Value of the modified Patient-Generated Subjective Global Assessment in indicating the need for nutrition intervention and predicting overall survival in patients with malignant tumors in at least two organs.

BACKGROUND: Although the Patient-Generated Subjective Global Assessment (PG-SGA) is a reference standard used to assess a patient's nutrition status, it is cumbersome to administer. The aim of the present study was to estimate the value of a simpler and easier-to-use modified PG-SGA (mPG-SGA) to evaluate the nutrition status and need for intervention in patients with malignant tumors present in at least two organs. METHODS: A total of 591 patients (343 male and 248 female) were included from the INSCOC study. A Pearson correlation analysis was conducted to assess the correlation between the mPG-SGA and nutrition-related factors, with the optimal cut-off defined by a receiver operating characteristic curve (ROC). The consistency between the mPG-SGA and PG-SGA was compared in a concordance analysis. A survival analysis was used to determine the effects of nutritional intervention among different nutrition status groups. Univariable and multivariable Cox analyses were applied to evaluate the association of the mPG-SGA with the all-cause mortality. RESULTS: The mPG-SGA showed a negative association with nutrition-related factors. Individuals with an mPG-SGA ≥ 5 (rounded from 4.5) were considered to need nutritional intervention. Among the malnourished patients (mPG-SGA ≥ 5), the overall survival (OS) of those who received nutrition intervention was significantly higher than that of patients who did not. However, the OS was not significantly different in the better-nourished patients (mPG-SGA < 5). CONCLUSION: Our findings support that the mPG-SGA is a feasible tool that can be used to guide nutritional interventions and predict the survival of patients with malignant tumors affecting at least two organs.

Association of possible sarcopenia with all-cause mortality in patients with solid cancer: A nationwide multicenter cohort study.

OBJECTIVES: The concept of possible sarcopenia (PS) was recently introduced to enable timely intervention in settings without the technologies required to make a full diagnosis of sarcopenia. This study aimed to investigate the association between PS and all-cause mortality in patients with solid cancer. DESIGN: Retrospective observational study. SETTING AND PARTICIPANTS: 13,736 patients with 16 types of solid cancer who were ≥18 years old. MEASUREMENTS: The presence of both a low calf circumference (men <34 cm or women <33 cm) and low handgrip strength (men <28 kg or women <18 kg) was considered to indicate PS. Harrell's C-index was used to assess prognostic value and the association of PS with mortality was estimated by calculating multivariable-adjusted hazard ratios (HRs). RESULTS: The study enrolled 7207 men and 6529 women (median age = 57.8 years). During a median follow-up of 43 months, 3150 deaths occurred. PS showed higher Harrell's C-index (0.549, 95%CI = [0.541, 0.557]) than the low calf circumference (0.541, 95%CI = [0.531, 0.551], P = 0.037) or low handgrip strength (0.542, 95%CI = [0.532, 0.552], P = 0.026). PS was associated with increased mortality risk in both univariate (HR = 1.587, 95%CI = [1.476, 1.708]) and multivariable-adjusted models (HR = 1.190, 95%CI = [1.094, 1.293]). Sensitivity analyses showed that the association of PS with mortality was robust in different covariate subgroups, which also held after excluding those patients who died within the first 3 months (HR = 1.162, 95%CI = [1.060, 1.273]), 6 months (HR = 1.150, 95%CI = [1.039, 1.274]) and 12 months (HR = 1.139, 95%CI = [1.002, 1.296]) after enrollment. CONCLUSION: PS could independently and robustly predict all-cause mortality in patients with solid cancer. These findings imply the importance of including PS assessment in routine cancer care to provide significant prognostic information to help mitigate sarcopenia-related premature deaths.

Comparison of the performance of the GLIM criteria, PG-SGA and mPG-SGA in diagnosing malnutrition and predicting survival among lung cancer patients: A multicenter study.

BACKGROUND & AIMS: The present study aimed to compare the ability of the GLIM criteria, PG-SGA and mPG-SGA to diagnose malnutrition and predict survival among Chinese lung cancer (LC) patients. METHODS: This was a secondary analysis of a multicenter, prospective, nationwide cohort study, 6697 LC inpatients were enrolled between July 2013 and June 2020. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), area under the curve (AUC), and quadratic weighted Kappa coefficients were calculated to compare the ability to diagnose malnutrition. There were 754 patients who underwent follow-up for a median duration of 4.5 years. The associations between the nutritional status and survival were analyzed by the Kaplan-Meier method and multivariable Cox proportional hazard regression models. RESULTS: The median age of LC patients was 60 (53, 66), and 4456 (66.5%) were male. There were 617 (9.2%), 752 (11.2%), 1866 (27.9%), and 3462 (51.7%) patients with clinical stage Ⅰ, Ⅱ, Ⅲ, and Ⅳ LC, respectively. Malnutrition was present in 36.1%-54.2% (as evaluated using different tools). Compared with the PG-SGA (used as the diagnostic reference), the sensitivity of the mPG-SGA and GLIM was 93.7% and 48.3%; the specificity was 99.8% and 78.4%; and the AUC was 0.989 and 0.633 (P < 0.001). The weighted Kappa coefficients were 0.41 for the PG-SGA vs. GLIM, 0.44 for the mPG-SGA vs. GLIM, and 0.94 for the mPG-SGA vs PG-SGA in patients with stage Ⅰ-Ⅱ LC. These values were respectively 0.38, 0.39, and 0.93 in patients with stage Ⅲ-Ⅳ of LC. In a multivariable Cox analysis, the mPG-SGA (HR = 1.661, 95%CI = 1.348-2.046, P < 0.001), PG-SGA (HR = 1.701, 95%CI = 1.379-2.097, P < 0.001) and GLIM (HR = 1.657, 95%CI = 1.347-2.038, P < 0.001) showed similar death hazard ratios. CONCLUSIONS: The mPG-SGA provides nearly equivalent power to predict the survival of LC patients as the PG-SGA and the GLIM, indicating that all three tools are applicable for LC patients. The mPG-SGA has the potential to be an alternative replacement for quick nutritional assessment among LC patients.

Triceps skinfold-albumin index significantly predicts the prognosis of cancer cachexia: A multicentre cohort study.

BACKGROUND: The fat mass and nutritional status play important roles in the onset and progression of cancer cachexia. The present study evaluated the joint prognostic value of the fat mass, as indicated by the triceps skinfold thickness (TSF), and the serum albumin level, for mortality in patients with cancer cachexia. METHODS: We performed a multicentre cohort study including 5134 patients with cancer cachexia from January 2013 to April 2019. The sum of the TSF (mm) and serum albumin (g/L) was defined as the triceps skinfold-albumin index (TA). Harrell's C index, a time-dependent receiver operating characteristic (ROC) curve analysis and the area under the curve (AUC) were used to evaluate the prognostic performance of the TA and other indices. Optimal stratification was used to identify the thresholds to define a low TA, and the association of the TA with all-cause mortality was evaluated using Kaplan-Meier analysis and Cox proportional hazard regression models. RESULTS: The study enrolled 2408 women and 2726 men with a median age of 58.6 years and a median follow-up of 44 months. A total of 607 women (TA < 49.9) and 817 men (TA < 45.6) were classified as having a low TA. The TA showed better discrimination performance (C index = 0.621, 95% confidence interval [CI] = 0.607-0.636) to predict mortality in patients with cancer cachexia than the handgrip strength, the nutritional risk index, the prognostic nutritional index, the controlling nutritional status index, the systemic immune-inflammation index, the modified Glasgow prognostic score, and the TSF or albumin alone in the study population (all P < 0.05). The 1-, 3- and 5-year time-dependent ROC analyses (AUC = 0.647, 0.625 and 0.630, respectively) showed that the TA had the highest prognostic value among all indices investigated (all P < 0.05). Univariate analysis showed that a lower TA was associated with an increased death hazard (hazard ratio [HR] = 1.859, 95% CI = 1.677-2.062), regardless of the sex and cancer type. Multivariable survival analysis showed that a lower TA was independently associated with an increased death hazard (HR = 1.381, 95% CI = 1.223-1.560). This association was significantly strengthened in patients who did not receive curative chemotherapy (HR = 1.491, 95% CI = 1.298-1.713), those who had higher serum total protein levels (HR = 1.469, 95% CI = 1.284-1.681) and those with better physical performance (HR = 1.453, 95% CI = 1.271-1.662). CONCLUSIONS: This study defined and evaluated a new prognostic index, the TA, which may improve the selection of intervention strategies to optimize the survival of patients with cancer cachexia.

GLIM-defined malnutrition and overall survival in cancer patients: A meta-analysis.

BACKGROUND: Malnutrition defined by the Global Leadership Initiative on Malnutrition (GLIM) has been associated with cancer mortality, but the effect is limited and inconsistent. We performed this meta-analysis aiming to assess this relationship in patients with cancer. METHODS: We systematically searched Embase, PubMed, Web of Science, Cochrane, CINAHL, CNKI, Wanfang, and VIP databases from January 1, 2019, to July 1, 2022. Studies evaluating the prognostic effect of GLIM-defined malnutrition on cancer survival were included. A fixed-effect model was fitted to estimate the combined hazard ratio (HR) with a 95% CI. Heterogeneity of studies was analyzed using the I2 statistic. Quality assessment were performed using the Newcastle-Ottawa Scale (NOS) and the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) tool. RESULTS: The search strategy identified 4378 articles in all databases combined. Nine studies (8829 patients) meeting the inclusion criteria were included for quantitative analysis. Meta-analysis revealed significant associations between GLIM-defined pooled malnutrition (HR = 1.75; 95% CI, 1.43-2.15), moderate malnutrition (HR = 1.44; 95% CI, 1.29-1.62), and severe malnutrition (HR = 1.79; 95% CI, 1.58-2.02) with all-cause mortality. Sensitivity analysis supported the robustness of these associations. The between-study heterogeneity was low (all I2 < 50%), and study quality assessed with NOS was high (all scores > 6). The evidence quality according to the GRADE tool was very low. CONCLUSIONS: Our meta-analysis suggests a significant negative association of malnutrition, as defined by the GLIM, with overall survival in patients with cancer. However, definitive conclusions cannot be made, owing to the low quality of the source data.

Development and validation of an online dynamic nomogram system for predicting cancer cachexia among inpatients: a real-world cohort study in China.

BACKGROUND: Early recognition of cachexia is essential for ensuring the prompt intervention and treatment of cancer patients. However, the diagnosis of cancer cachexia (CC) usually is delayed. This study aimed to establish an accurate and high-efficiency diagnostic system for CC. METHODS: A total of 4834 cancer inpatients were enrolled in the INSCOC project from July 2013 to June 2020. All cancer patients in the study were randomly assigned to a development cohort (n=3384, 70%) and a validation cohort (n=1450, 30%). The least absolute shrinkage and selection operator (LASSO) method and multivariable logistic regression were used to identify the independent predictors for developing the dynamic nomogram. Discrimination and calibration were adopted to evaluate the ability of nomogram. A decision curve analysis (DCA) was used to evaluate clinical use. RESULTS: We combined 5 independent predictive factors (age, NRS2002, PG-SGA, QOL by the QLQ-C30, and cancer categories) to establish the online dynamic nomogram system. The C-index, sensitivity, and specificity of the nomo-system to predict CC was 0.925 (95%CI, 0.916-0.934, P < 0.001), 0.826, and 0.862 in the development set, while the values were 0.923 (95%CI, 0.909-0.937, P < 0.001), 0.854, and 0.829 in the validation set. In addition, the calibration curves of the diagnostic nomogram also presented good agreement with the actual situation. DCA showed that the model is clinically useful and can increase the clinical benefit in cancer patients. CONCLUSIONS: This study developed an online dynamic nomogram system with outstanding accuracy to help clinicians and dieticians estimate the probability of cachexia. This simple-to-use online nomogram can increase the clinical benefit in cancer patients and is expected to be widely adopted.

Identifying cancer cachexia in patients without weight loss information: machine learning approaches to address a real-world challenge.

BACKGROUND: Diagnosing cancer cachexia relies extensively on patient-reported historic weight, and failure to accurately recall this information can lead to severe underestimation of cancer cachexia. OBJECTIVES: The present study aimed to develop inexpensive tools to facilitate the identification of cancer cachexia in patients without weight loss information. METHODS: This multicenter cohort study included 12,774 patients with cancer. Cachexia was retrospectively diagnosed using Fearon et al.'s framework. Baseline clinical features, excluding weight loss, were modeled to mimic a situation where the patient is unable to recall their weight history. Multiple machine learning (ML) models were trained using 75% of the study cohort to predict cancer cachexia, with the remaining 25% of the cohort used to assess model performance. RESULTS: The study enrolled 6730 males and 6044 females (median age = 57.5 y). Cachexia was diagnosed in 5261 (41.2%) patients and most diagnoses were made based on the weight loss criterion. A 15-variable logistic regression (LR) model mainly comprising cancer types, gastrointestinal symptoms, tumor stage, and serum biochemistry indexes was selected among the various ML models. The LR model showed good performance for predicting cachexia in the validation data (AUC = 0.763; 95% CI: 0.747, 0.780). The calibration curve of the model demonstrated good agreement between predictions and actual observations (accuracy = 0.714, κ = 0.396, sensitivity = 0.580, specificity = 0.808, positive predictive value = 0.679, negative predictive value = 0.733). Subgroup analyses showed that the model was feasible in patients with different cancer types. The model was deployed as an online calculator and a nomogram, and was exported as predictive model markup language to permit flexible, individualized risk calculation. CONCLUSIONS: We developed an ML model that can facilitate the identification of cancer cachexia in patients without weight loss information, which might improve decision-making and lead to the development of novel management strategies in cancer care. This trial was registered at https://www.chictr.org.cn as ChiCTR1800020329.

A New Nomogram Model for Predicting 1-Year All-Cause Mortality After Hip Arthroplasty in Nonagenarians With Hip Fractures: A 20-Year Period Retrospective Cohort Study.

Background: China has become an ageing society and as it continues to age, it will face an increasing number of hip fractures in nonagenarians. However, few preoperative assessment tools to determine the postoperative mortality risk in nonagenarians with hip fracture were available. The aim of this study was to identify all-cause mortality risk factors after hip arthroplasty in nonagenarians with hip fractures and to establish a new nomogram model to optimize the individualized hip arthroplasty in nonagenarians with hip fractures. Methods: We retrospectively studied 246 consecutive nonagenarians diagnosed with hip fracture from August 2002 to February 2021 at our center. During the follow-up, 203 nonagenarians with a median age of 91.9 years treated with hip arthroplasty were included, of which 136 were females and 67 were males, and 43 nonagenarians were excluded (40 underwent internal fixation and 3 were lost to follow-up). The full cohort was randomly divided into training (50%) and validation (50%) sets. The potential predictive factors for 1-year all-cause mortality after hip arthroplasty were assessed by univariate and multivariate COX proportional hazards regression on the training set, and then, a new nomogram model was established and evaluated by concordance index (C-index) and calibration curves. Results: After analyzing 44 perioperative variables including demographic characteristics, vital signs, surgical data, laboratory tests, we identified that age-adjusted Charlson Comorbidity Index (aCCI) (p = 0.042), American Society of Anesthesiologists (ASA) classification (p = 0.007), Urea (p = 0.028), serum Ca2+ (p = 0.011), postoperative hemoglobin (p = 0.024) were significant predictors for 1-year all-cause mortality after hip arthroplasty in the training set. The nomogram showed a robust discrimination, with a C-index of 0.71 (95%CIs, 0.68-0.78). The calibration curves for 1-year all-cause mortality showed optimal agreement between the probability as predicted by the nomogram and the actual probability in training and validation sets. Conclusion: A novel nomogram model integrating 5 independent predictive variables were established and validated. It can effectively predict 1-year all-cause mortality after hip arthroplasty in nonagenarians with hip fracture and lead to a more optimized and rational therapeutic choice.

Accuracy of the GLIM criteria for diagnosing malnutrition: A systematic review and meta-analysis.

BACKGROUND & AIMS: Although malnutrition remains a global public health concern, and has proved to be a major contributor to death and illness, there has been a foundational lack of a gold standard for diagnostic testing for clinical application. The Global Leadership Initiative on Malnutrition (GLIM) criteria were established to normalize the diagnosis of malnutrition, but their use remains controversial. Therefore, we carried out a meta-analysis based on the published literature to assess the accuracy of the GLIM criteria for diagnosing malnutrition. METHODS: We utilized publication databases (including CENTRAL, MEDLINE, and EMBASE) to acquire research studies published from the initial use of the GLIM criteria in 2019 until January 22, 2022 that used the criteria to diagnose malnutrition. We conducted this meta-analysis with reference to the recommendations from the PRISMA-DTA statement. We separately calculated the amalgamated sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and AUC with 95%CI for the GLIM criteria. Then, we aggregated and presented the data by drawing forest plots to assess the real accuracy of the criteria. A subgroup analysis was also carried out to identify the potential sources of heterogeneity. RESULTS: After the initial search of the CENTRAL, EMBASE, and MEDLINE databases, a total of 451 unique studies were identified. Twenty studies met our selection standards and 10,781 total patients were included in the meta-analysis. We noted that 4761 of the 10,781 patients (44.2%) were malnourished. The amalgamated sensitivity of the GLIM criteria was 0.72 (95%CI, 0.64-0.78), the specificity was 0.82 (95%CI, 0.72-0.88), the PLR was 3.9 (95%CI, 2.6-6.1), NLR was 0.35 (95%CI, 0.27-0.44), DOR was 11 (95%CI, 6-20), and AUC was 0.82 (95%CI, 0.79-0.85). Based on the results of a subgroup analysis using the SGA as a reference standard, the GLIM criteria had better diagnostic value (sensitivity, 0.81; specificity, 0.80; DOR, 17; AUC, 0.87). CONCLUSIONS: The GLIM criteria have high diagnostic accuracy for distinguishing patients with malnutrition, and the GLIM criteria seem to have the potential to be used as a gold standard for diagnosing malnutrition in clinical practice. Moreover, the subgroup analysis showed a better diagnostic value for the GLIM criteria compared to the SGA used as a reference standard. Large-scale diagnostic trials and additional refinements to simplify the criteria are urgently needed to increase the clinical utilization of the GLIM criteria in the future.

De novo Creation and Assessment of a Prognostic Fat-Age-Inflammation Index "FAIN" in Patients With Cancer: A Multicenter Cohort Study.

Background and Aims: Malnutrition is highly prevalent and is related to multiple impaired clinical outcomes in cancer patients. This study aimed to de novo create an objective, nutrition-related index specially for prognostic purposes in oncology populations. Methods: We performed a multicenter cohort study including 14,134 cancer patients. The prognostic impact for each baseline characteristic was estimated by calculating Harrell's C-index. The optimal parameters reflecting the nutritional and inflammatory impact on patients' overall survival were selected to develop the fat-age-inflammation (FAIN) index. The associations of the FAIN with the nutritional status, physical performance, quality of life, short-term outcomes and mortality of patients were comprehensively evaluated. Independent external validation was performed to further assess the prognostic value of the FAIN. Results: The study enrolled 7,468 men and 6,666 women with a median age of 57 years and a median follow-up of 42 months. The FAIN index was defined as: (triceps skinfold thickness + albumin) / [age + 5 × (neutrophil count/lymphocyte count)]. There were significant associations of the FAIN with the nutritional status, physical performance, quality of life and short-term outcomes. The FAIN also showed better discrimination performance than the Nutritional Risk Index, the Prognostic Nutritional Index and the Controlling Nutritional Status index (all P < 0.05). In multivariable-adjusted models, the FAIN was independently associated with a reduced death hazard both as a continuous variable (HR = 0.57, 95%CI = 0.47-0.68) and per one standard deviation (HR = 0.83, 95%CI = 0.78-0.88). External validation in a multicenter lung cancer cohort (n = 227) further confirmed the prognostic value of the FAIN. Conclusions: This study created and assessed the prognostic FAIN index, which might act as a feasible option to monitor the nutritional status and help develop intervention strategies to optimize the survival outcomes of cancer patients.

A Dose-Response Meta-Analysis of Dietary Fiber Intake and Breast Cancer Risk.

Whether dietary fiber intake could reduce the risk of breast cancer (BC) is still controversial. The articles related to breast cancer and dietary fiber were retrieved through PubMed and Web of Science database. Summary relative risk (RR) and attributable risk percentage (ARP) for dietary fiber intake on the development of breast cancer were calculated. Dose-response meta-analysis modeled the relationship between dietary fiber intake and breast cancer risk. A total of 10 studies were included in this study. Meta-analysis showed that dietary fiber intake was negatively associated with breast cancer (RR = 0.83). In dose-response analysis, the risk of breast cancer showed a statistically significant linear trend with increasing dietary fiber dose: when adding 10 g per day, the risk decreased by 4.7% (RR = 0.95). The ARP results demonstrated that the breast cancer dietary fiber-attributed percentage was 33.33% in Asia, which was higher than 16.28% in North America and 9.89% in Europe. In conclusion, dietary fiber intake may have a positive effect on reducing breast cancer risk, especially in high doses.

Several anthropometric measurements and cancer mortality: predictor screening, threshold determination, and joint analysis in a multicenter cohort of 12138 adults.

BACKGROUND: Anthropometric measurements (AMs) are cost-effective surrogates for evaluating body size. This study aimed to identify the optimal prognostic AMs, their thresholds, and their joint associations with cancer mortality. METHODS: We performed an observational cohort study including 12138 patients with cancer at five institutions in China. Information on demographics, disease, nutritional status, and AMs, including the body mass index, mid-arm muscle circumference, mid-arm circumference, handgrip strength, calf circumference (CC), and triceps-skinfold thickness (TSF), was collected and screened as mortality predictors. The optimal stratification was used to determine the thresholds to categorize those prognostic AMs, and their associations with mortality were estimated independently and jointly by calculating multivariable-adjusted hazard ratios (HRs). RESULTS: The study included 5744 females and 6394 males with a mean age of 56.9 years. The CC and TSF were identified as better mortality predictors than other AMs. The optimal thresholds were women 30 cm and men 32.8 cm for the CC, and women 21.8 mm and men 13.6 mm for the TSF. Patients in the low CC or low TSF group had a 13% (HR = 1.13, 95% CI = 1.03-1.23) and 22% (HR = 1.22, 95% CI = 1.12-1.32) greater mortality risk compared with their normal CC/TSF counterparties, respectively. Concurrent low CC and low TSF showed potential joint effect on mortality risk (HR = 1.39, 95% CI = 1.25-1.55). CONCLUSIONS: These findings support the importance of assessing the CC and TSF simultaneously in hospitalized cancer patients to guide interventions to optimize their long-term outcomes.

Fat mass assessment using the triceps skinfold thickness enhances the prognostic value of the Global Leadership Initiative on Malnutrition criteria in patients with lung cancer.

The present study evaluated whether fat mass assessment using the triceps skinfold (TSF) thickness provides additional prognostic value to the Global Leadership Initiative on Malnutrition (GLIM) framework in patients with lung cancer (LC). We performed an observational cohort study including 2672 LC patients in China. Comprehensive demographic, disease and nutritional characteristics were collected. Malnutrition was retrospectively defined using the GLIM criteria, and optimal stratification was used to determine the best thresholds for the TSF. The associations of malnutrition and TSF categories with survival were estimated independently and jointly by calculating multivariable-adjusted hazard ratios (HR). Malnutrition was identified in 808 (30·2 %) patients, and the best TSF thresholds were 9·5 mm in men and 12 mm in women. Accordingly, 496 (18·6 %) patients were identified as having a low TSF. Patients with concurrent malnutrition and a low TSF had a 54 % (HR = 1·54, 95 % CI = 1·25, 1·88) greater death hazard compared with well-nourished individuals, which was also greater compared with malnourished patients with a normal TSF (HR = 1·23, 95 % CI = 1·06, 1·43) or malnourished patients without TSF assessment (HR = 1·31, 95 % CI = 1·14, 1·50). These associations were concentrated among those patients with adequate muscle mass (as indicated by the calf circumference). Additional fat mass assessment using the TSF enhances the prognostic value of the GLIM criteria. Using the population-derived thresholds for the TSF may provide significant prognostic value when used in combination with the GLIM criteria to guide strategies to optimise the long-term outcomes in patients with LC.

Association study of SNPs in LncRNA CDKN2B-AS1 with breast cancer susceptibility in Chinese Han population.

BACKGROUND: The study aimed to analysis the genetic variation of the lncRNA CDKN2B-AS1 SNPs, and explored the regulation of SNPs on the invasion and metastasis of Breast cancer (BC). METHODS: The SNPs (Single Nucleotide Polymorphisms) was screened for genotyping among 504 Chinese Han patients and 505 controls, which were frequency-matched for age ( ± 2 years). Logistic analysis was to explore the relationship between SNPs and the BC risk. Interactions between SNPs and reproductive factors was explored using the multifactor dimensionality reduction (MDR) method. qRT-PCR was conducted to detect the CDKN2B-AS1 expression in plasma of different rs10965215 and rs2518723 genotypes. The effect of rs10965215 A>G mutation on the binding ability of CDKN2B-AS1 and miR-4440 was verified by dual luciferase experiment. CCK-8, scratch and Transwell experiment were performed to explore the effect of miR-4440 over-expression on BC cell proliferation, migration and invasion. RESULTS: A total of 13 SNP was screened. The individuals with SNPs rs2518723C>T, rs10965215 A>G, rs77792598C>G, rs4977753 T > C, rs75917766C>T and rs78545330C>G mutations might increase the BC risk. MDR results revealed that individuals with rs10965215 G genotype who age at menarche≥ 13 and regardless of the number of abortion< 2 or ≥ 2 had a higher risk of BC. The relative expression of CDKN2B-AS1 in rs10965215 homozygous wild AA genotype (8.88 ± 3.43) was lower than heterozygous GA (11.08 ± 2.90) and homozygous mutant GG genotype (11.31 ± 2.90). When rs10965215 wild A genotype was carried, there was an interaction between CDKN2B-AS1 and miR-4440. The CCK-8, Transwell, and scratch experiment were all found that miR-4440 over-expression might enhance the proliferation, invasion and migration of BC cells. - CONCLUSION: CDKN2B-AS1 gene polymorphism might be related to the susceptibility of BC, CDKN2B-AS1 rs10965215 A/G genotype probably affect the proliferation, invasion and migration of BC cells by modulating the interactions with of miR-4440.

Polymorphisms in lncRNA MIR2052HG and susceptibility to breast cancer in Chinese population.

BACKGROUND: Published studies based on pharmacokinetics have explored the relationship between the lncRNA MIR2052HG and the prognosis of breast cancer (BC) resistance and recurrence. However, the underlying association of MIR2052HG SNPs with BC development remains unclear. METHODS: Combining bioinformatics and databases, SNPs (Single Nucleotide Polymorphisms) in the MIR2052HG gene were screened, and SNPs in the lncRNA MIR2052HG were selected for genotyping among 504 Chinese Han patients and 505 healthy controls, which were frequency-matched for age (±2 years). Logistic regression analysis was used to explore the association between MIR2052HG SNPs and the BC risk. Interactions between the MIR2052HG SNPs and reproductive factors were further evaluated using the multifactor dimensionality reduction (MDR) method. qRT-PCR was performed to detect MIR2052HG expression in individuals with different genotypes of rs34841297. The target miRNA, miR-4456 of MIR2052HG rs34841297 was predicted by websites and confirmed by performing dual luciferase gene reporter assays. CCK-8 and Transwell experiments were designed to explore the effects of miR-4456 on the proliferation, invasion and migration of BC cells. RESULTS: In this study, nine SNPs were screened. After adjusting for age, menarche age, menopausal status, number of pregnancies, history of abortions, breast feeding history and family history of BC, the results of the logistic regression analysis showed the rs34841297 A/- gene polymorphism was positively correlated with the incidence of BC. Compared with the AA genotype, patients with the A-+-- genotype of rs34841297 at age<50 years, and menarche age<14 years, Premenopausal status, history of abortion, no history of breastfeeding and no family history of tumors in first-degree relatives had an increased risk of BC. MDR results revealed that individuals with rs34841297 - (homozygous deletion) of the A allele who were not menopausal and had no history of breastfeeding had a higher risk of BC. qRT-PCR results revealed that homozygous deletion (1.68±1.37) of the rs34841297 A- genotype resulted in higher MIR2052HG expression than the heterozygous deletion genotype (0.95±0.94) and wild AA genotype (0.26±0.12). Binding between MIR2052HG and miR-4456 was occurred when rs34841297 carried the AA genotype. Moreover, preliminary functional studies indicated that the overexpression of miR-4456 increased the proliferation, invasion and migration of BC cells. CONCLUSION: Our study showed that the MIR2052HG gene polymorphism may be related to BC susceptibility, and the MIR2052HG rs34841297 A/- genotype may probably affect the proliferation, invasion and migration of BC cells by modulating the interactions with of miR-4456.

A fusion decision system to identify and grade malnutrition in cancer patients: Machine learning reveals feasible workflow from representative real-world data.

BACKGROUND AND AIMS: Most nutritional assessment tools are based on pre-defined questionnaires or consensus guidelines. However, it has been postulated that population data can be used directly to develop a solution for assessing malnutrition. This study established a machine learning (ML)-based, individualized decision system to identify and grade malnutrition using large-scale data from cancer patients. METHODS: This was an observational, nationwide, multicenter cohort study that included 14134 cancer patients from five institutions in four different geographic regions of China. Multi-stage K-means clustering was performed to isolate and grade malnutrition based on 17 core nutritional features. The effectiveness of the identified clusters for reflecting clinical characteristics, nutritional status and patient outcomes was comprehensively evaluated. The study population was randomly split for model derivation and validation. Multiple ML algorithms were developed, validated and compared to screen for optimal models to implement the cluster prediction. RESULTS: A well-nourished cluster (n = 8193, 58.0%) and a malnourished cluster with three phenotype-specific severity levels (mild = 2195, 15.5%; moderate = 2491, 17.6%; severe = 1255, 8.9%) were identified. The clusters showed moderate agreement with the Patient-Generated Subjective Global Assessment and the Global Leadership Initiative on Malnutrition criteria. The severity of malnutrition was negatively associated with the nutritional status, physical status, quality of life, and short-term outcomes, and was monotonically correlated with reduced overall survival. A multinomial logistic regression was found to be the optimal ML algorithm, and models built based on this algorithm showed almost perfect performance to predict the clusters in the validation data. CONCLUSIONS: This study developed a fusion decision system that can be used to facilitate the identification and severity grading of malnutrition in patients with cancer. Moreover, the study workflow is flexible, and might provide a generalizable solution for the artificial intelligence-based assessment of malnutrition in a wider variety of scenarios.

Comparison of the AWGS and optimal stratification-defined handgrip strength thresholds for predicting survival in patients with lung cancer.

OBJECTIVES: Handgrip strength (HGS) is related to cancer mortality. The aim of this study was to compare the performance of the Asian Working Group for Sarcopenia 2019 (AWGS)- and optimal stratification (OS)-defined HGS thresholds for predicting the survival of patients with lung cancer (LC). METHODS: We performed an observational cohort study including 3230 patients with LC admitted to five institutions in China from November 2011 to January 2019. Comprehensive baseline and follow-up information was documented. Sex-specific thresholds for identifying patients with a low HGS were defined based on the AWGS (<28 kg in men and <18 kg in women) and the OS. The associations of a low HGS with survival were estimated by calculating multivariable-adjusted hazard ratios (HRs), and the relationships were flexibly modeled using restricted cubic splines. RESULTS: The study included 1041 women and 2189 men with a mean age of 60 y and a median follow-up time of 761 d. The OS-calculated HGS thresholds were <31.2 kg in men and <22.4 kg in women. There were significant associations between a low HGS defined by the AWGS (n = 1392; 43.1%) or the OS (n = 2034; 63%) and various nutritional characteristics. An AWGS-defined low HGS was associated with prolonged hospitalization. The OS-defined low HGS group was associated with a 23% greater death hazard than the normal HGS group (HR, 1.23; 95% confidence interval, 1.08-1.40). An n-shaped non-linear association was observed between the HGS and survival in women (P = 0.003). CONCLUSIONS: The OS-defined HGS thresholds show better performance than the AWGS for predicting the survival of patients with LC. Additionally, the HGS had n-shaped associations with the overall mortality among female patients with LC.

Sedentary behavior and risk of breast cancer: a dose-response meta-analysis from prospective studies.

BACKGROUND: Emerging studies examined the association between sedentary behavior and risk of breast cancer, however, the dose-response relationship remained unclear. We aim to explore dose-response relationship of sedentary behavior and breast cancer risk based on relevant cohort studies. METHODS: Online database (PubMed, Web of Science, EMBASE and Cochrane Library) were searched up to March 29, 2019. Overall relative risk (RR) with 95% confidence interval (CI) were pooled, and generalized least squares (GLS) method and restricted cubic splines were applied to evaluate the linear or nonlinear relation. Attributable risk proportion (ARP) was used to assess the health hazards of sedentary behavior in different countries. RESULTS: Eight prospective studies were included in the meta-analysis, containing 17 048 breast cancer cases and 426 506 participants. The borderline statistical association was detected between prolonged sedentary behavior and risk of breast cancer (RR 1.08, 95% CI 0.99-1.19). Linear association between sedentary and breast cancer was observed (Pnonlinearity = 0.262), and for 1 h/d increment of sedentary behavior, there was 1% increase of breast cancer risk (RR 1.01, 95% CI1.00-1.02). Similar results were also found between TV viewing and risk of breast cancer (Pnonlinearity = 0.551), with 1 h/day increment of TV viewing daily attributing to 2% increase of breast cancer risk (RR 1.02, 95% CI 1.00-1.04). Moreover, sedentary behavior may statistically increase the risk of breast cancer by 21.6% for Asian countries, 8.26% for North America. CONCLUSIONS: Sedentary behavior was validated as a risk factor of breast cancer through dose-response analysis, especially TV viewing.

Alcohol Consumption by Beverage Type and Risk of Breast Cancer: A Dose-Response Meta-Analysis of Prospective Cohort Studies.

AIMS: Alcohol intake has been shown to increase the risk of breast cancer. However, the dose-response analysis of different alcoholic beverages (spirits, wine and beer) is not clear. Our meta-analysis aims to provide a dose-response estimation between different alcohols and breast cancer risk. METHODS: Search of PubMed and Web of Science and manual searches were conducted up to 1 December 2018, and summary relative risks (RRs) and attributable risk percentage (ARP) for alcohol intake on the development of breast cancer were calculated. Dose-response meta-analysis modeled relationships between drinking type and breast cancer risk. Sources of heterogeneity were explored, and sensitivity analyses were conducted to test the robustness of findings. RESULTS: In total, 22 cohort studies and 45,350 breast cancer cases were included. Current drinkers for ER+ had an increased risk compared with never drinkers. In dose-response analysis, there was a statistically significant linear trend with breast cancer risk increasing gradually by total alcohol and wine dose: when adding 10 g per day, the risk increased by 10.5% (RR = 1.10, 95%CI = 1.08-1.13) in total alcohol and 8.9% (RR = 1.08, 95%CI = 1.04-1.14) in wine. For postmenopausal women, the risk increases by 11.1% (RR = 1.11, 95%CI = 1.09-1.13) with every 10 g of total alcohol increase. Furthermore, the breast cancer alcohol-attributed percentage is higher in Europe than in North America and Asia. CONCLUSIONS: The effect of drinking on the incidence of breast cancer is mainly manifested in ER+ breast cancer. Quantitative analysis showed total drinking had a significant risk for breast cancer, especially for postmenopausal women. However, for different alcohols, just wine intake has the similar results.