of systemic inflammation in our study, SIRI was the strongest predictor of a poor outcome in the multivariate regression model.

Filipa Alexandre A., Stoelzel M., Kiran A., Garcia-Hernandez A., Morga A., Kalra P.A.

Abstract Background Established cardiovascular risk assessment tools lack chronic kidney disease–specific clinical factors and may underestimate cardiovascular risk in non–dialysis-dependent chronic kidney disease (CKD) patients. Methods A retrospective analysis of a cohort of patients with stage 3–5 non–dialysis-dependent chronic kidney disease in the Salford Kidney Study (UK, 2002–2016) was performed. Multivariable Cox regression models with backward selection and repeated measures joint models were used to evaluate clinical risk factors associated with cardiovascular events (individual and composite cardiovascular major adverse cardiovascular events), mortality (all-cause and cardiovascular-specific), and need for renal replacement therapy. Models were established using 70% of the cohort and validated on the remaining 30%. Hazard ratios ([95% CIs]) were reported. Results Among 2192 patients, mean follow-up was 5.6 years. Cardiovascular major adverse cardiovascular events occurred in 422 (19.3%) patients; predictors included prior history of diabetes (1.39 [1.13–1.71]; P = 0.002) and serum albumin reduction of 5 g/L (1.20 [1.05–1.36]; P = 0.006). All-cause mortality occurred in 740 (33.4%) patients, median time to death was 3.8 years; predictors included reduction of estimated glomerular filtration of 5 mL/min/1.73 m2 (1.05 [1.01–1.08]; P = 0.011) and increase of phosphate of 0.1 mmol/L (1.04 [1.01–1.08]; P = 0.021), whereas a 10 g/L hemoglobin increase was protective (0.90 [0.85–0.95]; P < 0.001). In 394 (18.0%) patients who received renal replacement therapy, median time to event was 2.3 years; predictors included halving of estimated glomerular filtration rate (3.40 [2.65–4.35]; P < 0.001) and antihypertensive use (1.23 [1.12–1.34]; P < 0.001). Increasing age, albumin reduction, and prior history of diabetes or cardiovascular disease were risk factors for all outcomes except renal replacement therapy. Conclusions Several chronic kidney disease–specific cardiovascular risk factors were associated with increased mortality and cardiovascular event risk in patients with non–dialysis-dependent chronic kidney disease. Graphical abstract

Xiu J., Lin X., Chen Q., Yu P., Lu J., Yang Y., Chen W., Bao K., Wang J., Zhu J., Zhang X., Pan Y., Tu J., Chen K., Chen L.

ObjectiveInflammation plays an important role in the pathophysiology of hypertension (HTN). Aggregate index of systemic inflammation (AISI), as a new inflammatory and prognostic marker has emerged recently. Our goal was to determine whether there was a relationship between HTN and AISI.MethodsWe analyzed patients with HTN from the National Health and Nutrition Examination Survey (NHANES) from 1999 to 2018. The primary end point was cardiovascular mortality. A total of 23,765 participants were divided into four groups according to the AISI quartile level. The association between AISI and cardiovascular mortality in patients with HTN was assessed by survival curves and Cox regression analyses based on NHANES recommended weights.ResultsHigh levels of AISI were significantly associated with cardiovascular mortality in patients with HTN. After full adjustment for confounders, there was no significant difference in the risk of cardiovascular mortality in Q2 and Q3 compared to Q1, while Q4 (HR: 1.91, 95% CI: 1.42–2.58; P &lt; 0.001) had a higher risk of cardiovascular mortality compared to Q1. Results remained similar in subgroup analyses stratified by age (P for interaction = 0.568), gender (P for interaction = 0.059), and obesity (P for interaction = 0.289).ConclusionsIn adults with HTN, elevated AISI levels are significantly associated with an increased risk of cardiovascular mortality and may serve as an early warning parameter for poor prognosis.

Deng J., Tang R., Chen J., Zhou Q., Zhan X., Long H., Peng F., Wang X., Wen Y., Feng X., Su N., Tang X., Tian N., Wu X., Xu Q.

AbstractBackground and AimsRemnant cholesterol (RC) adversely contributes to cardiovascular disease (CVD) and overall survival in various diseases. However, its role in CVD outcomes and all-cause mortality in patients undergoing peritoneal dialysis (PD) is limited. Therefore, we aimed to investigate the association between RC and all-cause and CVD mortality in patients undergoing PD.Methods and ResultsBased on lipid profiles recorded using standard laboratory procedures, fasting RC levels were calculated in 2,710 incident patients undergoing PD who were enrolled between January 2006 and December 2017 and followed up until December 2018. Patients were divided into four groups according to the quartile distribution of baseline RC levels (Q1:

Hosseninia S., Ghobadi H., Garjani K., Hosseini S.A., Aslani M.R.

Abstract Background The role of leukocytes and systemic inflammation indicators in predicting the severity and mortality of inflammatory diseases has been well reported, such as the neutrophil to lymphocyte ratio (NLR), platelet to lymphocyte ratio (PLR), monocyte to lymphocyte ratio (MLR), neutrophil/lymphocyte*platelet ratio (NLPR), derived neutrophil/lymphocyte ratio (dNLR), aggregate index of systemic inflammation (AISI), as well as systemic inflammation response index (SIRI) and systemic inflammation index (SII). The purpose of the present study was to investigate the prognostic role of systemic inflammatory indicators in the mortality of chronic obstructive pulmonary disease (COPD) patients with COVID-19. Methods This retrospective study included 169 COPD patients hospitalized with COVID-19. Demographic, clinical, and laboratory data were obtained from the patients’ electronic records. The ability of systemic inflammation indeces to distinguish the severity of COVID-19 was determined by receiver operating characteristic (ROC) analysis, and survival probability was determined by the mean of Kaplan–Meier curves, with the endpoint being death. Results ROC curves showed that the AUD level was significant for WBC, MLR, SIRI, and AISI. Interestingly, Kaplan-Meier survival curves revealed that survival was lower with higher MLR (HR = 2.022, 95% CI = 1.030 to 3.968, P < 0.05) and AISI (HR = 2.010, 95% CI = 1.048 to 3.855, P < 0.05) values. However, the multivariate Cox regression model showed that only AISI was significantly associated with survival. Conclusion AISI in COPD patients with COVID-19 was a reliable predictor of mortality.

Wang H., Wei Q., Yang Y., Lu T., Yan Y., Wang F.

Abstract Background Multiple perioperative inflammatory markers are considered important factors affecting the long-term survival of esophageal cancer (EC) patients. Hematological parameters, whether single or combined, have high predictive value. Aim To investigate the inflammatory status of patients with preoperative EC using blood inflammatory markers, and to establish and validate competing risk nomogram prediction models for overall survival (OS) and progression-free survival (PFS) in EC patients. Methods A total of 508 EC patients who received radical surgery (RS) treatment in The First Affiliated Hospital of Zhengzhou University from August 5, 2013, to May 1, 2019, were enrolled and randomly divided into a training cohort (356 cases) and a validation cohort (152 cases). We performed least absolute shrinkage and selection operator (LASSO)-univariate Cox- multivariate Cox regression analyses to establish nomogram models. The index of concordance (C-index), time-dependent receiver operating characteristic (ROC) curves, time-dependent area under curve (AUC) and calibration curves were used to evaluate the discrimination and calibration of the nomograms, and decision curve analysis (DCA) was used to evaluate the net benefit of the nomograms. The relative integrated discrimination improvement (IDI) and net reclassification improvement (NRI) were calculated to evaluate the improvement in predictive accuracy of our new model compared with the AJCC staging system and another traditional model. Finally, the relationship between systemic inflammatory response markers and prognostic survival was explored according to risk plot, time-dependent AUC, Kaplan–Meier and restricted cubic spline (RCS). Results Based on the multivariate analysis for overall survival (OS) in the training cohort, nomograms with 10 variables, including the aggregate index of systemic inflammation (AISI) and lymphocyte-to-monocyte ratio (LMR), were established. Time-dependent ROC, time-dependent AUC, calibration curves, and DCA showed that the 1-, 3-, and 5 year OS and PFS probabilities predicted by the nomograms were consistent with the actual observations. The C-index, NRI, and IDI of the nomograms showed better performance than the AJCC staging system and another prediction model. Moreover, risk plot, time-dependent AUC, and Kaplan–Meier showed that higher AISI scores and lower LMR were associated with poorer prognosis, and there was a nonlinear relationship between them and survival risk. Conclusion AISI and LMR are easy to obtain, reproducible and minimally invasive prognostic tools that can be used as markers to guide the clinical treatment and prognosis of patients with EC.

Chen Y., Yu J., Shi L., Han S., Chen J., Sheng Z., Deng M., Jin X., Zhang Z.

Xie W., Xu Z., Qiu Y., Ye W., Zhang Z., Wang C., Zang J.

Background. This study is aimed at constructing a nomogram to predict the risk of clinically significant prostate cancer (csPCa) based on the aggregate index of systemic inflammation (AISI) and prostate imaging-reporting and data system version (PIRADS) score. Methods. Clinical data on patients who had undergone initial prostate biopsy from January 2019 to December 2021 were collected. Patients were randomized in a 7 : 3 ratio to the training cohort and the validation cohort. Potential risk factors for csPCa were identified by univariable and multivariate logistic regression. Nomogram was conducted with these independent risk factors, and calibration curves, the receiver operating characteristic (ROC), and decision curve analysis (DCA) were employed to assess the nomogram’s ability for prediction. Results. A total of 1219 patients were enrolled in this study. Multivariate logistic regression identified that age, AISI, total prostatic specific-antigen (tPSA), free to total PSA (f/tPSA), prostate volume (PV), and PIRADS score were potential risk predictors of csPCa, and the nomogram was developed based on these factors. The area under the curve (AUC) of the training cohort and validation cohort was 0.884 (95% CI: 0.862-0.906) and 0.899 (95% CI: 0.867-0.931). The calibration curves showed that the apparent curves were closer to the ideal curves. The DCA results revealed that the nomogram model seemed to have clinical application value per DCA. Conclusion. The nomogram model can efficiently predict the risk of csPCa and may assist clinicians in determining if a prostate biopsy is necessary.

Guan J., Xie H., Wang H., Gong S., Wu X., Gong T., Shen S.

Cardiovascular events (CVE) are the leading cause of death in peritoneal dialysis (PD) patients. The predictive value of cardiac valve calcification (CVC) for CVE in dialysis patients remains controversial. In particular, such studies are limited in PD patients. We aimed to examine the predictive role of CVC for CVE and cardiovascular mortality in PD patients. A retrospective analysis was performed on patients who initiated PD in our hospital. According to the result of echocardiography, patients were divided into CVC group and non-CVC group. The differences in baseline demographic characteristics, biochemical variables, comorbidities, and clinical outcomes between the two groups were compared. Kaplan–Meier method was used to obtain survival curves. The Cox regression model was used to evaluate the influence of CVC for cardiovascular outcomes. The inverse probability of treatment weighting (IPTW) was used to eliminate influence of the confounders in the groups. 458 peritoneal dialysis patients were enrolled in this study. 77 patients were in CVC group and 381 patients in non-CVC group. The average follow-up time was (32 ± 21) months. At baseline, the absolute standardized difference (ASD) of age, BMI, history of CVE, diabetes, LVEF, LVMI, albumin, calcium, phosphorus, triglycerides, hsCRP, urine volume, Kt/V, statins and vitamin D intake rate were greater than 0.1 between the two groups. All of ASD dropped to less than 0.1 after IPTW, which meant that the balance had been reached between the two groups. Multivariable logistic analysis showed that advanced age, diabetes, and hyperphosphatemia were associated with CVC. The Kaplan–Meier survival curve showed the cumulative CVE-free survival rate and cardiovascular survival rate of CVC group were significantly lower than that of non-CVC group before and after IPTW (log-rank P < 0.05). After IPTW was used to eliminate the effect of confounders, multivariate Cox regression analysis still showed CVC was an independent risk factor for CVE (HR = 2.383, 95% CI 1.331~4.264, P = 0.003) and cardiovascular mortality (HR = 2.347, 95% CI 1.211~4.548, P = 0.012) in PD patients. The prevalence of CVC is high in peritoneal dialysis patients. CVC is an independent risk factor for CVE and cardiovascular mortality in peritoneal dialysis patients.

Niculescu R., Russu E., Arbănași E.M., Kaller R., Arbănași E.M., Melinte R.M., Coșarcă C.M., Cocuz I.G., Sabău A.H., Tinca A.C., Stoian A., Vunvulea V., Mureșan A.V., Cotoi O.S.

Background: Carotid endarterectomy (CEA) is the first-line surgical intervention for cases of severe carotid stenoses. Unfortunately, the restenosis rate is high after CEA. This study aims to demonstrate the predictive role of carotid plaque features and inflammatory biomarkers (monocyte-to-lymphocyte ratio (MLR), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), systemic inflammatory index (SII), Systemic Inflammation Response Index (SIRI), and Aggregate Index of Systemic Inflammation (AISI)) in carotid restenosis and mortality at 12 months following CEA. Methods: The present study was designed as an observational, analytical, retrospective cohort study and included all patients over 18 years of age with a minimum of 70% carotid stenosis and surgical indications for CEA admitted to the Vascular Surgery Clinic, Emergency County Hospital of Targu Mures, Romania between 2018 and 2021. Results: According to our results, the high pre-operative values of inflammatory biomarkers—MLR (OR: 10.37 and OR: 6.11; p < 0.001), NLR (OR: 34.22 and OR: 37.62; p < 0.001), PLR (OR: 12.02 and OR: 16.06; p < 0.001), SII (OR: 18.11 and OR: 31.70; p < 0.001), SIRI (OR: 16.64 and OR: 9.89; p < 0.001), and AISI (OR: 16.80 and OR: 8.24; p < 0.001)—are strong independent factors predicting the risk of 12-month restenosis and mortality following CEA. Moreover, unstable plaque (OR: 2.83, p < 0.001 and OR: 2.40, p = 0.04) and MI (OR: 3.16, p < 0.001 and OR: 2.83, p = 0.005) were independent predictors of all outcomes. Furthermore, AH (OR: 2.30; p = 0.006), AF (OR: 1.74; p = 0.02), tobacco (OR: 2.25; p < 0.001), obesity (OR: 1.90; p = 0.02), and thrombotic plaques (OR: 2.77; p < 0.001) were all independent predictors of restenosis, but not for mortality in all patients. In contrast, antiplatelet (OR: 0.46; p = 0.004), statin (OR: 0.59; p = 0.04), and ezetimibe (OR:0.45; p = 0.03) therapy were protective factors against restenosis, but not for mortality. Conclusions: Our data revealed that higher preoperative inflammatory biomarker values highly predict 12-month restenosis and mortality following CEA. Furthermore, age above 70, unstable plaque, cardiovascular disease, and dyslipidemia were risk factors for all outcomes. Additionally, AH, AF, smoking, and obesity were all independent predictors of restenosis but not of mortality in all patients. Antiplatelet and statin medication, on the other hand, were protective against restenosis but not against mortality.

Halmaciu I., Arbănași E.M., Kaller R., Mureșan A.V., Arbănași E.M., Bacalbasa N., Suciu B.A., Cojocaru I.I., Runcan A.I., Grosu F., Vunvulea V., Russu E.

Background: Numerous tools, including inflammatory biomarkers and lung injury severity scores, have been evaluated as predictors of disease progression and the requirement for intensive therapy in COVID-19 patients. This study aims to verify the predictive role of inflammatory biomarkers [monocyte to lymphocyte ratio (MLR), neutrophil to lymphocyte ratio (NLR), systemic inflammatory index (SII), Systemic Inflammation Response Index (SIRI), Aggregate Index of Systemic Inflammation (AISI), and interleukin-6 (IL-6)] and the total system score (TSS) in the need for invasive mechanical ventilation (IMV) and mortality in COVID-19 patients. Methods: The present study was designed as an observational, analytical, retrospective cohort study and included all patients over 18 years of age with a diagnosis of COVID-19 pneumonia, confirmed through real time-polymerase chain reaction (RT-PCR) and radiological chest CT findings admitted to County Emergency Clinical Hospital of Targu-Mureș, Romania, and Modular Intensive Care Unit of UMFST “George Emil Palade” of Targu Mures, Romania between January 2021 and December 2021. Results: Non-Survivors patients were associated with higher age (p = 0.01), higher incidence of cardiac disease [atrial fibrillation (AF) p = 0.0008; chronic heart failure (CHF) p = 0.01], chronic kidney disease (CKD; p = 0.02), unvaccinated status (p = 0.001), and higher pulmonary parenchyma involvement (p < 0.0001). Multivariate analysis showed a high baseline value for MLR, NLR, SII, SIRI, AISI, IL-6, and TSS independent predictor of adverse outcomes for all recruited patients. Moreover, the presence of AF, CHF, CKD, and dyslipidemia were independent predictors of mortality. Furthermore, AF and dyslipidemia were independent predictors of IMV need. Conclusions: According to our findings, higher MLR, NLR, SII, SIRI, AISI, IL-6, and TSS values at admission strongly predict IMV requirement and mortality. Moreover, patients above 70 with AF, dyslipidemia, and unvaccinated status highly predicted IMV need and fatality. Likewise, CHF and CKD were independent predictors of increased mortality.