Polygenic risk for prostate cancer: Decreasing relative risk with age but little impact on absolute risk.

Polygenic risk scores (PRSs) for a variety of diseases have recently been shown to have relative risks that depend on age, and genetic relative risks decrease with increasing age. A refined understanding of the age dependency of PRSs for a disease is important for personalized risk predictions and risk stratification. To further evaluate how the PRS relative risk for prostate cancer depends on age, we refined analyses for a validated PRS for prostate cancer by using 64,274 prostate cancer cases and 46,432 controls of diverse ancestry (82.8% European, 9.8% African American, 3.8% Latino, 2.8% Asian, and 0.8% Ghanaian). Our strategy applied a novel weighted proportional hazards model to case-control data to fully utilize age to refine how the relative risk decreased with age. We found significantly greater relative risks for younger men (age 30-55 years) compared with older men (70-88 years) for both relative risk per standard deviation of the PRS and dichotomized according to the upper 90th percentile of the PRS distribution. For the largest European ancestral group that could provide reliable resolution, the log-relative risk decreased approximately linearly from age 50 to age 75. Despite strong evidence of age-dependent genetic relative risk, our results suggest that absolute risk predictions differed little from predictions that assumed a constant relative risk over ages, from short-term to long-term predictions, simplifying implementation of risk discussions into clinical practice.

Estimating risk of consequences following hypoglycaemia exposure using the Hypo-RESOLVE cohort: a secondary analysis of pooled data from insulin clinical trials.

AIMS/HYPOTHESIS: Whether hypoglycaemia increases the risk of other adverse outcomes in diabetes remains controversial, especially for hypoglycaemia episodes not requiring assistance from another person. An objective of the Hypoglycaemia REdefining SOLutions for better liVEs (Hypo-RESOLVE) project was to create and use a dataset of pooled clinical trials in people with type 1 or type 2 diabetes to examine the association of exposure to all hypoglycaemia episodes across the range of severity with incident event outcomes: death, CVD, neuropathy, kidney disease, retinal disorders and depression. We also examined the change in continuous outcomes that occurred following a hypoglycaemia episode: change in eGFR, HbA1c, blood glucose, blood glucose variability and weight. METHODS: Data from 84 trials with 39,373 participants were pooled. For event outcomes, time-updated Cox regression models adjusted for age, sex, diabetes duration and HbA1c were fitted to assess association between: (1) outcome and cumulative exposure to hypoglycaemia episodes; and (2) outcomes where an acute effect might be expected (i.e. death, acute CVD, retinal disorders) and any hypoglycaemia exposure within the last 10 days. Exposures to any hypoglycaemia episode and to episodes of given severity (levels 1, 2 and 3) were examined. Further adjustment was then made for a wider set of potential confounders. The within-person change in continuous outcomes was also summarised (median of 40.4 weeks for type 1 diabetes and 26 weeks for type 2 diabetes). Analyses were conducted separately by type of diabetes. RESULTS: The maximally adjusted association analysis for type 1 diabetes found that cumulative exposure to hypoglycaemia episodes of any level was associated with higher risks of neuropathy, kidney disease, retinal disorders and depression, with risk ratios ranging from 1.55 (p=0.002) to 2.81 (p=0.002). Associations of a similar direction were found when level 1 episodes were examined separately but were significant for depression only. For type 2 diabetes cumulative exposure to hypoglycaemia episodes of any level was associated with higher risks of death, acute CVD, kidney disease, retinal disorders and depression, with risk ratios ranging from 2.35 (p<0.0001) to 3.00 (p<0.0001). These associations remained significant when level 1 episodes were examined separately. There was evidence of an association between hypoglycaemia episodes of any kind in the previous 10 days and death, acute CVD and retinal disorders in both type 1 and type 2 diabetes, with rate ratios ranging from 1.32 (p=0.017) to 2.68 (p<0.0001). These associations varied in magnitude and significance when examined separately by hypoglycaemia level. Within the range of hypoglycaemia defined by levels 1, 2 and 3, we could not find any evidence of a threshold at which risk of these consequences suddenly became pronounced. CONCLUSIONS/INTERPRETATION: These data are consistent with hypoglycaemia being associated with an increased risk of adverse events across several body systems in diabetes. These associations are not confined to severe hypoglycaemia requiring assistance.

Serum electrolyte concentrations and risk of atrial fibrillation: an observational and mendelian randomization study.

BACKGROUND: Atrial fibrillation (AF) is a prevalent arrhythmic condition resulting in increased stroke risk and is associated with high mortality. Electrolyte imbalance can increase the risk of AF, where the relationship between AF and serum electrolytes remains unclear. METHODS: A total of 15,792 individuals were included in the observational study, with incident AF ascertainment in the Atherosclerosis Risk in Communities (ARIC) study. The Cox regression models were applied to calculate the hazard ratio (HR) and 95% confidence interval (CI) for AF based on different serum electrolyte levels. Mendelian randomization (MR) analyses were performed to examine the causal association. RESULTS: In observational study, after a median 19.7 years of follow-up, a total of 2551 developed AF. After full adjustment, participants with serum potassium below the 5th percentile had a higher risk of AF relative to participants in the middle quintile. Serum magnesium was also inversely associated with the risk of AF. An increased incidence of AF was identified in individuals with higher serum phosphate percentiles. Serum calcium levels were not related to AF risk. Moreover, MR analysis indicated that genetically predicted serum electrolyte levels were not causally associated with AF risk. The odds ratio for AF were 0.999 for potassium, 1.044 for magnesium, 0.728 for phosphate, and 0.979 for calcium, respectively. CONCLUSIONS: Serum electrolyte disorders such as hypokalemia, hypomagnesemia and hyperphosphatemia were associated with an increased risk of AF and may also serve to be prognostic factors. However, the present study did not support serum electrolytes as causal mediators for AF development.

Estimating and presenting hazard ratios and absolute risks from a Cox model with complex nonlinear interactions.

Interaction analysis is a critical component of clinical and public health research and represents a key topic in precision health and medicine. In applied settings, however, interaction assessment is usually limited to the test of a product term in a regression model and to the presentation of results stratified by levels of additional covariates. Stratification of results often relies on categorizing or making linearity assumptions for continuous covariates, with substantial loss of precision and of relevant information. In time-to-event analysis, moreover, interaction assessment is often limited to the multiplicative hazard scale by inclusion of a product term in a Cox regression model, disregarding the clinically relevant information that is captured by the absolute risk scale. In this paper we present a user-friendly procedure, based on the prediction of individual absolute risks from the Cox model, for the estimation and presentation of interactive effects on both the multiplicative and additive scales in survival analysis. We describe how to flexibly incorporate interactions with continuous covariates, which potentially operate in a nonlinear fashion, provide software for replicating our procedure, and discuss different approaches to deriving CIs. The presented approach will allow clinical and public health researchers to assess complex relationships between multiple covariates as they relate to a clinical endpoint, and to provide a more intuitive and precise depiction of the results in applied research papers focusing on interaction and effect stratification.

A novel model for predicting prognosis and response to immunotherapy in nasopharyngeal carcinoma patients.

Blood-based biomarkers of immune checkpoint inhibitors (ICIs) response in patients with nasopharyngeal carcinoma (NPC) are lacking, so it is necessary to identify biomarkers to select NPC patients who will benefit most or least from ICIs. The absolute values of lymphocyte subpopulations, biochemical indexes, and blood routine tests were determined before ICIs-based treatments in the training cohort (n = 130). Then, the least absolute shrinkage and selection operator (Lasso) Cox regression analysis was developed to construct a prediction model. The performances of the prediction model were compared to TNM stage, treatment, and Epstein-Barr virus (EBV) DNA using the concordance index (C-index). Progression-free survival (PFS) was estimated by Kaplan-Meier (K-M) survival curve. Other 63 patients were used for validation cohort. The novel model composed of histologic subtypes, CD19+ B cells, natural killer (NK) cells, regulatory T cells, red blood cells (RBC), AST/ALT ratio (SLR), apolipoprotein B (Apo B), and lactic dehydrogenase (LDH). The C-index of this model was 0.784 in the training cohort and 0.735 in the validation cohort. K-M survival curve showed patients with high-risk scores had shorter PFS compared to the low-risk groups. For predicting immune therapy responses, the receiver operating characteristic (ROC), decision curve analysis (DCA), net reclassifcation improvement index (NRI) and integrated discrimination improvement index (IDI) of this model showed better predictive ability compared to EBV DNA. In this study, we constructed a novel model for prognostic prediction and immunotherapeutic response prediction in NPC patients, which may provide clinical assistance in selecting those patients who are likely to gain long-lasting clinical benefits to anti-PD-1 therapy.

A nomogram and heat map based on LASSO-Cox regression for predicting the risk of early-stage severe fever with thrombocytopenia syndrome patients developing into critical illness at 7-day and 14-day.

Severe fever with thrombocytopenia syndrome (SFTS) represents an emerging infectious disease characterized by a substantial mortality risk. Early identification of patients is crucial for effective risk assessment and timely interventions. In the present study, least absolute shrinkage and selection operator (LASSO)-Cox regression analysis was conducted to identify key risk factors associated with progression to critical illness at 7-day and 14-day. A nomogram was constructed and subsequently assessed for its predictive accuracy through evaluation and validation processes. The risk stratification of patients was performed using X-tile software. The performance of this risk stratification system was assessed using the Kaplan-Meier method. Additionally, a heat map was generated to visualize the results of these analyses. A total of 262 SFTS patients were included in this study, and four predictive factors were included in the nomogram, namely viral copies, aspartate aminotransferase (AST) level, C-reactive protein (CRP), and neurological symptoms. The AUCs for 7-day and 14-day were 0.802 [95% confidence interval (CI): 0.707-0.897] and 0.859 (95% CI: 0.794-0.925), respectively. The nomogram demonstrated good discrimination among low, moderate, and high-risk groups. The heat map effectively illustrated the relationships between risk groups and predictive factors, providing valuable insights with high predictive and practical significance.

Using the theory of planned behavior to predict parents' disclosure of donor conception to their children: a longitudinal study.

STUDY QUESTION: Can the application of the theory of planned behavior (TPB) help predict heterosexual parents' disclosure of donor conception to their children? SUMMARY ANSWER: Parents with a stronger will to act in accordance with social norms favoring disclosure were more likely to start the disclosure process within the next 5-9 years. WHAT IS KNOWN ALREADY: In contrast to single mothers by choice and same-sex couples, heterosexual couples need to make an active decision to disclose their use of donor conception to their child. While disclosure at an early age is encouraged by international guidelines, many heterosexual-couple parents struggle with this. A previous study has found an association between parental scores of TPB factors and disclosure intention, but so far, no study has applied the TPB to predict parents' disclosure behavior. STUDY DESIGN, SIZE, DURATION: The present study is based on the fourth and fifth waves of data collection (T4 and T5) in a nation-wide longitudinal study. Participating parents had conceived through identity-release oocyte donation (n = 68, response rate 65%) and sperm donation (n = 62, response rate 56%) as part of a heterosexual couple. PARTICIPANTS/MATERIALS, SETTING, METHODS: The present study is part of the prospective longitudinal Swedish Study on Gamete Donation (SSGD). Consecutive recruitment of couples starting oocyte or sperm donation treatment was conducted at all seven fertility clinics providing gamete donation in Sweden during a 3-year period (2005-2008). Participants were requested to complete postal surveys at five time points. The present study includes heterosexual-couple parents following oocyte or sperm donation who participated at the two latest time points when their children were 7-8 years old (T4), and 13-17 years old (T5). At T4, participants completed the study-specific TPB Disclosure Questionnaire (TPB-DQ) measuring attitudes and intentions to disclose the donor conception to the child, and disclosure behavior was assessed at both T4 and T5. Data from those participants who had not yet disclosed at T4 were analyzed using survival analysis with Cox regressions. MAIN RESULTS AND THE ROLE OF CHANCE: Forty participants had not disclosed the donor conception to their children at T4 and, out of these, 13 had still not disclosed at T5. We found a significant association between scores of the TPB factor Subjective norms at T4 and their subsequent disclosure behavior at T5 (HR = 2.019; 95% CI: 1.36-3.01). None of the other factors were significantly associated with disclosure behavior. LIMITATIONS, REASONS FOR CAUTION: The present study concerns heterosexual-couple parents with children conceived following treatment with gametes from open-identity donors, which limits the generalizability of our findings to other groups and contexts. Other limitations include the risk of systematic attrition due to the longitudinal study design and decreased statistical power due to few participants. WIDER IMPLICATIONS OF THE FINDINGS: Our findings highlight the importance of perceived subjective norms for parents' disclosure behavior and indicate that the co-parent's opinion about disclosure is of particular relevance in this regard. Counselors should focus on supporting prospective parents to initiate and maintain a healthy and open dialogue about concerns around building a family with donor conception. STUDY FUNDING/COMPETING INTEREST(S): The study was funded by the Swedish Research Council. The authors have no competing interests to declare. TRIAL REGISTRATION NUMBER: N/A.

Association Between Type 1 Diabetes Mellitus and Incident Gastrointestinal Cancer in Korean Population: A Nationwide Retrospective Cohort Study.

BACKGROUND: The age-standardised incidence ratio of gastrointestinal cancers in type 1 diabetes (T1D) patients has been reported to be higher than that in the general population. After adjusting for shared risk factors, we aimed to explore the association between T1D and gastrointestinal cancer and examine how this relationship varies by age and sex. MATERIALS AND METHODS: This retrospective cohort study included 268,179 participants from the Korean National Health Insurance Service-National Sample Cohort. The primary outcome is the incident of gastrointestinal cancers, based on diagnostic codes. Multivariate Cox regression analyses were performed to assess the association between T1D and gastrointestinal cancers. RESULTS: Of the 268,179 participants, 2681 had T1D at baseline and were followed for 12.98 (± 2.92) years. Compared with non-T1D, T1D patients had a significantly increased risk of all gastrointestinal cancer (adjusted hazard ratio [aHR]: 1.403, 95% confidence interval [CI]: 1.253-1.573). T1D patients increased risks of oesophageal cancer (aHR: 1.864, 95% CI: 1.038-3.349), gastric cancer (aHR: 1.313, 95% CI: 1.066-1.616), colon cancer (aHR: 1.365, 95% CI: 1.110-1.678), liver cancer (aHR: 1.388, 95% CI: 1.115-1.727), and pancreatic cancer (aHR: 1.716, 95% CI: 1.182-2.492). The consistency of this association persisted among both male and female, with its strength increasing with older age. CONCLUSIONS: The risk of gastrointestinal cancer was significantly increased in T1D patients. Older male T1D patients exhibit a higher risk, suggesting the need for targeted attention in their care.

Development of preoperative and postoperative models to predict recurrence in postoperative glioma patients: a longitudinal cohort study.

BACKGROUND: Glioma recurrence, subsequent to maximal safe resection, remains a pivotal challenge. This study aimed to identify key clinical predictors influencing recurrence and develop predictive models to enhance neurological diagnostics and therapeutic strategies. METHODS: This longitudinal cohort study with a substantial sample size (n = 2825) included patients with non-recurrent glioma who were pathologically diagnosed and had undergone initial surgical resection between 2010 and 2018. Logistic regression models and stratified Cox proportional hazards models were established with the top 15 clinical variables significantly influencing outcomes screened by the least absolute shrinkage and selection operator (LASSO) method. Preoperative and postoperative models predicting short-term (within 6 months) postoperative recurrence in glioma patients were developed to explore the risk factors associated with short- and long-term recurrence in glioma patients. RESULTS: Preoperative and postoperative logistic models predicting short-term recurrence had accuracies of 0.78 and 0.87, respectively. A range of biological and early symptomatic characteristics linked to short- and long-term recurrence have been pinpointed. Age, headache, muscle weakness, tumor location and Karnofsky score represented significant odd ratios (t > 2.65, p < 0.01) in the preoperative model, while age, WHO grade 4 and chemotherapy or radiotherapy treatments (t > 4.12, p < 0.0001) were most significant in the postoperative period. Postoperative predictive models specifically targeting the glioblastoma and IDH wildtype subgroups were also performed, with an AUC of 0.76 and 0.80, respectively. The 50 combinations of distinct risk factors accommodate diverse recurrence risks among glioma patients, and the nomograms visualizes the results for clinical practice. A stratified Cox model identified many prognostic factors for long-term recurrence, thereby facilitating the enhanced formulation of perioperative care plans for patients, and glioblastoma patients displayed a median progression-free survival (PFS) of only 11 months. CONCLUSION: The constructed preoperative and postoperative models reliably predicted short-term postoperative glioma recurrence in a substantial patient cohort. The combinations risk factors and nomograms enhance the operability of personalized therapeutic strategies and care regimens. Particular emphasis should be placed on patients with recurrence within six months post-surgery, and the corresponding treatment strategies require comprehensive clinical investigation.

Predicting the unpredictable: a robust nomogram for predicting recurrence in patients with ampullary carcinoma.

OBJECTIVE: To screen the risk factors affecting the recurrence risk of patients with ampullary carcinoma (AC)after radical resection, and then to construct a model for risk prediction based on Lasso-Cox regression and visualize it. METHODS: Clinical data were collected from 162 patients that received pancreaticoduodenectomy treatment in Hebei Provincial Cancer Hospital from January 2011 to January 2022. Lasso regression was used in the training group to screen the risk factors for recurrence. The Lasso-Cox regression and Random Survival Forest (RSF) models were compared using Delong test to determine the optimum model based on the risk factors. Finally, the selected model was validated using clinical data from the validation group. RESULTS: The patients were split into two groups, with a 7:3 ratio for training and validation. The variables screened by Lasso regression, such as CA19-9/GGT, AJCC 8th edition TNM staging, Lymph node invasion, Differentiation, Tumor size, CA19-9, Gender, GPR, PLR, Drinking history, and Complications, were used in modeling with the Lasso-Cox regression model (C-index = 0.845) and RSF model (C-index = 0.719) in the training group. According to the Delong test we chose the Lasso-Cox regression model (P = 0.019) and validated its performance with time-dependent receiver operating characteristics curves(tdROC), calibration curves, and decision curve analysis (DCA). The areas under the tdROC curves for 1, 3, and 5 years were 0.855, 0.888, and 0.924 in the training group and 0.841, 0.871, and 0.901 in the validation group, respectively. The calibration curves performed well, as well as the DCA showed higher net returns and a broader range of threshold probabilities using the predictive model. A nomogram visualization is used to display the results of the selected model. CONCLUSION: The study established a nomogram based on the Lasso-Cox regression model for predicting recurrence in AC patients. Compared to a nomogram built via other methods, this one is more robust and accurate.

Regression models for average hazard.

Limitations of using the traditional Cox's hazard ratio for summarizing the magnitude of the treatment effect on time-to-event outcomes have been widely discussed, and alternative measures that do not have such limitations are gaining attention. One of the alternative methods recently proposed, in a simple 2-sample comparison setting, uses the average hazard with survival weight (AH), which can be interpreted as the general censoring-free person-time incidence rate on a given time window. In this paper, we propose a new regression analysis approach for the AH with a truncation time τ. We investigate 3 versions of AH regression analysis, assuming (1) independent censoring, (2) group-specific censoring, and (3) covariate-dependent censoring. The proposed AH regression methods are closely related to robust Poisson regression. While the new approach needs to require a truncation time τ explicitly, it can be more robust than Poisson regression in the presence of censoring. With the AH regression approach, one can summarize the between-group treatment difference in both absolute difference and relative terms, adjusting for covariates that are associated with the outcome. This property will increase the likelihood that the treatment effect magnitude is correctly interpreted. The AH regression approach can be a useful alternative to the traditional Cox's hazard ratio approach for estimating and reporting the magnitude of the treatment effect on time-to-event outcomes.

Cox regression with linked data.

Record linkage is increasingly used, especially in medical studies, to combine data from different databases that refer to the same entities. The linked data can bring analysts novel and valuable knowledge that is impossible to obtain from a single database. However, linkage errors are usually unavoidable, regardless of record linkage methods, and ignoring these errors may lead to biased estimates. While different methods have been developed to deal with the linkage errors in the generalized linear model, there is not much interest on Cox regression model, although this is one of the most important statistical models in clinical and epidemiological research. In this work, we propose an adjusted estimating equation for secondary Cox regression analysis, where linked data have been prepared by a third-party operator, and no information on matching variables is available to the analyst. Through a Monte Carlo simulation study, the proposed method is shown to lead to substantial bias reductions in the estimation of the parameters of the Cox model caused by false links. An asymptotically unbiased variance estimator for the adjusted estimators of Cox regression coefficients is also proposed. Finally, the proposed method is applied to a linked database from the Brest stroke registry in France.

Balancing versus modelling in weighted analysis of non-randomised studies with survival outcomes: A simulation study.

Weighting methods are widely used for causal effect estimation in non-randomised studies. In general, these methods use the propensity score (PS), the probability of receiving the treatment given the covariates, to arrive at the respective weights. All of these "modelling" methods actually optimize prediction of the respective outcome, which is, in the PS model, treatment assignment. However, this does not match with the actual aim of weighting, which is eliminating the association between covariates and treatment assignment. In the "balancing" approach, covariates are thus balanced directly by solving systems of numerical equations, explicitly without fitting a PS model. To compare modelling, balancing and hybrid approaches to weighting we performed a large simulation study for a binary treatment and a survival outcome. For maximal practical relevance all simulation parameters were selected after a systematic review of medical studies that used PS methods for analysis. We also introduce a new hybrid method that uses the idea of the covariate balancing propensity score and matching weights, thus avoiding extreme weights. In addition, we present a corrected robust variance estimator for some of the methods. Overall, our simulations results indicate that balancing approach methods work worse than expected. However, among the considered balancing methods, entropy balancing consistently outperforms the variance balancing approach. All methods estimating the average treatment effect in the overlap population perform well with very little bias and small standard errors even in settings with misspecified propensity score models. Finally, the coverage using the standard robust variance estimator was too high for all methods, with the proposed corrected robust variance estimator improving coverage in a variety of settings.

The effect of number of clusters and magnitude of within-cluster homogeneity in outcomes on the performance of four variance estimators for a marginal multivariable Cox regression model fit to clustered data in the context of observational research.

Researchers often estimate the association between the hazard of a time-to-event outcome and the characteristics of individuals and the clusters in which individuals are nested. Lin and Wei's robust variance estimator is often used with a Cox regression model fit to clustered data. Recently, alternative variance estimators have been proposed: the Fay-Graubard estimator, the Kauermann-Carroll estimator, and the Mancl-DeRouen estimator. Using Monte Carlo simulations, we found that, when fitting a marginal Cox regression model with both individual-level and cluster-level covariates: (i) in the presence of weak to moderate within-cluster homogeneity of outcomes, the Lin-Wei variance estimator can result in estimates of the SE with moderate bias when the number of clusters is fewer than 20-30, while in the presence of strong within-cluster homogeneity, it can result in biased estimation even when the number of clusters is as large as 100; (ii) when the number of clusters was less than approximately 20, the Fay-Graubard variance estimator tended to result in estimates of SE with the lowest bias; (iii) when the number of clusters exceeded approximately 20, the Mancl-DeRouen estimator tended to result in estimated standard errors with the lowest bias; (iv) the Mancl-DeRouen estimator used with a t-distribution tended to result in 95% confidence that had the best performance of the estimators; (v) when the magnitude of within-cluster homogeneity in outcomes was strong or very strong, all methods resulted in confidence intervals with lower than advertised coverage rates even when the number of clusters was very large.

Washed microbiota transplantation improves symptoms and intestinal barrier function in patients with functional bowel disorders: a propensity-score matching analysis.

BACKGROUND: Alterations in the intestinal microbiota may play a role in the pathogenesis of functional bowel disorders (FBDs). Probiotics are widely used to improve intestinal dysbacteriosis in FBDs. In the context of FBDs, washed microbiota transplantation (WMT) appear to be a promising therapeutic option. We aimed to compare probiotics with WMT by using a propensity-score matching analysis (PSMA). METHODS: We conducted a retrospective investigation of 103 patients with FBDs, including irritable bowel syndrome (IBS), functional constipation (FC), functional diarrhea (FDr), functional abdominal bloating (FAB). Patients were divided into the WMT group or probiotics group (taking probiotics capsules). Data on the following parameters were matched for PSMA: age; sex; disease course; body mass index; anxiety; insomnia; tobacco smoking; alcohol consumption; and levels of D-lactate, diamine oxidase, and lipopolysaccharide. Intestinal barrier function (IBF) and symptoms were evaluated both before and after treatment initiation. Prognostic factors were assessed by Cox proportional hazards regression analysis. RESULTS: PSMA identified in 34 matched pairs (11 IBS, 12 FC, 7 FDr, and 4 FAB in the probiotics group and 14 IBS, 13 FC, 5 FDr, and 2 FAB in the WMT group. Improvement of FBD symptoms was greater with WMT than probiotics (P = 0.002). The WMT group had significantly fewer patients with intestinal barrier damage than the probiotics group (38.2% vs. 67.6%, P = 0.041). This improvement of FBD with WMT was further reflected as a reduction in D-lactate levels (P = 0.031). Increased D-lactate levels which were identified as a prognostic factor for FBDs (HR = 0.248, 95%CI 0.093-0.666, P = 0.006) in multivariate Cox regression analysis. CONCLUSION: WMT could improve symptoms and IBF in patients with FBDs. Increased D-lactate levels in patients with FBDs may predict a favorable response to WMT treatment.

TES and SLC40A1 as Potential Biomarkers for Predicting Survival in T-Cell Acute Lymphoblastic Leukemia.

INTRODUCTION: Identifying patients with high-risk T-cell acute lymphoblastic leukemia (T-ALL) is crucial for personalized therapy; however, the lack of robust biomarkers hinders prognosis assessment. To address this issue, our study aimed to screen and validate genes whose expression may serve as predictive indicators of outcomes in T-ALL patients while also investigating the underlying molecular mechanisms. METHODS: Differentially expressed genes (DEGs) between T-ALL patients and healthy controls were identified by integrating data from three independent public datasets. Functional annotation of these DEGs and protein-protein interactions were also conducted. Further, we enrolled a prospective cohort of T-ALL patients (n = 20) at our center, conducting RNA-seq analysis on their bone marrow samples. Survival-based univariate Cox analysis was employed to identify gene expressions related to survival, and an intersection algorithm was sequentially applied. Furthermore, we validated the identified genes using cases from the Therapeutically Applicable Research to Generate Effective Treatments database, plotting Kaplan-Meier curves for secondary validation. RESULTS: Through the integration of survival-related genes with DEGs identified in T-ALL, our analysis revealed six T-ALL-specific genes, the expression levels of which were linked to prognostic value. Notably, the independent prognostic value of SLC40A1 and TES expression levels was confirmed in both an external cohort and a prospective cohort at our center. CONCLUSION: In summary, our preliminary study indicates that the expression levels of TES and SLC40A1 genes show promise as potential indicators for predicting survival outcomes in T-ALL patients.

Evaluating the prognostic value of tumor deposits in non-metastatic lymph node-positive colon adenocarcinoma using Cox regression and machine learning.

BACKGROUND: The 8th AJCC TNM staging for non-metastatic lymph node-positive colon adenocarcinoma patients(NMLP-CA) stages solely by lymph node status, irrespective of the positivity of tumor deposits (TD). This study uses machine learning and Cox regression to predict the prognostic value of tumor deposits in NMLP-CA. METHODS: Patient data from the SEER registry (2010-2019) was used to develop CSS nomograms based on prognostic factors identified via multivariate Cox regression. Model performance was evaluated by c-index, dynamic calibration, and Schmid score. Shapley additive explanations (SHAP) were used to explain the selected models. RESULTS: The study included 16,548 NMLP-CA patients, randomized 7:3 into training (n = 11,584) and test (n = 4964) sets. Multivariate Cox analysis identified TD, age, marital status, primary site, grade, pT stage, and pN stage as prognostic for cancer-specific survival (CSS). In the test set, the gradient boosting machine (GBM) model achieved the best C-index (0.733) for CSS prediction, while the Cox model and GAMBoost model optimized dynamic calibration(6.473) and Schmid score (0.285), respectively. TD ranked among the top 3 most important features in the models, with increasing predictive significance over time. CONCLUSIONS: Positive tumor deposit status confers worse prognosis in NMLP-CA patients. Tumor deposits may confer higher TNM staging. Furthermore, TD could play a more significant role in the staging system.

Plasma boron concentrations and risk of all-cause mortality in the general population.

PURPOSE: Higher dietary intake or higher circulating levels of the trace element boron have been associated with beneficial effects on human health. However, the relationship between plasma boron levels and survival in the general population is not known. Therefore, we aimed to assess the association between plasma boron concentrations and all-cause mortality in a population-based cohort from northern Germany (n = 863 individuals; median age 62.3 years, 42.8% women). METHODS: Plasma boron concentrations (median 31.9 µg/L [22.9; 43.5]) were measured by inductively coupled plasma-mass spectrometry. Cox proportional hazards regression models adjusted for relevant confounders were used to associate plasma boron concentrations with all-cause mortality. RESULTS: After a median follow-up time of 11 years, n = 99 individuals had died. In the overall sample, plasma boron concentrations were associated with all-cause mortality in the crude model (HR: 1.07 [95% CI 1.03-1.11] per 5-unit-increment). However, multivariable adjustment rendered the association non-significant (HR: 1.03 [95% CI 0.99-1.07]). Sex-stratified analyses revealed slightly higher mortality hazards with increasing plasma boron concentrations in women (HR: 1.11 [95% CI 1.03-1.18], pInteraction = 0.034), but not in men (HR: 1.00 [95% CI 0.95-1.06]). CONCLUSION: We conclude that in a moderate-sized sample from the general population, higher plasma boron concentrations were associated with a higher risk of all-cause mortality in women, but not in men. Due to the low number of events in the female subsample (n = 27), this observation has to be interpreted with caution.

Greater adherence to the Healthy Nordic Food Index is associated with lower all-cause mortality in a population-based sample from northern Germany.

PURPOSE: Dietary pattern scores reflecting a high intake of beneficial food groups were associated with reduced mortality risk. Data on associations of such dietary pattern scores in population-based samples from northern Germany are lacking. Therefore, we examined the association of three dietary pattern scores with all-cause mortality in a moderate-sized prospective sample from northern Germany. METHODS: The study sample comprised 836 participants (43.8% females, median age 62.4 years). Based on a validated, self-administered Food Frequency Questionnaire, the dietary scores Dietary Approaches to Stop Hypertension (DASH), Modified Mediterranean Diet Score (MMDS), and Healthy Nordic Food Index (HNFI) were calculated. Cox proportional hazard regression models, adjusted for age, sex, body mass index, waist to hip ratio, education, smoking status, total energy intake, and physical activity, were used to separately relate DASH, MMDS, and HNFI to all-cause mortality. RESULTS: During a median follow-up period of 11 years, 93 individuals died. While DASH and MMDS scores were not associated with all-cause mortality, greater adherence to HNFI was associated with lower mortality hazards (HR: 0.47 [95% CI 0.25-0.89] when comparing the highest score quartile to the lowest; HR: 0.79 [95% CI 0.64-0.98] for HNFI modeled as a 1-Standard Deviation increment). Among different HNFI components, higher intake of oats and cereals displayed the most conclusive association with all-cause mortality (HR: 0.59 [95% CI 0.38-0.91] when comparing high and low intake). CONCLUSION: In an elderly general population sample from northern Germany, we observed greater adherence to HNFI to be associated with lower all-cause mortality.

An MRI-based radiomics nomogram to predict progression-free survival in patients with endometrial cancer.

Aim: This study aimed to explore the importance of an MRI-based radiomics nomogram in predicting the progression-free survival (PFS) of endometrial cancer.Methods: Based on clinicopathological and radiomic characteristics, we established three models (clinical, radiomics and combined model) and developed a nomogram for the combined model. The Kaplan-Meier method was utilized to evaluate the association between nomogram-based risk scores and PFS.Results: The nomogram had a strong predictive ability in calculating PFS with areas under the curve (ROC) of 0.905 and 0.901 at 1 and 3 years, respectively. The high-risk groups identified by the nomogram-based scores had shorter PFS compared with the low-risk groups.Conclusion: The radiomics nomogram has the potential to serve as a noninvasive imaging biomarker for predicting individual PFS of endometrial cancer.

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