The inflammatory burden index is a superior systemic inflammation biomarker for the prognosis of non-small cell lung cancer

Elevated inflammatory burden index increases mortality in adults with chronic inflammatory airway diseases: a nationwide cohort study

OBJECTIVE

The objective of this study was to investigate the potential association between the inflammatory burden index (IBI) and the prevalence of chronic inflammatory airway diseases (CIAD), as well as mortality rates among individuals diagnosed with CIAD.

METHODS

Participants were sourced from the National Health and Nutrition Examination Survey (NHANES) conducted between 1999 and 2010. The IBI was calculated using the formula: IBI = C-reactive protein * neutrophils / lymphocytes. CIAD comprised self-reported asthma, chronic bronchitis, and chronic obstructive pulmonary disease (COPD). Mortality outcomes, including all-cause and respiratory disease mortality, were determined through linked data from the National Death Index (NDI) up to December 2019.

RESULTS

A total of 27,495 adults were included. IBI was divided into quartiles, with the lowest quartile as the reference group. After adjusting for confounding variables, a positive correlation was observed between higher IBI and increased prevalence of total CIAD (OR = 1.383 [1.215-1.575]), asthma (OR = 1.267 [1.096-1.465]), chronic bronchitis (OR = 1.568 [1.263-1.946]), and COPD (OR = 1.907 [1.311-2.774]). Over a median follow-up of 12.33 [9.92-16.00] years, there were 1221 deaths from all causes and 220 deaths from respiratory disease among 4499 patients with CIAD. Following multivariate adjustments, the fourth quartile was significantly associated with increased risk of all-cause mortality (HR = 2.227 [1.714-2.893]) and respiratory disease mortality (HR = 2.748 [1.383-5.459]) compared to the first quartile of IBI in CIAD participants. Moreover, variable importance analysis using a random survival forest model demonstrated the significance of IBI in predicting mortality from both all-cause and respiratory diseases.

CONCLUSION

IBI exhibited an association with the prevalence of CIAD, with higher IBI levels correlating with elevated all-cause and respiratory disease mortality among individuals with CIAD.

Inflammatory burden index: associations between osteoarthritis and all-cause mortality among individuals with osteoarthritis

BACKGROUND

The newly described inflammatory burden index (IBI) reflects a patient's inflammatory burden. This study aimed to estimate the association between IBI, osteoarthritis (OA), and all-cause mortality in patients with OA.

METHODS

We extracted the data of adults from the National Health and Nutrition Examination Survey database between 1999 and 2018. After using appropriate survey weights to correct for sample bias, we conducted multivariate logistic regression analyses to explore the association between IBI and OA across three models: in the unadjusted model, partially adjusted model (adjusting age, sex, race, education level, marital status, PIR, BMI, smoking status, drinking status, stroke, CVD, DM, and hypertension) and fully adjusted model (which included additional variables: HBA1C, ALT, AST, BUN, TC, and HDL). And the odds ratios (OR) and 95% confidence intervals (CI) were calculated. Similarly, using comparable survey weights and covariates adjustments, we employed Cox proportional hazards regression analysis to investigate the association between IBI and all-cause mortality in the other 3 models. The Cox proportional hazards regression models were fitted to calculate the hazard ratios (HR) and 95% CI of the association between IBI and all-cause mortality. A restricted cubic spline (RCS) was used to explore the nonlinear relationships between association effects. Subgroup analysis was performed to validate the reliability of their effects.

RESULTS

In total, 22,343 eligible participants were included. Multiple logistic regression models revealed that participants with the highest IBI had 2.54 times (95%CI, 2.23, 2.90)) higher risk of OA than those with the lowest IBI in Model 1, whereas the OR was 1.21 (95%CI, 1.03, 1.42) in Model 2 and 1.23 (95%CI,1.05, 1.45) in Model 3. Multiple Cox regression models showed participants with the highest IBI had 186% (95%CI, 1.50, 2.31) times risk of developing all-cause death than those with the lowest IBI in Model 1. This trend remained stable in Models 2 (HR,1.54; 95%CI,1.22, 1.95) and 3 (HR, 1.41; 95%CI, 1.10, 1.80). The RCS revealed a significant positive association between IBI and OA risk. With respect to the association between IBI and all-cause mortality, a slight decrease in mortality was observed from the lowest quartile to the second quartile of IBI, and the mortality risk increased with increasing IBI. Subgroup analyses showed that age, cardiovascular disease, and hypertension were pivotal in the association of IBI with all-cause mortality, whereas the association of IBI with OA remained stable after stratification by other factors such as sex, race, education level, marital, smoking, and drinking status, hypertension, and most serological indices.

CONCLUSIONS

This study provides evidence of a positive association between IBI, OA, and all-cause mortality. IBI may be a promising signature for assessing the inflammatory burden in patients with OA, which, in turn, is conducive to precise references for high-risk population recognition, anti-inflammatory guidance, and reducing mortality intervention.

Association between systemic inflammation biomarkers and mortality in patients with sepsis-associated acute kidney injury receiving intensive care and continuous kidney replacement therapy: results from the RENERGY (REsearches for NEphRology and epidemioloGY) study

BACKGROUND

Identifying risk factors and improving prognostication for mortality among patients with sepsis-associated acute kidney injury (AKI) undergoing continuous kidney replacement therapy (CKRT) is important in improving the adverse prognosis of this patient population. This study aimed to compare the prognostic value of existing systemic inflammation biomarkers and determine the optimal systemic inflammation biomarker in patients with sepsis-associated AKI receiving CKRT.

METHODS

This multi-center, retrospective, observational cohort study included 1,500 patients with sepsis-associated AKI treated with intensive care and CKRT. The main predictor was a panel of 13 different systemic inflammation biomarkers. The primary outcome was 28-day mortality after CKRT initiation. Secondary outcomes included 90-day mortality after CKRT initiation, CKRT duration, kidney replacement therapy dependence at discharge, and lengths of intensive care unit (ICU) and hospital stays.

RESULTS

When added to the widely accepted Acute Physiology and Chronic Health Evaluation II score, platelet-to-albumin ratio (PAR) and neutrophil-platelet score (NPS) had the highest improvements in prognostication of 28-day mortality, where the corresponding increases in C-statistic were 0.01 (95% confidence interval [CI], 0.00-0.02) and 0.02 (95% CI, 0.01-0.03). Similar findings were observed for 90-day mortality. The 28- and 90-day mortality rates were significantly lower for the higher PAR and NPS quartiles. These associations remained significant even after adjustment for potential confounding variables in multivariable Cox proportional hazards models.

CONCLUSION

Of the available systemic inflammation biomarkers, the addition of PAR or NPS to conventional ICU prediction models improved the prognostication of patients with sepsis-associated AKI receiving intensive care and CKRT.

A novel inflammatory nutrient index for predicting survival outcomes in patients with non-small cell lung cancer

Background

Lung cancer is one of the most common contributors to cancer-related deaths worldwide. This study aimed to develop a new blood index on the basis of the patient's systemic inflammation and nutritional status, which can be used to predict the prognosis of patients with non-small cell lung cancer (NSCLC).

Methods

Pre-treatment blood markers were analyzed in 556 NSCLC patients from 2010 to 2019. A least absolute shrinkage and selection operator (LASSO) method was used to select indicators to establish a new integrated biomarker (PNAGR). Kaplan-Meier survival curves were used to assess the prognostic impact of platelet-to-lymphocyte ratio (PLR), albumin (ALB), and the PNAGR. The prognostic value was verified using univariate and multivariate Cox analyses.

Results

We used four biomarkers including PLR, ALB, 1/albumin-to-globulin ratio (1/AGR), and neutrophil/albumin-to-globulin ratio (N/AGR) were used to screen for the PNAGR using LASSO. Patients with high PNAGR demonstrated lower overall survival (OS) compared to those with low PNAGR. In both univariate and multivariate analyses, PNAGR was revealed as an independent prognostic factor for OS. The predictive power of PNAGR [area under the curve (AUC): 0.753] was higher than that of the metrics alone.

Conclusions

PNAGR is a novel and effective clinical prognostic tool with good clinical predictive value for NSCLC patients.

Enhancing the revelation of key genes and interaction networks in non-small cell lung cancer with major depressive disorder: A bioinformatics analysis

Background and Aims

Lung cancer is ranked as the second most prevalent form of cancer worldwide. Nonsmall cell lung cancer (NSCLC) represents the predominant histological subtype. Research suggests that one-third of lung cancer patients also experiencing depression. Antidepressants play an indispensable role in the management of NSCLC. Despite significant advancements in treatment, lung cancer patients still face a high mortality rate. Major depressive disorder (MDD) and related antidepressants involved in treatment efficacy and prognosis of NSCLC. However, there has been a lack of screening and analysis regarding genes and networks associated with both NSCLC and MDD.

Methods

To investigate the correlation between MDD and NSCLC, our discovery and validation analysis included four datasets from the Gene Expression Omnibus database from NSCLC or MDD. Differential gene expression (DEGs) analysis, GO and KEGG Pathway, and protein-protein interaction network analyzes to identify hub genes, networks, and associated observations link between MDD and NSCLC.

Results

The analysis of two datasets yielded a total of 84 downregulated and 52 upregulated DEGs. Pathway enrichment analyzes indicated that co-upregulated genes were enriched in the regulation of positive regulation of cellular development, collagen-containing extracellular matrix (ECM), cytokine binding, and axon guidance. We identified 20 key genes, which were further analyzed using the MCODE plugin to identify two core subnetworks. The integration of functionally similar genes provided valuable insights into the potential involvement of these hub genes in diverse biological processes including angiogenesis humoral immune response regulation inflammatory response organization ECM network.

Conclusion

We have identified a total of 136 DEGs that participate in multiple biological signaling pathways. A total of 20 hub genes have demonstrated robust associations, potentially indicating novel diagnostic and therapeutic targets for both diseases.

Prognostic Biomarkers of Systemic Inflammation in Non-Small Cell Lung Cancer: A Narrative Review of Challenges and Opportunities

Non-small cell lung cancer (NSCLC) is a common malignancy and is associated with poor survival outcomes. Biomarkers of systemic inflammation derived from blood tests collected as part of routine clinical care offer prognostic information for patients with NSCLC that may assist clinical decision making. They are an attractive tool, as they are inexpensive, easily measured, and reproducible in a variety of healthcare settings. Despite the wealth of evidence available to support them, these inflammatory biomarkers are not yet routinely used in clinical practice. In this narrative review, the key inflammatory indices reported in the literature and their prognostic significance in NSCLC are described. Key challenges limiting their clinical application are highlighted, including the need to define the optimal biomarker of systemic inflammation, a lack of understanding of the systemic inflammatory landscape of NSCLC as a heterogenous disease, and the lack of clinical relevance in reported outcomes. These challenges may be overcome with standardised recording and reporting of inflammatory biomarkers, clinicopathological factors, and survival outcomes. This will require a collaborative approach, to which this field of research lends itself. This work may be aided by the rise of data-driven research, including the potential to utilise modern electronic patient records and advanced data-analysis techniques.

The prognostic value and model construction of inflammatory markers for patients with non-small cell lung cancer

The aim of this study was to investigate and analyse the predictive value of systemic inflammatory markers based on peripheral blood biomarkers for the prognosis of non-small cell lung cancer (NSCLC) patients. Based on a retrospective monitoring cohort of 973 NSCLC patients from an Affiliated Tumor Hospital from 2012 to 2023. The log-rank test and Cox proportional risk regression model were used to identify independent prognostic inflammatory markers. Subsequently, a nomogram prediction model was constructed and evaluated. The results of multivariate Cox regression analysis showed that patients with high NLR group (HR = 1.238, 95% CI 1.015-1.510, P = 0.035), and high CAR group (HR = 1.729, 95% CI 1.408-2.124, P < 0.001) were risk factors affecting the prognosis of NSCLC patients. The nomogram that includes age, tumor stage, smoking history, BMI, NLR, and CAR can effectively predict the prognosis of NSCLC patients.The inflammatory markers NLR and CAR, which combine inflammatory and nutritional status, are effective predictors of the prognosis of NSCLC patients. The combination of clinical information and these easily accessible inflammatory markers has significant research value for prognostic assessment, clinical treatment, and follow-up monitoring of NSCLC patients.

Association between platelet‑to‑lymphocyte ratio and serum prostate specific antigen

There is evidence that the systemic inflammatory response may have an impact on prostate-specific antigen (PSA) levels. However, the relationship between the platelet-to-lymphocyte ratio (PLR) and PSA remains unclear. As a result, the relationship between PLR and PSA using the National Health and Nutrition Examination Survey (NHANES) database was examined. After the screening, 6,638 participants out of 52,186 in the NHANES survey conducted between 2001 to 2010 were suitable for the present study. The PLR was the independent variable in the present study, and PSA was the dependent variable. The selected subjects in the present study had an average age of 58.563±11.848 years. After controlling for covariates, the results showed that with every increase in PLR, the PSA concentration increased by 0.004 ng/ml (0.001, 0.007). This difference was statistically significant. Furthermore, a smoothing curve based on a fully adjusted model was created to investigate the possibility of a linear relationship between PLR and PSA concentration in men from USA. In men from USA, an independent and positive correlation between PLR and PSA was identified, which could potentially result in overdiagnosis of asymptomatic prostate cancer in populations with higher PLR levels.

Clinical Significance of the Preoperative Inflammatory Burden Index in Esophageal Cancer

INTRODUCTION

The inflammatory burden index (IBI) serves as a prognostic marker for several cancers. Here, we evaluated the predictive value of preoperative IBI associated with the surgical and oncological outcomes of patients with esophageal cancer (EC).

METHODS

The IBI was formulated as C-reactive protein × neutrophil/lymphocyte. We retrospectively analyzed preoperative IBI of 147 EC patients receiving esophagectomy between 2008 and 2018. Cox proportional hazards models and multivariable logistic regression were employed to identify independent risk factors of surgical site infection and prognosis.

RESULTS

Increased preoperative IBI significantly correlated with higher tumor stage. Patients with high IBI experienced shorter overall survival (p = 0.0002) and disease-free survival (p = 0.002) compared with those with low IBI. In the adjusted Cox proportional hazards regression models, increased IBI served as an independent prognostic factor for overall survival (hazard ratio, 3.56; 95% confidence interval, 1.79-7.34; p = 0.0003) and disease-free survival (hazard ratio, 3.03; 95% confidence interval, 1.60-5.92; p = 0.007). Multivariable analysis identified preoperative high IBI which served as an independent risk factor for overall surgical site infection (odds ratio, 2.53; 95% confidence interval, 1.00-6.38; p = 0.049).

CONCLUSION

Preoperative IBI may serve as a useful predictor of prognosis and surgical site infection of patients with EC after esophagectomy.

Clinical utility of preoperative pan-immune-inflammation value (PIV) for prognostication in patients with esophageal squamous cell carcinoma

BACKGROUND

Several researches have shown that pan-immune-inflammation value (PIV) is related to cancer prognosis in recent years. In esophageal squamous cell carcinoma (ESCC), nevertheless, the prognostic impact of PIV remains unclear. The present study sought to investigate the prognostic impact of preoperative PIV in ESCC with radical resection.

METHODS

The data of 294 ESCC patients who received radical resection were retrospectively analyzed. Based on analyzing the non-linear relationship between PIV and cancer-specific survival (CSS), the optimal cutoff value for PIV was calculated by the restricted cubic spline (RCS) model. Cox proportional hazards regression was carried out to identify the prognostic factors. A risk stratification model was established by recursive partitioning analysis (RPA). The performance of the RPA-based model was assessed by the decision curve analysis (DCA) and receiver operating characteristic (ROC).

RESULTS

The RCS visualized the non-linear relationship between PIV and CSS (P < 0.0001). Then patients were then divided into high and low groups based on the optimal threshold of 308.2. The 5-year CSS (17.7 % vs. 48.3 %, P < 0.001) was significantly worse in patients with high PIV than those in the low group. Subgroup analyses confirmed that patients with low PIV also achieved better 5-year survival at different pathological tumor node metastasis (pTNM) stages (pTNM I: P = 0.022; pTNM II: P = 0.001; pTNM III: P = 0.011). PIV served as an independent prognostic factor of CSS (hazard ratio = 1.983, P < 0.001). A new staging involving three risk groups with significantly different CSS was developed using RPA algorithms based on pTNM and PIV. Compared with the pTNM classification, the RPA-based model exhibited significantly superior performance for prognostication.

CONCLUSION

The present study confirmed the prognostic impact of PIV in ESCC who treated with radical resection. PIV was associated with the tumor stage and prognosis, which might be useful in the preoperative assessment of ESCC.

Association Between Inflammatory Burden Index and Unfavorable Prognosis After Endovascular Thrombectomy in Acute Ischemic Stroke

Background

Inflammatory burden index (IBI) is a systemic inflammation indicator that reflects the inflammatory status. We aimed to investigate the prognostic value of IBI after endovascular thrombectomy (EVT) in patients with acute ischemic stroke.

Methods

We enrolled patients treated with EVT from a multicenter cohort between June 2020 and December 2021. The IBI was calculated as C-reaction protein × neutrophil / lymphocyte count. The primary outcome was the unfavorable functional outcome (90-day modified Rankin scale score 3-6). C-statistics and net reclassification indexes were used to assess the predictive accuracy. Multivariable logistic regression models were used to investigate the association between IBI and unfavorable outcome.

Results

A total of 295 patients (mean age, 64.0 ± 12.8 years; male, 63.7%) were enrolled in this study. In multivariable models, higher IBI levels were associated with an increased risk of 90-day unfavorable outcome after EVT (per 1-SD: odds ratio, 1.754; 95% confidence interval, 1.241-2.587; P = 0.002). Restricted cubic spline curve displayed a linear relationship between the IBI level and 90-day unfavorable outcome (P for nonlinearity = 0.410). Besides, IBI was a more accurate biomarker for predicting unfavorable outcomes with the highest predictive accuracy and reclassification indexes.

Conclusion

This study demonstrated that higher IBI was associated with an increased risk of 90-day unfavorable outcome in acute ischemic stroke treated with EVT.