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 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.

Anthropometrics and cancer prognosis: a multi-center cohort study.

BACKGROUND: Anthropometric indicators have been shown to be associated with the prognosis of patients with cancer. However, any single anthropometric index has limitation in predicting the prognosis. OBJECTIVE: This study aimed to observe the predictive role of seven anthropometric indicators based on body size on the prognosis of patients with cancer. DESIGN: A principal component analysis (PCA) on seven anthropometric measurements: height, weight, body mass index (BMI), hand grip strength (HGS), triceps skinfold thickness (TSF), mid-upper arm circumference (MAC), and calf circumference (CAC) was conducted. Principal components (PCs) were derived from this analysis. Cox regression analysis was used to investigate the association between the prognosis of patients with cancer and the PCs. Subgroups and sensitivity analyses were also conducted. RESULTS: Through PCA, four distinct PCs were identified, collectively explaining 88.3% of the variance. PC1, primarily characterized by general obesity, exhibited a significant inverse association with the risk of cancer death (adjusted HR=0.86, 95% CI (0.83, 0.88)). PC2 (short stature with high TSF) was not significantly associated with cancer prognosis. PC3 (high BMI coupled with low HGS) demonstrated a significant increase with the risk of cancer-related death (adjusted HR=1.08, 95% CI (1.05, 1.11)). PC4 (tall stature with high TSF) exhibited a significant association with increased cancer risk (adjusted HR=1.05, 95% CI (1.02, 1.07)). These associations varied across different cancer stages. The stability of the results was confirmed through sensitivity analyses. CONCLUSION: Different body sizes are associated with distinct prognostic outcomes in patients with cancer. The impact of BMI on prognosis is influenced by both HGS and subcutaneous fat. This finding may influence the clinical care of cancer and improve the survival of cancer patients.

The relationship between different fatty acids intake and the depressive symptoms: A population-based study.

BACKGROUND: Depression is a common psychological disorder worldwide, affecting mental and physical health. Previous studies have explored the benefits of polyunsaturated fatty acids (PUFAs) intake in depressive symptoms; however, few studies have focused on the association between all types of fatty acids intake and depressive symptoms. Therefore, we explored the relationship between the intake of different fatty acids intake and the risk of depressive symptoms. METHODS: The study was based on the data from the 2005-2018 National Health and Nutrition Examination Survey (NHANES), a large US-based database. We used a nutrient residual model and multi-nutrient density model for the analysis. We calculated the nutrient density and residual in men and women separately, and the fatty acids intake was divided into quartiles based on the sex distribution. The relationship between the depressive symptoms and the intake of different fatty acids was examined using logistic regression; furthermore, we explored the relationships separately in men and women. RESULTS: The intake of monounsaturated fatty acids (MUFAs) and PUFAs, particularly n-3 and n-6 PUFAs, were associated with reduced odds ratios for depressive symptoms. The inverse relationship between the intake of MUFAs, PUFAs, n-3, and n-6 PUFAs and depressive symptoms was stronger in women. The inverse relationship between total fatty acid (TFAs) intake and depressive symptoms existed only in a single model. In contrast, saturated fatty acid (SFAs) intake was not related to depressive symptoms. CONCLUSION: Consuming MUFAs and PUFAs can counteract the depressive symptoms, especially in women.

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.

The association of metabolic syndrome scores 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.

Evaluation and validation of neutrophil to albumin ratio as a promising prognostic marker for all-cause mortality in patients with cancer: a multicenter cohort study.

OBJECTIVES: The practicality and effectiveness of using the prognostic value of the neutrophil-to-albumin ratio (NAR) in evaluating patients with cancer remain unclear, and research is needed to fully understand its potential application in the cancer population. METHODS: The Kaplan-Meier method was used for survival analysis, and the log-rank test was employed for comparison. Univariate and multivariate Cox proportional hazards models were used to determine the prognostic biomarkers, and Logistic regression analysis was conducted to investigate the relationship between NAR and 90-day outcomes and cachexia. RESULTS: The study included 14 682 patients with cancer, divided into discovery (6592 patients), internal validation (2820 patients), and external validation groups (5270 patients). Patients with high NAR had higher all-cause mortality than those with low NAR in the discovery (50.15% versus 69.29%, P < 0.001), internal validation (54.18% versus 70.91%, P < 0.001), and external validation cohorts (40.60% versus 66.68%, P < 0.001). In the discovery cohort, high NAR was observed to be independently associated with all-cause mortality in patients (HR 1.16, 95% CI 1.12-1.19; P < 0.001). Moreover, we validated the promising prognostic value of NAR as a predictor of survival in patients with cancer through internal validation (HR 1.21, 95% CI 1.16-1.27, P < 0.001) and external validation cohorts (HR 1.27, 95% CI 1.21-1.34, P < 0.001). Additionally, in the subgroup analysis by tumor type, high NAR was identified as a risk factor for most cancers, except for breast cancer. CONCLUSIONS: This study showed that NAR is a feasible and promising biomarker for predicting prognosis and cancer cachexia in cancer patients.

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.

Age Differences in the Association of Sleep Duration Trajectory With Cancer Risk and Cancer-Specific Mortality: Prospective Cohort Study.

BACKGROUND: Baseline sleep duration is associated with cancer risk and cancer-specific mortality; however, the association between longitudinal patterns of sleep duration and these risks remains unknown. OBJECTIVE: This study aimed to elucidate the association between sleep duration trajectory and cancer risk and cancer-specific mortality. METHODS: The participants recruited in this study were from the Kailuan cohort, with all participants aged between 18 and 98 years and without cancer at baseline. The sleep duration of participants was continuously recorded in 2006, 2008, and 2010. Latent mixture modeling was used to identify shared sleep duration trajectories. Furthermore, the Cox proportional risk model was used to examine the association of sleep duration trajectory with cancer risk and cancer-specific mortality. RESULTS: A total of 53,273 participants were included in the present study, of whom 40,909 (76.79%) were men and 12,364 (23.21%) were women. The average age of the participants was 49.03 (SD 11.76) years. During a median follow-up of 10.99 (IQR 10.27-11.15) years, 2705 participants developed cancers. Three sleep duration trajectories were identified: normal-stable (44,844/53,273, 84.18%), median-stable (5877/53,273, 11.03%), and decreasing low-stable (2552/53,273, 4.79%). Compared with the normal-stable group, the decreasing low-stable group had increased cancer risk (hazard ratio [HR] 1.39, 95% CI 1.16-1.65) and cancer-specific mortality (HR 1.54, 95% CI 1.18-2.06). Dividing the participants by an age cutoff of 45 years revealed an increase in cancer risk (HR 1.88, 95% CI 1.30-2.71) and cancer-specific mortality (HR 2.52, 95% CI 1.22-5.19) only in participants younger than 45 years, rather than middle-aged or older participants. Joint analysis revealed that compared with participants who had a stable sleep duration within the normal range and did not snore, those with a shortened sleep duration and snoring had the highest cancer risk (HR 2.62, 95% CI 1.46-4.70). CONCLUSIONS: Sleep duration trajectories and quality are closely associated with cancer risk and cancer-specific mortality. However, these associations differ with age and are more pronounced in individuals aged <45 years. TRIAL REGISTRATION: Chinese Clinical Trial Registry ChiCTR-TNRC-11001489; http://tinyurl.com/2u89hrhx.

Temporal relationship between inflammation and metabolic disorders and their impact on cancer risk.

Background: Inflammation and metabolic disorders are closely associated with cancer. Whether inflammation leads to metabolic disorders or vice versa during cancer initiation remains unclear. In this study, we explored this temporal relationship and the co-exposure effect on cancer risk. Methods: This prospective study had two phases. Initially, we examined the temporal relationship between inflammation (high-sensitivity C-reactive protein (CRP)) and metabolic disorders (metabolic syndrome severity Z-score (MetS-Z)) using a 3.98-year survey and cross-lagged analysis. Subsequently, we assessed the connection of co-exposure to inflammation and metabolic disorders, and the risks of overall cancer, as well as specific obesity-related, non-obesity-related, digestive system, lung, and other cancers using an 11.04-year survey and Cox proportional hazard models. Results: The cross-lagged analysis revealed that the path coefficient from baseline CRP to follow-up MetS-Z (ß2 = 0.032; 95% confidence interval (CI) = 0.026, 0.046) was more significant than the path coefficient from baseline MetS-Z to follow-up CRP (ß1 = 0.009; 95% CI = -0.001, 0.019). During the follow-up, 2304 cases of cancer occurred. Compared with the risk of cancer of patients with low average cumulative CRP and MetS-Z, patients with high value had a significantly increased risk (hazard ratio = 1.54, 95% CI = 1.30, 1.83). The mediation analysis showed that MetS-Z mediated the association between CRP levels and overall cancer (12.67%), digestive system cancer (10.16%), and obesity-related cancer risk (13.87%). Conclusions: Inflammation had a greater impact on metabolic disorders than vice versa. Co-exposure to inflammation and metabolic disorders significantly increased the risk of cancer, particularly digestive system and obesity-related cancers. Registration: Chinese Clinical Trial Registry: ChiCTR-TNRC-11001489.

Systemic inflammation and insulin resistance-related indicator predicts poor outcome in patients with cancer cachexia.

BACKGROUND: The C-reactive protein (CRP)-triglyceride-glucose (TyG) index (CTI), which is a measure representing the level of inflammation and insulin resistance (IR), is related to poor cancer prognosis; however, the CTI has not been validated in patients with cancer cachexia. Thus, this study aimed to explore the potential clinical value of the CTI in patients with cancer cachexia. METHODS: In this study, our prospective multicenter cohort included 1411 patients with cancer cachexia (mean age 59.45 ± 11.38, 63.3% male), which was a combined analysis of multiple cancer types. We randomly selected 30% of the patients for the internal test cohort (mean age 58.90 ± 11.22% 61.4% male). Additionally, we included 307 patients with cancer cachexia in the external validation cohort (mean age 61.16 ± 11, 58.5% male). Receiver operating characteristic (ROC) and calibration curves were performed to investigate the prognostic value of CTI. The prognostic value of the CTI was also investigated performing univariate and multivariate survival analyses. RESULTS: The survival curve indicated that the CTI showed a significant prognostic value in the total, internal, and external validation cohorts. Prognostic ROC curves and calibration curves revealed that the CTI showed good consistency in predicting the survival of patients with cancer cachexia. Multivariate survival analysis showed that an elevated CTI increased the risk of death by 22% (total cohort, 95% confidence interval [CI] = 1.13-1.33), 34% (internal test cohort, 95%CI = 1.11-1.62), and 35% (external validation cohort, 95%CI = 1.14-1.59) for each increase in the standard deviation of CTI. High CTI reliably predicted shorter survival (total cohort, hazard ratio [HR] = 1.45, 95%CI = 1.22-1.71; internal test cohort, HR = 1.62, 95%CI = 1.12-2.36; external validation cohort, HR = 1.61, 95%CI = 1.15-2.26). High CTI significantly predicted shorter survival in different tumor subgroups, such as esophageal [HR = 2.11, 95%CI = 1.05-4.21] and colorectal cancer [HR = 2.29, 95%CI = 1.42-3.71]. The mediating effects analysis found that the mediating proportions of PGSGA, ECOG PS, and EORTC QLQ-C30 on the direct effects of CTI were 21.72%, 19.63%, and 11.61%, respectively We found that there was a significant positive correlation between the CTI and 90-day [HR = 2.48, 95%CI = 1.52-4.14] and 180-day mortality [HR = 1.77,95%CI = 1.24-2.55] in patients with cancer cachexia. CONCLUSION: The CTI can predict the short- and long-term survival of patients with cancer cachexia and provide a useful prognostic tool for clinical practice.

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.

The age-related obesity paradigm: results from two large prospective cohort studies.

BACKGROUND: The obesity paradigm has been a health concern globally for many years, its meaning is controversial. In this study, we assess the characteristics and causes of obesity paradigm and detail the mediation of obesity and inflammation on survival. METHODS: The original cohort included participants from the US National Health and Nutrition Examination Survey (NHANES) from 1999 to 2018, a prospective cohort of a nationally representative sample of adult participants; the oncology validation cohort included patients from the Investigation on Nutrition Status and Clinical Outcome of Common Cancers (INSCOC) from 2013 to 2021, a prospective cohort of Chinese patients with cancer. Survival analysis was performed using weighted (NHANES) or unweighted (INSCOC) Cox survival analyses. The normal BMI group was used as a reference for all comparisons. Systemic inflammation was defined as neutrophil-to-lymphocyte ratio (NLR) > 3. Model-based causal mediation analysis was used to identify the mediators. RESULTS: A total of 52 270 (weighted population: 528506229) participants of the NHANES [mean follow-up times: 10.2 years; mean age (SD): 47 (19.16) years] were included in the original cohort; and a total of 17 418 patients with cancer of INSCOC [mean follow-up times: 2.9 years; mean age (SD): 57.37 (11.66) years] were included in the validation cohort. In the subgroups of all the participants, the obesity paradigm was more apparent in older participants and participants with disease [HR (95% CI): age ≥ 65 years, 0.84 (0.76, 0.93); with cancer, 0.84 (0.71, 0.99); with CVD, 0.74 (0.65, 0.85)]. As aged, the protective effect of a high BMI on survival gradually increased and a high BMI showed the effect of a protective factor on older participants [for obese II, HR (95% CI): young adults, 1.91 (1.40, 2.62); middle age, 1.56 (1.28, 1.91); old adults, 0.85 (0.76, 0.96]). The aged-related obesity paradigm in patients with cancer from the NHANES was verified in the INSCOC cohorts [for obese, HR (95%CI): 0.65 (0.52, 0.81)]. The NLR is an important mediator of the effect of BMI on survival (proportion of mediation = 15.4%). CONCLUSIONS: The obesity paradigm has a strong correlation with age. Relative to normal weight, obese in young people was association with higher all-cause mortality, and obese in elderly people was not association with higher mortality. The protection of obesity is association with systemic inflammation.

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.

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 the age of onset of metabolic syndrome with the risk of all cancer types.

BACKGROUND: The association between the age at onset of metabolic syndrome and cancer risk remains unknown. This study explored the association between age at metabolic syndrome onset and the risk of overall and site-specific cancer incidence. METHODS: This study included 31,688 participants with new-onset metabolic syndrome and 31,688 participants matched according to sex, age (±1 y), and examination year among the 179,328 participants who underwent Kailuan health examinations from 2006 to 2017 in Tangshan, China. Weighted Cox regression was used to calculate the hazard ratios and 95% confidence intervals of new-onset metabolic syndrome for overall and site-specific cancer incidence across age groups. Population-attributable risk proportions were used to estimate the number of cases that could be prevented by eliminating the risk factors from the population. RESULTS: During an average follow-up period of 10.22 y, we identified 2,710 cases of cancer and 4,218 deaths that occurred before the diagnosis of cancer. With an increase in metabolic syndrome onset age, the hazards of overall cancer incidence were gradually attenuated. The average hazard ratios (95% confidence intervals) of overall cancer were 1.94 (1.25-2.99) for metabolic syndrome onset age <45 year old, 1.41 (1.15-1.71) for age 45-54 years old, 1.38 (1.11-1.73) for age 55-64 years old, and 1.07 (0.89-1.28) for age ≥65 years old, respectively (p for interaction = 0.005). Similar results were obtained for colorectal, liver, and breast cancers in the site-specific analysis. CONCLUSIONS: New-onset metabolic syndrome was associated with a higher risk of overall cancer and incidence of several types of cancer, and the associations were stronger with a younger age of onset. TRIAL REGISTRATION: Kailuan Study, ChiCTR2000029767 (Registered February 12, 2020, https://www.chictr.org.cn/showprojEN.html?proj=48316).

Predicted lean body mass trajectories, and cancer risk and cancer-specific and all-cause mortality: A prospective cohort study.

BACKGROUND: Although many studies have investigated the association between body composition, cancer risk and mortality, predicting these risks through a single body composition measurement undoubtedly increases the limitations of the study. Few studies have explored the association between the trajectory of changes in body composition and the risk of cancer and death. We aimed to explore the association of predicted lean mass trajectories with cancer risk, cancer-specific mortality and all-cause mortality. METHODS: The participants in this study were all from the Kailuan cohort, a prospective, periodic, resurvey cohort study initiated in 2006. Latent mixture modelling was used to identify predicted lean mass trajectories for 2006-2010. The hazard ratios (HRs) and 95% confidence intervals (95% CIs) of the Cox proportional hazard models were used to describe the association between predicted lean mass trajectories and cancer risk and cancer-specific and all-cause mortality during follow-up (2010-2021). RESULTS: A total of 44 374 participants (average age, 53.01 ± 11.41 years, 78.99% men and 21.01% women) were enrolled in this study. Five distinct trajectories were identified: low-stable (n = 12 060), low-increasing (n = 8027), moderately stable-decreasing (n = 4725), moderately stable-increasing (n = 8053) and high-stable (n = 11 509). During the 11-year follow-up period, 2183 cancer events were recorded. After adjusting for age, predicted fat mass in 2010, sex, BMI, sedentary, physical activity, smoke, alcohol use, salt consumption, high-fat diet, high-sensitivity C-reactive protein, serum creatinine, family history of tumour, hypertension, diabetes mellitus, compared with the low-stable group, participants in the low-increasing group (HR = 0.851, 95% CI, 0.748-0.969), moderately stable-increasing group (HR = 0.803, 95% CI, 0.697-0.925) and high-stable group (HR = 0.770, 95% CI, 0.659-0.901) had a lower cancer risk, but not in the moderately stable-decreasing group (HR = 0.864, 95% CI, 0.735-1.015). Compared with the low-stable group, the risk of cancer-specific mortality was reduced by 25.4% (8.8-38.9%), 36.5% (20.3-49.4%) and 35.4% (17.9-49.2%), and the risk of all-cause mortality was reduced by 24.2% (16.9-30.8%), 37.0% (30.0-43.2%) and 47.4% (41.0-53.1%) in the low-increasing, moderately stable-increasing group and high-stable groups, respectively. CONCLUSIONS: Predicted lean mass trajectories may be closely associated with cancer risk and cancer-specific and all-cause mortality. Regular monitoring of body composition is necessary.

Individualized threshold of the involuntary weight loss in prognostic assessment of cancer.

BACKGROUND: Involuntary weight loss (WL) is a common symptom in cancer patients and is associated with poor outcomes. However, there is no standardized definition of WL, and it is unclear what magnitude of weight loss should be considered significant for prognostic purposes. This study aimed to determine an individualized threshold for WL that can be used for prognostic assessment in cancer patients. METHODS: Univariate and multivariate analyses of overall survival (OS) were performed using Cox proportional hazard models. The Kaplan-Meier method was performed to estimate the survival distribution of different WL levels. Logistic regression analysis was used to determine the relationship between WL and 90-day outcomes. Restricted cubic splines with three knots were used to examine the effects of WL on survival under different body mass index (BMI) conditions. RESULTS: Among the 8806 enrolled patients with cancer, median survival time declined as WL increased, from 25.1 to 20.1, 17.8 and 16.4 months at <2%, 2-5%, 5-10% and ≥10% WL, respectively (P < 0.001). Multivariate adjusted Cox regression analysis showed that the risk of adverse prognosis increased by 18.1% based on the SD of WL (5.45 U) (HR: 1.181, 95% CI: 1.144-1.219, P < 0.001). Similarly, categorical WL was independently associated with OS in patients with cancer. With the worsening of WL, the risk of a poor prognosis in patients increases stepwise. Compared with <2% WL, all-cause mortalities were 15.1%, 37% and 64.2% higher in 2-5%, 5-10%, and ≥10% WL, respectively. WL can effectively stratify the prognosis of both overall and site-specific cancers. The clinical prognostic thresholds for WL based on different BMI levels were 4.21% (underweight), 5.03% (normal), 6.33% (overweight), and 7.60% (obese). Multivariate logistic regression analysis showed that WL was independently associated with 90-day outcomes in patients with cancer. Compared with patients with <2% WL, those with ≥10% WL had more than twice the risk of 90-day outcomes (OR: 3.277, 95% CI: 2.287-4.694, P < 0.001). Systemic inflammation was a cause of WL deterioration. WL mediates 6.3-10.3% of the overall association between systemic inflammation and poor prognoses in patients with cancer. CONCLUSIONS: An individualized threshold for WL based on baseline BMI can be used for prognostic assessment in cancer patients. WL and BMI should be evaluated simultaneously in treatment decision-making, nutritional intervention, and prognosis discussions of patients with cancer.

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.