A Novel Inflammatory-Nutritional Prognostic Scoring System for Patients with Early-Stage Breast Cancer

A cross-sectional study investigating the relationship between urinary albumin creatinine ratio and abdominal aortic calcification in adults

Introduction

The presence of abdominal aortic calcification (AAC) is strongly linked to the development of atherosclerosis and the incidence of morbidity and mortality related to cardiovascular diseases (CVD). Urinary albumin creatinine ratio (UACR) was found related with the increased risk of CVD. The aim of this study is to explore the relationship between the UACR and severe AAC (SAAC).

Methods and Results

This study included a total of 2,379 individuals aged over 40 years, and their information was obtained from the National Health and Nutrition Examination Survey conducted (NHANES) in 2013-2014. The measurement of AAC was conducted through dual-energy x-ray absorptiometry and assessed using the Kauppila scoring system. SAAC was characterized by a Kauppila score of 6 or higher. Multivariate regression models were used to analyze the relationship between UACR level and SAAC, with covariate adjustment. In the completely adapted model, the top third subgroup exhibits increased likelihood of SAAC (odds ratio 1.50; 95%CI: 0.98, 2.29; p = 0.030) in contrast to the bottom third subgroup. The subgroup analyses revealed a more pronounced correlation among the older participants (p-value for interaction = 0.013).

Discussion

In the United States, SAAC was more likely to occur in adults who had a higher probability of UACR. The use of UACR has the potential to be a valuable method for forecasting the likelihood of SAAC.

Systemic inflammation mediates the association between environmental tobacco smoke and depressive symptoms: A cross-sectional study of NHANES 2009-2018

BACKGROUND

Depression is associated with both environmental tobacco smoke (ETS) and inflammation. However, whether systemic inflammation mediates the ETS-depression relationship is unclear.

METHODS

We analyzed 19,612 participants from the 2009-2018 National Health and Nutrition Examination Survey (representing approximately 206,284,711 USA individuals), utilizing data of depressive symptoms (assessed by Patient Health Questionnaire-9), blood cotinine level (an ETS biomarker), dietary inflammatory index (DII, assessed by 24-h dietary recall) and inflammation, represented by immune-inflammation index (SII) and systemic inflammation response index (SIRI).

RESULTS

Weighted multivariable logistic regression showed that a higher blood cotinine level is significantly associated with a higher depressive symptoms risk (OR = 1.79, 1.35-2.38). After adjusting for covariates, the effect in smokers (OR = 1.220, 95 % CI: 1.140-1.309) is larger than that in non-smokers (OR = 1.150, 95 % CI: 1.009-1.318). Compared to the lowest level, depressive symptoms risks in participants with the highest level of SII, SIRI and DII are 19 % (OR = 1.19, 1.05-1.35), 15 % (OR = 1.15, 1.01-1.31) and 88 % (OR = 1.88, 1.48-2.39) higher, respectively. Weighted linear regression demonstrated positive correlations of SII (β = 0.004, 0.001-0.006), SIRI (β = 0.009, 0.005-0.012) and DII (β = 0.213, 0.187-0.240) with blood cotinine level. Restricted cubic splines model showed a linear dose-response relationship between blood cotinine and depressive symptoms (Pnon-linear = 0.410), with decreasing risk for lower DII. And SII and SIRI respectively mediate 0.21 % and 0.1 % of the association between blood cotinine and depressive symptoms.

LIMITATION

Cross-sectional design, and lack of medication data for depression.

CONCLUSIONS

Positive association of ETS (blood cotinine) with depressive symptoms risk is partly mediated by systemic inflammation, and anti-inflammatory diet could be beneficial.

A Novel Inflammatory-Nutritional Prognostic Scoring System for Patients with Diffuse Large B Cell Lymphoma

Purpose

This study aimed to examine the predictive ability of inflammatory and nutritional markers and further establish a novel inflammatory nutritional prognostic scoring (INPS) system.

Patients and Methods

We collected clinicopathological and baseline laboratory data of 352 patients with DLBCL between April 2010 and January 2023 at the First affiliated hospital of Ningbo University. Eligible patients were randomly divided into training and validation cohorts (n = 281 and 71, respectively) in an 8:2 ratio. We used the least absolute shrinkage and selection operator (LASSO) Cox regression model to determine the most important factors among the eight inflammatory-nutritional variables. The impact of INPS on OS was evaluated using the Kaplan-Meier curve and the Log rank test. A prognostic nomogram was developed based on the multivariate Cox regression method. Then, we used the concordance index (C-index), calibration plot, and time-dependent receiver operating characteristic (ROC) analysis to evaluate the prognostic performance and predictive accuracy of the nomogram.

Results

Seven inflammatory-nutritional biomarkers, including neutrophil-lymphocyte ratio (NLR), prognostic nutritional index (PNI), body mass index (BMI), monocyte-lymphocyte ratio (MLR), prealbumin, C reactive protein, and D-dimer were selected using the LASSO Cox analysis to construct INPS, In the multivariate analysis, IPI-High-intermediate group, IPI-High group, high INPS were independently associated with OS, respectively. The prognostic nomogram for overall survival consisting of the above two indicators showed excellent discrimination. The C-index for the nomogram was 0.94 and 0.95 in the training and validation cohorts. The time-dependent ROC curves showed that the predictive accuracy of the nomogram for OS was better than that of the NCCN-IPI system.

Conclusion

The INPS based on seven inflammatory-nutritional indexes was a reliable and convenient predictor of outcomes in DLBCL patients.

Risk stratification model integrating nutritional and inflammatory factors for predicting 1-year mortality after valvular heart surgery: a retrospective cohort study

INTRODUCTION

Existing risk-scoring systems for cardiac surgery include only standard preoperative factors without considering nutritional and inflammatory status or intraoperative factors. The objective of this study was to develop a comprehensive prediction model for mortality incorporating nutritional, inflammatory, and perioperative factors in patients undergoing valvular heart surgery.

MATERIALS AND METHODS

In this retrospective review of 2046 patients who underwent valvular heart surgery, Cox and LASSO regression analyses were performed to identify independent prognostic factors for 1-year postoperative mortality among various perioperative factors known to affect prognosis, including objective nutritional and inflammatory indices. A novel nomogram model incorporating selected prognostic factors was developed, and its discrimination ability was evaluated using the C-index. The model was validated in internal and external cohorts.

RESULTS

The 1-year mortality rate after valvular heart surgery was 5.1% (105 of 2046 patients) and was significantly associated with several preoperative objective inflammatory and nutritional indices. Cox and LASSO analyses identified the following five independent prognostic factors for mortality: monocyte-to-lymphocyte ratio (an objective inflammatory index), EuroSCORE II, Controlling Nutritional Status score, cardiopulmonary bypass time, and number of erythrocyte units transfused intraoperatively. The nomogram model incorporating these five factors had a C-index of 0.834 (95% CI: 0.791-0.877), which was higher than that of EuroSCORE II alone (0.744, 95% CI: 0.697-0.791) ( P <0.001). The nomogram achieved good discrimination ability, with C-indices of 0.836 (95% CI: 0.790-0.878) and 0.727 (95% CI: 0.651-0.803) in the internal and external validation cohorts, respectively, and showed well-fitted calibration curves.

CONCLUSIONS

A nomogram model incorporating five inflammatory, nutritional, and perioperative factors, as well as EuroSCORE II, was a better predictor of 1-year mortality after valvular heart surgery than EuroSCORE II alone, with good discrimination and calibration power for predicting mortality in both internal and external validation cohorts.

Pan-Immune-Inflammatory Value in Patients with Non-Small-Cell Lung Cancer Undergoing Neoadjuvant Immunochemotherapy

Background

We aimed to investigate the predictive value of a systematic serum inflammation index, pan-immune-inflammatory value (PIV), in pathological complete response (pCR) of patients treated with neoadjuvant immunotherapy to further promote ideal patients' selection.

Methods

The clinicopathological and baseline laboratory information of 128 NSCLC patients receiving neoadjuvant immunochemotherapy between October 2019 and April 2022 were retrospectively reviewed. We performed least absolute shrinkage and selection operator (LASSO) algorithm to screen candidate serum biomarkers for predicting pCR, which further entered the multivariate logistic regression model to determine final biomarkers. Accordingly, a diagnostic model for predicting individual pCR was established. Kaplan-Meier method was utilized to estimate curves of disease-free survival (DFS), and the Log rank test was analyzed to compare DFS differences between patients with and without pCR.

Results

Patients with NSCLC heterogeneously responded to neoadjuvant immunotherapy, and those with pCR had a significant longer DFS than patients without pCR. Through LASSO and the multivariate logistic regression model, PIV was identified as a predictor for predicting pCR of patients. Subsequently, a diagnostic model integrating with PIV, differentiated degree and histological type was constructed to predict pCR, which presented a satisfactory predictive power (AUC, 0.736), significant agreement between actual and our nomogram-predicted pathological response.

Conclusion

Baseline PIV was an independent predictor of pCR for NSCLC patients receiving neoadjuvant immunochemotherapy. A significantly longer DFS was achieved in patients with pCR rather than those without pCR; thus, the PIV-based diagnostic model might serve as a practical tool to identify ideal patients for neoadjuvant immunotherapeutic guidance.

A single center retrospective study assessing the prognostic significance of pre-treatment neutrophil/lymphocyte ratio in locally advanced nasopharyngeal carcinoma

Background

In light of the growing evidence suggesting the impact of inflammatory parameters on the survival of individuals with cancer, this research assessed the prognostic significance of the neutrophil-to-lymphocyte ratio (NLR) in individuals diagnosed with locally advanced nasopharyngeal carcinoma (NPC) prior to undergoing intensity-modulated radiation therapy (IMRT).

Methods

A total of 163 individuals diagnosed with locally advanced NPC treated with IMRT at our hospital between January 2012 and December 2017 were included in this research. For each patient, the absolute counts of neutrophils and lymphocytes were recorded, and the NLR was calculated at the first diagnosis. To determine the optimal cut-off values for NLR, receiver operating characteristic (ROC) curve analysis was conducted. The effects of the determined cut-off value on local failure-free survival (LFFS), overall survival (OS), progression-free survival (PFS), and distant failure-free survival (DFFS) were evaluated employing the Cox regression model.

Results

The median follow-up duration for the individuals in this study was 15 months (ranging from 6 to 79 months). According to the determined NLR cut-off value of 3.27, individuals were classified into two groups (high NLR and low NLR). Individuals in the high-NLR group had remarkably poorer 3-year OS (62.8% vs. 91.7%), PFS (51.4% vs. 82.4%), and DFFS (70.7% vs. 89.6%) compared to the low-NLR group. Furthermore, the outcomes of univariate and multivariate survival analyses revealed that NLR served as an independent predictor of DFFS (HR: 2.81, 95% CI: 1.195-6.608, P=0.018), OS (HR: 3.1, 95% CI: 1.211-7.935, P=0.018), and PFS (HR: 2.21, 95% CI: 1.133-4.292, P=0.02).

Conclusions

Elevated NLR exhibited a significant correlation with reduced OS, DFFS, and PFS. These findings suggest that NLR holds promise as a cost-effective and reliable marker for the prediction of clinical outcomes among patients with locoregionally advanced nasopharyngeal carcinoma (LANPC). Furthermore, incorporating NLR into clinical decision-making regarding LANPC treatment strategies may contribute to a more targeted approach aimed at reducing the risk of distant failure.

Prognostic significance of the novel nutrition-inflammation marker of lymphocyte-C-reactive protein ratio in patients with nasopharyngeal carcinoma receiving concurrent chemoradiotherapy

Background

Recent studies indicate that the novel lymphocyte-C-reactive protein ratio (LCR) is strongly associated with the survival of various tumors, but its prognostic value in nasopharyngeal carcinoma (NPC) is understudied. This study aimed to explore the relationship between LCR and overall survival (OS) in NPC and develop a predictive model.

Methods

A total of 841 NPC patients who received concurrent chemoradiotherapy (CCRT) between January 2010 and December 2014 were retrospectively enrolled and randomly divided into a training cohort (n = 589) and a validation cohort (n = 252), and 122 patients between January 2015 and March 2015 were included as an additional validation cohort. Univariate and multivariate Cox analyses were performed to identify variables associated with OS and construct a predictive nomogram. The predictive accuracy of the nomogram was evaluated and independently validated.

Results

The LCR score differentiated NPC patients into two groups with distinct prognoses (HR = 0.53; 95% CI: 0.32-0.89, P = 0.014). Multivariate analysis showed that age, T stage, N stage, EBV-DNA status, and LCR score were independently associated with OS, and a predictive nomogram was developed. The nomogram had a good performance for the prediction of OS [C-index = 0.770 (95% CI: 0.675-0.864)]. and outperformed the traditional staging system [C-index = 0.589 (95% CI: 0.385-0.792)]. The results were internally and additionally validated using independent cohorts.

Conclusion

The pretreatment LCR could independently predict the overall survival in NPC patients. A novel LCR-based prognostic model of an easy-to-use nomogram was established, and it outperformed the conventional staging system in terms of predictive power. Further external verification remains necessary.

Prognostic Significance of Preoperative Lactate Dehydrogenase to Albumin Ratio in Breast Cancer: A Retrospective Study

Purpose

We explored the relationship between platelet count to lymphocyte count ratio (PLR), monocyte count to lymphocyte count ratio (MLR), lactate dehydrogenase to albumin ratio (LAR), and long-term survival in patients with breast cancer.

Patients and Methods

We retrospectively analyzed the clinical and follow-up data of 134 patients with breast cancer. The receiver operating characteristic curve (ROC curve) was used to distinguish between the low and high ratio groups. The chi-square test or Fisher's exact test was used to calculate the differences among the investigation factors. The Kaplan-Meier method was used to draw the survival curves. Log rank test was used for univariate analysis, and Cox proportional hazards regression model was used for multivariate analysis. A P value of <0.05 was considered statistically significant.

Results

The median follow-up time was 45 months. The PFS rates in the low group (LAR≤3.4066) at 18 months, 24 months, and 36 months were 100%, 100%, and 97.6%, and those in the high group (LAR > 3.4066) were 97.7%, 94.3%, and 87.3%, respectively. LAR was associated with Age (P=0.002) and BMI (body mass index) (P=0.002). Univariate analysis showed that Tumor size (P=0.027), Node positivity (P<0.001), TNM (tumor-node-metastasis) stage (P<0.001), PLR (P=0.034), MLR (P=0.038), and LAR (P=0.035) were significantly associated with PFS (progression-free survival) in breast cancer patients. Multivariate analysis showed that Node positivity (P<0.001) and LAR (P=0.035) were associated with PFS, while PLR and MLR were not independent prognostic indicators.

Conclusion

Preoperative high LAR will be an independent predictor of prognosis in patients with breast cancer.

Systemic Immune Inflammation Index (SII), System Inflammation Response Index (SIRI) and Risk of All-Cause Mortality and Cardiovascular Mortality: A 20-Year Follow-Up Cohort Study of 42,875 US Adults

BACKGROUND AND AIM

Chronic low-grade inflammation is associated with various health outcomes, including cardiovascular diseases (CVDs) and cancers. Systemic immune inflammation index (SII) and system inflammation response index (SIRI) have lately been explored as novel prognostic markers for all-cause mortality and cardiovascular mortality. However, studies on prediction value in nationwide representative population are scarce, which limit their generalization. To bridge the knowledge gap, this study aims to prospectively assess the association of SII, SIRI with all-cause mortality and cardiovascular mortality in the National Health and Nutrition Examination Survey (NHANES).

METHODS

From 1999 to 2018, 42,875 adults who were free of pregnancy, CVDs (stroke, acute coronary syndrome), cancers, and had follow-up records and participated in the NHANES were included in this study. SII and SIRI were quantified by calculating the composite inflammation indicators from the blood routine. To explore the characteristics of the population in different SII or SIRI levels, we divided them according to the quartile of SII or SIRI. The associations between SII, SIRI, and all-cause mortality and cardiovascular mortality events were examined using a Cox regression model. To investigate whether there was a reliable relationship between these two indices and mortalities, we performed subgroup analysis based on sex and age.

RESULTS

A total of 42,875 eligible individuals were enrolled, with a mean age of 44 ± 18 years old. During the follow-up period of up to 20 years, 4250 deaths occurred, including 998 deaths from CVDs. Cox proportional hazards modeling showed that adults with SII levels of >655.56 had higher all-cause mortality (hazard ratio [HR], 1.29; 95% confidence interval [CI], 1.18-1.41) and cardiovascular mortality (HR, 1.33; 95% CI, 1.11-1.59) than those with SII levels of <335.36. Adults with SIRI levels of >1.43 had higher risk of all-cause (HR, 1.39; 95% CI, 1.26-1.52) and cardiovascular death (HR, 1.39; 95% CI, 1.14-1.68) than those with SIRI levels of <0.68. In general population older than 60 years, the elevation of SII or SIRI was associated with the risk of all-cause death.

CONCLUSION

Two novel inflammatory composite indices, SII and SIRI, were closely associated with cardiovascular death and all-cause death, and more attention should be paid to systemic inflammation to provide better preventive strategies.

A Novel Inflammatory and Nutritional Prognostic Scoring System for Nonpathological Complete Response Breast Cancer Patients Undergoing Neoadjuvant Chemotherapy

Background

It has been demonstrated that inflammatory and nutritional variables are associated with poor breast cancer survival. However, some studies do not include these variables due to missing data. To investigate the predictive potential of the INPS, we constructed a novel inflammatory-nutritional prognostic scoring (INPS) system with machine learning.

Methods

This retrospective analysis included 249 patients with malignant breast tumors undergoing neoadjuvant chemotherapy (NAC). After comparing seven potent machine learning models, the best model, Xgboost, was applied to construct an INPS system. K-M survival curves and the log-rank test were employed to determine OS and DFS. Univariate and multivariate analyses were carried out with the Cox regression model. Additionally, we compared the predictive power of INPS, inflammatory, and standard nutritional variables using the Z test.

Results

After comparing seven machine learning models, it was determined that the XGBoost model had the best OS and DFS performance (AUC = 0.865 and 0.771, respectively). For overall survival (OS, cutoff value = 0.3917) and disease-free survival (cutoff value = 0.4896), all patients were divided into two groups by the INPS. Those with low INPS had higher 5-year OS and DFS rates (77.2% vs. 50.0%, P < 0.0001; and 59.6% vs. 32.1%, P < 0.0001, respectively) than patients with high INPS. For OS and DFS, the INPS exhibited the highest AUC compared to the other inflammatory and nutritional variables (AUC = 0.615, P = 0.0003; AUC = 0.596, P = 0.0003, respectively).

Conclusion

The INPS was an independent predictor of OS and DFS and exhibited better predictive ability than BMI, PNI, and MLR. For patients undergoing NAC for nonpCR breast cancer, INPS was a crucial and comprehensive biomarker. It could also forecast individual survival in breast cancer patients with low HER-2 expression.

Prognostic prediction by a novel integrative inflammatory and nutritional score based on least absolute shrinkage and selection operator in esophageal squamous cell carcinoma

BACKGROUND

This study aimed to establish and validate a novel predictive model named integrative inflammatory and nutritional score (IINS) for prognostic prediction in esophageal squamous cell carcinoma (ESCC).

MATERIALS AND METHODS

We retrospectively recruited 494 pathologically confirmed ESCC patients with surgery and randomized them into training (n = 346) or validation group (n = 148). The least absolute shrinkage and selection operator (LASSO) Cox proportional hazards (PH) regression analysis was initially used to construct a novel predictive model of IINS. The clinical features and prognostic factors with hazard ratio (HRs) and 95% confidence intervals (CIs) grouped by IINS were analyzed. Nomogram was also established to verify the prognostic value of IINS.

RESULTS

According to the LASSO Cox PH regression analysis, a novel score of IINS was initially constructed based on 10 inflammatory and nutritional indicators with the optimal cut-off level of 2.35. The areas under the curve (AUCs) of IINS regarding prognostic ability in 1-year, 3-years, and 5-years prediction were 0.814 (95% CI: 0.769-0.854), 0.748 (95% CI: 0.698-0.793), and 0.792 (95% CI: 0.745-0.833) in the training cohort and 0.802 (95% CI: 0.733-0.866), 0.702 (95% CI: 0.621-0.774), and 0.748 (95% CI: 0.670-0.816) in the validation cohort, respectively. IINS had the largest AUCs in the two cohorts compared with other prognostic indicators, indicating a higher predictive ability. A better 5-years cancer-specific survival (CSS) was found in patients with IINS ≤ 2.35 compared with those with IINS > 2.35 in both training cohort (54.3% vs. 11.1%, P < 0.001) and validation cohort (53.7% vs. 18.2%, P < 0.001). The IINS was then confirmed as a useful independent factor (training cohort: HR: 3.000, 95% CI: 2.254-3.992, P < 0.001; validation cohort: HR: 2.609, 95% CI: 1.693-4.020, P < 0.001). Finally, an IINS-based predictive nomogram model was established and validated the CSS prediction (training set: C-index = 0.71 and validation set: C-index = 0.69, respectively).

CONCLUSION

Preoperative IINS is an independent predictor of CSS in ESCC. The nomogram based on IINS may be used as a potential risk stratification to predict individual CSS and guide treatment in ESCC with radical resection.

Plasma metabolomics analyses highlight the multifaceted effects of noise exposure and the diagnostic power of dysregulated metabolites for noise-induced hearing loss in steel workers

Noise exposure can lead to various kinds of disorders. Noise-induced hearing loss (NIHL) is one of the leading disorders confusing the noise-exposed workers. It is essential to identify NIHL markers for its early diagnosis and new therapeutic targets for its treatment. In this study, a total of 90 plasma samples from 60 noise-exposed steel factory male workers (the noise group) with (NIHL group, n = 30) and without NIHL (non-NIHL group, n = 30) and 30 male controls without noise exposure (control group) were collected. Untargeted human plasma metabolomic profiles were determined with HPLC-MS/MS. The levels of the metabolites in the samples were normalized to total peak intensity, and the processed data were subjected to multivariate data analysis. The Wilcoxon test and orthogonal partial least square-discriminant analysis (OPLS-DA) were performed. With the threshold of p &lt; 0.05 and the variable importance of projection (VIP) value &gt;1, 469 differential plasma metabolites associated with noise exposure (DMs-NE) were identified, and their associated 58 KEGG pathways were indicated. In total, 33 differential metabolites associated with NIHL (DMs-NIHL) and their associated 12 KEGG pathways were identified. There were six common pathways associated with both noise exposure and NIHL. Through multiple comparisons, seven metabolites were shown to be dysregulated in the NIHL group compared with the other two groups. Through LASSO regression analysis, two risk models were constructed for NIHL status predication which could discriminate NIHL from non-NIHL workers with the area under the curve (AUC) values of 0.840 and 0.872, respectively, indicating their efficiency in NIHL diagnosis. To validate the results of the metabolomics, cochlear gene expression comparisons between susceptible and resistant mice in the GSE8342 dataset from Gene Expression Omnibus (GEO) were performed. The immune response and cell death-related processes were highlighted for their close relations with noise exposure, indicating their critical roles in noise-induced disorders. We concluded that there was a significant difference between the metabolite’s profiles between NIHL cases and non-NIHL individuals. Noise exposure could lead to dysregulations of a variety of biological pathways, especially immune response and cell death-related processes. Our results might provide new clues for noise exposure studies and NIHL diagnosis.