Prediction of immune-related adverse events in non-small cell lung cancer patients treated with immune checkpoint inhibitors based on clinical and hematological markers: Real-world evidence

Systemic immunity-inflammation index is associated with body fat distribution among U.S. adults: evidence from national health and nutrition examination survey 2011-2018

OBJECTIVE

The systemic immunity-inflammation index (SII) is a newly developed biomarker that provides an integrated measure of inflammation in the body. We aim to evaluate the relationship between SII and body fat distribution.

METHODS

Adults from the National Health and Nutrition Examination Survey (NHANES) 2011-2018 were included. The SII was computed using lymphocyte (LC), neutrophil (NC), and platelet (PC) counts as its components. Body fat distribution was assessed by (total, android, gynoid) percentage fat, total abdominal fat area, subcutaneous adipose tissue area, visceral adipose tissue area, and the ratio of visceral to subcutaneous adipose tissue area (V/S ratio). Multivariable weighted linear regression and subgroup analysis were use to examine the relationships between fat distribution and SII. Restricted cubic splines (RCS) and threshold effect analysis were used to examine analyze nonlinear associations.

RESULTS

After exclusions, a total of 11,192 adults with a weighted mean age of 38.46 ± 0.26 years were studied. In multivariable weighted linear regression, each level increase in log2SII was associated with increased of 0.23 SDs total percentage fat (95% CI = 0.03, 0.43) and 0.26 SDs android percentage fat (95% CI = 0.06, 0.47). Besides, the subgroup analysis showed that the positive association between SII and android percentage fat was mainly among obese individuals (BMI > 30 kg/m2) and non-obese individuals without DM or hypertension. Meanwhile, the relationship between SII and the V/S ratio was found to be significant in the female subgroup, the obese subgroup, individuals with non-alcoholic fatty liver disease (NAFLD), and those without diabetes mellitus. Finally, SII exhibited an inverted U-shaped relationship with total percentage fat, android percent fat and total abdominal fat. Accordingly, threshold effect analysis indicated a positive association between lower SII levels and total percentage fat, android percentage fat and total abdominal fat area.

CONCLUSIONS

In the nationwide study, it was observed that the SII exhibited a significant correlation with higher levels of body fat, specifically android fat. This association was particularly noticeable within specific subgroups of the population.

Inverted U-Shaped relationship Between Systemic Immune-Inflammation Index and Pulmonary Function: A Large Population-Based Study in US Adults

Background

Systemic immune-inflammation index (SII) is a novel comprehensive inflammatory marker. Inflammation is associated with impaired lung function. We aimed to explore the possible relationship between SII and lung function to examine the potential of SII in predicting lung function decline.

Methods

A cross-sectional survey was conducted using the data of the NHANES from 2007 to 2012. Multiple linear regression models were used to analyze the linear relationship between SII and pulmonary functions. Sensitivity analyses, subgroup analyses, and interaction tests were used to examine the robustness of this relationship across populations. Fitted smooth curves and threshold effect analysis were used to describe the nonlinear relationships.

Results

A total of 10,125 patients were included in this study. After adjusting for all covariates, multiple linear regression model analysis showed that high Log2-SII level was significantly associated with decreased FVC(β, -23.4061; 95% CI, -42.2805- -4.5317), FEV1(β, -46.7730; 95% CI, -63.3371- -30.2089), FEV1%(β, -0.7923; 95% CI, -1.1635- -0.4211), FEV1/FVC(β, -0.6366; 95% CI, -0.8328- -0.4404) and PEF(β, -121.4468; 95% CI,-164.1939- -78.6998). The negative correlation between Log2-SII and pulmonary function indexes remained stable in trend test and stratified analysis. Inverted U-shaped relationships between Log2-SII and FVC, FEV1, FEV1%, and PEF were observed, while a negative linear correlation existed between FEV1/FVC and Log2-SII. The cutoff values of the nonlinear relationship between Log2-SII and FVC, FEV1, FEV1%, PEF were 8.3736, 8.0688, 8.3745, and 8.5255, respectively. When SII exceeded the critical value, the lung function decreased significantly.

Conclusion

This study found a close correlation between SII and pulmonary function indicators. This study investigated the SII threshold when lung functions began to decline in the overall population. SII may become a promising serological indicator for predicting lung function decline. However, prospective studies were needed further to establish the causal relationship between these two factors.

The pan immune inflammatory value in relation to non-alcoholic fatty liver disease and hepatic fibrosis

BACKGROUND

Inflammation played a critical role in non-alcoholic fatty liver disease (NAFLD). Here, we aimed to explore the relationship between inflammatory biomarkers and the prevalence of NAFLD and hepatic fibrosis in US participants.

METHODS

Individuals with complete data from National Health and Nutrition Examination Survey (NHANES), 2017-2020 pre-pandemic cycle dataset were referred to this study. We identified NAFLD by vibration-controlled transient elastography (VCTE) on the basis of controlling attenuation parameter (CAP) ≥274dB/m. Liver fibrosis was confirmed by liver stiffness measurement (LSM) ≥8.2kPa. Multivariate logistic regression models were applied to estimate the correlations between inflammatory biomarkers and the prevalence of NAFLD and hepatic fibrosis based on sample weights.

RESULTS

All together 5026 subjects were incorporated into the study cohort. Among these subjects, 2209 were classified as having NAFLD, and 8.35 % were diagnosed with hepatic fibrosis. Pan immune inflammatory value (PIV), instead of systemic immune inflammatory index (SII), was positively correlated with the rate of NAFLD or hepatic fibrosis. Subgroup analysis for NAFLD revealed that the positive relationships of the PIV existed in males (OR=1.52, 95 % CI: 1.01-2.28, p = 0.046) and participants below 60 years of age (OR=1.49, 95 % CI: 1.05-2.1, p = 0.028). Moreover, subgroup analysis for hepatic fibrosis revealed that the positive relationships of the PIV existed in females (OR=2.09, 95 % CI: 1.2-3.63, p = 0.014) and participants below 60 years of age (OR=1.74, 95 % CI: 1.09-2.77, p = 0.023).

CONCLUSIONS

A higher PIV, but not SII, is associated with a higher likelihood of NAFLD and liver fibrosis, suggesting that the PIV is a more valuable inflammatory marker for assessing NAFLD and liver fibrosis in participants, especially for those who are below 60 years of age.

Association of systemic immune-inflammation index with insulin resistance and prediabetes: a cross-sectional study

Background and Objective

Previous research suggested a relationship between the Systemic Immune-Inflammation Index (SII) and multiple adverse health conditions. However, the role of SII in prediabetes and insulin resistance (IR) remains poorly understood. Therefore, this study aims to explore the potential relationship between SII and prediabetes and IR, providing data support for effective diabetes prevention by reducing systemic inflammation.

Methods

Linear regression models were used to assess the correlation between continuous SII and risk markers for type 2 diabetes (T2D). Subsequently, multivariate logistic regression models and subgroup analyses were employed to evaluate the association between SII tertiles and prediabetes and IR, controlling for various confounding factors. Finally, restricted cubic spline graphs were used to analyze the nonlinear relationship between SII and IR and prediabetes.

Results

After controlling for multiple potential confounders, SII was positively correlated with fasting blood glucose (FBG) (β: 0.100; 95% CI: 0.040 to 0.160), fasting serum insulin (FSI) (β: 1.042; 95% CI: 0.200 to 1.885), and homeostasis model assessment of insulin resistance (HOMA-IR) (β: 0.273; 95% CI: 0.022 to 0.523). Compared to participants with lower SII, those in the highest tertile had increased odds of prediabetes (OR: 1.17; 95% CI: 1.02-1.34; p for trend < 0.05) and IR (OR: 1.35; 95% CI: 1.18 to 1.51; p for trend<0.001).

Conclusions

Our study results demonstrate an elevated association between SII levels and both IR and prediabetes, indicating SII as a straightforward and cost-effective method identifying individuals with IR and prediabetes.

Immune checkpoint inhibitor-related pneumonitis: research advances in prediction and management

The advent of immune-checkpoint inhibitors (ICIs) has revolutionized the treatment of malignant solid tumors in the last decade, producing lasting benefits in a subset of patients. However, unattended excessive immune responses may lead to immune-related adverse events (irAEs). IrAEs can manifest in different organs within the body, with pulmonary toxicity commonly referred to as immune checkpoint inhibitor-related pneumonitis (CIP). The CIP incidence remains high and is anticipated to rise further as the therapeutic indications for ICIs expand to encompass a wider range of malignancies. The diagnosis and treatment of CIP is difficult due to the large individual differences in its pathogenesis and severity, and severe CIP often leads to a poor prognosis for patients. This review summarizes the current state of clinical research on the incidence, risk factors, predictive biomarkers, diagnosis, and treatment for CIP, and we address future directions for the prevention and accurate prediction of CIP.

Significance of the autoantibody assay in predicting the development of immune-related adverse events in patients receiving atezolizumab plus bevacizumab combination therapy for unresectable hepatocellular carcinoma

AIM

Atezolizumab plus bevacizumab (AB) combination therapy is the first-line treatment for unresectable hepatocellular carcinoma (u-HCC). The management of immune-related adverse events (irAEs) is an important issue associated with achieving a good therapeutic response in patients receiving AB therapy. However, few studies have reported irAE development in patients receiving AB therapy. This study focused on the association between irAE development and autoantibodies at baseline in patients receiving AB therapy.

METHODS

Sixty-one patients receiving AB therapy were enrolled. For autoantibodies, the following antibodies were tested before the start of AB therapy: antinuclear antibodies, rheumatoid factor (RF), anti-thyroglobulin antibodies, thyroid peroxidase antibodies, anti-thyroid stimulating hormone receptor antibodies, and acetylcholine receptor antibodies. A patient was considered to have pre-existing antibodies if any of the listed antibodies were present at baseline.

RESULTS

Ten patients (16%) developed irAEs during the observation period. The irAEs included liver injury, hypothyroidism, adrenal insufficiency, adrenocorticotropic hormone deficiency, and rhabdomyolysis. Patients with irAE (n = 10) were more likely to be positive for any autoantibody (hazard ratio [HR] 3.7, p = 0.047) and RF at baseline (HR 5.4, p = 0.035) and to achieve complete response (HR 5.8, p = 0.027) than those without. The presence of autoantibodies at baseline was an independent factor associated with irAE development.

CONCLUSION

In the real world, 16% of patients receiving AB therapy for u-HCC developed irAEs. Patients with autoantibodies at baseline are at high risk of developing irAEs and require cautious follow-up.

Early Changes in LIPI Score Predict Immune-Related Adverse Events: A Propensity Score Matched Analysis in Advanced Non-Small Cell Lung Cancer Patients on Immune Checkpoint Blockade

In advanced cancer patients undergoing immune checkpoint blockade, the burden of immune-related adverse events (irAEs) is high. The need for reliable biomarkers for irAEs remains unfulfilled in this expanding therapeutic field. The lung immune prognostic index (LIPI) is a noninvasive measure of systemic inflammation that has consistently shown a correlation with survival in various cancer types when assessed at baseline. This study sought to determine whether early changes in the LIPI score could discriminate the risk of irAEs and different survival outcomes in advanced non-small cell lung cancer (NSCLC) patients receiving PD-(L)1 blockade-based therapies. We included consecutive patients diagnosed with metastatic NSCLC who received pembrolizumab, nivolumab, or atezolizumab as second-line therapy following platinum-based chemotherapy, or first-line pembrolizumab either alone or in combination with platinum-based chemotherapy. The LIPI score relied on the combined values of derived neutrophil/lymphocyte ratio (dNLR) and lactate dehydrogenase. Their assessment at baseline and after two cycles of treatment allowed us to categorize the population into three subgroups with good (LIPI-0), intermediate (LIPI-1), and poor (LIPI-2) prognosis. Between April 2016 and May 2023, we enrolled a total of 345 eligible patients, 165 (47.8%) and 180 (52.2%) of whom were treated as first- and second-line at our facility, respectively. After applying propensity score matching, we considered 83 relevant patients in each cohort with a homogeneous distribution of all characteristics across the baseline LIPI subgroups. There was a noticeable change in the distribution of LIPI categories due to a significant decrease in dNLR values during treatment. Although no patients shifted to a worse prognosis category, 20 (24.1%) transitioned from LIPI-1 to LIPI-0, and 7 (8.4%) moved from LIPI-2 to LIPI-1 (p < 0.001). Throughout a median observation period of 7.3 (IQR 3.9-15.1) months, a total of 158 irAEs (63.5%) were documented, with 121 (48.6%) and 39 (15.7%) patients experiencing mild to moderate and severe adverse events, respectively. Multivariate logistic regression analysis showed that the classification and changes in the LIPI score while on treatment were independent predictors of irAEs. The LIPI-0 group was found to have significantly increased odds of experiencing irAEs. Following a median follow-up period of 21.1 (95% CI 17.9-25.8) months, the multivariable Cox model confirmed LIPI categorization at any given time point as a significant covariate with influence on overall survival, irrespective of the treatment line. These findings suggest that reassessing the LIPI score after two cycles of treatment could help pinpoint patients particularly prone to immune-related toxicities. Those who maintain a good LIPI score or move from the intermediate to good category would be more likely to develop irAEs. The continuous assessment of LIPI provides prognostic insights and could be useful for predicting the benefit of PD-(L)1 checkpoint inhibitors.

Baseline Circulating Blood Cell Counts and Ratios and Changes Therein for Predicting Immune-Related Adverse Events during Immune Checkpoint Inhibitor Therapy: A Multicenter, Prospective, Observational, Pan-Cancer Cohort Study with a Gender Perspective

Several factors have been associated with the occurrence of immune-related adverse events (irAEs) induced by immune checkpoint inhibitor (ICI) therapy. Despite their availability, the predictive value of circulating blood cell parameters remains underexplored. Our aim was to investigate whether baseline values of and early changes in absolute neutrophil count (ANC), absolute lymphocyte count (ALC), other blood cell counts, and lymphocyte-related ratios can predict irAEs and whether sex may differentially influence this potential predictive ability. Of the 145 patients included, 52 patients (35.8%) experienced at least one irAE, with a 1-year cumulative incidence of 41.6%. Using Fine and Gray competing risk models, we identified female sex (hazard ratio (HR) = 2.17, 95% confidence interval (CI) = 1.20-3.85), high ALC before ICI initiation (HR = 1.63, 95% CI = 1.09-2.45), and low ANC after ICI initiation (HR = 0.81, 95% CI = 0.69-0.96) as predictors of irAEs. However, ALC and ANC may only have an impact on the risk of irAEs in women (stratified for female sex, ALC-related HR = 2.61, 95% CI = 1.40-4.86 and ANC-related HR = 0.57, 95% CI = 0.41-0.81). Priority should be given to developing models to predict ICI-related toxicity and their validation in various settings, and such models should assess the impact of patient sex on the risk of toxicity.

Association between systemic immune-inflammation index and metabolic syndrome and its components: results from the National Health and Nutrition Examination Survey 2011-2016

BACKGROUND

Metabolic syndrome (MetS), a worldwide public health problem, affects human health and quality of life in a dramatic manner. A growing evidence base suggests that MetS is strongly associated with levels of systemic immune inflammation. The present study aimed to investigate the possible relationship between the systemic immune-inflammation index (SII), a novel inflammatory marker, and MetS to provide data support for effective MetS prevention by reducing the systemic inflammatory response.

METHODS

We included adult participants with complete SII and MetS information from the 2011-2016 National Health and Nutrition Examination Survey (NHANES). MetS was defined as using the criteria developed by the Adult Treatment Program III of the National Cholesterol Education Program. The formula for SII was as follows: SII = platelet counts × neutrophil counts/ lymphocyte counts. Weighted linear regression was used to assess differences in variables across SII quartile groups after the SII score was divided into 4 quartiles. The independent interaction between SII and MetS was investigated using weighted multivariate logistic regression analysis and subgroup analysis, and the relationship between SII levels and 5 particular MetS items was further explored in depth.

RESULTS

A total of 12,402 participants, 3,489 of whom were diagnosed with MetS, were included in this study. After correcting for covariates, the results of a logistic regression of multistage weighted complex sampling data revealed that participants with higher SII scores had a higher chance of developing MetS (odds ratio (OR) = 1.33, 95% confidence interval (CI): 1.14-1.55) and that SII levels could be used as an independent risk factor to predict that likelihood of MetS onset. In the Q1-Q4 SII quartile group, the risk of developing MetS was 1.33 times higher in the Q4 group, which had the highest level of systemic immune inflammation than in the Q1 group. After adjusting for all confounding factors, SII scores were found to have a negative correlation with high-density lipoprotein cholesterol (OR = 1.29; 95% CI, 0.99-1.67, P = 0.056) and a significant positive correlation with waist circumference (OR = 2.17; 95% CI, 1.65-2.87, P < 0.001) and blood pressure (BP) (OR = 1.65; 95% CI, 1.20-2.27, P = 0.003). Gender, age, and smoking status were shown to alter the positive association between SII and MetS in subgroup analyses and interaction tests (p for interaction < 0.05). Additionally, we demonstrated a nonlinear correlation between SII and MetS. The findings of the restricted cubic spline indicated that there was an inverted U-shaped association between SII and MetS.

CONCLUSIONS

Our findings imply that increased SII levels are related to MetS, and SII may be a simple and cost-effective method to identify individuals with MetS. Therefore, protective measures such as early investigation and anti-inflammatory interventions are necessary to reduce the overall incidence of MetS.

Common methodological pitfalls in ICI pneumonitis risk prediction studies

Background

Pneumonitis is one of the most common adverse events induced by the use of immune checkpoint inhibitors (ICI), accounting for a 20% of all ICI-associated deaths. Despite numerous efforts to identify risk factors and develop predictive models, there is no clinically deployed risk prediction model for patient risk stratification or for guiding subsequent monitoring. We believe this is due to systemic suboptimal approaches in study designs and methodologies in the literature. The nature and prevalence of different methodological approaches has not been thoroughly examined in prior systematic reviews.

Methods

The PubMed, medRxiv and bioRxiv databases were used to identify studies that aimed at risk factor discovery and/or risk prediction model development for ICI-induced pneumonitis (ICI pneumonitis). Studies were then analysed to identify common methodological pitfalls and their contribution to the risk of bias, assessed using the QUIPS and PROBAST tools.

Results

There were 51 manuscripts eligible for the review, with Japan-based studies over-represented, being nearly half (24/51) of all papers considered. Only 2/51 studies had a low risk of bias overall. Common bias-inducing practices included unclear diagnostic method or potential misdiagnosis, lack of multiple testing correction, the use of univariate analysis for selecting features for multivariable analysis, discretization of continuous variables, and inappropriate handling of missing values. Results from the risk model development studies were also likely to have been overoptimistic due to lack of holdout sets.

Conclusions

Studies with low risk of bias in their methodology are lacking in the existing literature. High-quality risk factor identification and risk model development studies are urgently required by the community to give the best chance of them progressing into a clinically deployable risk prediction model. Recommendations and alternative approaches for reducing the risk of bias were also discussed to guide future studies.

Predictive Biomarkers for Checkpoint Inhibitor Immune-Related Adverse Events

Immune-checkpoint inhibitors (ICIs) are antagonists of inhibitory receptors in the immune system, such as the cytotoxic T-lymphocyte-associated antigen-4, the programmed cell death protein-1 and its ligand PD-L1, and they are increasingly used in cancer treatment. By blocking certain suppressive pathways, ICIs promote T-cell activation and antitumor activity but may induce so-called immune-related adverse events (irAEs), which mimic traditional autoimmune disorders. With the approval of more ICIs, irAE prediction has become a key factor in improving patient survival and quality of life. Several biomarkers have been described as potential irAE predictors, some of them are already available for clinical use and others are under development; examples include circulating blood cell counts and ratios, T-cell expansion and diversification, cytokines, autoantibodies and autoantigens, serum and other biological fluid proteins, human leucocyte antigen genotypes, genetic variations and gene profiles, microRNAs, and the gastrointestinal microbiome. Nevertheless, it is difficult to generalize the application of irAE biomarkers based on the current evidence because most studies have been retrospective, time-limited and restricted to a specific type of cancer, irAE or ICI. Long-term prospective cohorts and real-life studies are needed to assess the predictive capacity of different potential irAE biomarkers, regardless of the ICI type, organ involved or cancer site.

Predicting survival of NSCLC patients treated with immune checkpoint inhibitors: Impact and timing of immune-related adverse events and prior tyrosine kinase inhibitor therapy

Introduction

Immune checkpoint inhibitors (ICIs) produce a broad spectrum of immune-related adverse events (irAEs) affecting various organ systems. While ICIs are established as a therapeutic option in non-small cell lung cancer (NSCLC) treatment, most patients receiving ICI relapse. Additionally, the role of ICIs on survival in patients receiving prior targeted tyrosine kinase inhibitor (TKI) therapy has not been well-defined.

Objective

To investigate the impact of irAEs, the relative time of occurrence, and prior TKI therapy to predict clinical outcomes in NSCLC patients treated with ICIs.

Methods

A single center retrospective cohort study identified 354 adult patients with NSCLC receiving ICI therapy between 2014 and 2018. Survival analysis utilized overall survival (OS) and real-world progression free survival (rwPFS) outcomes. Model performance matrices for predicting 1-year OS and 6-month rwPFS using linear regression baseline, optimal, and machine learning modeling approaches.

Results

Patients experiencing an irAE were found to have a significantly longer OS and rwPFS compared to patients who did not (median OS 25.1 vs. 11.1 months; hazard ratio [HR] 0.51, confidence interval [CI] 0.39- 0.68, P-value <0.001, median rwPFS 5.7 months vs. 2.3; HR 0.52, CI 0.41- 0.66, P-value <0.001, respectively). Patients who received TKI therapy before initiation of ICI experienced significantly shorter OS than patients without prior TKI therapy (median OS 7.6 months vs. 18.5 months; P-value < 0.01). After adjusting for other variables, irAEs and prior TKI therapy significantly impacted OS and rwPFS. Lastly, the performances of models implementing logistic regression and machine learning approaches were comparable in predicting 1-year OS and 6-month rwPFS.

Conclusion

The occurrence of irAEs, the timing of the events, and prior TKI therapy were significant predictors of survival in NSCLC patients on ICI therapy. Therefore, our study supports future prospective studies to investigate the impact of irAEs, and sequence of therapy on the survival of NSCLC patients taking ICIs.

Blood cell counts can predict adverse events of immune checkpoint inhibitors: A systematic review and meta-analysis

Background

Cancer is concerning owing to its high mortality rate. Consequently, methods of prolonging the life of patients with cancer have become the primary focus of attention research. In recent years, immune checkpoint inhibitors (ICIs) have achieved good clinical efficacy as antitumor drugs; however, their severe adverse effects have made their use challenging. In order to clarify the predictors of adverse effects, scientists have conducted a series of studies. Blood counts can potentially monitor risk factors associated with the occurrence of immune-related adverse events (irAEs). Herein, a meta-analysis was performed to clarify further the guiding significance of blood counts in the clinical setting.

Methods

Studies that satisfied the inclusion criteria were obtained by searching the database. Included studies were those in which irAEs had been observed, and evidence of an association between blood counts and irAEs was reported. The included ones were evaluated for quality. In addition to sensitivity analysis and subgroup analysis, a meta-analysis was performed using the odds ratio (OR) and 95% confidence interval (CI) for each study.

Results

A total of 18 articles were included in our study. The analyses were performed separately according to different blood cell count indicators. The blood cell count metrics associated with irAEs were: absolute eosinophil count, neutrophil: lymphocyte ratio, and platelet: lymphocyte ratio.

Conclusion

Our review and meta-analysis of studies suggest that absolute eosinophil count, neutrophil: lymphocyte ratio, and platelet: lymphocyte ratio may serve as predictors of the emergence of irAEs. Given the small number of studies focusing on the relationship between patient blood cell counts and the risk of irAEs, future studies need to further explore the mechanisms of occurrence and potential associations.

The Predictive Model Construction for Immune-Related Adverse Events in Non-Small Cell Lung Cancer Patients Receiving Immunotherapy

INTRODUCTION

It is possible to predict immune-related adverse events (irAEs) in the treatment of immune checkpoint inhibitors (ICIs) based on clinical and hematological parameters. Nevertheless, the specific parameters which can predict irAEs are still in the exploration. The purpose of this retrospective study was to develop a predictive model for irAEs in non-small cell lung cancer (NSCLC) patients in the treatment of ICIs.

METHODS

Researchers enrolled NSCLC patients treated with at least 1 type of ICIs at Harbin Medical University Cancer Hospital between January 30, 2019 and December 31, 2021. Baseline parameters including demographic, clinicopathology, treatment information, and peripheral blood markers were selected retrospectively. Type, onset time, grade, and treatment of irAEs were also assessed. By analyzing the risk factors for irAEs, an irAEs prediction model was established using univariate and multivariate logistic regression.

RESULTS

In a total of 484 patients, 81 patients experienced 112 irAEs in which thyroid dysfunction was the most common irAE (n = 38, 33.9%) and ICI pneumonitis was the most serious irAE (n = 6, 33.3%). Finally, a prediction model based on lines and combination therapy of ICIs, ECOG performance status, neutrophils/lymphocytes ratio (NLR), platelet (PLT), and lymphocyte (LYM) was established. Multivariate logistic regression analysis showed that 2 or ≥3 lines of immunotherapy, ICIs combination therapy, and ECOG PS 1-2 were independent risk factors for irAEs. Baseline LYM was positively associated with irAEs (OR = 2.599, P = 0.048) while baseline NLR and PLT were negatively associated with irAEs (OR = 0.392, P = 0.047; OR = 0.992, P = 0.035, respectively). The model showed great prediction performance with the AUC value of 0.851 and 0.779 in the training cohort and validation cohort, respectively.

CONCLUSION

Our study identified the risk factors related to irAEs occurrence and constructed and assessed the predictive model of irAEs in patients with NSCLC treated by ICIs using clinical and hematological parameters, thus guiding clinicians to select precisely the population receiving immunotherapy and develop individualized treatment therapy.

Role of pan immune inflammatory value in the evaluation of hepatosteatosis in children and adolescents with obesity

OBJECTIVES

Inflammation is a feature of non-alcoholic fatty liver disease progression and plays an important role in hepatic steatosis and fibrosis. Since there are no studies in the literature showing the relationship between hepatosteatosis with the systemic immune-inflammation index (SII) and pan-immune inflammation value (PIV), we aimed to evaluate the relationship between these biomarkers and hepatosteatosis in childhood.

METHODS

We included 133 consecutive obese children and adolescents aged 6-18 years into this single-center, retrospective, and cross-sectional study. Anthropometric, physical examination, radiological and laboratory data were obtained and recorded from the file records of each case.

RESULTS

When we grouped the patient population according to the grade of hepatosteatosis, there was a statistically significant difference between the groups in terms of SII and PIV values (p<0.05, for both). In the analyzes performed to identify independent predictors of hepatosteatosis pubertal status (p=0.019) and PIV value (p<0.001) were found to be significant as independent predictors. Moreover, in the analysis performed to predict severity of hepatic steatosis, regression analysis was performed by dividing the groups into groups with and without severe adiposity. As a result of this analysis, HOMA-IR (p=0.019) and PIV value (p=0.028) were found to be significant in the prediction of severe hepatic adiposity.

CONCLUSIONS

Our findings showed that increased PIV levels were associated with the presence and severity of hepatic steatosis, but not with SII.

Association between SII and hepatic steatosis and liver fibrosis: A population-based study

Background

The systemic immune-inflammation index (SII) is a novel marker of inflammation, and hepatic steatosis and fibrosis are associated with inflammation. This study aimed to investigate the possible relationship between SII and hepatic steatosis and fibrosis.

Methods

The datasets from the National Health and Nutrition Examination Survey (NHANES) 2017–2020 were used in a cross-sectional investigation. Multivariate linear regression models were used to examine the linear connection between SII and controlled attenuation parameter (CAP) and liver stiffness measurement (LSM). Fitted smoothing curves and threshold effect analysis were used to describe the nonlinear relationship.

Results

This population-based study included a total of 6,792 adults aged 18–80 years. In a multivariate linear regression analysis, a significant positive association between SII and CAP was shown [0.006 (0.001, 0.010)]. This positive association in a subgroup analysis was maintained in men [0.011 (0.004, 0.018)] but not in women. Furthermore, the association between SII and CAP was nonlinear; using a two-segment linear regression model, we found an inverted U-shaped relationship between SII and CAP with an inflection point of 687.059 (1,000 cells/µl). The results of the multiple regression analysis showed that the relationship between SII and LSM was not significant (P = 0.263).

Conclusions

Our findings imply that increased SII levels are linked to hepatic steatosis, but SII is not linked to liver fibrosis. To confirm our findings, more large-scale prospective investigations are needed.