Deciphering the folate puzzle: Unraveling the impact of genetic variations and metabolites on cancer risk.

The C677T polymorphism in the MTHFR gene and its role in folate metabolism, impacting serum folate metabolites like THF and 5-MTHF, is a critical but underexplored area in cancer research. This nested case-control study utilized data from CHHRS, involving 87,492 hypertensive adults without prior cancer. During a median of 2.02 years, we identified 1332 cancer cases and matched controls based on age, sex, and residency. Serum levels of folate, THF, and 5-MTHF were measured, and the MTHFR C677T gene polymorphism was considered. Statistical analyses included restricted cubic spline regression and conditional logistic regression models. Serum THF levels were inversely associated with overall cancer risk (ORper SD = 0.90, 95% CI = 0.82-0.99), while 5-MTHF levels showed a negative association in the general cohort (ORQ3 vs. Q1 = 0.76, 95% CI = 0.60-0.96; ORQ4 vs. Q1 = 0.75, 95% CI = 0.58-0.98) and in individuals with MTHFR C677T (CC + CT) polymorphism (ORper SD = 0.87, 95% CI = 0.77-0.99; ORQ4 VS. Q1 = 0.79, 95% CI = 0.61-0.98), but a positive association in the MTHFR C677T (TT) subgroup (ORper SD = 1.89, 95% CI = 1.02-3.72; ORQ4 VS. Q1 = 2.17, 95% CI = 1.06-8.21). The impact of folate, THF, and 5-MTHF on cancer risk varied significantly across different cancer types and MTHFR C677T genotypes. This study provides novel insights into the variable effects of folate and its metabolites on cancer risk, influenced by genetic factors like the MTHFR C677T polymorphism and cancer type.

The association of metabolic syndrome score trajectory patterns with risk of all cancer types.

BACKGROUND: Metabolic syndrome (MetS) elevates cancer risk. However, a single MetS assessment does not fully reveal the long-term association with cancer. Inflammation, alongside MetS, could synergistically expedite both the onset and advancement of cancer. This study aims to investigate MetS score trajectories and cancer risk in a large, prospective cohort study. METHODS: The authors prospectively examined the relationship between MetS score trajectory patterns and new-onset cancer in 44,115 participants. Latent mixture modeling was used to identify the MetS score trajectories. Cox proportional hazards regression models were used to evaluate the association between MetS score trajectory patterns and the risk of overall and site-specific cancers. RESULTS: Four MetS score trajectory patterns were identified: low-stable (n = 4657), moderate-low (n = 18,018), moderate-high (n = 18,288), and elevated-increasing (n = 3152). Compared to participants with a low-stable trajectory pattern, the elevated-increasing trajectory pattern was associated with an elevated risk of overall (hazard ratio [HR], 1.27; 95% confidence interval [CI], 1.04-1.55), breast (HR, 2.11; 95% CI, 1.04-4.34), endometrial (HR, 3.33; 95% CI, 1.16-6.77), kidney (HR, 4.52; 95% CI, 1.17-10.48), colorectal (HR, 2.54; 95% CI, 1.27-5.09), and liver (HR, 1.61; 95% CI, 1.09-4.57) cancers. Among participants with chronic inflammation (C-reactive protein levels ≥3 mg/L), the elevated-increasing trajectory pattern was significantly associated with subsequent breast, endometrial, colorectal, and liver cancers. CONCLUSIONS: Trajectories of MetS scores are associated with the occurrence of cancers, especially breast, endometrial, kidney, colorectal, and liver cancers, emphasizing the importance of long-term monitoring and evaluation of MetS. PLAIN LANGUAGE SUMMARY: The association between long-term elevated metabolic syndrome (MetS) scores and a heightened risk of various cancers is a pivotal finding of our study. Our research further indicates that individuals with MetS, particularly when coupled with chronic inflammation, are at an increased risk of cancer. We propose that sustained monitoring and management of MetS could be beneficial in reducing cancer risk.

Geriatric Nutritional Risk Index Combined with Calf Circumference Can be a Good Predictor of Prognosis in Patients Undergoing Surgery for Gastric or Colorectal Cancer.

OBJECTIVES: To explore the effect of combined hematological and physical measurement indicators on the prognosis of patients undergoing surgery for gastric or colorectal cancer and to screen for the best prognostic indicators. INTRODUCTION: Gastric and colorectal cancer is a widespread health concern worldwide and one of the major contributors to cancer-related death. The hematological and physical measurement indicators have been shown to associate with the prognosis of patients undergoing surgery for gastric or colorectal cancer, respectively, but it is still unclear whether the combination of the two can reflect the prognosis more effectively. METHODS: Thirteen hematological indicators and 5 physical measurement indicators were selected in this study, and the most promising ones were screened using LASSO regression. Then, the best prognostic indicators were selected by time-ROC curves. Survival curves were constructed using the Kaplan-Meier method, and the effects of hematological and physical measurement indicators on the prognosis of patients undergoing surgery for gastric or colorectal cancers were evaluated by Cox proportional risk regression analysis. In addition, the relationship between hematological and physical measurement indicators on secondary outcomes, including length of stay, hospitalization costs, intensive care unit (ICU) admission, and patients' subjective global assessment scores (PGSGA), was explored. RESULTS: After initial screening, among the hematological indicators, the geriatric nutritional risk index (GNRI) showed the highest mean area under the curve (AUC) values. Among body measures, calf circumference (CC) showed the highest mean AUC value. Further analyses showed that the combination of combined nutritional prognostic index (GNRI) and calf circumference (CC) (GNRI-CC) had the best performance in predicting the prognosis of patients undergoing surgery for gastric or colorectal cancers. Low GNRI, low CC, and low GNRI-low CC increased the risk of death by 44%, 48%, and 104%, respectively. Sensitivity analyses showed the same trend. In addition, low GNRI-low CC increased the risk of malnutrition by 17%. CONCLUSION: This study emphasizes that a combination of blood measures and body measures is essential to accurately assess the prognosis of patients undergoing surgery for gastric or colorectal cancers. The GNRI-CC is a good prognostic indicator and can also assess the risk of possible malnutrition.

Association of systemic inflammation with the obesity paradox in cancer: results from multi-cohort studies.

AIMS: This study aimed to explore whether the obesity paradox exists in overall and specific cancers and to investigate the role of systemic inflammation in the obesity paradox. METHODS: The Cox proportional hazard model was used to explore the relationship between body mass index (BMI) and all-cause mortality. The mediated effect was used to investigate the proportion of systemic inflammation mediating the relationship between BMI and cancer survival risk. RESULTS: The survival probability showed a step-like increase with an increase in BMI regardless of pathological stage. Approximately 10.8%-24.0% of the overall association between BMI and all-cause mortality in cancer was mediated by inflammation. In the internal validation, we found evidence of the obesity paradox in all body composition obtained using BIA, with inflammation remaining an important mediating factor. Furthermore, we also validated the existence of the obesity paradox of cancer in NHANES. Systemic inflammation remains an important factor in mediating the association between BMI and prognosis in cancer patients. CONCLUSIONS: The obesity paradox is prevalent in most cancers, except for hepatic biliary cancer and breast cancer. Inflammation may be one of the true features of the obesity paradox in cancer.

Machine learning to identify precachexia and cachexia: a multicenter, retrospective cohort study.

BACKGROUND: Detection of precachexia is important for the prevention and treatment of cachexia. However, how to identify precachexia is still a challenge. OBJECTIVE: This study aimed to detect cancer precachexia using a simple method and distinguish the different characteristics of precachexia and cachexia. METHODS: We included 3896 participants in this study. We used all baseline characteristics as input variables and trained machine learning (ML) models to calculate the importance of the variables. After filtering the variables based on their importance, the models were retrained. The best model was selected based on the receiver operating characteristic value. Subsequently, we used the same method and process to identify patients with precachexia in a noncachexia population using the same method and process. RESULTS: Participants in this study included 2228 men (57.2%) and 1668 women (42.8%), of whom 471 were diagnosed with precachexia, 1178 with cachexia, and the remainder with noncachexia. The most important characteristics of cachexia were eating changes, arm circumference, high-density lipoprotein (HDL) level, and C-reactive protein albumin ratio (CAR). The most important features distinguishing precachexia were eating changes, serum creatinine, HDL, handgrip strength, and CAR. The two logistic regression models for screening for cachexia and diagnosing precachexia had the highest area under the curve values of 0.830 and 0.701, respectively. Calibration and decision curves showed that the models had good accuracy. CONCLUSION: We developed two models for identifying precachexia and cachexia, which will help clinicians detect and diagnose precachexia.

Remnant cholesterol is an effective biomarker for predicting survival in patients with breast cancer.

BACKGROUND: Breast cancer is the most common malignancy in women worldwide. The relationship between remnant cholesterol (RC) and the prognosis of patients with breast cancer has not been clearly reported. This study investigated the prognostic value of RC in predicting mortality in patients with breast cancer. METHODS: This study prospectively analysed 709 women patients with breast cancer from the Investigation on Nutrition Status and Clinical Outcome of Common Cancers (INSCOC) project. Restricted cubic splines were used to analyse the dose-response relationship between RC and breast cancer mortality. The Kaplan-Meier method was used to evaluate the overall survival of patients with breast cancer. A Cox regression analyses was performed to assess the independent association between RC and breast cancer mortality. Inverse probability of treatment weighting (IPTW) using the propensity score was used to reduce confounding. Sensitivity analysis was performed after excluding patients with underlying diseases and survival times shorter than one year. RESULTS: A linear dose-response relationship was identified between RC and the risk of all-cause mortality in patients with breast cancer (p = 0.036). Kaplan-Meier survival analysis and log-rank test showed that patients with high RC levels had poorer survival than those with low RC levels (p = 0.007). Univariate and multivariate Cox regression analyses showed that RC was an independent risk factor for mortality in women patients with breast cancer. IPTW-adjusted analyses and sensitivity analyses showed that CR remained a prognostic factor. CONCLUSIONS: RC is an independent risk factor for the prognosis of patients with breast cancer, and patients with higher RC levels have poorer survival.

Exploring the impact of women-specific reproductive factors on phenotypic aging and the role of life's essential 8.

BACKGROUND: Aging is an inevitable biological process. Accelerated aging renders adults more susceptible to chronic diseases and increases their mortality rates. Previous studies have reported the relationship between lifestyle factors and phenotypic aging. However, the relationship between intrinsic factors, such as reproductive factors, and phenotypic aging remains unclear. METHODS: This study utilized data from the National Health and Nutrition Examination Survey (NHANES), spanning from 1999 to 2010 and 2015-2018, with 14,736 adult women. Random forest imputation was used to handle missing covariate values in the final cohort. Weighted linear regression was utilized to analyze the relationship between women-specific reproductive factors and PhenoAgeAccel. Considering the potential impact of menopausal status on the results, additional analyses were conducted on premenopausal and postmenopausal participants. Additionally, the Life's Essential 8 (LE8) was used to investigate the impact of healthy lifestyle and other factors on the relationship between women-specific reproductive factors and PhenoAgeAccel. Stratified analyses were conducted based on significant interaction p-values. RESULTS: In the fully adjusted models, delayed menarche and gynecological surgery were associated with increased PhenoAgeAccel, whereas pregnancy history were associated with a decrease. Additionally, early or late ages of menopause, first live birth, and last live birth can all negatively impact PhenoAgeAccel. The relationship between women-specific reproductive factors and PhenoAgeAccel differs between premenopausal and postmenopausal women. High LE8 scores positively impacted the relationship between certain reproductive factors (age at menarche, age at menopause, age at first live birth, and age at last live birth) and phenotypic age acceleration. Stratified analysis showed significant interactions for the following variables: BMI with age at menarche, pregnancy history, and age at menopause; ethnicity with age at menopause, age at first live birth, and parity; smoking status with use of contraceptive pills and gynecologic surgery; hypertension with use of contraceptive pills, pregnancy history, and age at menopause. CONCLUSION: Delayed menarche, gynecological surgery, and early or late ages of menopause, first live birth, and last live birth are associated with accelerated phenotypic aging. High LE8 score may alleviate the adverse effects of reproductive factors on phenotypic aging.

Cholesterol-modified prognostic nutritional index (CPNI) as an effective tool for assessing the nutrition status and predicting survival in patients with breast cancer.

BACKGROUND: Malnutrition is associated with poor overall survival (OS) in breast cancer patients; however, the most predictive nutritional indicators for the prognosis of patients with breast cancer are not well-established. This study aimed to compare the predictive effects of common nutritional indicators on OS and to refine existing nutritional indicators, thereby identifying a more effective nutritional evaluation indicator for predicting the prognosis in breast cancer patients. METHODS: This prospective study analyzed data from 776 breast cancer patients enrolled in the "Investigation on Nutritional Status and its Clinical Outcome of Common Cancers" (INSCOC) project, which was conducted in 40 hospitals in China. We used the time-dependent receiver operating characteristic curve (ROC), Kaplan-Meier survival curve, and Cox regression analysis to evaluate the predictive effects of several nutritional assessments. These assessments included the patient-generated subjective nutrition assessment (PGSGA), the global leadership initiative on malnutrition (GLIM), the controlling nutritional status (CONUT), the nutritional risk index (NRI), and the prognostic nutritional index (PNI). Utilizing machine learning, these nutritional indicators were screened through single-factor analysis, and relatively important variables were selected to modify the PNI. The modified PNI, termed the cholesterol-modified prognostic nutritional index (CPNI), was evaluated for its predictive effect on the prognosis of patients. RESULTS: Among the nutritional assessments (including PGSGA, GLIM, CONUT, NRI, and PNI), PNI showed the highest predictive ability for patient prognosis (time-dependent ROC = 0.58). CPNI, which evolved from PNI, emerged as the superior nutritional index for OS in breast cancer patients, with the time-dependent ROC of 0.65. It also acted as an independent risk factor for mortality (p < 0.05). Moreover, the risk of malnutrition and mortality was observed to increase gradually among both premenopausal and postmenopausal age women, as well as among women categorized as non-overweight, overweight, and obese. CONCLUSIONS: The CPNI proves to be an effective nutritional assessment tool for predicting the prognosis of patients with breast cancer.

Muscle distribution in relation to all-cause and cause-specific mortality in young and middle-aged adults.

BACKGROUND: The relationship between muscle and prognosis, especially that between muscle distribution across different body parts, and the related prognosis is not well established. OBJECTIVE: To investigate the relationship between muscle distribution and all-cause and cause-specific mortality and their potential modifiers. DESIGN: Longitudinal cohort study. C-index, IDI, and NRI were used to determine the best indicator of prognosis. COX regression analysis was performed to explore the relationship between variables and outcomes. Interaction and subgroup analyses were applied to identify the potential modifiers. PARTICIPANTS: A total of 5052 participants (weighted: 124,841,420) extracted from the NHANES 2003-2006 of median age 45 years and constituting 50.3% men were assessed. For validation, we included 3040 patients from the INSCOC cohort in China. MAIN MEASURES: Muscle mass and distribution. KEY RESULTS: COX regression analysis revealed that upper limbs (HR = 0.41, 95% CI 0.33-0.51), lower limbs (HR = 0.54, 95% CI 0.47-0.64), trunk (HR = 0.71, 95% CI, 0.59-0.85), gynoid (HR = 0.47, 95% CI 0.38-0.58), and total lean mass (HR = 0.55, 95% CI 0.45-0.66) were all associated with the better survival of participants (P trend < 0.001). The changes in the lean mass ratio of the upper and lower limbs and the lean mass ratio of the android and gynoid attenuated the protective effect of lean mass. Age and sex acted as potential modifiers, and the relationship between lean mass and the prognosis was more significant in men and middle-aged participants when compared to that in other age groups. Sensitive analyses depicted that despite lean mass having a long-term impact on prognosis (15 years), it has a more substantial effect on near-term survival (5 years). CONCLUSION: Muscle mass and its distribution affect the prognosis with a more significant impact on the near-term than that on the long-term prognosis. Age and sex acted as vital modifiers.

Habitually Skipping Breakfast Is Associated with the Risk of Gastrointestinal Cancers: Evidence from the Kailuan Cohort Study.

BACKGROUND: Habitually skipping breakfast may promote the initiation and progression of gastrointestinal (GI) cancers, which have never been systematically explored in large-scale prospective studies. METHODS: We prospectively examined the effects of breakfast frequency on the occurrence of GI cancers among 62,746 participants. The hazard ratios (HRs) and 95% confidence intervals (95% CIs) of GI cancers were calculated by Cox regression. The CAUSALMED procedure was used to perform the mediation analyses. RESULTS: During a median follow-up of 5.61 (5.18 ~ 6.08) years, 369 incident GI cancer cases were identified. Participants who consumed 1-2 times breakfasts per week exhibited an increased risk of stomach (HR = 3.45, 95% CI: 1.06-11.20) and liver cancer (HR = 3.42, 95% CI: 1.22-9.53). Participants who did not eat breakfast had an elevated risk of esophageal (HR = 2.72, 95% CI: 1.05-7.03), colorectal (HR = 2.32, 95% CI: 1.34-4.01), liver (HR = 2.41, 95% CI: 1.23-4.71), gallbladder, and extrahepatic bile duct cancer (HR = 5.43, 95% CI: 1.34-21.93). In the mediation effect analyses, BMI, CRP, and TyG (fasting triglyceride-glucose) index did not mediate the association between breakfast frequency and the risk of GI cancer incidence (all P for mediation effect > 0.05). CONCLUSIONS: Habitually skipping breakfast was associated with a greater risk of GI cancers including esophageal, gastric, colorectal, liver, gallbladder, and extrahepatic bile duct cancer. TRIAL REGISTRATION: Kailuan study, ChiCTR-TNRC-11001489. Registered 24 August, 2011-Retrospectively registered, http://www.chictr.org.cn/showprojen.aspx?proj=8050.

Association between the Dietary Inflammatory Index and All-Cause Mortality in Adults with Obesity.

INTRODUCTION: The dietary inflammatory index (DII) is associated with numerous chronic noncommunicable diseases. Previous studies have shown that the pro-inflammatory DII categories are associated with abdominal and simple obesity. However, the association between DII and mortality in patients with abdominal obesity and simple overweight or obesity remains unclear. METHODS: We used data from the US National Health and Nutrition Examination Survey (NHANES) from 2007 to 2018. A DII >0 (positive DII) was defined as a pro-inflammatory diet. A restricted cubic spline curve was used to describe the trend between DII and all-cause mortality. We then examined the association between DII and all-cause mortality in different body types using a Cox regression analysis and investigated the differences between sexes. Finally, the mediating effects of systemic inflammation were explored. RESULTS: A pro-inflammatory diet increased all-cause mortality in adults with abdominal obesity (aHR: 1.31, 95% confidence interval [CI]: 1.11-1.54; p < 0.001) and with simple overweight or obesity (aHR: 1.30, 95% CI: 1.11-1.53; p < 0.001). In addition, the most pro-inflammatory DII increased the risk of mortality by 43% (hazard ratio [HR]: Q4 vs. Q1 = 1.43, 95% CI = 1.14-1.79; p = 0.002; p for trend = 0.003) and 39% (HR: Q4 vs. Q1 = 1.39, 95% CI = 1.13-1.74; p = 0.003; p for trend = 0.009) in participants with abdominal obesity and with simple overweight or obesity, respectively. However, this association was not present in normal-sized participants. Compared with men, women resisted the effects of a pro-inflammatory diet. Mediation analysis showed that white blood cell and neutrophil were mediators of the association between DII and all-cause mortality (p < 0.001). CONCLUSION: A pro-inflammatory diet is associated with all-cause mortality in adults with abdominal obesity and simple overweight or obesity, and this effect differs between men and women. Systemic inflammation may mediate the association between DII and all-cause mortality.

Construction and validation of a predictive model of pneumonia for ICU patients with traumatic brain injury (TBI).

The incidence of pneumonia in ICU patients with TBI is very high, seriously affecting the prognosis. This study aims to construct a predictive model for pneumonia in ICU patients with TBI and provide help for the prevention of TBI-related pneumonia.Clinical data of ICU patients with TBI were collected from the Medical Information Mart for Intensive Care (MIMIC)-IV database and hospital data. Variables were screened by lasso and multivariate logistic regression to construct a predictive nomogram model, verified in internal validation cohort and external validation cohort by receiver operator characteristic (ROC) curve, calibration curve and decision curve analysis (DCA).A total of 1850 ICU patients with TBI were enrolled in the study from the MIMIC-IV database, including 1298 in the training cohort and 552 in internal validation cohort. The external validation cohort included 240 ICU patients with TBI from hospital data. Nine variables were selected from the training cohort by lasso regression and multivariate logistic regression, and a pneumonia prediction nomogram was constructed. This nomogram has a high discrimination in training, internal validation and external validation cohorts (AUC = 0.857, 0.877, 0.836). The calibration curve and DCA showed that this nomogram had a high calibration and better clinical decision-making efficiency.The nomogram showed excellent discrimination and clinical utility to predict pneumonia, and could identify pneumonia high-risk patients early, thus providing personalised treatment strategies for ICU patients with TBI.

Intraoperative cerebrospinal fluid leakage and residual tumors in endoscopic transsphenoidal surgery for pituitary adenoma: risk analysis and nomogram development.

BACKGROUND: Endoscopic transsphenoidal surgery is the primary method used to treat pituitary adenomas (PAs) at present; however, this technique is associated with certain risks, including cerebrospinal fluid leakage (CFL) and residual tumors (RTs). In this study, we aimed to identify specific risk factors for intraoperative CFL (ioCFL) and postoperative RT in patients with pituitary adenoma and construct a corresponding nomogram for risk assessment. METHODS: We collected a range of information from 782 patients who underwent endoscopic transsphenoidal PA resection in the Department of Neurosurgery at Beijing Tiantan Hospital between 2019 and 2021. Patients were then randomly assigned to training and validation groups (in a 8:2 ratio) with R software. Univariate and multivariable logistic regression models were then used to screen variables related to ioCFL and RT. These variables were then used to construct a predictive nomogram. Finally, the accuracy of the nomogram was validated by receiver operating characteristic curve (ROC) analysis, calibration plots, and decision curve analysis (DCA). RESULTS: Univariate and multivariable logistic regression models identified four risk factors for ioCFL (Hardy grade, tumor size, position, and consistency) and five risk factors for RT (operation time, tumor size, consistency, Knosp grade, and primary/recurrence type). The area under the ROC curve (AUC) for the ioCFL risk model was 0.666 and 0.697 for the training and validation groups, respectively. For RT, the AUCs for the two groups were 0.788 and 0.754, respectively. The calibration plots for the ioCFL and RT models showed high calibration quality and DCA analysis yielded excellent efficiency with regards to clinical decision making. CONCLUSION: Tumor size, growth characteristics, and invasion location were identified as the main factors affecting intraoperative CFL and RT. With our novel nomogram, surgeons can identify high-risk patients according to preoperative and intraoperative tumor performance and reduce the probability of complications.

Association of lymphocyte subsets with efficacy and prognosis of immune checkpoint inhibitor therapy in advanced non-small cell lung carcinoma: a retrospective study.

BACKGROUND: Immune checkpoint inhibitors (ICIs) have achieved promising effects in patients with non-small cell lung cancer (NSCLC). However, not all patients with NSCLC benefit from immunotherapy. There is an urgent need to explore biomarkers that could predict the survival outcomes and therapeutic efficacy in advanced NSCLC patients treated with immunotherapy. In this study, we aimed to assess the changes in peripheral blood lymphocyte subsets and their association with the therapeutic efficacy and clinical prognosis of advanced NSCLC patients treated with immunotherapy. METHODS: A total of 276 patients with advanced NSCLC were enrolled. Peripheral blood lymphocyte subsets including CD4+ T cells, CD8+ T cells, CD4+/CD8+ ratio, NK cells, Tregs and B cells were collected before any treatment, before immunotherapy or chemotherapy, and after 4 cycles of immunotherapy or chemotherapy. T-test was used to analyze the factors influencing lymphocyte subsets and their changes before and after therapy. Logistic regression was used to plot ROC curves and analyze the relationship between lymphocyte subsets and therapeutic efficacy. Log-rank test and Cox regression model were used to evaluate the relationship between lymphocyte subsets and progression-free survival (PFS). RESULTS: Gender, distant metastasis, and EGFR mutation status are known to affect the proportion of peripheral blood lymphocyte subsets in patients with advanced NSCLC. The proportions of CD4+ T cells, CD8+ T cells, Tregs and B cells were found to decrease after chemotherapy as compared to the baseline. The proportion of CD4+ T cells, CD8+ T cells, CD4+/CD8+ ratio, NK cells and Tregs were higher after immunotherapy than after chemotherapy. Compared to the baseline, the effective group showed significant increase in the proportions of CD4+ T cells, CD4+/CD8+ ratio, NK cells and Tregs, and the number of CD8+ T cells was significantly lower in the peripheral blood after 4 cycles of immunotherapy. On the contrary, the ineffective group did not show any significant differences in the above parameters. Baseline CD4+ T cells and NK cells were independent predictors of immunotherapy efficacy and PFS. Baseline Tregs were independent predictor of immunotherapy efficacy. CONCLUSION: Immune checkpoint inhibitors induced changes in the proportion of peripheral blood lymphocyte subsets in patients that responded well to immunotherapy. The levels of the different lymphocyte subsets could serve as valuable predictive biomarkers of efficacy and clinical prognosis for NSCLC patients treated with immunotherapy.

Prevalence and risk factors of severe postpartum hemorrhage: a retrospective cohort study.

BACKGROUND: Although maternal deaths are rare in developed regions, the morbidity associated with severe postpartum hemorrhage (SPPH) remains a major problem. To determine the prevalence and risk factors of SPPH, we analyzed data of women who gave birth in Guangzhou Medical Centre for Critical Pregnant Women, which received a large quantity of critically ill obstetric patients who were transferred from other hospitals in Southern China. METHODS: In this study, we conducted a retrospective case-control study to determine the prevalence and risk factors for SPPH among a cohort of women who gave birth after 28 weeks of gestation between January 2015 and August 2019. SPPH was defined as an estimated blood loss ≥1000 mL and total blood transfusion≥4 units. Logistic regression analysis was used to identify independent risk factors for SPPH. RESULTS: SPPH was observed in 532 mothers (1.56%) among the total population of 34,178 mothers. Placenta-related problems (55.83%) were the major identified causes of SPPH, while uterine atony without associated retention of placental tissues accounted for 38.91%. The risk factors for SPPH were maternal age < 18 years (adjusted OR [aOR] = 11.52, 95% CI: 1.51-87.62), previous cesarean section (aOR = 2.57, 95% CI: 1.90-3.47), history of postpartum hemorrhage (aOR = 4.94, 95% CI: 2.63-9.29), conception through in vitro fertilization (aOR = 1.78, 95% CI: 1.31-2.43), pre-delivery anemia (aOR = 2.37, 95% CI: 1.88-3.00), stillbirth (aOR = 2.61, 95% CI: 1.02-6.69), prolonged labor (aOR = 5.24, 95% CI: 3.10-8.86), placenta previa (aOR = 9.75, 95% CI: 7.45-12.75), placenta abruption (aOR = 3.85, 95% CI: 1.91-7.76), placenta accrete spectrum (aOR = 8.00, 95% CI: 6.20-10.33), and macrosomia (aOR = 2.30, 95% CI: 1.38-3.83). CONCLUSION: Maternal age < 18 years, previous cesarean section, history of PPH, conception through IVF, pre-delivery anemia, stillbirth, prolonged labor, placenta previa, placental abruption, PAS, and macrosomia were risk factors for SPPH. Extra vigilance during the antenatal and peripartum periods is needed to identify women who have risk factors and enable early intervention to prevent SPPH.

The quality of life impacting factors in malnourished patients with gastric cancer.

Introduction: Malnutrition is prevalent among individuals with gastric cancer and notably decreases their quality of life (QOL). However, the factors impacting QOL are yet to be clearly defined. This study aimed to identify essential factors impacting QOL in malnourished patients suffering from gastric cancer. Methods: By using the Patient-Generated Subjective Global Assessment (PG-SGA) to assess the nutritional status (≥4 defined malnutrition) of hospitalized cancer patients, 4,586 gastric cancer patients were ultimately defined as malnourished. Spearman method was used to calculate the relationship between clinical features and the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire (EORTC QLQ-C30). Then, univariate and multivariate logistic regression were used to observe which factors affected QOL, and subgroup analysis was performed in young and old population respectively. In addition, we used univariate and multivariate logistic regression to explore whether and how self-reported frequent symptoms in the last 2 weeks of the PG-SGA score affected QOL. Results: In multivariate logistic regression analysis of clinical features of patients with malnourished gastric cancer, women, stage II, stage IV, WL had an independent correlation with a low global QOL scores. However, BMI, secondary education, higher education, surgery, chemotherapy, HGS had an independent correlation with a high global QOL scores. In multivariate logistic regression analysis of symptoms in self-reported PG-SGA scores in patients with malnourished gastric cancer, having no problem eating had an independent correlation with a high global QOL scores. However, they have no appetite, nausea, vomiting, constipation and pain had an independent correlation with a lower global QOL scores. The p values of the above statistical results are both < 0.05. Conclusion: This study demonstrates that QOL in malnourished patients with gastric cancer is determined by female sex, stage II, stage IV, BMI, secondary and higher education or above, surgery, chemotherapy, WL, and HGS. Patients' self-reported symptoms of nearly 2 weeks, obtained by using PG-SGA, are also further predictive of malnourished gastric cancer patients. Detecting preliminary indicators of low QOL could aid in identifying patients who might benefit from an early referral to palliative care and assisted nursing.

Obesity-associated metabolic inflammation promotes triple-negative breast cancer progression through the interleukin-6/STAT3/pentraxin 3/matrix metalloproteinase 7 axis.

BACKGROUND: This study aimed to investigate the regulatory mechanism of the adipose factor interleukin (IL)-6 in promoting pentraxin 3 (PTX3) expression in triple-negative breast cancer (TNBC). METHODS: We established an in vitro coculture model of mature adipocytes and TNBC cells using a Transwell system. Cell scratch, Transwell migration, and matrix invasion assays were used to evaluate the migration and invasion abilities of TNBC cells cocultured with adipocytes. Next, we used lentivirus-mediated functional depletion experiments to study PTX3's role in the adipocyte-dependent migration of TNBC cells. RESULTS: After coculturing TNBC cells with adipocytes, PTX3 expression was upregulated, which accompanied enhanced cell migration and invasion. Using GEO data and RNA-seq analysis, we identified PTX3 as a key target gene influenced by the adipose TNBC microenvironment. IL-6 upregulation in the conditioned medium of mature adipocytes and in the serum of high-fat diet mice was associated with this effect, and the recombinant protein IL-6 significantly promoted the migration and invasion of TNBC cells along with the phosphorylation of intracellular STAT3 and the upregulation of PTX3. PTX3 knockdown inhibited TNBC cell migration and eliminated the enhanced migration caused by coculturing with adipocytes. Furthermore, in vivo experiments confirmed that the PTX3 knockdown reduced obesity-induced lung metastasis. Subsequent experiments with cytokines and drug inhibitors confirmed that adipocyte-derived IL-6 promoted PTX3 expression by activating the STAT3 signaling pathway. Additionally, bioinformatic analysis indicated that PTX3 promotes TNBC metastasis by regulating the matrix metalloproteinase (MMP) family. CONCLUSION: Our study elucidated Obesity-related metabolic inflammation promotes the progression via the IL-6/STAT3/PTX3/MMP7 axis.

AWGC2023 cachexia consensus as a valuable tool for predicting prognosis and burden in Chinese patients with cancer.

BACKGROUND: The Asian Working Group for Cachexia (AWGC) proposed the first consensus report on diagnostic criteria for cachexia in Asians in 2023. However, the current consensus lacks cohort evidence to validate its effectiveness and practicality. We aimed to explore the value of the AWGC2023 criteria for predicting the prognosis and medical burden of patients with cancer through a retrospective post hoc cross-sectional analysis of the Investigation on Nutrition Status and its Clinical Outcome of Common Cancers (INSCOC) project in China. METHODS: Cox regression analyses were performed to assess the independent association between cachexia and long-term survival. We utilized C-reactive protein (CRP), neutrophil-to-lymphocyte ratio (NLR), inflammatory burden index (IBI), albumin (ALB) and Glasgow prognostic score (GPS) as diagnostic markers for cachexia, designating them as CRP-based cachexia, NLR-based cachexia, IBI-based cachexia, ALB-based cachexia and GPS-based cachexia, respectively. Additionally, we diagnosed cachexia using body mass index (BMI) cutoff values of 18.5, 20, 21 and 22 kg/m2, respectively, and subsequently compared their prognostic predictive value through Harrell's concordance index (C-index). Logistic regression models were used to assess the association between cachexia and medical burden. RESULTS: A total of 5426 patients with cancer were enrolled in this study. Cox regression analysis confirmed that cachexia based on the AWGC2023 criteria was an independent predictor of long-term survival in patients with cancer. Patients with cachexia had significantly poorer long-term survival than patients without cachexia (66.4% vs. 49.7%, P < 0.001). Inflammatory biomarker-based cachexia was as an independent predictor of prognosis in patients with cancer, with inflammatory burden index (IBI)-based cachexia demonstrating the optimal prognostic discriminatory ability. The C-index indicated that cachexia based on BMI cutoff values of 18.5, 20, and 22 kg/m2 did not perform as well as a BMI cutoff value of 21 kg/m2. Logistic regression models revealed that using the AWGC2023 criteria, patients with cachexia had a 16.6% higher risk of prolonged hospitalization and a 16.0% higher risk of high medical expenses than patients without cachexia. CONCLUSION: The AWGC2023 criteria represent a valuable tool for predicting survival and medical burden among Chinese patients with cancer. Encouragement for further validation in other Asian populations is warranted for the AWGC2023 criteria.

Performance of anthropometry-based and bio-electrical impedance-based muscle-mass indicators in the Global Leadership Initiative on Malnutrition criteria for predicting prognosis in patients with cancer.

BACKGROUND: Reduced muscle mass is a criterion for diagnosing malnutrition using the Global Leadership Initiative on Malnutrition (GLIM) criteria; however, the choice of muscle-mass indicators within the GLIM criteria remains contentious. This study aimed to establish muscle-measurement-based GLIM criteria using data from bio-electrical impedance analysis (BIA) and anthropometric evaluations and evaluate their ability to predict overall survival (OS), short-term outcomes, and healthcare burden in patients with cancer. METHODS: This was a multicenter, prospective study that commenced in 2013 and enrolled participants from various clinical centers across China. We constructed GLIM criteria based on various muscle measurements, including fat-free mass index (FFMI), skeletal muscle index (SMI), calf circumference (CC), midarm circumference (MAC), midarm muscle circumference (MAMC), and midarm muscle area (MAMA). Survival was estimated using the Kaplan-Meier method and survival curves were compared using the log-rank test. Cox proportional hazards regression was used to assess the independent association between the GLIM criteria and OS. The discriminatory performance of different muscle-measurement-based GLIM criteria for mortality was evaluated using Harrell's concordance index (C-index). Logistic regression was used to evaluate the association of the GLIM criteria with short-term outcomes and healthcare burden. RESULTS: A total of 4769 patients were included in the analysis, of whom 1659 (34.8%) died during the study period. The Kaplan-Meier curves demonstrated that all muscle-measurement-based GLIM criteria significantly predicted survival in patients with cancer (all p < 0.001). The survival rate of malnourished patients was approximately 10% lower than that of non-malnourished patients. Cox proportional hazards regression showed that all the muscle-measurement-based GLIM could independently predict the OS of patients (all p < 0.001). The prognostic discrimination was as follows: MAMC (Chi-square: 79.61) > MAMA (Chi-square: 79.10) > MAC (Chi-square: 64.09) > FFMI (Chi-square: 62.33) > CC (Chi-square: 58.62) > ASMI (Chi-square: 57.29). In comparison to the FFMI-based GLIM criteria, the ASMI-based criteria (-0.002, 95% CI: -0.006 to 0.002, p = 0.334) and CC-based criteria (-0.003, 95% CI: -0.007 to 0.002, p = 0.227) did not exhibit a significant advantage. However, the MAC-based criteria (0.001, 95% CI: -0.003 to 0.004, p = 0.776), MAMA-based criteria (0.004, 95% CI: 0.000-0.007, p = 0.035), and MAMC-based criteria (0.005, 95% CI: 0.000-0.007, p = 0.030) outperformed the FFMI-based GLIM criteria. Logistic regression showed that muscle measurement-based GLIM criteria predicted short-term outcomes and length of hospital stay in patients with cancer. CONCLUSION: All muscle measurement-based GLIM criteria can effectively predict OS, short-term outcomes, and healthcare burden in patients with cancer. Anthropometric measurement-based GLIM criteria have potential for clinical application as an alternative to BIA-based measurement.