Systemic nutrition and inflammation status is recognized for its influence on cancer survival, yet its role in perioperative outcomes remains poorly defined. This study aimed to refine the assessment of systemic nutrition and inflammation status in non-small cell lung cancer (NSCLC) patients and to elucidate its impact on perioperative outcomes.

All patients underwent video-assisted thoracoscopic lobectomy, with their nutrition and inflammation status assessed based on preoperative blood tests. The development cohort, comprising 1497 NSCLC patients from two centers, evaluated the predictive value of systemic nutrition/inflammation indicators for perioperative endpoints and formulated the systemic nutrition-inflammation index (SNII). The tertiles of SNII were used to classify the nutrition/inflammation risk as high (< 15.6), moderate (15.6-23.1), and low (> 23.1). An external validation cohort of 505 NSCLC patients was utilized to confirm the effectiveness of SNII in guiding perioperative management.

In the development cohort, the SNII tool, calculated as the product of total cholesterol and total lymphocytes divided by total monocytes, demonstrated a stronger correlation with perioperative outcomes compared to 11 existing nutrition/inflammation indicators. A low SNII score, indicative of high nutrition/inflammation risk, was independently predictive of increased complication incidence and severity, as well as prolonged chest tube duration and hospital stay. These findings were corroborated in the validation cohort. Upon combining the development and validation cohorts, the superiority of the SNII in predicting perioperative outcomes was further confirmed over the existing nutrition/inflammation indicators. Additionally, comprehensive subgroup analyses revealed the moderately variable efficacy of SNII across different patient populations.

This study developed and validated the SNII as a tool for identifying systemic nutrition and inflammation risk, which can enhance perioperative managements in NSCLC patients. Patients identified with high risk may benefit from prehabilitation and intensive treatments, highlighting the need for further research.

The Advanced Lung Cancer Inflammation Index (ALI) reflects levels of systemic inflammation and nutrient metabolism in patients. However, The connection between ALI and hepatic steatosis and fibrosis remains unclear. This study aims to explore the potential association between ALI, the Controlled Attenuation Parameter (CAP), and Liver Stiffness Measurement (LSM), offering new perspectives for the prevention of hepatic steatosis and fibrosis.

Using data from the National Health and Nutrition Examination Survey database spanning from 2017 to 2020, a cross-sectional study of 6591 participants aged 20 years and older was performed to assess the relationship between ALI and hepatic steatosis and hepatic fibrosis. Smooth curve fitting and generalized additive models were used to further evaluate whether there was a nonlinear association between ALI, CAP, and LSM, and threshold effect analysis was used to find the inflection point. A two-part linear regression model was applied to estimate threshold effects. Subgroup analysis and interaction were used to evaluate the potential association between ALI, CAP, and LSM. Furthermore, to verify the efficacy of ALI, we used ROC curves to compare ALI with fibrosis and nutritional markers (FIB-4 index, GNRI) that have been reported to be associated with liver disease.

The ALI levels in the MASLD and fibrosis 4 groups were considerably elevated than the control group, with statistical significance (PMASLD<0.001, PAHF =0.013). Multiple linear regression analyses indicated significant positive associations between ALI and its quartiles with both CAP[b(95%CI) ALI=0.510(0.465,0.555), P<0.001] and LSM levels [b(95%CI) ALI=0.011(0.009,0.013), P<0.001). There was a positive smooth curve fitting relationship between ALI and the levels of CAP and LSM, with threshold effect inflection points at 88.287 and 98.420 (PLog likelihood ratio<0.001), respectively. ALI interacts with CAP in relation to gender [OR(95%CI)female=0.095(0.039-0.150), OR(95%CI)male=0.174(0.118-0.230), Pfor interaction=0.044] and smoking [OR(95% CI)<100cigarettes in life=0.086(0.035-0.137), OR(95% CI)>100cigarettes in life=0.177(0.115-0.240), Pfor interaction=0.023], and with LSM in patients with HL [OR(95% CI)HL=0.014(0.008-0.019), Pfor interaction<0.001].

The findings suggest a positive correlation between elevated ALI levels and the levels of CAP and LSM. Maintaining ALI within an appropriate range may help mitigate the prevalence of hepatic steatosis and hepatic fibrosis.

Changes in systemic inflammation, nutritional status and serum vitamin D level are important characteristics of asthma. However, role and importance of nutritional inflammatory indicators or serum vitamin D concentrations in predicting the prognosis of asthma remain unclear. The advanced lung cancer inflammation index (ALI), based on body mass index (BMI), serum albumin and neutrophil-lymphocyte ratio (NLR), is a comprehensive index to assess systemic inflammation and nutrition. This study aimed to evaluate their independent and combined predictive value of mortality in asthma patients.

This study analyzed data from the National Health and Nutrition Examination Survey (NHANES) 2001-2018. Cox regression analysis was used to assess the independent or joint effect of ALI and serum vitamin D on mortality risks of asthma. Receiver operator characteristic curve analysis was used to compare the prognostic ability of ALI with its component factors, including NLR, albumin, neutrophil, lymphocyte and BMI.

A total of 2870 eligible asthma patients were included. After adjustment, higher ALI correlated significantly with reduced all-cause and respiratory disease mortality (adjusted hazard ratio [aHR] = 0.64 and 0.34; P < 0.05). Meanwhile, vitamin D deficiency correlated significantly with increased all-cause and respiratory disease mortality (aHR = 2.06 and 2.73; P < 0.05). The area under the curve of ALI in predicting 1-year, 5-year or 10-year all-cause mortality surpassed that of its five component indices. Joint analyses showed that individuals with higher levels of ALI and vitamin D had the lowest risks of all-cause and respiratory disease mortality (aHR = 0.31 and 0.17; P < 0.05).

ALI and serum vitamin D are robust independent and combined predictors of mortality in asthma patients. ALI offers superior predictive capability over its components, and sufficient vitamin D levels are beneficial for survival outcomes. The synergistic effect of high ALI and adequate vitamin D highlights the benefit of integrating both metrics into clinical practice for enhanced prognostic accuracy.

Acute gouty arthritis (AGA) is an inflammatory joint disease. The advanced lung cancer inflammation index (ALI) evaluates inflammation. This study investigates the association between ALI and AGA.

We included 652 participants in this cohort study, dividing them into two groups: those with AGA and a control group without AGA. We employed logistic regression to examine the ALI-AGA relationship, using restricted cubic splines (RCS) to assess dose-response relationships, performing subgroup analyses, and conducting interaction tests. K-fold cross-validation was applied for model assessment. Receiver operating characteristic (ROC) curves were applied to visualize and compare the predictive value of ALI and other inflammatory indices for AGA.

Among the 652 participants, the AGA group exhibited significantly lower ALI values compared to the control group. Multivariate logistic regression identified an inverse relationship between ALI and AGA. RCS analysis indicated an L-shaped non-linear relationship between ALI levels and AGA, with inflection points at an ALI of 23.38. Subgroup analyses showed no significant interactions between ALI levels and AGA after stratifying by age, hypertension, coronary heart disease (CHD), and diabetes (DM). The results from the ROC curves indicate that ALI may serve as a better predictor of AGA.

This cross-sectional study found a significant inverse correlation between ALI levels and AGA prevalence. Moreover, the ALI could serve as a more accurate diagnostic tool for AGA and offer a novel approach for further investigating the relationship between inflammation and AGA.

Cachexia is common in patients with oesophagogastric cancer. The syndrome is characterised by tissue wasting (muscle and fat), anorexia, and reduced physical function, which result from complex interactions between the tumour and its host. Heterogeneity in the diagnostic criteria used for cachexia has hindered their clinical utilisation. This study aimed to compare the two established cachexia definitions (Fearon's consensus definition and the Global Leadership Initiative on Malnutrition [GLIM] criteria) and their relationships with survival in patients with oesophagogastric cancer.

Consecutive patients newly diagnosed with oesophagogastric cancer (January 2019 to December 2020) were identified from a prospective regional database. Involuntary weight loss, BMI, CT body composition analyses, and neutrophil-lymphocyte ratios were recorded at clinical staging. These data were used to assess patients for cachexia according to Fearon and GLIM diagnostic criteria. The primary outcome of interest was overall survival.

Overall, 465 patients (66.9% male, median 71 years) were diagnosed with oesophagogastric cancer during the 2-year study period. Cachectic proportions differed between definitions (Fearon: 59.1% vs. GLIM: 44.1%), and only 49.1% of the 322 patients who met one set of diagnostic criteria were cachectic according to both. Patients who met the GLIM criteria were significantly more comorbid and had a poorer performance status; however, no such difference was evident when using the Fearon definition. Those patients who met either set of diagnostic criteria had shorter survival than those who met neither (p < 0.001). Following adjustment for confounders, GLIM-defined cachexia was more strongly associated with reduced survival (aHR: 1.57 [95% CI: 1.25-1.96], p < 0.001) than Fearon-defined cachexia (aHR: 1.41 [95% CI: 1.13-1.76], p = 0.002). Patients who only met the Fearon diagnostic criteria had prolonged survival (median: 363 days) when compared to those who met only GLIM (median: 158 days) or both definitions (median: 120 days). A secondary analysis of those patients who met the GLIM diagnostic criteria (n = 205) compared the three potential phenotypical criteria used in this definition. Only reduced muscle mass, and not low BMI or weight loss, was associated with poorer survival (aHR: 1.88 [95% CI: 1.15-3.07], p = 0.012) in this group.

Cancer cachexia is strongly associated with shortened survival in patients with oesophagogastric cancer. Classification using the GLIM criteria provides more effective prognostication and this definition should be utilised in multidisciplinary patient care.

Blood inflammation index has been shown to correlate with the prognosis of patients with gastric cancer. However, few studies have compared the efficacy of existing blood inflammatory markers in predicting the prognosis of patients with locally advanced gastric cancer in combination with neoadjuvant chemotherapy and immunotherapy.

The objective of this study was to compare the prognostic value of existing commonly used blood inflammatory index in patients with advanced gastric cancer treated with neoadjuvant chemotherapy combined with immunotherapy.

The clinicopathological data of patients with advanced gastric cancer from three centers in China were analyzed retrospectively. Univariate COX regression analysis was used to analyze the independent risk factors of poor tumor regression and overall survival (OS) in this part of patients, and the predictive value of different inflammatory indexes on prognosis was compared by C-index index. Finally, Inflammatory burden index(IBI) was grouped by X-tile software, and Kaplan-Meier method was used to compare the survival difference between groups.

A total of 163 patients were enrolled in this study. The median age was 63 years(56-68). The median cycle of neoadjuvant therapy was 4(3-4). The median survival time was 85.1%(1 years), 65.6%(2 years), and 47.4%(3 years).Univariate analysis showed that IBI was an independent risk factor for non-TR(residual tumor cells>50%) (HR=1.08,95%CI:1.00-1.45,p<0.001)and OS(HR=1.04,95%CI:1.03-1.05,p<0.001). IBI is the best predictor of OS (C-index: 0.82, 95% CI: 0.78-0.87) among all inflammatory indexes. The IBI cutoff value was 52.1. It was found that the high IBI group had a higher incidence of postoperative complications(32.1%vs14.3%, p=0.001), the proportion of non-TR patients was significantly higher than that of the low IBI group(64.3%vs35.7%, p =0.001), and the high IBI group had a significantly lower OS((47.6% vs 87.6%, p < 0.001).

IBI is the best inflammatory index to predict the prognosis of advanced gastric cancer treated with neoadjuvant chemotherapy combined with immunotherapy, which will help guide patients' treatment decisions. This result still needs to be verified by large prospective studies.

Lower-grade glioma (LGG) exhibits significant heterogeneity in clinical outcomes, and current prognostic markers have limited predictive value. Despite the growing recognition of histone modifications in tumor progression, their role in LGG remains poorly understood. This study aimed to develop a histone modification-based risk signature and investigate its relationship with drug sensitivity to guide personalized treatment strategies.

We performed single-cell RNA sequencing analysis on LGG samples (n = 4) to characterize histone modification patterns. Through integrative analysis of TCGA-LGG (n = 513) and CGGA datasets (n = 693 and n = 325), we constructed a histone modification-related risk signature (HMRS) using machine learning approaches. The model's performance was validated in multiple independent cohorts. We further conducted comprehensive analyses of molecular mechanisms, immune microenvironment, and drug sensitivity associated with the risk stratification.

We identified distinct histone modification patterns across five major cell populations in LGG and developed a robust 20-gene HMRS from 129 candidate genes that effectively stratified patients into high- and low-risk groups with significantly different survival outcomes (training set: AUC = 0.77, 0.73, and 0.71 for 1-, 3-, and 5-year survival; P < 0.001). Integration of HMRS with clinical features further improved prognostic accuracy (C-index >0.70). High-risk tumors showed activation of TGF-β and IL6-JAK-STAT3 signaling pathways, and distinct mutation profiles including TP53 (63% vs 28%), IDH1 (68% vs 85%), and ATRX (46% vs 20%) mutations. The high-risk group demonstrated significantly elevated immune and stromal scores (P < 0.001), with distinct patterns of immune cell infiltration, particularly in memory CD4+ T cells (P < 0.001) and CD8+ T cells (P = 0.001). Drug sensitivity analysis revealed significant differential responses to six therapeutic agents including Temozolomide and targeted drugs (P < 0.05).

Our study establishes a novel histone modification-based prognostic model that not only accurately predicts LGG patient outcomes but also reveals potential therapeutic targets. The identified associations between risk stratification and drug sensitivity provide valuable insights for personalized treatment strategies. This integrated approach offers a promising framework for improving LGG patient care through molecular-based risk assessment and treatment selection.

The aim of this study was to evaluate the combined prognostic value of calf circumference (CC) and serum albumin on mortality in patients with cancer cachexia aged ≥65 years.

This multicenter cohort study involved 5322 older patients in hospital with cancer cachexia. The combined indicator of CC and albumin was defined as the calf circumference-albumin (CCA) index. Harrell's C index, a time-dependent receiver operating characteristic curve analysis, was used to assess the prognostic performance of the CCA index and other indices. The optimal thresholds method was used to determine the cutoff values of CC and albumin, and the association between the CCA index and all-cause mortality was assessed using Kaplan-Meier analysis and Cox proportional hazard regression models.

A total of 3875 men and 1447 women with a mean age of 72.0 years (range: 68.0-78.0 years) and a mean follow-up time of 55.0 months (range: 25.0-85.0 months) were included in the study. A total of 1269 patients were classified into the low CCA index group (0 score) by the optimal thresholds method. In the overall population, the CCA index showed better differentiating power at predicting mortality in older patients with cancer cachexia compared with CC or albumin alone (C index = 0.639; 95% CI: 0.612-0.666; P < 0.05). The time-dependent receiver operating characteristic curve showed that the CCA index had the highest prognostic value of all the measures studied (P < 0.05). In the overall population, male and female patients with a high CCA index (2 score) showed better performance than those with a low CCA index (0 or 1 score).

The CCA index could significantly predict the mortality of older patients with cancer cachexia, which might provide renewed assistance for future clinical management.

Cachexia is a poor prognostic factor in many advanced cancers. Cachexia diagnostic criteria of the European Palliative Care Research Collaboration (EPCRC) may underestimate cachexia in Asians; therefore, new criteria have been proposed by the Asian Working Group for Cachexia (AWGC). We compared both criteria to determine differences in diagnostic rates and their association with lung cancer prognosis.

This single-center, retrospective cohort study considered lung cancer outpatients receiving chemotherapy. Survival was analyzed using Kaplan-Meier curves and log-rank tests. The association between cachexia diagnosis and prognosis was examined for each set of criteria using a Cox proportional hazards model. C-statistic analysis was performed to compare the discriminative power for prognosis.

Among the 106 participants analyzed (median age, 75 [71-79] years; 75 males [70.8%]; 91 [85.9%] with performance status [PS] 0-1), 58 (54.7%) and 77 (72.6%) cachexia cases were diagnosed using the EPCRC and AWGC criteria, respectively. The latter encompassed all but one patient diagnosed using the EPCRC criteria. Patients with cachexia had a significantly poorer prognosis according to both criteria (EPCRC, p = 0.002; AWGC, p = 0.001). Both criteria had almost equal discriminative power for prognosis (EPCRC, C-statistic = 0.658; AWGC, C-statistic = 0.658). CRP in the AWGC criteria was most strongly related to prognosis.

Cachexia was an independent poor prognostic factor in lung cancer patients receiving chemotherapy under the AWGC and EPCRC criteria, both of which had similar prognostic discriminatory power. Among CRP, anorexia, and grip strength, elevated CRP may be the most prognostically relevant parameter in the AWGC criteria.

Vascular compliance is an important predictor of cardiovascular disease and mortality. Pulse pressure index (PPI) is a reliable indicator for evaluating vascular compliance. However, the association between PPI, all-cause mortality (ACM), and cardiovascular mortality (CVM) in patients with hypertension is still unclear. In this study, we aimed to investigate the association of PPI with ACM and CVM in patients with hypertension. Kaplan-Meier survival curves, Cox proportional hazards regression models, restricted cubic splines, and subgroup and interaction analyses were used to investigate the association of PPI with ACM and CVM. U-shaped associations were observed between PPI and both ACM and CVM, and the inflection points for ACM and CVM were at PPI values of 0.327 and 0.363, respectively. Time-dependent receiver operating characteristic curves indicated that PPI showed good predictive value for both ACM and CVM occurrence at 1, 3, 5, and 10 years, and its predictive value was higher than PP for ACM and CVM at 5 and 10 years. These results showed that PPI can be used to identify patients with hypertension who are at a high risk of mortality and can guide more aggressive anti-hypertensive treatment strategies. Moreover, these findings demonstrate that PPI is a superior vascular compliance indicator than PP.

Inflammation and nutritional status are closely associated with the mortality risk of survivors of cardio-cerebrovascular events. This study aims to evaluate the relationship between inflammation and nutritional indices and mortality among, identifying the most predictive indices.

This study included cohort data of the survivors of major adverse cardiovascular and cerebrovascular events (MACCE) from the National Health and Nutrition Examination Survey (NHANES) in 1999-2010. MACCE is defined as a composite of myocardial infarction, heart failure and stroke, and at least one of the three events occurs. The main outcomes were all-cause mortality and cardiovascular mortality. Kaplan-Meier analysis and receiver operating characteristic curves were used to compare the correlation between seven inflammatory nutritional indices (such as Advanced Lung Cancer Inflammation Index, ALI) and mortality among the survivors. A multivariable-adjusted Cox regression and restricted cubic splines analysis identified the most predictive index, with the optimal number of nodes determined by the Akaike information criterion. Subgroup and sensitivity analyses were conducted to assess model stability.

A total of 2,045 MACCE survivors were included. The higher levels of ALI and serum albumin were significantly associated with lower risks of all-cause and cardiovascular mortality among these individuals. Increases in C-reactive protein to Lymphocyte Ratio, Neutrophil to Serum Albumin Ratio, Neutrophil-to-Lymphocyte Ratio, Systemic Immune-Inflammation Index (SII), and C-reactive protein were similarly correlated with higher mortality risk. ALI outperformed other indices, displaying a distinct L-shaped nonlinear relationship with both all-cause and cardiovascular mortality among MACCE survivors, with an inflection point at 90 indicating the lowest risk. To the left of this inflection, each unit increase in ALI was associated with a 1.3% decrease in all-cause and cardiovascular mortality risk among MACCE patients. To the right, the risk might increase by 0.2%, although the change was not statistically significant. Subgroup analyses and sensitivity analyses showed that the association between ALI and risk of mortality remained stable in most MACCE survivor populations.

Routine and dynamic monitoring of ALI is helpful for clinicians to assess the mortality risk among MACCE survivors. Anti-inflammatory therapies and appropriate nutritional support are crucial for reducing mortality in these individuals.

Glucose-to-lymphocyte ratio (GLR) plays an important role in the prognosis of various tumors. The aim of this study was to comprehensively evaluate the prognostic value of GLR in solid tumors through the meta-analysis.

A comprehensive search of eligible studies was performed by scrutinizing the Pubmed, Embase and Web of science databases until May 30, 2024. The pooled hazard ratios (HRs) with 95% confidence intervals (CIs) were calculated to evaluate overall survival (OS), disease-free survival (DFS) and recurrence free survival (RFS).

A total of 22 studies from 14 articles involving 9472 patients were included in the study. The pooled analysis showed that cancer patients with high GLR was significantly associated with unfavorable OS (HR:1.48,95% CI:1.34-1.63) and DFS/RFS (HR:2.20,95% CI:1.66-2.92). Subgroup analysis further showed that high GLR had better predictive value in liver cancer (HR:2.66, 95%CI:1.80-3.93), breast cancer (HR:2.13, 95%CI:1.10-4.13) and pancreatic cancer (HR:1.92, 95%CI:1.30-2.84).

GLR can be used as an effective prognostic marker in patients with solid tumors.

Osteoarthritis (OA) is the most prevalent form of arthritis worldwide. Inflammation and nutrition status play crucial roles in the development and progression of OA. The advanced lung cancer inflammation index (ALI) serves as a composite indicator for evaluating inflammation and nutritional status, while the systemic immune inflammation index (SII) is a novel marker for assessing immune-related inflammation. The study aimed to investigate the associations of the ALI and SII with all-cause and cardiovascular mortality among US adults with OA.

A total of 2,602 individuals aged 20 years and above with OA were included in the study from the National Health and Nutrition Examination Survey (NHANES) spanning from 1999 to 2018. Participants were categorized into higher or lower ALI and SII groups using cut-off values determined by the maximally selected rank statistics method. The Kaplan-Meier analysis, Cox proportional hazards models, and Fine Gray competing risk regression models were employed to assess the associations between the ALI/SII and mortality in OA patients. Additionally, stratified and subgroup analyses were conducted to enhance the robustness of the findings. Furthermore, time-dependent receiver operating characteristic (ROC) analysis was used to evaluate the predictive capacity of ALI and SII for mortality.

Higher SII levels were associated with a 2-fold increase in the risk of all-cause mortality (HR: 2.00, 95% CI: 1.59-2.52, p < 0.001), whereas individuals with higher ALI in the OA group exhibited a significantly reduced risk of all-cause mortality (HR: 0.49, 95% CI: 0.39-0.60, p < 0.001). Notably, in Model 3, individuals with higher ALI demonstrated a substantially lower risk of cardiovascular mortality (HR: 0.60, 95% CI: 0.44-0.82, p < 0.001). Conversely, in fully adjusted models, those with higher SII experienced a significantly higher risk (HR: 1.83, 95% CI: 1.29-2.60, p < 0.001). The RCS analysis revealed a J-shaped non-linear relationship between SII levels and all-cause mortality (p overall < 0.001; p non-linear < 0.001), and an L-shaped non-linear association between ALI levels and all-cause mortality (p overall < 0.001; p non-linear = 0.002). The time-dependent ROC curves illustrated that ALI and SII displayed a reasonably good and consistent predictive performance for both short- and long-term mortality in OA patients.

Lower ALI and higher SII values were correlated with increased risks of all-cause and cardiovascular mortality among US adults with OA.

In China, lung cancer ranks first in both incidence and mortality among all malignant tumors. Non-small cell lung cancer (NSCLC) constitutes the vast majority of cases, accounting for 80% to 85% of cases. Immune checkpoint inhibitors (ICIs), either as monotherapies or combined with other treatments, have become the standard first-line therapy for NSCLC patients. This study aimed to establish a nomogram model for NSCLC patients receiving immunotherapy incorporating demographic information, clinical characteristics, and laboratory indicators.

From January 1, 2019, to December 31, 2022, a prospective longitudinal cohort study involving 1321 patients with NSCLC undergoing immunotherapy was conducted at Chongqing University Cancer Hospital. Clinical and pathological characteristics, as well as follow-up data, were collected and analyzed. To explore prognostic factors affecting overall survival (OS), a Cox regression model was used to test the significance of various variables. Independent prognostic indicators were identified through multivariate analysis and then used to construct a nomogram prediction model. To validate the accuracy and practicality of this model, the concordance index (C-index), area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA) were used to assess the predictive performance of the nomogram.

In the final model, 11 variables from the training cohort were identified as independent risk factors for patients with NSCLC: age, KPS score, BMI, diabetes, targeted therapy, Hb, WBC, LDH, CRP, PLR, and LMR. The C-index for OS in the training cohort was 0.717 (95% CI, 0.689-0.745) and 0.704 (95% CI, 0.660-0.750) in the validation cohort. Calibration curves for survival probability showed good concordance between the nomogram predictions and actual observations. The AUCs for 1-year, 2-year, and 3-year OS in the training cohort were 0.724, 0.764, and 0.79, respectively, and 0.725, 0.736, and 0.818 in the validation cohort. DCA demonstrated that the nomogram model had a greater overall net benefit.

A prognostic model for OS in NSCLC patients receiving immunotherapy was established, providing a simple and reliable tool for predicting patient survival (https://icisnsclc.shinyapps.io/DynNomapp/). This model offers valuable guidance for clinicians in making treatment decisions and recommendations.

Asthma exacerbation is associated with obesity and systemic inflammatory diseases, and advanced lung cancer inflammation index (ALI) is a novel biomarker of nutritional inflammation. The purpose of this study was to investigate the potential relationship between ALI and unstable asthma.

This cross-sectional study utilized data from the 2007-2018 National Health and Nutrition Examination Survey (NHANES). Asthma was assessed through self-reported questionnaires. Multifactorial logistic regression, subgroup analyses, interaction assessments, smoothed curve fitting, and threshold effect analysis models were conducted to investigate the association between ALI and unstable asthma.

The study included 1,822 subjects with current asthma, and we found a linear positive association between ALI and unstable asthma, with higher levels of ALI significantly associated with an increased risk of asthma exacerbations in fully corrected models. However, the associations were not entirely consistent across subgroups. In subgroup analyses by body mass index (BMI) and race, unstable asthma and ALI were independently significant in the BMI (25-29.9) range and the Non-Hispanic White group. Interaction analysis suggested that BMI moderated the relationship between ALI and unstable asthma. Furthermore, smoothed curve fitting showed an inverted U-shaped relationship between log ALI and unstable asthma in subjects with a BMI <25 and male individuals, with inflection points observed at 1.53 and 2.13, respectively.

We found a linear positive association between ALI and unstable asthma, which remained constant in the fully adjusted model. These findings suggest that higher levels of ALI were significantly associated with an increased risk of asthma exacerbation, particularly in asthmatic populations with BMI in the 25-29.9 range. However, more prospective studies are required to confirm our findings.

This study aimed to assess different combinations of visceral proteins and to elucidate their value in predicting progression-free survival (PFS) and overall survival (OS) in patients with colon cancer. The visceral protein ratios included the albumin-globulin ratio (AGR), prealbumin-globulin ratio (PGR), and albumin-prealbumin-globulin ratio (APGR). Compared with AGR and PGR, APGR had the best time-dependent area under the receiver operating characteristic curves for predicting the outcomes. High AGR/PGR/APGR levels were associated with an increased risk of mortality. High AGR (HR = 0.816, 95%CI: 0.719-0.925, p = 0.001), PGR (HR = 0.831, 95%CI: 0.724-0.953, p = 0.008), and APGR (HR = 0.789, 95%CI: 0.688-0.904, p < 0.001) were independent risk factors for PFS. For every SD increase in AGR, PGR, and APGR, the risk of poor OS in patients with colon cancer was reduced by 16.9 % (HR = 0.831, 95%CI, 0.733-0.943; p = 0.001), 15.1 % (HR = 0.849, 95%CI, 0.739-0.976; p = 0.021), and 19.1 % (HR = 0.809, 95%CI, 0.705-0.928; p = 0.002), respectively. Logistic regression models showed that AGR, PGR, and APGR were independent factors that affected recurrence. Visceral protein ratios are independent predictors of PFS and OS. Compared to the existing visceral protein ratios (AGR and PGR), APGR is a more accurate and sensitive indicator for predicting the outcomes of patients with colon cancer.

Immune checkpoint inhibitors (ICIs) have transformed advanced gastric cancer treatment, yet patient responses vary, highlighting the need for effective biomarkers. Common markers, such as programmed cell death ligand-1 (PD-L1), microsatellite instability/mismatch repair (MSI/MMR), tumor mutational burden, tumor-infiltrating lymphocytes, and Epstein-Barr virus, face sampling challenges and high costs. This study seeks practical, minimally invasive biomarkers to enhance patient selection and improve outcomes.

This multicenter retrospective study analyzed 617 patients with advanced gastric or gastroesophageal junction cancer treated with programmed cell death protein-1 (PD-1)/PD-L1 inhibitors from January 2019 to March 2023. Clinical data and peripheral blood marker data were collected before and after treatment. The primary endpoints were overall survival (OS) and progression-free survival (PFS); the secondary endpoints included the objective response rate (ORR) and disease control rate (DCR). Least absolute shrinkage and selection operator (LASSO)-Cox and LASSO logistic regression analyses identified independent factors for OS, PFS, and ORR. Predictive nomograms were validated using receiver operating characteristic (ROC) curves, areas under the curve (AUCs), C-indices, and calibration curves, with clinical utility assessed via decision curve analysis (DCA), net reclassification improvement (NRI), and integrated discrimination improvement (IDI).

OS-related factors included treatment line, T stage, ascites, pretreatment indirect bilirubin (pre-IBIL), posttreatment CA125, CA199, CA724, and the PLR. PFS-related factors included treatment lines, T stage, metastatic sites, pre-IBIL, posttreatment globulin (GLOB), CA125, and CA199 changes. ORR-related factors included treatment line, T stage, N stage, liver metastasis, pretreatment red cell distribution width-to-platelet ratio (RPR), CA125, and CA724 changes. The nomograms showed strong predictive performance and clinical utility.

Early treatment, lower T stage, the absence of ascites, and lower pre-IBIL, post-CA125, CA199, CA724, and PLR correlate with better OS. Factors for improved PFS include early treatment, lower T stage, fewer metastatic sites, and lower pre-IBIL, post-GLOB, and post-CA125 levels. Nomogram models can help identify patients who may benefit from immunotherapy, providing valuable clinical guidance.

The combined effect of depression and nutritional-inflammatory status on mortality in the chronic kidney disease (CKD) population is unclear.

We prospectively analyzed 3,934 (weighted population: 22,611,423) CKD participants from the National Health and Nutrition Examination Survey (2007-2018). Depression and nutritional-inflammatory status were assessed with Patient Health Questionnaire 9 (PHQ-9) and Advanced Lung Cancer Inflammation Index (ALI), respectively. Weighted multivariate COX regression models, restricted cubic splines (RCS) models, and stratified analyses were used to investigate the association of PHQ-9 scores and ALI with all-cause mortality.

During a median follow-up of 5.8 years (interquartile range 3.4-8.6 years), a total of 985 patients died (25.0%). Each point increase in a patient's PHQ-9 score increased the risk of all-cause mortality by 4% (HR, 1.04; 95% CI, 1.02-1.06; p < 0.001), in the full adjusted model. However, an increase in ALI levels was associated with a decreased risk. HRs (95% CI) of 0.76 (0.65-0.90), 0.70 (0.57-0.86), and 0.51 (0.41-0.64) in the Q2, Q3, and Q4 of ALI compared with the Q1 of ALI, respectively. In addition, the joint analysis showed that CKD patients without depression and with higher ALI were associated with a reduced risk of all-cause mortality. Namely, patients in the highest ALI group (Q4) without depression had the lowest risk (HR, 0.32; 95% CI, 0.21-0.48). Furthermore, this combined effect was consistent across all subgroups, and no significant interaction was found (p > 0.05 for interaction).

In a nationally representative sample of US patients with CKD, coexisting depression and poorer nutrition-inflammation were associated with a significantly increased risk of all-cause mortality.

Skin cancer ranks as one of the most prevalent malignant tumors affecting humans. This study was designed to explore the correlation between the advanced lung cancer inflammation index (ALI), a metric that gauged both nutrition and inflammation statuses, in skin cancer patients and their subsequent prognosis.

Data from the National Health and Nutrition Examination Survey (NHANES) spanning 1999-2018 were scrutinized, along with mortality tracking extending to December 31, 2019. Kaplan-Meier survival curves and COX regression analysis, utilizing NHANES-recommended weights, delineated the association between ALI levels and skin cancer prognosis. To decipher the potential non-linear relationship, a restricted cubic spline analysis was applied. Additionally, stratified analysis was conducted to affirm the robustness of our findings.

The 1,149 patients participating in NHANES 1999-2018 were enrolled. We observed a reverse J-shaped non-linear relationship between ALI and both skin cancer all-cause mortality and cancer mortality, with inflection points at 81.13 and 77.50, respectively.

The ALI served as a comprehensive indicator of a patient's nutrition and inflammation status and was demonstrably linked to the prognosis in skin cancer cases. The meticulous evaluation and continuous monitoring of these parameters in skin cancer patients bear clinical importance.

Chronic kidney disease (CKD) is one of the common chronic diseases, and malnutrition and inflammation play a key role in the development of CKD. The advanced lung cancer inflammation index (ALI) is a novel index of nutrition and inflammation, and its association with CKD has not yet been clarified. The aim of this study was to explore the potential association between ALI and CKD.

We conducted a cross-sectional survey using data extracted from the National Health and Nutrition Examination Survey (NHANES, 2003-2018). Weighted multivariate logistic regression was used to assess the association between ALI and CKD, and smoothed curve fitting and threshold effect analyses were used to describe the nonlinear association between ALI and CKD. Subgroup analyses were performed to further assess the influence of other covariates on the relationship between ALI and CKD.

A total of 39,469 adult participants were included in the study, of whom 7,204 (18.25%) were diagnosed with CKD. After adjusting for multiple confounders, we found a significant negative correlation between ALI and CKD (OR = 0.93; 95%CI, 0.91-0.95; p < 0.0001). The risk of CKD tended to decrease with increasing quartiles of ALI. Smoothed curve fitting showed an L-shaped negative correlation between ALI and CKD. Threshold analysis showed a saturation effect of ALI at the inflection point of 55.09. Subgroup analyses and interaction tests showed that this negative association was maintained across age, sex, race, BMI, diabetes, hypertension, cardiovascular disease, and cancer subgroups (P for interaction >0.05).

Our findings suggest a significant correlation between ALI and CKD in the US adult population. However, more large-scale prospective studies are still needed to further confirm our findings.

Lung cancer is a major health burden globally and smoking is a well-known risk factor. It has been observed that chronic inflammation contributes to lung cancer progression, with immune cells and inflammatory cytokines implicated in tumor development. Clarifying the causal links between these immune components and lung cancer could enhance prevention and therapy.

We performed Mendelian randomization (MR) to explore causal connections between immune cells, inflammatory markers, and lung cancer risk, using genetic variants as instruments. Data from GWAS on these variables underpinned our MR analyses.

Our findings indicated an inverse association between some immune cells and lung cancer risk, implying that more immune cells might be protective. NK T cells (CD16-CD56) and myeloid cells (HLA DR+ on CD33dim HLA DR+ CD11b+) had an inverse correlation with lung cancer risk. Furthermore, a direct relationship was observed between inflammatory cytokines and these immune cells. In contrast, IL-18 was inversely associated with lung cancer, while IL-13 showed a direct correlation.

The study underscores the role of immune and inflammatory factors in lung cancer. These insights could lead to new therapeutic strategies for combating lung cancer.

Previous studies have hinted at the benefits of following an anti-inflammatory diet for potentially reducing breast cancer prevalence. However, the combined influence of diet and inflammation on breast cancer remains unclear.

The advanced lung cancer inflammation index (ALI) was used to assess inflammation and nutritional status. Statistical methods, such as multivariable logistic regression, eXtreme Gradient Boosting (XGBoost) model, and subgroup analysis, were employed to analyze the impact of ALI on prevalence of BC. Additionally, a two-piece-wise logistic regression model with smoothing was used to determine the ALI threshold for BC prevalence. The study aimed to understand the mechanistic association between ALI levels and BC development.

The mean (SD) age of the study population was 50.0 (17.7) years, with 40.0% of individuals classified as obese. Comparing ALI tertiles to the lowest tertile, the odds ratios (95% CI) for breast cancer (BC) were 0.78 (0.62, 0.98) and 0.68 (0.52, 0.87) for T2-T3. The XGBoost machine learning model was employed to assess the importance of selected factors, revealing ALI as one of the top five variables influencing BC. Subgroup analysis identified a correlation between ALI, alcohol consumption, and menopausal status. Additionally, ALI levels were associated with decreased estradiol (E2) levels, increased total testosterone (TT)/E2 ratio, and TT/sex hormone-binding globulin (SHBG) ratio.

This study indicates a potential protective effect of ALI levels against breast cancer, possibly related to sex hormone disruption. The findings support the use of optimal therapeutic strategies for preventing breast cancer.

To assess the efficacy and safety of combining Programmed Death-1/Programmed Death-Ligand 1 (PD-1/L1) inhibitors with platinum-containing chemotherapy for treating late-stage Non-Small Cell Lung Cancer (NSCLC) patients who have developed resistance to Epidermal Growth Factor Receptor-Tyrosine Kinase Inhibitors (EGFR-TKIs).

A retrospective analysis was conducted at Baoji Traditional Chinese Medicine Hospital involving 133 patients with advanced NSCLC who had shown resistance to EGFR-TKIs and were treated from October 2018 to May 2021. The cohort was categorized into two groups: one treated with immune checkpoint inhibitors (ICIs) plus chemotherapy and antiangiogenic agents (ICIs+BCP group), and the other treated with ICIs alone (ICIs group). Baseline data collected included demographic factors, smoking status, PD-L1 Tumor Proportion Score (TPS), EGFR mutation, Eastern Cooperative Oncology Group (ECOG) score, and routine blood markers prior to second-line therapy. Computed Tomography (CT) scans were performed every two treatment courses to evaluate the treatment efficacy.

The ICIs+BCP group exhibited a statistically significant improvement in Overall Survival (OS) compared to the ICIs group (P=0.001). Cox survival analysis uncovered age (P=0.012), PD-L1 TPS expression (P<0.001), treatment regimen (P=0.006), Neutrophil-to-Lymphocyte Ratio (NLR) (P=0.024), and Platelet-to-Lymphocyte Ratio (PLR) (P=0.005) as independent factors influencing OS in patients with advanced NSCLC resistant to primary-line EGFR-TKI therapy. The nomogram model, based on these prognostic factors, exhibited Area Under the Curve (AUC) values of 0.823 and 0.769, indicating its predictive accuracy for 1-year and 2-year survival, respectively.

Combining ICIs with BCP prolongs OS in patients with NSCLC resistant to EGFR-TKIs. This study underscores the importance of personalized treatment plans and biomarker evaluations to improve outcomes in drug-resistant cases.

The team aimed to explore the possible functional significance of M6A regulation in Pan-programmed cell death (PCD) among patients with bladder cancer (BLCA). In BLCA patients, the analysis was conducted on the13 patterns of programmed cell death (PCD) and the regulation of M6A. Transcriptome, genomics, and clinical data were collected from TCGA-BLCA, GEO32548, and IMvigor210. Consensus clustering analysis, functional enrichment analysis, and other prognostic tools were used to validate the Pan-PCD. Finally, in vitro experiments and transcription sequencing were performed to understand the potential influence of the PI3K pathway on Pan-PCD in BLCA patients. Diverse PCD patterns were simultaneously activated, and M6A regulators exhibited significant variability in bladder malignant tissues. The machine learning algorithm established an 8-gene M6A-related Pan-PCD signature. This signature was validated in three independent datasets, and BLCA patients with higher risk scores had worse prognosis. An unsupervised clustering approach identified activated and suppressed Pan-PCD subgroups of BLCA patients, with distinct responses to immunotherapy and drug sensitivity. In addition, the PI3K pathway was identified as a key mechanism for various forms of programmed cell death, encompassing apoptosis, pyroptosis, autophagy, and cell death dependent on lysosomes. This research revealed that the Pan-PCD model was a more promising approach for BLCA patients under M6A regulation. A new signature from M6A-related Pan-PCD was proposed, with prognostic value for survival or drug sensitivity. The PI3K pathway was a key mechanism for multiple PCDs in BLCA patients.

Circulating tumor cells (CTCs) has shown important prognostic value in non-small cell lung cancer (NSCLC). However, the present low sensitivity of CTC capture technology restricts their clinical application. This study aims to explore the feasibility of combining the peripheral blood cell (PBC)-derived inflammation-based score with CTCs to increase the prognostic value of CTCs in NSCLC.

Sixty volunteers diagnosed with NSCLC were recruited. CTC count and six inflammation-based scores were examined and the association with progression-free survival (PFS) and overall survival (OS) was explored. The changes in the CTC counts before and after the immunotherapy were observed.

Multivariate analysis showed that CTCs >7 [hazard ratio (HR) =9.07; 95% confidence interval (CI): 3.68-22.37, P<0.001] and monocytes-to-lymphocytes ratio (MLR) > 0.2 (HR =3.07; 95% CI: 1.21-7.84; P=0.01) were associated with shorter OS and PFS in patients with NSCLC. Patients with CTCs >7 and MLR >0.2 had 12.30 times increased risk of death (P<0.001) and 6.10 times increased risk of disease progression (P=0.002) compared with those with CTCs ≤7 and MLR ≤0.2. Decreased CTC counts after immunotherapy were closely related to disease control (r=0.535, P=0.01).

CTCs and MLR are both independent risk factors for prognosis in patients with NSCLC. The combination of CTCs with MLR significantly increased the prognostic value of CTCs, which would contribute to stratification of NSCLC patients and providing precise treatment. Dynamic monitoring of CTCs efficiently shows the immunotherapy response in NSCLC.

Numerous biomarkers that reflect host status have been identified for patients with metastatic colorectal cancer (mCRC). However, there has been a paucity of biomarker studies that comprehensively indicate body composition, nutritional assessment, and systemic inflammation status. The advanced lung cancer inflammation index (ALI), initially introduced as a screening tool for patients with non-small-cell lung cancer in 2013, emerges as a holistic marker encompassing all body composition, nutritional status, and systemic inflammation status. The index is calculated by the simple formula: body mass index × albumin value / neutrophil-to-lymphocyte ratio. Given its accessibility in routine clinical practice, the ALI has exhibited promising clinical utility in prognosticating outcomes for patients with multiple types of cancer. In this review, we focus on the significance of host status and the clinical applicability of the ALI in the treatment and management of patients with malignancies, including mCRC. We also suggest its potential in guiding the formulation of treatment strategies against mCRC and outline future perspectives.

Rheumatoid arthritis (RA) is associated with significant mortality, which is primarily due to cardiovascular complications. Despite advancements in RA treatment, mortality rates remain high, highlighting the need for reliable prognostic markers. The advanced lung cancer inflammation index (ALI), which integrates inflammatory and nutritional biomarkers, has shown promise in predicting outcomes in various medical conditions. However, its role in RA prognosis remains unclear.

This study aimed to investigate the associations between the ALI and all-cause mortality, as well as cardiovascular mortality, in RA patients using data from the National Health and Nutrition Examination Survey (NHANES). A total of 1568 RA patients were included, and the ALI was calculated using body mass index (BMI), serum ALB, and the neutrophil-to-lymphocyte ratio. Comprehensive demographic, lifestyle, and metabolic data from the NHANES enabled adjustments for potential confounders. Multivariate Cox regression and sensitivity analyses were conducted to assess the associations between the ALI and mortality outcomes.

Our findings demonstrate an inverse association between the ALI and both all-cause and cardiovascular mortality in RA patients. Furthermore, a nonlinear relationship was observed, with mortality risk increasing significantly below a certain ALI threshold. Stratified analyses revealed a protective effect of the ALI across various demographic and clinical subgroups, underscoring its potential as a prognostic marker in patients with RA.

The ALI holds promise as a valuable prognostic marker for identifying high-risk individuals and guiding personalized management strategies for patients with RA. However, further validation in prospective studies is warranted to confirm its clinical utility. Nonetheless, the potential implications of the ALI for improving the prognosis of patients with RA underscore the importance of its continued investigation in clinical practice.

Background: The aim of this research is to establish and validate a prognostic model for predicting prognosis in non-small cell lung cancer (NSCLC) patients with bone metastases. Methods: Overall, 176 NSCLC patients with bone metastases were retrospectively evaluated in the research. We employed the LASSO-Cox regression method to select the candidate indicators for predicting the prognosis among NSCLC patients complicated with bone metastases. We employed the receiver operating characteristic curve (ROC) and the concordance index (C-index) to assess the discriminative ability. Results: Based on the LASSO-Cox regression analysis, 9 candidate indicators were screened to build the prognostic model. The prognostic model had a higher C-index in the training cohort (0.738, 95% CI: 0.680-0.796) and the validation cohort (0.660, 95% CI: 0.566-0.754) than the advanced lung cancer inflammation index (ALI). Furthermore, the AUCs of the 1-, 2-, and 3-year OS predictions for the prognostic model were higher than ALI in both cohorts. Kaplan-Meier curves and the estimated restricted mean survival time (RMST) values showed that the patients in the low-risk subgroup had the lower probabilities of cancer-specific mortality than high-risk subgroup. Conclusions: The prognostic model could provide clinicians with precise information and facilitate individualized treatment for patients with bone metastases.

Gastroenteropancreatic neuroendocrine carcinomas (GEP-NECs) constitute a rare and aggressive group of malignancies usually with widespread disease. There are limited studies on GEP-NECs, and therefore, we aim to acquire more information on the clinical features, treatment regimens, and prognosis.

Data from advanced GEP-NECs patients who had not previously received systemic treatment for advanced disease at The First Affiliated Hospital of Nanjing Medical University from 2010 to 2022 were retrospectively collected. Relationships between clinical-pathological features, treatment regimens, and prognosis were investigated using Kaplan-Meier curves and cox regression models.

A total of fifty-four patients were enrolled in the study. The median age was 65.5 years and 79.6% were male. At diagnosis, 51.9% and 3.7% of patients developed liver and brain metastasis respectively. Sixteen (29.6%) patients received chemotherapy according to primary site of tumor (PST), while thirty-eight (70.4%) were treated with etoposide-platinum (EP) regimen, which based on the first-line treatment of advanced small cell lung cancer (SCLC). No significant differences on progression-free survival (PFS) and response rate were observed between these two groups. Univariate survival analysis showed that liver metastasis, elevated baseline serum carcinoembryonic antigen, elevated baseline serum neuron-specific enolase, elevated baseline serum lactate dehydrogenase, and elevated baseline serum neutrophil-to-lymphocyte ratio (NLR) were associated with shorter PFS. After multivariate analysis, elevated NLR was the only factor that remained significantly associated with shorter PFS (P=0.01).

GEP-NECs are aggressive neoplasms, of which elevated NLR is proven to be an independent negative predictor. Treatment regimens based on PST are not inferior to regiments based on SCLC (EP) for GEP-NECs patients. Large-scale, prospective randomized controlled trials are required to establish the standard of care.

The inflammatory response and the nutritional status are associated with overall survival (OS) in patients with non-small cell lung cancer (NSCLC), but it is unclear which biomarkers are better suited to predict prognosis. This study sought to determine which of the commonly existing inflammatory and nutritional indicators best predicted the OS.

This study included 15 compound indicators based on inflammation or nutrition, with cutoff points obtained through the receiver operating characteristic (ROC) curve. Univariate and multivariate Cox proportional risk models were used to evaluate the relationship between these predictors and OS. Kaplan-Meier curves were used for survival analysis, and log-rank tests were used to compare differences between groups. The C-index was calculated to evaluate the predictive ability of the different indicators.

The study included 899 patients with NSCLC. In the univariate analysis, all 15 measures were significantly associated with the OS of patients (all p < 0.05). The results of the C-index analysis showed that the fibrinogen-to-albumin ratio (FAR), the systemic immune-inflammation index (SII), and the albumin-to-alkaline phosphatase ratio (AAPR) were the three indices with the best predictive performance. Among them, FAR (C-index = 0.639) had the best predictive power for OS in patients with NSCLC. In the different subgroups, FAR had the highest C-index in male, non-smoking, adenocarcinoma, and stage II patients. The C-index of the platelet-to-lymphocyte ratio (PLR) in female patients was the highest. SII was the highest in smokers, in those aged <65 and ≥65 years, and in stage III patients. The C-index of AAPR was the highest in non-adenocarcinomas. The C-index of the pan-immune-inflammation value (PIV) was the highest in stage I patients. In the multivariate Cox regression analysis, among FAR, SII, and AAPR, only FAR was an independent predictor of OS in patients with NSCLC. A high FAR was associated with a higher risk of death in patients with NSCLC (HR = 1.601, 95% CI = 1.028-2.495). In order to further evaluate the potential prognostic value of FAR, SII, and AAPR in patients with different stages, Cox regression analysis was performed for those with stage I-II and stage III NSCLC. The results showed that FAR was an independent prognostic factor for OS in patients with stage I-II NSCLC.

For all patients with NSCLC, the prognostic power of FAR was superior to that of other inflammatory and nutritional indicators.

Chronic Obstructive Pulmonary Disease (COPD) progression in the elderly is notably influenced by nutritional, immune, and inflammatory status. This study aimed to investigate the impact of adequate energy supply on these indicators in COPD patients.

COPD patients meeting specific criteria were recruited and categorized into energy-adequate and energy-deficient groups based on their energy supply. Comparable demographic factors such as age, gender, smoking and drinking history, COPD duration, inhaled drug classification, and home oxygen therapy application were observed. Notable differences were found in BMI and inhaled drug use between the two groups.

The energy-adequate group exhibited significant improvements in various health indicators, including lymphocyte count, hemoglobin, CRP, total cholesterol, prealbumin, albumin, PNI, SII, SIRI, CAR, and CONUT scores in the secondary auxiliary examination. These positive changes suggest a notable enhancement in nutritional, immune, and inflammatory status.

This research highlights the substantial benefits of adequate energy supply in elderly COPD patients. The observed improvements in nutritional, immune, and inflammatory markers underscore the importance of addressing energy needs to positively influence disease-related outcomes in this population. These findings have implications for developing targeted interventions to optimize the well-being of elderly individuals with COPD.

To investigate the prognostic significance of the advanced lung cancer inflammation index (ALI) in patients with operable non-small-cell lung carcinoma (NSCLC). By constructing the nomogram model, it can provide a reference for clinical work.

A total of 899 patients with non-small cell lung cancer who underwent surgery in our hospital between January 2017 and June 2021 were retrospectively included. ALI was calculated by body mass index (BMI) × serum albumin/neutrophil to lymphocyte ratio (NLR). The optimal truncation value of ALI was obtained using the receiver operating characteristic (ROC) curve and divided into two groups. Survival analysis was represented by the Kaplan-Meier curve. The predictors of Overall survival (OS) were evaluated by the Cox proportional risk model using single factor and stepwise regression multifactor analysis. Based on the results of multi-factor Cox proportional risk regression analysis, a nomogram model was established using the R survival package. The bootstrap method (repeated sampling 1 000 times) was used for internal verification of the nomogram model. The concordance index (C-index) was used to represent the prediction performance of the nomogram model, and the calibration graph method was used to visually represent its prediction conformity. The application value of the model was evaluated by decision curve analysis (DCA).

The optimal cut-off value of ALI was 70.06, and the low ALI group (ALI < 70.06) showed a poor survival prognosis. In multivariate analyses, tumor location, pathological stage, neuroaggression, and ALI were independently associated with operable NSCLC-specific survival. The C index of OS predicted by the nomogram model was 0.928 (95% CI: 0.904-0.952). The bootstrap self-sampling method (B = 1000) was used for internal validation of the prediction model, and the calibration curve showed good agreement between the prediction and observation results of 1-year, 2-year, and 3-year OS. The ROC curves for 1-year, 2-year, and 3-year survival were plotted according to independent factors, and the AUC was 0.952 (95% CI: 0.925-0.979), 0.951 (95% CI: 0.916-0.985), and 0.939 (95% CI: 0.913-0.965), respectively. DCA shows that this model has good clinical application value.

ALI can be used as a reliable indicator to evaluate the prognosis of patients with operable NSCLC, and through the construction of a nomogram model, it can facilitate better individualized treatment and prognosis assessment.

Lung cancer, the most common cause of cancer-related death, is diagnosed mostly in advanced stages, and 5-year survival is approximately 5.8%. It is critical to identify reliable prognostic factors to optimize treatment responses, guide therapeutic strategies and pave the way to new research. In this study, we aimed to investigate the strongest prognostic factors for advanced non-small cell lung cancer (NSCLC).

We retrospectively analyzed 278 patients with NSCLC. We evaluated the association between potential prognostic factors and overall survival (OS) times using Kaplan-Meier analysis and Cox regression analysis.

The median OS in all patients was 15.3 months. In univariate analysis, gender, histologic type, performance status, immunotherapy, radiotherapy, hemoglobin level, serum albumin, sodium-globulin ratio (SGR), neutrophil-lymphocyte ratio (NLR), systemic immune inflammation index (SII), hemoglobin-albumin-lymphocyte-platelet score (HALP), and advanced lung cancer index (ALI) were associated with survival. Models were established for multivariate analyses. In the models, NLR, SGR, HALP, immunotherapy, radiotherapy, and Eastern Cooperative Oncology Group (ECOG) performance status showed independent prognostic features (p < 0.001, p = 0.003, p = 0.002, p < 0.001, p = 0.010, and p = 0.025, respectively). In addition, in the subgroup analysis, prognostic indexes (NLR, SGR, and HALP) were found to have a prognostic effect on survival in multiple subgroups.

Pretreatment NLR, SGR, HALP, immunotherapy, radiotherapy, and ECOG performance status are independent prognostic factors for advanced NSCLC patients. These prognostic factors can be used in clinical practice as easily accessible, simple, and useful tools for clinicians.

The objective of the present study was to explore the correlation between the advanced lung cancer inflammation index (ALI) and in-hospital mortality among patients diagnosed with community-acquired pneumonia (CAP).

Data from the Medical Information Mart for Intensive Care-IV database were adopted to analyze the in-hospital mortality of ICU patients with CAP. Upon admission to the ICU, fundamental data including vital signs, critical illness scores, comorbidities, and laboratory results, were collected. The in-hospital mortality of all CAP patients was documented. Multivariate logistic regression (MLR) models and restricted cubic spline (RCS) analysis together with subgroup analyses were conducted.

This study includes 311 CAP individuals, involving 218 survivors as well as 93 nonsurvivors. The participants had an average age of 63.57 years, and the females accounted for approximately 45.33%. The in-hospital mortality was documented to be 29.90%. MLR analysis found that ALI was identified as an independent predictor for in-hospital mortality among patients with CAP solely in the Q1 group with ALI ≤ 39.38 (HR: 2.227, 95% CI: 1.026-4.831, P = 0.043). RCS analysis showed a nonlinear relationship between the ALI and in-hospital mortality, with a turning point at 81, and on the left side of the inflection point, a negative correlation was observed between ALI and in-hospital mortality (HR: 0.984, 95% CI: 0.975-0.994, P = 0.002). The subgroup with high blood pressure showed significant interaction with the ALI.

The present study demonstrated a nonlinear correlation of the ALI with in-hospital mortality among individuals with CAP. Additional confirmation of these findings requires conducting larger prospective investigations.

Energy metabolism is a hub of governing all processes at cellular and organismal levels such as, on one hand, reparable vs. irreparable cell damage, cell fate (proliferation, survival, apoptosis, malignant transformation etc.), and, on the other hand, carcinogenesis, tumor development, progression and metastazing versus anti-cancer protection and cure. The orchestrator is the mitochondria who produce, store and invest energy, conduct intracellular and systemically relevant signals decisive for internal and environmental stress adaptation, and coordinate corresponding processes at cellular and organismal levels. Consequently, the quality of mitochondrial health and homeostasis is a reliable target for health risk assessment at the stage of reversible damage to the health followed by cost-effective personalized protection against health-to-disease transition as well as for targeted protection against the disease progression (secondary care of cancer patients against growing primary tumors and metastatic disease). The energy reprogramming of non-small cell lung cancer (NSCLC) attracts particular attention as clinically relevant and instrumental for the paradigm change from reactive medical services to predictive, preventive and personalized medicine (3PM). This article provides a detailed overview towards mechanisms and biological pathways involving metabolic reprogramming (MR) with respect to inhibiting the synthesis of biomolecules and blocking common NSCLC metabolic pathways as anti-NSCLC therapeutic strategies. For instance, mitophagy recycles macromolecules to yield mitochondrial substrates for energy homeostasis and nucleotide synthesis. Histone modification and DNA methylation can predict the onset of diseases, and plasma C7 analysis is an efficient medical service potentially resulting in an optimized healthcare economy in corresponding areas. The MEMP scoring provides the guidance for immunotherapy, prognostic assessment, and anti-cancer drug development. Metabolite sensing mechanisms of nutrients and their derivatives are potential MR-related therapy in NSCLC. Moreover, miR-495-3p reprogramming of sphingolipid rheostat by targeting Sphk1, 22/FOXM1 axis regulation, and A2 receptor antagonist are highly promising therapy strategies. TFEB as a biomarker in predicting immune checkpoint blockade and redox-related lncRNA prognostic signature (redox-LPS) are considered reliable predictive approaches. Finally, exemplified in this article metabolic phenotyping is instrumental for innovative population screening, health risk assessment, predictive multi-level diagnostics, targeted prevention, and treatment algorithms tailored to personalized patient profiles-all are essential pillars in the paradigm change from reactive medical services to 3PM approach in overall management of lung cancers. This article highlights the 3PM relevant innovation focused on energy metabolism as the hub to advance NSCLC management benefiting vulnerable subpopulations, affected patients, and healthcare at large.

The online version contains supplementary material available at 10.1007/s13167-024-00357-5.

Nutritional intervention prior to the occurrence of cachexia will significantly improve the survival rate of lung cancer patients. This study aimed to establish an ensemble learning model based on anthropometry and blood indicators without information on body weight loss to identify the risk factors of cachexia for early administration of nutritional support and for preventing the occurrence of cachexia in lung cancer patients.

This multicenter study included 4,712 lung cancer patients. The least absolute shrinkage and selection operator (LASSO) method was used to obtain the key indexes. The characteristics excluded weight loss information, and the study data were randomly divided into a training set (70%) and a test set (30%). The training set was used to select the optimal model among 18 models and verify the model performance. A total of 18 machine learning models were evaluated to predict the occurrence of cachexia, and their performance was determined using area under the curve (AUC), accuracy, precision, recall, F1 score, and Matthews correlation coefficient (MCC).

Among 4,712 patients, 1,392 (29.5%) patients were diagnosed with cachexia based on the framework of Fearon et al. A 17-variable gradient boosting classifier (GBC) model including body mass index (BMI), feeding situation, tumor stage, neutrophil-to-lymphocyte ratio (NLR), and some gastrointestinal symptoms was selected among the 18 machine learning models. The GBC model showed good performance in predicting cachexia in the training set (AUC = 0.854, accuracy = 0.819, precision = 0.771, recall = 0.574, F1 score = 0.658, MCC = 0.549, and kappa = 0.538). The abovementioned indicator values were also confirmed in the test set (AUC = 0.859, accuracy = 0.818, precision = 0.801, recall = 0.550, F1 score = 0.652, and MCC = 0.552, and kappa = 0.535). The learning curve, decision boundary, precision recall (PR) curve, the receiver operating curve (ROC), the classification report, and the confusion matrix in the test sets demonstrated good performance. The feature importance diagram showed the contribution of each feature to the model.

The GBC model established in this study could facilitate the identification of cancer cachexia in lung cancer patients without weight loss information, which would guide early implementation of nutritional interventions to decrease the occurrence of cachexia and improve the overall survival (OS).

The role of inflammation is increasingly understood to have a central influence on therapeutic outcomes and prognosis in lung adenocarcinoma (LUAD). However, the detailed molecular divisions involved in inflammatory responses are yet to be fully elucidated. Our study identified two main inflammation-oriented LUAD grades: the inflammation-low (INF-low) and the inflammation-high (INF-high) subtypes. Both presented with unique clinicopathological features, implications for prognosis, and distinctive tumor microenvironment profiles. Broadly, the INF-low grade, marked by its dominant immunosuppressive tumor microenvironment, was accompanied by less favorable prognostic outcomes and a heightened prevalence of oncogenic mutations. In contrast, the INF-high grade exhibited more optimistic clinical trajectories, underscored by its immune-active environment. In addition, our efforts led to the conceptualization and empirical validation of an inflammation-centric predictive model with considerable predictive potency. Our study paves the way for a refined inflammation-centric LUAD classification and fosters a deeper understanding of tumor microenvironment intricacies.

To investigate the differences and correlation between blood inflammatory indexes such as monocytes (MONO), lymphocytes (LYM), haemoglobin (HGB), neutrophils (NEU), platelets (PLT), ultrasensitive C-reactive protein, albumin and platelet/lymphocyte ratio (PLR), NEU/LYM ratio (NLR), MONO/LYM ratio (MLR) and clinicopathologic characteristics of patients with non-small cell lung cancer (NSCLC).

187 patients with NSCLC who were first diagnosed in 2017-2023 and 102 with healthy check-ups during the same period (control group) were retrospectively selected as study subjects to compare the differences in inflammatory indexes between the two groups and the levels of inflammatory indexes in NSCLC patients with different clinicopathologic characteristics.

Correlation analysis between blood inflammatory indexes and clinicopathologic features in NSCLC group showed that C-reactive protein, CAR, and PLR values were different in different pathologic types (P<0.05). The values of NEU, MONO, C-reactive protein, MLR, NLR, CAR and albumin were different among various degrees of differentiation (P<0.05). There were differences in LYM, albumin, MLR, NLR, CAR, and C-reactive protein among M stage subgroups (P<0.05). Analysis of the efficacy of early diagnosis of non-small cell lung cancer has been shown, the AUC of NLR was 0.796, sensitivity of 0.679, specificity of 0.176, 95% CI=0.743-0.849 (P<0.001). The AUC of albumin was 0.977, the sensitivity was 0.941, the specificity was 0.941, and 95% CI was 0.959-0.994 (P<0.001).

Blood inflammatory indexes are closely associated with NSCLC and vary according to pathologic features. Blood inflammatory indices can predict tumor pathologic staging and guide treatment for patients with NSCLC.

Insufficient evidence existed about the prognostic role of the advanced lung cancer inflammation index (ALI) for gastric cancer patients who underwent curative resection. The aim of this study was to identify the predictive ability of ALI for survival after curative gastrectomy.

We retrospectively analyzed 328 gastric cancer patients who received curative gastrectomy from the database of Chongqing University Cancer Hospital, and investigated the prognostic role of the preoperative ALI compared with clinicopathological variables and other serum biomarkers, such as preoperative neutrophil-to-lymphocyte ratio (NLR), platelet to lymphocyte ratio (PLR) and Lymphocyte-monocyte ratio (LMR). To minimize intergroup differences, propensity score matching (PSM) analysis was employed. Additionally, we performed a meta-analysis of four cohort studies published up to October 2023 following the PRISMA guidelines.

In the overall cohort, patients in the low ALI group had a significantly worse overall survival compared to those in the high ALI group (P < 0.0001). Subgroup analysis identified that ALI maintained its prognostic significance across different subgroups. In addition, ROC analysis showed that ALI had a higher AUC value for 3-year overall survival compared to NLR, PLR, and LMR (0.576 vs. 0.573 vs. 0.557 vs. 0.557). Multivariate analysis indicated that ALI, other than other serum biomarkers, was an independent risk factor for decreased overall survival in GC patients following curative surgery (HR = 1.449; 95%CI: 1.028-2.045; P = 0.034). Consistently, PSM analysis supported all of these findings. The meta-analysis including 4 studies evaluating 2542 patients, confirmed the association between the low ALI and poor survival outcomes.

The preoperative ALI was an independent prognostic factor for survival in gastric cancer patients who underwent curative gastrectomy.

The aim of the study was to investigate the prognostic significance of the advanced lung cancer inflammation index (ALI) in patients with limited-stage small-cell lung cancer (LS-SCLC) undergoing definite chemo-radiotherapy (CRT). We included 87 patients with LS-SCLC from South Korea, treated between 2005 and 2019 with definite CRT. ALI was calculated using body mass index, serum albumin, and neutrophil-lymphocyte ratio. We categorized 38 patients into the high ALI group (ALI ≥ 44.3) and 48 into the low ALI group (ALI < 44.3). Patients in the high ALI group exhibited longer overall survival (OS) than patients in the low ALI group. In multivariate analysis, prophylactic cranial irradiation (hazard ratio [HR] = 0.366, 95% confidence interval [CI] 0.20-0.66, P = 0.0008), and high ALI (HR = 0.475, 95% CI 0.27-0.84, P = 0.0103) were identified as independent prognostic factors for predicting better OS. Notably, a high ALI score was particularly indicative of longer survival in patients treated with the combination of etoposide and cisplatin. In conclusion, this study demonstrated that a high pretreatment ALI was significantly associated with better OS in patients with LS-SCLC undergoing definite CRT. This suggests that ALI could be a useful tool for predicting prognosis and guiding chemotherapy regimen selections in clinical practice for LS-SCLC.

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 aim of the present study was to explore the relationship between tumor metabolic glycolysis and inflammatory or nutritional status in patients with advanced non-small cell lung cancer (NSCLC) who received programmed death-1 (PD-1) blockade. A total of 186 patients were registered in the present study. All of patients underwent 18F-FDG PET imaging before initial PD-1 blockade, and maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV) and total lesion glycolysis (TLG) were assessed as indicators of 18F-FDG uptake. As inflammatory and nutritional index, neutrophil to lymphocyte ratio (NLR), platelet to lymphocyte ration (PLR), systemic immune inflammation index (SII), prognostic nutritional index (PNI), advanced lung cancer inflammation index (ALI) and Glasgow prognostic score (GPS) were evaluated based on previous assessment. 18F-FDG uptake by MTV and TLG significantly correlated with the scores of NLR, PLR, SII, PNI and ALI, in addition to the level of albumin, lactate dehydrogenase, C-reactive protein, white blood cells, neutrophils, lymphocytes and body mass index. The count of NLR, PLR and SII was significantly higher in patients with <1 year overall survival (OS) compared with in those with ≥1 year OS, and that of PNI and ALI was significantly lower in those with <1 year OS compared with those with ≥1 year OS. High MTV under the high PLR, SII and low ALI were identified as significant factors for predicting the decreased PFS and OS after PD-1 blockade in a first-line setting. In second or more lines, high MTV was identified as a significant prognostic predictor regardless of the levels of PLR, SII, ALI and GPS. In conclusion, metabolic tumor glycolysis determined by MTV was identified as a predictor for the outcome of PD-1 blockade under the high inflammatory and low nutritional conditions, in particular, when treated with a first-line PD-1 blockade. A high MTV under high PLR and SII and low ALI in the first-line setting could be more predictive of ICI treatment than other combinations.